Compare commits

...

282 Commits

Author SHA1 Message Date
phernandez 552a835669 chore: update version to 0.20.3 for v0.20.3 release 2026-03-26 23:09:57 -05:00
phernandez 888e3c2909 docs: add v0.20.3 changelog entry
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-26 23:09:20 -05:00
Paul Hernandez d1320f671e fix: (cloud) CLI cloud commands now use API key when configured (#698)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-26 22:56:45 -05:00
phernandez 94bdfe77e4 fix: detect cloud mode in resolve_runtime_mode
BASIC_MEMORY_CLOUD_MODE env var was never checked in resolve_runtime_mode(),
so cloud deployments always ran as LOCAL mode. This caused file sync to start
in the cloud container, which then failed with "DATABASE_URL must be set when
using Postgres backend" because there's no local DB in cloud mode.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-26 14:53:12 -05:00
Paul Hernandez 4791e19685 feat: add Logfire phased instrumentation (#692)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-24 20:39:42 -05:00
Paul Hernandez 36848410a1 feat(core): add default_search_type config setting (#676)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-17 22:19:53 -05:00
jope-bm a77b51a28e fix(core): allow double-dot filenames while still blocking path traversal (#673)
Signed-off-by: Joe P <joe@basicmemory.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-16 07:47:49 -06:00
Paul Hernandez 1a6a65571e fix(mcp): add project detection from memory:// URLs in edit_note and delete_note (#668)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-15 19:14:22 -05:00
Paul Hernandez c8b00449d2 fix(core): exclude stale entity rows from embedding coverage stats (#675)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-15 18:45:36 -05:00
Paul Hernandez 013864ebf0 fix(cli): use resolved project path in doctor command (#667)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-15 18:43:23 -05:00
Drew Cain dd91b49054 chore: update version to 0.20.2 for v0.20.2 release 2026-03-10 23:13:59 -05:00
Drew Cain 7c96a0777d fix(cli): handle brew outdated exit code 1 as outdated, not error
Signed-off-by: Drew Cain <groksrc@gmail.com>
2026-03-10 23:13:54 -05:00
Drew Cain 148e07c580 chore: update version to 0.20.1 for v0.20.1 release 2026-03-10 23:06:21 -05:00
Drew Cain 21334cc29b docs: add v0.20.1 changelog entry
Signed-off-by: Drew Cain <groksrc@gmail.com>
2026-03-10 23:06:15 -05:00
Drew Cain db60942267 fix(core): invalidate config cache when file is modified by another process (#662)
Signed-off-by: Drew Cain <groksrc@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-10 23:05:55 -05:00
Drew Cain 7bfac158df fix(cli): project list MCP column shows transport type instead of DB presence (#661)
Signed-off-by: Drew Cain <groksrc@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-10 23:05:48 -05:00
Drew Cain 87616924ff chore: update version to 0.20.0 for v0.20.0 release 2026-03-10 22:08:00 -05:00
Drew Cain 5cb0502ed2 docs: add v0.20.0 changelog entry
Signed-off-by: Drew Cain <groksrc@gmail.com>
2026-03-10 22:07:49 -05:00
Paul Hernandez a94a717b1b feat(cli): add default-on auto-update system and bm update command (#643)
Signed-off-by: phernandez <paul@basicmachines.co>
Signed-off-by: Drew Cain <groksrc@users.noreply.github.com>
Co-authored-by: Drew Cain <groksrc@users.noreply.github.com>
2026-03-10 22:06:33 -05:00
phernandez 6e4bb72f10 chore: update version to 0.19.2 for v0.19.2 release 2026-03-09 23:42:10 -05:00
phernandez 11b0e31e24 docs: add v0.19.2 changelog entry
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-09 23:41:40 -05:00
Paul Hernandez a5c9e77f16 fix: coerce string params to list/dict in MCP tools (#657)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-09 22:19:07 -05:00
Paul Hernandez 30a89357cb fix(core): handle SQLite and Windows semantic regressions (#655)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-09 22:18:17 -05:00
phernandez 222ec5d3b6 chore: update version to 0.19.1 for v0.19.1 release 2026-03-08 18:09:04 -05:00
phernandez d42aec7ea9 docs: add v0.19.1 changelog entry
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-08 18:04:30 -05:00
Paul Hernandez 9809b469c6 fix: enforce strict entity resolution in destructive MCP tools (#650)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-08 15:56:07 -05:00
phernandez 76ac880f2d feat(api): add GET /knowledge/graph endpoint for full graph visualization
Returns all entities and resolved relations in a flat node/edge format
optimized for graph rendering. Replaces the frontend's use of the
recent memory endpoint which only returned a subset of relations.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-08 11:52:17 -05:00
Paul Hernandez ad3f2650d9 feat: add insert_before_section and insert_after_section edit operations (#648)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-08 10:54:10 -05:00
dependabot[bot] d6508d985c chore(deps): bump authlib from 1.6.6 to 1.6.7 in the uv group across 1 directory (#645)
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-03-08 10:53:52 -05:00
phernandez 7b95b9f37b docs: add What's New in v0.19.0 section to README
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-08 10:29:06 -05:00
phernandez 0bce4be1a6 chore: update version to 0.19.0 for v0.19.0 release 2026-03-07 14:27:43 -06:00
phernandez a316424edf docs: add v0.19.0 changelog entry
Comprehensive changelog for 114 commits since v0.18.5 covering semantic
vector search, schema system, per-project cloud routing, FastMCP 3.0
upgrade, CLI overhaul, and numerous bug fixes.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-07 14:27:29 -06:00
phernandez af71cf4896 fix(test): clear search_vector_chunks before embedding backfill test
Test was polluted by other tests leaving rows in search_vector_chunks,
causing _needs_semantic_embedding_backfill to return False.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-07 13:54:58 -06:00
phernandez e846ae85d8 fix: semantic embeddings not generated on fresh DB or upgrade
The previous backfill trigger relied on Alembic revision tracking, but
alembic_version only stores the head revision — intermediate revisions
(like the backfill trigger) are invisible after a multi-step upgrade or
fresh DB creation.

Three changes fix this:

1. Replace Alembic revision check with a simple "entities exist but
   embeddings are empty" check that works regardless of migration path
2. Generate embeddings during sync — after FTS indexing, batch-embed all
   synced entities at the end of the sync operation
3. Add background backfill at MCP startup for the upgrade path (entities
   already exist, no embeddings) without blocking server readiness

Also adds clear startup logging for semantic embedding status so issues
are easy to spot in the logs.

📋 Covers: fresh DB, upgrade from pre-embedding version, db reset,
   interrupted backfill

Signed-off-by: Pedro Hernandez <pedro@basicmachines.co>
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-07 13:23:39 -06:00
phernandez 63e4bcdf1d fix: clarify search_notes parameter naming and fix note_types case sensitivity
- Add Annotated descriptions to note_types and entity_types parameters so
  LLMs can distinguish frontmatter type filtering from knowledge graph item
  type filtering (search.py, ui_sdk.py)
- Lowercase note_types values at filter time so "Chapter" matches stored
  "chapter"
- Fix misleading entity_types references in schema.py guidance strings
  (should be note_types)
- Add permalink pattern documentation note about full path matching
- Add test for note_types case-insensitive lowercasing

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-05 15:08:52 -06:00
phernandez f23dd0474b fix(test): patch API fallback in project_context tests for Postgres
In Postgres test mode, stale dependency_overrides on the module-level
FastAPI app allow _resolve_default_project_from_api() to query a live
database and return 'test-project' even when the test sets
default_project=None. Monkeypatch the async fallback in the three
affected tests to isolate config-based resolution from API leakage.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-04 15:34:35 -06:00
phernandez fced804438 fix(test): update integration test for DB default project fallback (#644)
The test previously asserted that write_note fails when ConfigManager
has no default_project. With the API fallback, it now correctly
resolves to the database is_default project. Updated the test to
verify this fallback behavior instead of expecting an error.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-04 13:27:03 -06:00
phernandez 1fdc9fdc69 fix: resolve_project_parameter falls back to projects API for default (#644)
In cloud mode, ConfigManager has no local config so default_project
is always None. Add API fallback in resolve_project_parameter that
queries /v2/projects/ for the default_project field. This fixes all
MCP tools that rely on project resolution (recent_activity, etc).

Removed discovery mode tests that simulated an invalid state by
clearing is_default — there must always be a default project.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-04 13:17:16 -06:00
phernandez 2feecdfaf7 fix: ChatGPT search/fetch tools broken in cloud mode (#644)
Both search() and fetch() read default_project from ConfigManager,
which returns None in cloud mode. Remove the manual ConfigManager
lookup and let the underlying search_notes/read_note resolve the
project via get_project_client(), which works in both modes.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-04 12:40:40 -06:00
phernandez 195229f78e fix: resolve default_project returning null in cloud mode (#644)
In cloud mode, ConfigManager has no local config file so
default_project always returned None. Add async
get_default_project_name() on ProjectService that falls back
to the database is_default flag when ConfigManager returns None.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-04 11:57:15 -06:00
phernandez d15f6a8427 Add batched vector sync orchestration across repositories 2026-03-03 16:36:06 -06:00
phernandez b8a3a14ad2 Add semantic query timing and FastEmbed parallel guardrails
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-03 14:04:03 -06:00
phernandez 9b199c6dcb fix: add FastEmbed runtime tuning knobs and provider caching
Add configurable cache_dir, threads, and parallel settings for FastEmbed
to support cloud deployments where defaults fail. Cache embedding providers
at the process level to avoid re-creating heavy ONNX model instances.

- Add semantic_embedding_cache_dir, semantic_embedding_threads, and
  semantic_embedding_parallel config fields
- Thread-safe provider cache with double-checked locking in factory
- Forward runtime knobs through to TextEmbedding and embed() calls
- Fix if/elif chain in factory for correct error handling

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-03 10:26:47 -06:00
phernandez fe4a7b1622 fix: update analytics test mocks for non-daemon thread change
Thread constructor no longer receives daemon=True, update mock
signatures to match. Also assert on the new "type": "event" field.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-02 21:26:54 -06:00
phernandez 7d2012a82c fix(cli): fix Umami analytics event delivery
Three issues prevented CLI analytics from reaching the Umami dashboard:

1. Wrong API endpoint — cloud.umami.is rejects /api/send, the JS tracker
   uses api-gateway.umami.dev
2. Missing "type": "event" top-level field required by Umami v2 API
3. Non-browser User-Agent ("basic-memory-cli/...") triggers Umami's bot
   detection, which silently drops events with {"beep":"boop"} 🤖
4. Daemon thread was killed before HTTP request completed on fast commands

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-02 19:24:10 -06:00
phernandez 7f2d4d2a6f fix: three cloud-testing bugs (#640, #641, #642)
🔧 #640 — LinkResolver selects worst match instead of best
Replace `min(results, key=lambda x: x.score)` with `results[0]`.
Both SQLite and Postgres return results sorted best-first in SQL,
so using `results[0]` is backend-agnostic and correct.

🔧 #641 — search_notes output_format="text" returns raw Pydantic model
Add `_format_search_markdown()` that formats SearchResponse as readable
markdown with title, permalink, score, and matched snippet per result.
Update prompts to use `output_format="json"` since they need structured
data for result counting and branching logic.

🔧 #642 — metadata_filters with `note_type` key returns empty results
Add `_METADATA_KEY_ALIASES` mapping at the tool level that aliases
`note_type` → `type` before passing metadata_filters to the search query.
The frontmatter field is `type`, not `note_type`.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-02 12:58:22 -06:00
phernandez 8b7f39ee9b docs: update AI assistant guides for v0.19.0, fix multi-tag search parsing
Update both the compact and extended AI assistant guides with v0.19.0 changes:
- 📝 write_note overwrite guard: callout, edit_note examples, overwrite=True
- 🔍 Expanded search section: all search types, tag: shorthand, filter-only
  searches, metadata_filters operators, min_similarity
- ⚠️ "Note already exists" error handling pattern
- 📋 Tool quick reference: updated params, added list_workspaces
- 🔗 memory:// URL: added cross-project format
- ✏️ Best practice: prefer edit_note for updates

Fix tag: shorthand parsing to handle multiple tags anywhere in the query.
Old parser only handled queries starting with "tag:" and broke on
"tag:coffee AND tag:brewing". New parser uses re.findall to extract all
tag:value tokens, strips boolean connectors, and preserves remaining text.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-02 09:50:22 -06:00
phernandez a368d06fd2 fix: improve cloud CLI status and error messages
- Simplify `bm cloud status` output: remove verbose health check details
  (status/version/timestamp), show simple "Cloud connected" / "Cloud not
  connected" message instead
- Improve `bm reindex --project` error for cloud projects: distinguish
  between "project not found" and "project is cloud-only" with a helpful
  message explaining reindexing is a local operation
- Improve `bm project list` cloud error message: show the actual error
  and soften the credentials suggestion
- Add tests for cloud status command (5 tests)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-01 20:24:11 -06:00
phernandez 3a2b80b7e9 fix: remove broken CI coverage infrastructure
The coverage collection had multiple issues:
- Wrong pytest markers caused 0 tests to run
- Postgres jobs silently skipped artifact uploads
- Coverage Summary job failed when artifacts were missing
- uv venv picked wrong Python version for coverage jobs

Simplify: every job just runs tests via `just` recipes. No more
dual code paths, artifact uploads, or summary aggregation job.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-01 13:18:51 -06:00
phernandez bd5099120f fix: CI Postgres coverage jobs used wrong pytest marker (-m postgres)
The coverage code paths for Postgres unit and integration jobs filtered
with `-m postgres`, but no tests use that marker. Postgres is selected
via BASIC_MEMORY_TEST_POSTGRES=1 env var. This caused 0 tests to run
on Python 3.12 (the only version with coverage: true).

- Postgres unit: remove `-m postgres` (matches `just test-unit-postgres`)
- Postgres integration: use `-m "not semantic"` (matches `just test-int-postgres`)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-01 12:47:38 -06:00
phernandez 8e64caf784 fix: list_workspaces bypasses factory pattern on cloud MCP server (#636)
Add set_workspace_provider() injection point so the cloud MCP server can
list workspaces by querying its own database directly, instead of making
an HTTP round-trip to the control-plane API with credentials it doesn't have.

🔧 Mirrors the existing set_client_factory() pattern in async_client.py
🧪 Adds 3 tests for provider injection, fallback, and context caching
🩹 Updates build_context test assertions for v0.18 backward compat fields

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-01 12:08:25 -06:00
phernandez ccb5740920 fix: create backup before config migration overwrites old format (#637)
When load_config() detects a legacy config format and resaves it, the old
config.json was overwritten in-place with no recovery path. Users switching
between dev and released versions would lose their config.

Now creates config.json.bak before the migration resave so users can revert
if needed.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-01 12:08:25 -06:00
phernandez 373893a7ee fix: restore API backward compatibility for v0.18.x clients (#638)
v0.18.5 clients (homebrew) fail against v0.19.0 servers because:
- `entity_type` was renamed to `note_type` in EntityResponse
- 13 fields gained `Field(exclude=True)` and vanished from JSON

🔧 EntityResponse: add `entity_type` computed_field mirroring `note_type`
🔧 memory.py: remove `exclude=True` from 13 fields across EntitySummary,
   RelationSummary, ObservationSummary, and MemoryMetadata
🔧 Add v0.18 backward-compat contract tests

Closes #638

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-01 12:08:25 -06:00
phernandez 1987581ee3 docs: update v0.19.0 release notes and apply formatting fixes
Add #634 (stale schema metadata) to bug fixes section.
Apply ruff formatting to schema.py, write_note.py, test files.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-03-01 12:08:25 -06:00
Drew Cain 59134affa4 fix: read schema definitions from file instead of stale database metadata (#635)
Signed-off-by: Drew Cain <groksrc@gmail.com>
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: phernandez <paul@basicmachines.co>
2026-03-01 09:41:22 -06:00
phernandez c7d97decd6 docs: update v0.19.0 release notes with recent commits
Add write_note overwrite guard (#632) to new capabilities, 8 bug fixes
(#631, #630, #30, #31, #28, plus schema_validate/Post/frontmatter fixes),
and write_note idempotency breaking change to upgrade notes. Bump commit
count from 80+ to 90+.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-28 13:53:29 -06:00
phernandez 5d5efa02a5 fix: format schema_infer and schema_diff as markdown text (#28)
schema_infer and schema_diff returned raw Pydantic models in text mode,
causing LLMs to render field names as "undefined". Add text formatters
(_format_inference_report, _format_drift_report) matching the existing
_format_validation_report pattern. CLI paths are unaffected — they
always use output_format="json".

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-28 12:24:38 -06:00
phernandez 4f12182e28 fix: parse tag: prefix at MCP tool level to avoid hybrid search failure (#30)
When semantic search is enabled (default), `search_notes(query="tag:security")`
failed because the HYBRID retrieval mode requires non-empty text, but the
service-layer tag: parser clears the text after the mode is already set.

Parse tag: prefix at the tool level before search mode selection, converting it
to a tags filter. This works with all search modes (text, hybrid, vector).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-28 11:53:05 -06:00
phernandez 2c5b63c4a2 fix: default search_notes to entity-level results (#31)
search_notes was returning individual observations and relations as
separate top-level results, wasting the result limit and creating
confusing UX. Default entity_types to ["entity"] when the caller
doesn't specify it — the entity row already indexes full file content,
so no matches are lost. Users can still override with explicit
entity_types=["observation"] etc.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-28 11:28:51 -06:00
phernandez c4c9f842ea fix: schema_validate identifier resolution and text rendering
Fixes issues #29 and #33 from openclaw-basic-memory.

🔧 Identifier resolution (#33):
- Router used get_by_permalink() which only matched exact permalinks.
  Replaced with link_resolver.resolve_link() so titles, paths, and
  fuzzy matches work consistently with other tools like read_note.
- Set total_entities=1 and total_notes=len(results) on single-note
  path for consistency with batch path.
- Guards for "no notes" and "no schema" now fire for identifier-based
  validation too, not just note_type-based.

🎨 Text rendering (#29):
- Tool returned raw Pydantic model which LLMs rendered as
  "undefined — invalid". Now returns pre-formatted markdown.
- Router uses entity.title (with permalink fallback) as note_identifier
  for human-readable output in both text and JSON modes.
- JSON output (output_format="json") unchanged for CLI compatibility.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-28 10:40:01 -06:00
phernandez dc0ca71c18 fix: avoid Post(**metadata) crash when frontmatter contains 'content' or 'handler' keys
frontmatter.Post.__init__ takes `content` and `handler` as positional
parameters. When user YAML frontmatter contains these as field names,
unpacking metadata via **kwargs causes "got multiple values for argument
'content'".

Replace frontmatter.loads() with frontmatter.parse() + Post() + update()
in entity_parser, and replace the **metadata unpacking in
entity_service.update_entity() with the same safe pattern.

Fixes basic-memory-cloud#375

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-27 22:52:16 -06:00
phernandez 236ae268aa fix: coerce list frontmatter values to strings for title and type fields
YAML block sequence syntax can cause PyYAML to parse scalar fields like
`title` and `type` as lists instead of strings. Downstream code calls
.strip()/.casefold() on these values, crashing with
"'list' object has no attribute 'strip'".

Add _coerce_to_string() helper that joins list items with ", " and apply
it in entity_parser.parse_markdown_content() and
entity_service.fast_edit_entity() where these fields are extracted.

Fixes basic-memory-cloud#376

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-27 22:45:09 -06:00
Paul Hernandez bd5923a370 feat: add overwrite guard to write_note tool (#632)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-27 16:12:38 -06:00
jope-bm 4ea5396ddd fix: skip workspace resolution when client factory is active (#630)
Signed-off-by: Joe P <joe@basicmemory.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-27 14:40:04 -06:00
Paul Hernandez e97eafa55a fix: build_context related_results schema validation failure (#631)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-27 12:30:44 -06:00
bm-clawd 254e30423d docs: update v0.19.0 release notes with post-draft changes (#626)
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-27 09:36:27 -06:00
phernandez 496af07ced fix: resolve pyright possibly-unbound errors in edit_note
Initialize entity_id and result before the try/except block and
add assertion before the formatting section to help pyright prove
result is always bound on all code paths.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-26 20:10:35 -06:00
bm-clawd b5667f9b55 docs: Add UTM tracking to README links (#611) 2026-02-26 19:55:33 -06:00
phernandez 54b968b93c fix: reduce excessive log volume by demoting per-request noise to DEBUG (#613)
Demote high-frequency per-request/per-item logs from INFO to DEBUG:
- 🔇 Client routing decisions (async_client.py) — logged every MCP tool call
- 🔇 DB migration checks (db.py) — logged every ASGI client creation
- 🔇 Vector table ensure/ready (sqlite + postgres search repos) — logged every search
- 🔇 Per-entity search index start/complete (search_service.py) — logged every file sync
- 🔇 Incremental scan details + per-file permalink updates (sync_service.py)
- 🔇 MCP search tool params and no-results (search.py)
- 📉 Log retention: "10 days" → 5 files (~50MB cap)

API v2 request/response logs remain at INFO for observability.

Closes #613

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-26 19:48:24 -06:00
phernandez e59b5cb6d9 feat: edit_note append/prepend auto-create note if not found (#614)
When append or prepend targets a non-existent note, the tool now
creates the file automatically instead of returning an error. This
eliminates silent failures for plugins (like openclaw) that use
edit_note(append) to build daily conversation notes — on the first
message of each day, the note didn't exist yet.

- find_replace and replace_section still require an existing note
- JSON output now includes `fileCreated: bool` in all responses
- Path traversal security check applied to auto-created directories
- Updated error messages to suggest append/prepend for missing notes

🧪 25 unit tests, 14 MCP integration tests, 10 CLI integration tests — all passing

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-26 19:00:34 -06:00
phernandez f0335b998e fix: handle quoted picoschema enum strings in YAML frontmatter (#612)
The picoschema enum-with-description syntax `[val1, val2], description`
is invalid YAML. Users must quote it so YAML parses it as a string.
This adds `_parse_enum_string()` to extract enum values and description
from the resulting string value (e.g., "[active, blocked], current state").

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-26 18:10:48 -06:00
phernandez 66effb04a7 fix: upgrade cryptography and python-multipart for security advisories
- cryptography 46.0.3 → 46.0.5 (subgroup attack on SECT curves)
- python-multipart 0.0.21 → 0.0.22 (arbitrary file write via non-default config)
- pillow alert dismissed — blocked by fastembed 0.7.4 pinning <12.0 (tracking qdrant/fastembed#606)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-26 14:42:20 -06:00
phernandez 9331126ba1 feat: improve content hit rate in search results (#609)
Three changes to surface more answer text in search results:

- Populate matched_chunk_text for FTS-only hybrid results from content_snippet,
  preventing fallback to truncated content when vector search has no match
- Increase TOP_CHUNKS_PER_RESULT from 3 to 5, catching answers in deeper chunks
  for large notes (~2700 → ~4500 chars of matched context)
- Increase CONTENT_DISPLAY_LIMIT from 2000 to 4000, doubling the safety-net
  content truncation for results without matched_chunk

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-26 11:55:38 -06:00
phernandez 3bbb44af0b feat: add --json output to CLI commands for scripting and CI
Add machine-readable JSON output to five CLI commands:
- `bm status --json` — sync report
- `bm project list --json` — structured project list
- `bm schema validate --json` — validation report
- `bm schema infer --json` — inference report
- `bm schema diff --json` — drift report

Refactored `run_status()` to return data instead of printing directly,
improving testability. Follows the established `bm project info --json`
pattern using `print()` for clean JSON (no Rich markup).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-26 10:33:00 -06:00
phernandez f9b2a075a9 feat: return richer content context in search results (#609)
Two strategies to improve content hit rate when the right document is found:

1. Small notes (<=2000 chars): return full content_snippet as matched_chunk
   so the answer is always present for correctly-retrieved small notes
2. Large notes: return top-3 chunks by similarity joined with \n---\n
   instead of just the single best chunk (~2700 chars vs ~900 chars)

Also raises CONTENT_DISPLAY_LIMIT from 250 to 2000 for richer FTS results.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-26 08:36:54 -06:00
phernandez 0a3f3f07f8 fix: run coverage instead of tests on 3.12/ubuntu, not in addition to
Previous commit still ran tests twice on the coverage matrix entry.
Now the 3.12/ubuntu/main combo runs with coverage directly, and all
other matrix entries run without. No duplicate work.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-25 23:36:46 -06:00
phernandez 184ea6d9fa fix: collect coverage from test jobs instead of re-running all tests
The coverage summary job was re-running the entire SQLite + Postgres test
suite from scratch (~60 min), duplicating work already done by upstream jobs.
It consistently timed out.

Now each test job collects coverage data and uploads it as an artifact.
The coverage job just downloads, combines, and reports — should take <1 min.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-25 23:33:15 -06:00
phernandez f5a0e942b0 fix: replace RRF with score-based fusion in hybrid search (#577)
RRF compressed all fused scores to ~0.016, destroying ranking differentiation.
The new formula `max(vec, fts) + FUSION_BONUS * min(vec, fts)` preserves
dominant signals and rewards dual-source agreement.

Changes:
- Remove RRF_K constant; add FUSION_BONUS (0.3) and FTS_GATE_THRESHOLD (0.0)
- Use raw vector similarity scores instead of re-normalizing by vec_max
- Zero-score results now produce zero fused score (no 0.1 weight floor)
- Rename test_hybrid_rrf.py → test_hybrid_fusion.py with updated assertions
- Update docs and docstrings to reflect score-based fusion

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-25 22:53:00 -06:00
phernandez e46555bf2c fix: guard against closed streams in promo and missing vector tables (#579, #607)
- Wrap isatty() in _is_interactive_session() with try/except ValueError
  so MCP stdio transport shutdown no longer produces noisy tracebacks
- Check both search_vector_chunks AND search_vector_embeddings exist
  before running JOIN queries in get_embedding_status(), fixing
  OperationalError when only the chunks table is present
- Add test for closed-stream scenario

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-25 18:01:59 -06:00
phernandez 74e6afdc0f fix: create search_vector_chunks in test fixtures for Postgres compatibility
Embedding status tests were creating search_vector_chunks inline using
SQLite-only DDL (AUTOINCREMENT). Added Postgres DDL constants to
models/search.py and wired them into the test fixture so both backends
create the table at setup time — matching what the Alembic migration
does in production.

Also fixed stub search_vector_embeddings to use chunk_id (Postgres
column name) instead of rowid, and added inter-test cleanup to prevent
ordering-dependent failures.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-25 17:30:35 -06:00
phernandez aa635b8a8b fix: accept null for expected_replacements in edit_note (#606)
MCP clients may send explicit `null` for unused optional fields.
`expected_replacements: int = 1` caused FastMCP's JSON Schema validation
to reject null before the function body ran. Changed to `Optional[int] = None`
with an effective default resolved inside the function body.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-25 14:18:53 -06:00
phernandez 1856d4b462 fix: update test_project_info assertions for dashboard format
The htop-inspired dashboard (3004d0d1) changed the project info output
from "Basic Memory Project Info" / "Statistics" to project name title
with "Knowledge Graph" section. Update test assertions to match.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-25 13:38:01 -06:00
phernandez 73413486bc feat: merge search_by_metadata into search_notes with optional query
Make `query` optional in `search_notes` so it becomes the single search tool.
Remove `search_by_metadata` entirely — it was unreleased and redundant since
`search_notes` already supports `metadata_filters`, `tags`, and `status`.

- 🔧 `query` param is now `Optional[str] = None`
- 🛡️ Added None guards for project detection and URL resolution
-  Added `no_criteria()` validation with helpful error message
- 🗑️ Deleted `search_by_metadata` tool, imports, tests, and contract entry
- 📝 Updated docs, README, and v0.19.0 release notes

Closes #605

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-25 13:30:42 -06:00
phernandez b09eca1698 feat: add EmbeddingStatus schema and get_embedding_status() service method
Add EmbeddingStatus model to project_info schemas and wire it into
ProjectInfoResponse. ProjectService.get_embedding_status() queries
vector tables for chunk/embedding counts, detects orphaned chunks
and missing embeddings, and recommends reindex when appropriate.
Handles both SQLite and Postgres backends. 🔍

Includes 6 unit tests covering: disabled search, missing vector tables,
entities without chunks, orphaned chunks, healthy state, and integration
with get_project_info().

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-25 11:44:50 -06:00
phernandez 3004d0d1fe feat: replace project info with htop-inspired dashboard
Replace Layout-based display (expanded to terminal width) with a compact
Panel using Table.grid(expand=False). Add horizontal bar charts for note
types (top 5), embedding coverage bar with Unicode blocks, and colored
status dots. Removes verbose sections (most connected, recent activity,
available projects) in favor of a dense, visually engaging dashboard. 📊

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-25 11:11:58 -06:00
phernandez b6369d3d14 fix: cap sqlite-vec knn k parameter at 4096 limit
sqlite-vec enforces k <= 4096 for nearest-neighbor queries. Projects with
>4096 vector chunks would crash all vector/hybrid search because
candidate_limit = max(100, (limit + offset) * 10) exceeded this hard limit.

Clamp the knn k in _run_vector_query while keeping the outer SQL LIMIT
unclamped. Only affects SQLite — pgvector has no such constraint.

Fixes #604

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-24 23:23:15 -06:00
phernandez db6d0dcd9e docs: add tag: shorthand limitation note to search_notes docstring
The tag: query shorthand doesn't work with hybrid search (default when
semantic search is enabled) because it strips the text query. Document
the workaround: use search_type="text" or the tags parameter instead.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-24 20:45:42 -06:00
phernandez 5a5eb443ea docs: remove bm watch from v0.19.0 release notes
The watch command was removed from the codebase — drop the section
from the release notes so it doesn't advertise a non-existent feature.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-24 20:26:34 -06:00
phernandez e1df23d793 docs: add v0.19.0 release notes
Covers all 66 commits since v0.18.0: semantic vector search, schema
system, project-prefixed permalinks, per-project cloud routing,
FastMCP 3.0 upgrade, and 16 bug fixes.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-24 20:20:23 -06:00
phernandez 0462a7d4ba docs: add telemetry disclosure and opt-out instructions to README
Transparent about what we collect (promo/login events only), what we
don't (no PII, no file contents, no per-command tracking), and how
to opt out (BASIC_MEMORY_NO_PROMOS=1).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-24 19:52:56 -06:00
phernandez 79db876bd6 feat: hardcode Umami analytics defaults for FOSS usage tracking
Bake in cloud.umami.is host and basic-memory-foss site ID so all
open-source installs send anonymous CLI events by default. Users
can still opt out with BASIC_MEMORY_NO_PROMOS=1 or override the
endpoint via env vars.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-24 19:50:28 -06:00
phernandez 0130573342 fix: prompts call MCP tools directly, sync handles semantic errors, status uses local routing
- Prompts (search, continue_conversation) now call MCP tools directly
  instead of going through API endpoints, matching the recent_activity
  pattern and fixing #526 where prompts returned empty results
- sync_file catches SemanticDependenciesMissingError separately so
  entities are still returned successfully when vector embedding fails,
  with a clear warning instead of silent failure (#578)
- `bm status` and `bm doctor` default to local routing since they scan
  the local filesystem — cloud routing returned Docker-internal paths

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-24 19:36:11 -06:00
phernandez c372dfb09f fix: use LinkResolver fallback in build_context for flexible identifier matching (#582)
build_context now falls back to LinkResolver when an exact permalink lookup
returns empty results. This reuses the same resolution pipeline as read_note
(permalink candidates, title match, file path, FTS) so callers no longer
get empty results for valid note identifiers.

Also changes ensure_frontmatter_on_sync default to True — frontmatter is
now added during sync by default. Tests updated accordingly.

🔧 ContextService accepts optional LinkResolver, wired via DI in all 3 factory variants
 2027 unit + 278 integration tests passing

Closes #582

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-24 18:58:19 -06:00
phernandez d763d86798 fix: unify project path so path is always the local filesystem path
Cloud projects with bisync had a split-brain problem: `path` held a cloud
slug while the actual local directory lived in `local_sync_path`. This caused
`bm status` and file sync to fail for bisync'd cloud projects.

Changes:
- Config migration promotes `local_sync_path` → `path` for entries where
  `path` is a non-absolute cloud slug
- `ensure_project_paths_exists` skips cloud-only projects with slug paths
- `initialize_file_sync` and watch service now keep cloud projects that have
  an absolute local path (bisync copy) instead of skipping all cloud projects
- `sync-setup` and `project add --cloud --local-path` set both `path` and
  `local_sync_path` to the local directory
- `sync-setup` creates the project in the local DB for immediate MCP use
- `_get_sync_project` falls back from `local_sync_path` to `path`
- Config load errors now show user-friendly messages instead of stack traces

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-24 15:08:09 -06:00
phernandez 538af97cba feat: show spinner while fetching cloud projects in bm project list
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-24 12:05:33 -06:00
phernandez 054178d155 fix: parameterize SQL queries in search repository type filters
Replace f-string interpolation with parameterized queries for note_types,
search_item_types, and metadata filter paths to prevent SQL injection.

Fixes #591

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-23 22:16:46 -06:00
phernandez 18f00f861d fix: remove hardcoded "main" default from default_project (#575)
When a user's config.json had projects with names other than "main" and
no explicit default_project key, the hardcoded field default of "main"
would not match any project. The model_post_init fixup logic existed but
was untested and only handled the stale-name case, not the None case.

Changes:
- Change default_project field default from "main" to None
- Use model_fields_set to distinguish "config omitted the key" (auto-resolve
  to first project) from "user explicitly set None" (preserve for discovery mode)
- Split model_post_init into two branches: auto-resolve when not explicitly
  provided, correct stale names when explicitly set but invalid
- Remove # pragma: no cover from now-tested branches
- Add 10 new tests covering valid defaults, stale defaults, empty string,
  single project, config file round-trips, and discovery mode preservation

Fixes #575

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-23 21:59:15 -06:00
jope-bm da4d369c32 feat: add created_by and last_updated_by user tracking to Entity (#602)
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-23 11:20:59 -07:00
Paul Hernandez 0f3889fdd0 fix: Return matched chunk text in search results (#601)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-22 21:01:20 -06:00
Paul Hernandez c44291830c chore: rename entity_type to note_type (#600)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-22 20:28:57 -06:00
phernandez e1cccba72d docs: add metadata search reference
Document the full structured metadata filter system — operators, MCP tools,
tag shortcuts, and CLI flags — which previously had no dedicated documentation.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-22 00:45:10 -06:00
phernandez 6ff39076a0 fix: double-default display in project list + stale test updates
Use config.default_project as single source of truth for the Default
column in `bm project list`, removing checks against local DB and cloud
API is_default fields that could independently mark multiple projects.

Also updates tests that were out of date after get_project_mode changed
to default unknown projects to CLOUD:
- test_get_client_local_project_uses_asgi_transport: register "main" as LOCAL
- test_run_filters_cloud_projects_each_cycle: register local project in config
- test_new_project_addition_scenario: register projects as LOCAL in config
- test_get_project_mode_defaults_to_cloud: assert new CLOUD default

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-22 00:33:25 -06:00
phernandez 56cefbaafd add OSS discount code to README.md
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-21 22:28:13 -06:00
phernandez 3337c7d1ff fix cli test for project move local only
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-21 21:02:13 -06:00
phernandez e248763a73 fix: update tool contract test for list_memory_projects workspace param
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-21 20:12:47 -06:00
phernandez 2e5813d31e feat: CLI refactoring + workspace-aware cloud project listing
Refactor CLI commands to use typed ProjectClient instead of raw HTTP calls,
and add workspace metadata to cloud project listings so users can distinguish
personal vs organization projects.

Key changes:
- 🔧 CLI commands now use ProjectClient typed API clients instead of
  call_get/call_post with manual URL construction
- 🏢 Cloud project listings include workspace_name, workspace_type, and
  workspace_tenant_id for each cloud-sourced project
- Pass config.default_workspace when fetching cloud projects via
  _fetch_cloud_projects() and CLI list_projects
- Add --workspace flag to `bm project list` for explicit workspace override
- Add "Workspace" column to CLI project list table
- Add `bm tool list-projects` and `bm tool list-workspaces` JSON commands
- Comprehensive tests for workspace passthrough, merge behavior, and CLI routing

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-21 20:04:03 -06:00
phernandez 2cde8d2659 clean up cli commands
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-21 16:06:43 -06:00
Paul Hernandez 9515130b2a feat: upgrade fastmcp 2.12.3 to 3.0.1 with tool annotations (#598)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-21 12:28:30 -06:00
Paul Hernandez b86dd6fb53 feat: Fix bm CLI runtime defects and audit regressions (#596)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-20 22:59:29 -06:00
Paul Hernandez 8451f2b1d7 feat: add frontmatter validation to schema system (#597)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-20 22:57:19 -06:00
Paul Hernandez ee0397513d fix: recent_activity dedup + pagination across MCP tools (#595)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-20 21:48:58 -06:00
Drew Cain 9c9ff2931d fix: backend-specific distance-to-similarity conversion (#593)
Signed-off-by: Drew Cain <groksrc@gmail.com>
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Paul Hernandez <60959+phernandez@users.noreply.github.com>
Co-authored-by: phernandez <paul@basicmachines.co>
2026-02-20 20:15:46 -06:00
Paul Hernandez bbe6c1e8f3 chore: add ty as supplemental type checker (#594)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-20 20:14:46 -06:00
Paul Hernandez edb7991ccf fix: strip NUL bytes from content before PostgreSQL search indexing (#592)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-20 18:11:04 -06:00
phernandez d7faeb754e feat: Add destination_folder parameter to move_note tool
Allows callers to move a note into a folder while preserving its original
filename — no need for a separate read_note round-trip to extract the basename.

- destination_folder is mutually exclusive with destination_path
- Rejected for directory moves (is_directory=True)
- Uses Path().name and PureWindowsPath().as_posix() for cross-platform compat
- Validates resolved path against path traversal attacks
- Includes formatting fixes in promo.py and test_analytics.py

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-20 02:06:04 -06:00
jope-bm deef89a724 feat: Add display_name and is_private to ProjectItem (#574)
Signed-off-by: Joe P <joe@basicmemory.com>
Signed-off-by: phernandez <paul@basicmachines.co>
Signed-off-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Paul Hernandez <60959+phernandez@users.noreply.github.com>
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Paul Hernandez <phernandez@users.noreply.github.com>
Co-authored-by: phernandez <paul@basicmachines.co>
Co-authored-by: jope-bm <jope-bm@users.noreply.github.com>
2026-02-20 01:44:07 -06:00
Paul Hernandez 30499a9f61 feat: Let stdio MCP honor per-project cloud routing (#590)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-20 01:43:45 -06:00
bm-clawd 1ac65b944c feat: CLI analytics via Umami event collector (#572)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-20 00:09:42 -06:00
Paul Hernandez 306e562281 chore: Make semantic deps default, auto-backfill embeddings, and default search to semantic (#586)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-19 18:31:15 -06:00
phernandez ed82b0c417 Merge branch 'main' of github.com:basicmachines-co/basic-memory 2026-02-18 22:23:53 -06:00
Paul Hernandez 0cb3f95d67 feat: Add JSON output mode for BM MCP tools (default text) (#585)
Signed-off-by: phernandez <paul@basicmachines.co>
Signed-off-by: Paul Hernandez <60959+phernandez@users.noreply.github.com>
2026-02-18 22:18:29 -06:00
phernandez 0a36256f8a Merge branch 'main' of github.com:basicmachines-co/basic-memory 2026-02-18 10:50:58 -06:00
phernandez 36e67e6eec ci: split and speed up PR test matrix
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-18 10:41:02 -06:00
Paul Hernandez f2683291e4 feat: add workspace selection flow for MCP and CLI (#576)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-17 22:48:06 -06:00
phernandez 8c05a9ec80 fix: stabilize semantic search defaults, FTS fallback, and postgres project sync
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-17 15:36:46 -06:00
phernandez a6d8d4c0f6 Add ensure_frontmatter_on_sync with precedence warning
Add a new config option to enforce frontmatter on markdown sync when missing, writing derived title/type/permalink and updating in-memory metadata before upsert. Add startup warning when this option is combined with disable_permalinks to make precedence explicit. Add config/sync/initialization tests for the new behavior, and stabilize project list CLI integration assertions by forcing a wide terminal in tests to avoid Rich truncation.

Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-16 22:08:09 -06:00
phernandez 0239f4abb4 Simplify local/cloud routing and clarify project targeting
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-16 21:42:25 -06:00
Paul Hernandez 9259a7eb59 feat: min-similarity override, edit-note CLI, and strip-frontmatter (#571)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-16 17:56:00 -06:00
Paul Hernandez 55d675e278 feat: min_similarity override, cloud promo improvements (#570)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-16 16:44:02 -06:00
Drew Cain 6afe4fd0cc feat: expose external_id in EntityResponse and link resolver (#569)
Signed-off-by: Drew Cain <groksrc@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-15 20:33:37 -06:00
phernandez 113d1b6f1b formatting fix
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-14 09:56:37 -06:00
Paul Hernandez 545804f194 feat: Add project-prefixed permalinks and memory URL routing (#544)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-13 17:45:50 -06:00
Paul Hernandez 8bc03d1357 feat(mcp): add MCP UI variants and TUI output (#545)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-13 15:21:48 -06:00
Paul Hernandez f6e0a5b5bb fix: Speed up bm --version startup (#534)
Signed-off-by: phernandez <paul@basicmachines.co>
Signed-off-by: Paul Hernandez <60959+phernandez@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-02-13 11:12:52 -06:00
Paul Hernandez 7624a20d8d feat: isolate default sqlite db by config dir (#567)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-13 11:11:13 -06:00
Paul Hernandez d84708ca7f feat: add per-project local/cloud routing with API key auth (#555)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-13 09:52:50 -06:00
Paul Hernandez 1428d18de1 fix: make semantic search dependencies optional extras (#566)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-12 21:46:26 -06:00
Paul Hernandez 312662f382 feat: Add cloud discovery touchpoints to CLI and MCP (#546)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-12 21:01:45 -06:00
Paul Hernandez ed9487708e feat: enable default_project_mode by default (#560)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-12 20:34:46 -06:00
Paul Hernandez 8df88e4d02 feat: add basic-memory watch CLI command (#559)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-12 20:10:09 -06:00
Paul Hernandez 07778790d3 feat: add semantic vector search for SQLite and Postgres (#550)
Signed-off-by: phernandez <paul@basicmachines.co>
Signed-off-by: bm-clawd <clawd@basicmemory.com>
Co-authored-by: bm-clawd <clawd@basicmemory.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-12 15:44:47 -06:00
Paul Hernandez b609c4e531 fix: use global --header for Tigris consistency on all rclone transactions (#564)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-12 15:43:05 -06:00
Paul Hernandez f1a065bce3 chore: Release/v0.18.2 (#563)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-12 15:41:38 -06:00
phernandez 2b94d9a278 fix formatting for tigris headers
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-12 13:22:31 -06:00
Paul Hernandez 344e651693 fix: use VIRTUAL instead of STORED columns in SQLite migration (#562)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-11 22:24:51 -06:00
Paul Hernandez c97733d785 feat: Schema system for Basic Memory (#549)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-11 15:04:42 -06:00
phernandez 00537272c6 chore: update version to 0.18.1 for v0.18.1 release 2026-02-11 14:28:52 -06:00
phernandez b057912452 docs: add CHANGELOG entry for v0.18.1
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-11 14:24:26 -06:00
Paul Hernandez 8489a3d37e fix: add X-Tigris-Consistent headers to all rclone commands (#558)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-11 14:22:49 -06:00
Paul Hernandez a47c9c021f feat: add --format json to CLI tool commands (#552)
Signed-off-by: phernandez <paul@basicmachines.co>
Signed-off-by: bm-clawd <clawd@basicmemory.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: bm-clawd <clawd@basicmemory.com>
2026-02-08 14:56:29 -06:00
phernandez c46d7a6833 fix: add POST legacy compat routes for v0.18.0 CLI
The v0.18.0 CLI (Homebrew) calls POST /projects/projects for project add
and POST /projects/config/sync for config sync. The previous legacy compat
fix (a0e754b) only added GET for list_projects but missed POST endpoints.

This caused 405 Method Not Allowed when running `bm project add` in cloud mode.

🔗 Logfire trace: trace_id=019c398cb257f040c1255821b6d5e385

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-07 13:41:41 -06:00
Paul Hernandez 343a6e118b fix: Handle EntityCreationError as conflict (#541)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-03 22:28:58 -06:00
phernandez a0e754b7ae fix: restore legacy /projects/projects endpoint for older CLI versions
Older versions of basic-memory CLI (v0.17.4 and earlier) call
GET /projects/projects to list projects. This endpoint was removed
when we migrated to v2 routers.

Add explicit route at /projects/projects (without trailing slash) to
avoid 307 redirects that the cloud proxy doesn't follow.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-02 22:26:48 -06:00
Paul Hernandez 24ca5f6804 fix: recent_activity prompt defaults (#533)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-02 19:22:33 -06:00
Paul Hernandez f1d50c2ba7 feat: Support tag: query shorthand in search (#535)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-02 19:22:16 -06:00
Paul Hernandez 8072449a78 chore: Add fast feedback loop tooling (#538)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-01 23:04:59 -06:00
phernandez 45d3f58e4d Merge branch 'main' of github.com:basicmachines-co/basic-memory 2026-02-01 21:17:39 -06:00
phernandez d9c8923148 fix ci runner for tests
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-01 21:16:10 -06:00
phernandez 15bd6b95ef fix ci runner i
Signed-off-by: phernandez <paul@basicmachines.co>
2026-02-01 21:10:26 -06:00
phernandez 0715dcff3d run ubuntu tests on depot
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-31 23:25:46 -06:00
Paul Hernandez 009e84926d fix: stabilize metadata filters on postgres (#536)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-31 22:31:00 -06:00
phernandez 8838571509 Add metadata filter tests and fix fast write external_id
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-31 15:27:05 -06:00
Paul Hernandez 530cbac73f feat: fast edit entities, refactors for webui, enhance search (#532)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-31 15:16:52 -06:00
Paul Hernandez e3ced49d9d fix: prevent spurious 'metadata: {}' in frontmatter output (#530)
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-31 12:17:10 -06:00
Drew Cain 8f962fdd87 chore: update version to 0.18.0 for v0.18.0 release 2026-01-29 22:41:42 -06:00
Drew Cain fbb497f6dc docs: add CHANGELOG entry for v0.18.0 2026-01-29 22:41:14 -06:00
Drew Cain 0023e736ab feat: add context-aware wiki link resolution with source_path support (#527)
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-28 19:11:41 -06:00
Paul Hernandez 0b2080114b feat: add directory support to move_note and delete_note tools (#518)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-26 19:52:10 -06:00
Paul Hernandez 8730067f3a feat: Feature/517 local mcp cloud mode (#522)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-26 19:51:49 -06:00
Drew Cain e14ba92631 fix: resolve MCP prompt rendering errors (#524)
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-25 19:14:41 -06:00
phernandez 9d98892570 chore: update version to 0.17.9 for v0.17.9 release 2026-01-24 12:55:58 -06:00
phernandez 3be4495723 docs: add CHANGELOG entry for v0.17.9
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-24 12:55:27 -06:00
Paul Hernandez 17c0e0a29b fix: check config default_project only in local mode for remove_project (#523)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-24 12:53:44 -06:00
phernandez 7ebf16a95d chore: update version to 0.17.8 for v0.17.8 release 2026-01-24 11:43:44 -06:00
phernandez c05075f8d4 docs: add CHANGELOG entry for v0.17.8
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-24 11:43:21 -06:00
phernandez 4cef9281ca docs: add CHANGELOG entry for v0.17.7
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-24 11:39:41 -06:00
Paul Hernandez 6888effef2 fix: correct get_default_project() query to check for True instead of not NULL (#521)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-24 11:37:05 -06:00
Paul Hernandez 38616c345d fix: read default project from database in cloud mode (#520)
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-24 10:44:04 -06:00
phernandez f3c1aa895c fix links in README.md to remove smithery badge
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-22 12:58:03 -06:00
phernandez d978aba09b fix links in README.md to remove glama.ai
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-22 12:56:33 -06:00
phernandez 2aaee734c9 fix links in README.md to point to basicmemory.com instead of basicmachines.co
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-22 12:53:18 -06:00
Paul Hernandez 369ad37b3d feat: add SPEC-29 Phase 3 bucket snapshot CLI commands (#476)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-20 17:55:01 -06:00
Drew Cain 4e5f701d22 Fix server.json runtimeArguments format
- Use proper Argument object format instead of plain strings
- Add .mcpregistry tokens to .gitignore

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: Drew Cain <groksrc@gmail.com>
2026-01-19 12:23:29 -06:00
Drew Cain 9d9ea4d61c chore: update version to 0.17.7 for v0.17.7 release 2026-01-19 12:12:35 -06:00
Drew Cain 7a502e6474 feat: Add MCP registry publication files (#515)
Signed-off-by: Drew Cain <groksrc@gmail.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-19 11:57:50 -06:00
Paul Hernandez c7835a9d5c fix: ensure external_id is set on entity creation (#512) (#513)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-19 11:14:48 -06:00
Paul Hernandez 85835ae533 chore: Remove OpenPanel telemetry (#514)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-18 22:24:27 -06:00
phernandez 671e3d4db9 chore: update version to 0.17.6 for v0.17.6 release 2026-01-17 15:11:34 -06:00
phernandez e11aeff8d9 docs: add changelog entry for v0.17.6
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-17 15:11:17 -06:00
phernandez 803f3efe53 turn sync logging to debug
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-17 15:10:04 -06:00
phernandez d6dab8552c Merge branch 'main' of github.com:basicmachines-co/basic-memory 2026-01-17 15:09:04 -06:00
phernandez d1d433df15 remove logfire config, and specs docs, turn lifespan logging to debug
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-17 15:08:54 -06:00
Drew Cain 1799c94953 fix: Docker container Python symlink broken at runtime (#510)
Signed-off-by: Drew Cain <groksrc@gmail.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-14 11:59:35 -06:00
Drew Cain 07996181b3 chore: add doc update reminder to release commands
Remind developers to update docs.basicmemory.com and basicmachines.co
after running just release or just beta.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-14 10:21:23 -06:00
phernandez a1c37c1dba chore: update version to 0.17.5 for v0.17.5 release 2026-01-11 16:53:55 -06:00
phernandez aff53cca93 docs: add changelog entry for v0.17.5
- Python 3.14 compatibility fix for CLI commands hanging on exit
- Skip nest_asyncio on Python 3.14+
- Update pyright to 1.1.408 for Python 3.14 support
- Fix SQLAlchemy rowcount typing

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-11 16:53:38 -06:00
Paul Hernandez 863e0a4e24 fix: prevent CLI commands from hanging on exit (Python 3.14) (#505)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-11 16:47:35 -06:00
phernandez eeeade4f07 chore: update version to 0.17.4 for v0.17.4 release 2026-01-05 21:38:03 -06:00
phernandez 03793eaf7c docs: add v0.17.4 changelog entry
- Critical bug fix for search index preservation (#503)
- Major architecture refactor with composition roots (#502)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-05 21:36:14 -06:00
Paul Hernandez 26f7e98932 fix: preserve search index across server restarts (#503)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude <noreply@anthropic.com>
2026-01-05 21:31:35 -06:00
Paul Hernandez 5947f04bd3 refactor: composition roots, deps split, and typed API clients (#490 roadmap) (#502)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-04 11:05:58 -06:00
Drew Cain ba1439fefc chore: update version to 0.17.3 for v0.17.3 release 2026-01-03 13:08:11 -06:00
Drew Cain ef411ceb12 docs: add v0.17.3 changelog entry
🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-03 13:07:24 -06:00
Drew Cain c6baf58aa7 fix: update mcp to support protocol version 2025-11-25 (#501)
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-03 12:39:11 -06:00
phernandez 3c1748cc89 return external_id with directory response in tree
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-02 23:44:03 -06:00
phernandez 9206e7960a return external_id with directory response
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-02 23:43:14 -06:00
Paul Hernandez 53c4c20d22 fix: route ordering for cloud (#499)
Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-02 21:52:02 -06:00
Paul Hernandez b4486d20bd test: remove stdlib mocks, strengthen integration coverage (#489)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-02 14:22:01 -06:00
jope-bm a4000f64ce feat: add stable external_id (UUID) to Project and Entity models (#485)
Signed-off-by: Joe P <joe@basicmemory.com>
Signed-off-by: Paul Hernandez <60959+phernandez@users.noreply.github.com>
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Paul Hernandez <60959+phernandez@users.noreply.github.com>
Co-authored-by: phernandez <paul@basicmachines.co>
2026-01-02 13:00:35 -06:00
phernandez 88a1778798 fix(importers): return ImportResult from handle_error
Implement handle_error() in all importers so errors return a concrete ImportResult (success=false, error_message set) instead of None, preventing NoneType.success crashes.

Signed-off-by: phernandez <paul@basicmachines.co>
2026-01-01 11:47:42 -06:00
phernandez 4ce21984a4 fix: use upsert to prevent IntegrityError during parallel search indexing
Replace delete-then-insert pattern with INSERT ... ON CONFLICT for
PostgreSQL search index operations. This fixes race conditions where
parallel entity indexing could cause UniqueViolationError on the
uix_search_index_permalink_project constraint.

Changes:
- Add index_item() override in PostgresSearchRepository with upsert
- Update bulk_index_items() to use ON CONFLICT (permalink, project_id)
- Add CREATE_POSTGRES_SEARCH_INDEX_PERMALINK DDL for test fixtures
- Add tests for upsert behavior on duplicate permalinks

Technical note: Use column-based ON CONFLICT syntax instead of
ON CONSTRAINT (which only works for table constraints, not indexes).

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-30 21:55:43 -06:00
phernandez eb7fbaf0bf fix set_default_project in cloud mode
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-30 20:57:45 -06:00
phernandez 8adf1f4ed4 fix: use relative file paths in importers for cloud storage compatibility
Importers now use relative file paths (based on permalink) instead of
absolute paths. This enables proper S3 key generation in cloud environments.

Changes:
- write_entity() accepts str | Path for file_path parameter
- ensure_folder_exists() uses relative paths directly
- All importers pass relative paths to FileService
- FileService handles base_path resolution internally

This prevents S3 keys from including container filesystem paths like
`/app/basic-memory/imports/...` and instead uses clean relative paths
like `imports/20241010-file.md`.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-30 20:29:01 -06:00
phernandez 45ce1813e4 refactor: use FileService in importers for cloud compatibility
Refactor importers to use FileService for all file operations instead of
direct filesystem calls. This enables cloud environments to override file
operations via dependency injection (e.g., S3FileService).

Changes:
- Add `to_markdown_string()` method to MarkdownProcessor for content
  serialization without file I/O
- Update Importer base class to accept FileService and use it for:
  - `write_entity()` - now uses FileService.write_file()
  - `ensure_folder_exists()` - now async, uses FileService.ensure_directory()
- Fix direct `mkdir()` calls in:
  - claude_projects_importer.py
  - memory_json_importer.py
- Update deps.py to inject FileService into all importers (v1 and v2)
- Update CLI commands to create and pass FileService to importers
- Update tests to work with new FileService dependency

This follows the pattern used by /knowledge API and SyncService, enabling
cloud to override file operations by providing S3FileService via DI.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-30 19:21:32 -06:00
phernandez 2744c4b6a5 add info logging to index_entity_data background task
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-30 12:04:49 -06:00
Paul Hernandez fd732aa6fe fix: set_default_project skips config file update in cloud mode (#486)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2025-12-30 09:05:48 -06:00
Paul Hernandez 537e58ad7d fix: make RelationResponse.from_id optional to handle null permalinks (#484)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2025-12-30 08:12:29 -06:00
phernandez 48e6e84beb chore: update version to 0.17.2 for v0.17.2 release 2025-12-29 16:47:04 -06:00
phernandez 02c14acddb docs: add CHANGELOG entry for v0.17.2
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-29 16:46:26 -06:00
phernandez 0b5425f163 don't run full tests in release, just lint, typecheck
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-29 16:33:38 -06:00
phernandez 0bcda4a14a fix: allow recent_activity discovery mode in cloud mode
Add `allow_discovery` parameter to `resolve_project_parameter()` that
enables tools like `recent_activity` to work across all projects in
cloud mode without requiring a project parameter.

- Add `allow_discovery: bool = False` param to resolve_project_parameter
- In cloud mode with allow_discovery=True, return None instead of error
- Update recent_activity to use allow_discovery=True
- Fix circular import by deferring call_get import inside functions
- Add comprehensive tests for resolve_project_parameter

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-29 16:22:48 -06:00
phernandez 7a49f57dee chore: update version to 0.17.1 for v0.17.1 release
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-29 10:34:06 -06:00
phernandez 58db2817d2 docs: add CHANGELOG entry for v0.17.1
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-29 10:20:00 -06:00
Paul Hernandez 98fbd60527 fix: only set BASIC_MEMORY_ENV=test during pytest runs (#482)
Signed-off-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Paul Hernandez <phernandez@users.noreply.github.com>
2025-12-29 10:16:45 -06:00
phernandez 6281a81256 chore: update version to 0.17.0 for v0.17.0 release
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-28 16:06:03 -06:00
phernandez be1d0b169f style: format telemetry.py
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-28 16:05:07 -06:00
phernandez 148bf6f75a docs: add CHANGELOG entry for v0.17.0
🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-28 16:03:13 -06:00
phernandez 272a983709 add foss as telementry source, disable analytics for tests
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-28 13:41:40 -06:00
phernandez ef7adb7b99 fix: add cloud_mode check to initialize_app()
MCP server crashes in cloud mode with:
ValueError: DATABASE_URL must be set when using Postgres backend

Root cause: initialize_app() did not check cloud_mode_enabled before
trying to initialize the database. Only ensure_initialization() had
the check. In cloud mode, tenant DBs are per-request via headers,
not via DATABASE_URL environment variable.

Also includes minor formatting fix in telemetry.py.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-28 10:31:17 -06:00
phernandez 3cd9178415 refactor: centralize test environment detection in config.is_test_env
Add is_test_env property to BasicMemoryConfig that checks:
- config.env == "test"
- BASIC_MEMORY_ENV env var is "test"
- PYTEST_CURRENT_TEST is set

Replace duplicated test detection logic in:
- api/app.py
- mcp/server.py
- services/initialization.py
- telemetry.py (disables telemetry during tests)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-27 12:44:42 -06:00
Paul Hernandez 856737fe3c feat: add anonymous usage telemetry (Homebrew-style opt-out) (#478)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2025-12-26 22:09:48 -06:00
Paul Hernandez 1fd680c3f1 feat: add auto-format files on save with built-in Python formatter (#474)
Signed-off-by: Cedric Hurst <cedric@spantree.net>
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Cedric Hurst <cedric@spantree.net>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Sebastian B Otaegui <feniix@users.noreply.github.com>
Co-authored-by: Cedric Hurst <cedric@divideby0.io>
2025-12-24 15:39:22 -06:00
phernandez 38919d11cb docs: update CLAUDE.md with accurate CLI commands and code guidelines
- Add Code Change Guidelines section (full file read, minimize diffs, fail fast, no guessing)
- Add Literate Programming Style section (section headers, decision point comments)
- Fix CLI commands: `tools` -> `tool`, add project management commands
- Fix cloud commands to match current CLI (status, setup)
- Remove non-existent MCP tools (get_current_project, sync_status)
- Add ChatGPT-compatible tools (search, fetch)
- Remove non-existent json_canvas_spec prompt
- Add /importers to codebase architecture
- Remove unused python-developer and system-architect agents

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-24 15:16:02 -06:00
phernandez 85684f848f fix: handle UTF-8 BOM in frontmatter parsing
Fixes #452 - Imported conversations not fully indexed

Files with UTF-8 BOM (Byte Order Mark) at the start would fail frontmatter
detection, causing:
- Title to fall back to filename instead of frontmatter value
- Permalink to be null in the database

Added strip_bom() helper function and updated all frontmatter-related
functions to strip BOM before processing:
- has_frontmatter()
- parse_frontmatter()
- remove_frontmatter()
- EntityParser.parse_markdown_content()

Added comprehensive tests for BOM handling with various scenarios.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-24 14:28:50 -06:00
Paul Hernandez 14ce5a3bd0 fix: handle null titles in ChatGPT import (#475)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude <noreply@anthropic.com>
2025-12-24 13:50:30 -06:00
phernandez 45d6caf723 fix: remove MaxLen constraint from observation content
The API Pydantic schema had a MaxLen(1000) constraint on observation
content while the database uses SQLAlchemy's Text type (unlimited).
This mismatch caused validation errors when observations exceeded
1000 characters (e.g., JSON schemas with 1458+ chars).

Removed the MaxLen constraint to match the DB schema. Retained:
- BeforeValidator(str.strip) for whitespace cleaning
- MinLen(1) to ensure non-empty content

Added comprehensive tests to verify:
- Long content (10K+ chars) is accepted
- Very long content (50K+ chars) is accepted
- Empty/whitespace-only content is still rejected
- Whitespace stripping still works

Fixes #385

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-24 13:41:28 -06:00
phernandez 1652f862dd fix: handle FileNotFoundError gracefully during sync
When a file exists in the database but is missing from the filesystem,
the sync worker now treats this as a deletion instead of crashing.

The sync_file() method catches FileNotFoundError specifically and calls
handle_delete() to clean up the orphaned database record. This prevents
the sync from failing on database/filesystem inconsistencies that can
occur due to race conditions, manual file deletions, or cloud storage
caching issues.

Includes a test to verify the graceful handling behavior.

Fixes #386

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-24 13:34:59 -06:00
phernandez c23927d124 fix: use canonical project names in API response messages
Use database-retrieved project names (new_project.name, old_project.name)
instead of input parameters (project_data.name, name) in v1 API response
messages to ensure consistent project name casing.

The v2 API already did this correctly. This fixes issue #450 where project
names would display with different casing between add and remove operations.

Fixes #450

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-24 13:28:06 -06:00
jope-bm 1a74d85973 feat: Complete Phase 2 of API v2 migration - Update MCP tools to use v2 endpoints (#447)
Signed-off-by: Joe P <joe@basicmemory.com>
Signed-off-by: Paul Hernandez <60959+phernandez@users.noreply.github.com>
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Paul Hernandez <60959+phernandez@users.noreply.github.com>
Co-authored-by: phernandez <paul@basicmachines.co>
2025-12-24 12:59:14 -06:00
phernandez d71c6e8568 fix: suppress CLI warnings for cleaner output
Suppress DeprecationWarning from aiosqlite and LogfireNotConfiguredWarning
that were cluttering CLI output.

The key fix is applying warnings.filterwarnings("ignore") AFTER all imports
in main.py, because authlib (imported via cloud commands) adds a
DeprecationWarning filter that overrides earlier suppressions.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-24 12:21:14 -06:00
phernandez 63b98491be fix: prevent DEBUG logs from appearing on CLI stdout
Remove loguru's default handler at the very start of cli/app.py,
before any other imports. This prevents module-level code (like
TemplateLoader.__init__) from logging to stdout during import.

The import chain cli/commands/project.py -> mcp/async_client.py ->
api/app.py -> api/routers/prompt_router.py -> api/template_loader.py
triggers TemplateLoader() instantiation at DEBUG level before
init_cli_logging() can remove the default handler.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-24 11:42:25 -06:00
Paul Hernandez 622d37e4a8 fix: detect rclone version for --create-empty-src-dirs support (#473)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2025-12-24 11:27:49 -06:00
Paul Hernandez 916baf8971 fix: prevent CLI commands from hanging on exit (#471)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2025-12-24 11:27:11 -06:00
Drew Cain 95937c6d0a fix: make test-int-postgres compatible with macOS
Use gtimeout (Homebrew) or timeout (Linux), falling back to running
without timeout if neither is available. This fixes 'command not found'
errors on macOS which doesn't have GNU timeout by default.
2025-12-20 10:27:26 -06:00
Drew Cain 24dc9a2931 chore: update version to 0.16.3 for v0.16.3 release 2025-12-20 09:53:37 -06:00
Drew Cain 85c63e5a7a docs: add CHANGELOG entry for v0.16.3 2025-12-20 09:26:00 -06:00
Drew Cain f227ef6a86 fix: Pin FastMCP to 2.12.3 to fix MCP tools visibility (#464)
Signed-off-by: Drew Cain <groksrc@gmail.com>
Co-authored-by: Claude <noreply@anthropic.com>
2025-12-20 08:54:44 -06:00
Paul Hernandez 897b1edaa4 fix: Reduce watch service CPU usage by increasing reload interval (#458)
Signed-off-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Paul Hernandez <phernandez@users.noreply.github.com>
2025-12-17 11:37:01 -06:00
Paul Hernandez 0c12a39a98 test: Add integration test for issue #416 (read_note with underscored folders) (#453)
Signed-off-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Paul Hernandez <phernandez@users.noreply.github.com>
2025-12-17 09:56:45 -06:00
Paul Hernandez efbc758325 fix: await background sync task cancellation in lifespan shutdown (#456)
Signed-off-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Paul Hernandez <phernandez@users.noreply.github.com>
2025-12-17 09:53:34 -06:00
Paul Hernandez a0f20eb102 chore: more Tenantless fixes (#457)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2025-12-16 18:34:05 -06:00
Paul Hernandez 78673d8e51 chore: Cloud compatibility fixes and performance improvements (#454)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2025-12-15 20:07:55 -06:00
phernandez 126c0495c0 Merge branch 'main' of github.com:basicmachines-co/basic-memory 2025-12-13 15:24:43 -06:00
Paul Hernandez 4a43d7df4a remove logfire instrumentation
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-13 15:22:14 -06:00
Paul Hernandez c462faf046 Replace py-pglite with testcontainers for Postgres testing (#449)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
2025-12-10 22:17:56 -06:00
Cedric Hurst 70bb10be1d fix: respect --project flag in background sync (fixes #434) (#436)
Signed-off-by: Cedric Hurst <cedric@spantree.net>
2025-12-08 12:58:18 -06:00
phernandez fbf9045d78 use asyncpg for just db-migrate
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-05 15:01:35 -06:00
phernandez 1094210c52 fix broken sqlite migration
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-02 20:23:55 -06:00
phernandez 391feb639f add delete cascade to entity to delete search_index (postgres only)
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-02 10:04:21 -06:00
phernandez a920a9ff29 feat: Add project_id to Relation and Observation for efficient project-scoped queries
Denormalizes project_id onto Relation and Observation tables to enable
efficient project-scoped queries without joins. Migration backfills
from associated entity and adds pg_trgm extension with GIN indexes
for fuzzy link resolution on PostgreSQL.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-01 21:54:56 -06:00
phernandez 05efe8701c test: Verify update() returns entity with eager-loaded relations
Add test confirming entity_repository.update() returns the entity with
observations and relations eagerly loaded, eliminating the need for a
separate find_by_id() call after update.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-12-01 16:22:21 -06:00
phernandez 0eaf30bb06 remove conflict constraint name from relation_repository.py
Signed-off-by: phernandez <paul@basicmachines.co>
2025-11-30 19:31:09 -06:00
phernandez 0818bda565 feat: Add bulk insert with ON CONFLICT handling for relations
Add add_all_ignore_duplicates() method to RelationRepository for bulk
inserting relations with ON CONFLICT DO NOTHING. This handles cases
where the same [[wiki link]] appears multiple times in a document,
silently ignoring duplicates based on the (from_id, to_name, relation_type)
unique constraint.

Works with both SQLite and PostgreSQL dialects.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-11-30 14:59:52 -06:00
phernandez 6f99d2e551 perf: lightweight permalink resolution to avoid eager loading
Add optimized repository methods for resolve_permalink() that skip
eager loading of observations and relations:

- permalink_exists(): Check existence without loading entity
- get_file_path_for_permalink(): Get only file_path column
- get_permalink_for_file_path(): Get only permalink column
- get_all_permalinks(): Get all permalinks as strings
- get_permalink_to_file_path_map(): Bulk lookup mapping
- get_file_path_to_permalink_map(): Reverse mapping

Updated entity_service.resolve_permalink() to use these lightweight
methods instead of loading full entities with all relationships.

Also added logfire instrumentation to markdown utils.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-11-30 14:34:31 -06:00
phernandez 73d940e064 fix: observation parsing and permalink limits (#446)
1. Hashtag detection now checks for standalone words starting with #
   instead of just checking if # appears anywhere in content.
   This prevents HTML color codes like #4285F4 from being
   interpreted as hashtags.

2. Observation permalinks now truncate content to 200 chars
   to stay under PostgreSQL's btree index limit of 2704 bytes.

Added tests for both fixes.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-11-30 00:12:04 -06:00
phernandez c3678a11d2 truncate content_stems to fix Postgres 8KB index row limit
Large documents (like ~1MB conversation imports) exceed Postgres's 8KB
index row limit, causing ProgramLimitExceededError. Truncate content_stems
to 6000 characters (with headroom for other columns) before indexing.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-11-29 20:23:09 -06:00
phernandez 203d684c24 fix integrity error handling when setting forward relation refs
Signed-off-by: phernandez <paul@basicmachines.co>
2025-11-29 19:03:12 -06:00
phernandez a872220924 disable pooling for postgres db
Signed-off-by: phernandez <paul@basicmachines.co>
2025-11-29 13:24:47 -06:00
phernandez 7d763a66ff use entity.mtime for updated at in api
Signed-off-by: phernandez <paul@basicmachines.co>
2025-11-29 13:21:58 -06:00
phernandez b5d4fb559c fix: postgres/neon connection settings and search index dedupe
- Reduce db_pool_recycle from 3600s to 180s for Neon scale-to-zero
- Add connect_args for Neon serverless (statement cache, timeouts, app name)
- Dedupe observation permalinks in search indexing to avoid unique constraint violations
- Add tests for duplicate observation permalink handling

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
2025-11-29 11:24:08 -06:00
phernandez 830775276d remove record_return=True from logfire spans
Signed-off-by: phernandez <paul@basicmachines.co>
2025-11-28 18:36:09 -06:00
phernandez ed894fc3ed get db pool sizes from config
Signed-off-by: phernandez <paul@basicmachines.co>
2025-11-28 16:50:57 -06:00
phernandez 704338edcf remove logfire.instrument_fastapi(app) from app.py
Signed-off-by: phernandez <paul@basicmachines.co>
2025-11-28 13:52:18 -06:00
phernandez 0ca02a7ebe add logfire instrumentation to services and repository code
Signed-off-by: phernandez <paul@basicmachines.co>
2025-11-28 12:47:29 -06:00
jope-bm 28cc5225a7 feat: Implement API v2 with ID-based endpoints (Phase 1) (#441)
Signed-off-by: Joe P <joe@basicmemory.com>
Signed-off-by: phernandez <paul@basicmachines.co>
Signed-off-by: Claude <noreply@anthropic.com>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Paul Hernandez <60959+phernandez@users.noreply.github.com>
Co-authored-by: phernandez <paul@basicmachines.co>
2025-11-27 10:35:55 -06:00
phernandez 9b7bbc7116 formatting and logic change to resolve_relations, remove fuzzy search 2025-11-25 22:54:37 -06:00
phernandez 138c283d6c add postgres db type 2025-11-25 20:25:56 -06:00
phernandez 7a8954c37e add extra logic for cloud-indexing improvements 2025-11-25 13:52:58 -06:00
phernandez 10c7c19c03 fix db url for sqlite migrations
Signed-off-by: phernandez <paul@basicmachines.co>
2025-11-21 13:21:20 -06:00
Paul Hernandez fb5e9e1d77 feat: Add PostgreSQL database backend support (#439)
Signed-off-by: phernandez <paul@basicmachines.co>
Co-authored-by: Claude <noreply@anthropic.com>
2025-11-20 11:20:29 -06:00
phernandez 66b91b2847 ci: Add PostgreSQL testing to GitHub Actions workflow
Add Postgres service container and separate test step for PostgreSQL backend testing.
The Postgres tests only run on Linux runners since GitHub Actions service containers
are only available on Linux.

- Add postgres:17 service container with health checks
- Add 'Run tests (Postgres)' step with Linux-only condition
- Rename existing test step to 'Run tests (SQLite)' for clarity

This enables CI testing of dual database backend support introduced in the
postgres-support feature branch.

Signed-off-by: phernandez <paul@basicmachines.co>
2025-11-18 12:25:18 -06:00
Cedric Hurst b004565df9 fix: handle periods in kebab_filenames mode (#424) 2025-11-18 06:49:55 -05:00
636 changed files with 87549 additions and 37154 deletions
-154
View File
@@ -1,154 +0,0 @@
---
name: python-developer
description: Python backend developer specializing in FastAPI, DBOS workflows, and API implementation. Implements specifications into working Python services and follows modern Python best practices.
model: sonnet
color: red
---
You are an expert Python developer specializing in implementing specifications into working Python services and APIs. You have deep expertise in Python language features, FastAPI, DBOS workflows, database operations, and the Basic Memory Cloud backend architecture.
**Primary Role: Backend Implementation Agent**
You implement specifications into working Python code and services. You read specs from basic-memory, implement the requirements using modern Python patterns, and update specs with implementation progress and decisions.
**Core Responsibilities:**
**Specification Implementation:**
- Read specs using basic-memory MCP tools to understand backend requirements
- Implement Python services, APIs, and workflows that fulfill spec requirements
- Update specs with implementation progress, decisions, and completion status
- Document any architectural decisions or modifications needed during implementation
**Python/FastAPI Development:**
- Create FastAPI applications with proper middleware and dependency injection
- Implement DBOS workflows for durable, long-running operations
- Design database schemas and implement repository patterns
- Handle authentication, authorization, and security requirements
- Implement async/await patterns for optimal performance
**Backend Implementation Process:**
1. **Read Spec**: Use `mcp__basic-memory__read_note` to get spec requirements
2. **Analyze Existing Patterns**: Study codebase architecture and established patterns before implementing
3. **Follow Modular Structure**: Create separate modules/routers following existing conventions
4. **Implement**: Write Python code following spec requirements and codebase patterns
5. **Test**: Create tests that validate spec success criteria
6. **Update Spec**: Document completion and any implementation decisions
7. **Validate**: Run tests and ensure integration works correctly
**Technical Standards:**
- Follow PEP 8 and modern Python conventions
- Use type hints throughout the codebase
- Implement proper error handling and logging
- Use async/await for all database and external service calls
- Write comprehensive tests using pytest
- Follow security best practices for web APIs
- Document functions and classes with clear docstrings
**Codebase Architecture Patterns:**
**CLI Structure Patterns:**
- Follow existing modular CLI pattern: create separate CLI modules (e.g., `upload_cli.py`) instead of adding commands directly to `main.py`
- Existing examples: `polar_cli.py`, `tenant_cli.py` in `apps/cloud/src/basic_memory_cloud/cli/`
- Register new CLI modules using `app.add_typer(new_cli, name="command", help="description")`
- Maintain consistent command structure and help text patterns
**FastAPI Router Patterns:**
- Create dedicated routers for logical endpoint groups instead of adding routes directly to main app
- Place routers in dedicated files (e.g., `apps/api/src/basic_memory_cloud_api/routers/webdav_router.py`)
- Follow existing middleware and dependency injection patterns
- Register routers using `app.include_router(router, prefix="/api-path")`
**Modular Organization:**
- Always analyze existing codebase structure before implementing new features
- Follow established file organization and naming conventions
- Create separate modules for distinct functionality areas
- Maintain consistency with existing architectural decisions
- Preserve separation of concerns across service boundaries
**Pattern Analysis Process:**
1. Examine similar existing functionality in the codebase
2. Identify established patterns for file organization and module structure
3. Follow the same architectural approach for consistency
4. Create new modules/routers following existing conventions
5. Integrate new code using established registration patterns
**Basic Memory Cloud Expertise:**
**FastAPI Service Patterns:**
- Multi-app architecture (Cloud, MCP, API services)
- Shared middleware for JWT validation, CORS, logging
- Dependency injection for services and repositories
- Proper async request handling and error responses
**DBOS Workflow Implementation:**
- Durable workflows for tenant provisioning and infrastructure operations
- Service layer pattern with repository data access
- Event sourcing for audit trails and business processes
- Idempotent operations with proper error handling
**Database & Repository Patterns:**
- SQLAlchemy with async patterns
- Repository pattern for data access abstraction
- Database migration strategies
- Multi-tenant data isolation patterns
**Authentication & Security:**
- JWT token validation and middleware
- OAuth 2.1 flow implementation
- Tenant-specific authorization patterns
- Secure API design and input validation
**Code Quality Standards:**
- Clear, descriptive variable and function names
- Proper docstrings for functions and classes
- Handle edge cases and error conditions gracefully
- Use context managers for resource management
- Apply composition over inheritance
- Consider security implications for all API endpoints
- Optimize for performance while maintaining readability
**Testing & Validation:**
- Write pytest tests that validate spec requirements
- Include unit tests for business logic
- Integration tests for API endpoints
- Test error conditions and edge cases
- Use fixtures for consistent test setup
- Mock external dependencies appropriately
**Debugging & Problem Solving:**
- Analyze error messages and stack traces methodically
- Identify root causes rather than applying quick fixes
- Use logging effectively for troubleshooting
- Apply systematic debugging approaches
- Document solutions for future reference
**Basic Memory Integration:**
- Use `mcp__basic-memory__read_note` to read specifications
- Use `mcp__basic-memory__edit_note` to update specs with progress
- Document implementation patterns and decisions
- Link related services and database schemas
- Maintain implementation history and troubleshooting guides
**Communication Style:**
- Focus on concrete implementation results and working code
- Document technical decisions and trade-offs clearly
- Ask specific questions about requirements and constraints
- Provide clear status updates on implementation progress
- Explain code choices and architectural patterns
**Deliverables:**
- Working Python services that meet spec requirements
- Updated specifications with implementation status
- Comprehensive tests validating functionality
- Clean, maintainable, type-safe Python code
- Proper error handling and logging
- Database migrations and schema updates
**Key Principles:**
- Implement specifications faithfully and completely
- Write clean, efficient, and maintainable Python code
- Follow established patterns and conventions
- Apply proper error handling and security practices
- Test thoroughly and document implementation decisions
- Balance performance with code clarity and maintainability
When handed a specification via `/spec implement`, you will read the spec, understand the requirements, implement the Python solution using appropriate patterns and frameworks, create tests to validate functionality, and update the spec with completion status and any implementation notes.
-126
View File
@@ -1,126 +0,0 @@
---
name: system-architect
description: System architect who designs and implements architectural solutions, creates ADRs, and applies software engineering principles to solve complex system design problems.
model: sonnet
color: blue
---
You are a Senior System Architect who designs and implements architectural solutions for complex software systems. You have deep expertise in software engineering principles, system design, multi-tenant SaaS architecture, and the Basic Memory Cloud platform.
**Primary Role: Architectural Implementation Agent**
You design system architecture and implement architectural decisions through code, configuration, and documentation. You read specs from basic-memory, create architectural solutions, and update specs with implementation progress.
**Core Responsibilities:**
**Specification Implementation:**
- Read architectural specs using basic-memory MCP tools
- Design and implement system architecture solutions
- Create code scaffolding, service structure, and system interfaces
- Update specs with architectural decisions and implementation status
- Document ADRs (Architectural Decision Records) for significant choices
**Architectural Design & Implementation:**
- Design multi-service system architectures
- Implement service boundaries and communication patterns
- Create database schemas and migration strategies
- Design authentication and authorization systems
- Implement infrastructure-as-code patterns
**System Implementation Process:**
1. **Read Spec**: Use `mcp__basic-memory__read_note` to understand architectural requirements
2. **Design Solution**: Apply architectural principles and patterns
3. **Implement Structure**: Create service scaffolding, interfaces, configurations
4. **Document Decisions**: Create ADRs documenting architectural choices
5. **Update Spec**: Record implementation progress and decisions
6. **Validate**: Ensure implementation meets spec success criteria
**Architectural Principles Applied:**
- DRY (Don't Repeat Yourself) - Single sources of truth
- KISS (Keep It Simple Stupid) - Favor simplicity over cleverness
- YAGNI (You Aren't Gonna Need It) - Build only what's needed now
- Principle of Least Astonishment - Intuitive system behavior
- Separation of Concerns - Clear boundaries and responsibilities
**Basic Memory Cloud Expertise:**
**Multi-Service Architecture:**
- **Cloud Service**: Tenant management, OAuth 2.1, DBOS workflows
- **MCP Gateway**: JWT validation, tenant routing, MCP proxy
- **Web App**: Vue.js frontend, OAuth flows, user interface
- **API Service**: Per-tenant Basic Memory instances with MCP
**Multi-Tenant SaaS Patterns:**
- **Tenant Isolation**: Infrastructure-level isolation with dedicated instances
- **Database-per-tenant**: Isolated PostgreSQL databases
- **Authentication**: JWT tokens with tenant-specific claims
- **Provisioning**: DBOS workflows for durable operations
- **Resource Management**: Fly.io machine lifecycle management
**Implementation Capabilities:**
- FastAPI service structure and middleware
- DBOS workflow implementation
- Database schema design and migrations
- JWT authentication and authorization
- Fly.io deployment configuration
- Service communication patterns
**Technical Implementation:**
- Create service scaffolding and project structure
- Implement authentication and authorization middleware
- Design database schemas and relationships
- Configure deployment and infrastructure
- Implement monitoring and health checks
- Create API interfaces and contracts
**Code Quality Standards:**
- Follow established patterns and conventions
- Implement proper error handling and logging
- Design for scalability and maintainability
- Apply security best practices
- Create comprehensive tests for architectural components
- Document system behavior and interfaces
**Decision Documentation:**
- Create ADRs for significant architectural choices
- Document trade-offs and alternative approaches considered
- Maintain decision history and rationale
- Link architectural decisions to implementation code
- Update decisions when new information becomes available
**Basic Memory Integration:**
- Use `mcp__basic-memory__read_note` to read architectural specs
- Use `mcp__basic-memory__write_note` to create ADRs and architectural documentation
- Use `mcp__basic-memory__edit_note` to update specs with implementation progress
- Document architectural patterns and anti-patterns for reuse
- Maintain searchable knowledge base of system design decisions
**Communication Style:**
- Focus on implemented solutions and concrete architectural artifacts
- Document decisions with clear rationale and trade-offs
- Provide specific implementation guidance and code examples
- Ask targeted questions about requirements and constraints
- Explain architectural choices in terms of business and technical impact
**Deliverables:**
- Working system architecture implementations
- ADRs documenting architectural decisions
- Service scaffolding and interface definitions
- Database schemas and migration scripts
- Configuration and deployment artifacts
- Updated specifications with implementation status
**Anti-Patterns to Avoid:**
- Premature optimization over correctness
- Over-engineering for current needs
- Building without clear requirements
- Creating multiple sources of truth
- Implementing solutions without understanding root causes
**Key Principles:**
- Implement architectural decisions through working code
- Document all significant decisions and trade-offs
- Build systems that teams can understand and maintain
- Apply proven patterns and avoid reinventing solutions
- Balance current needs with long-term maintainability
When handed an architectural specification via `/spec implement`, you will read the spec, design the solution applying architectural principles, implement the necessary code and configuration, document decisions through ADRs, and update the spec with completion status and architectural notes.
+31 -1
View File
@@ -78,6 +78,34 @@ The GitHub Actions workflow (`.github/workflows/release.yml`) then:
2. Verify formula version matches release
3. Test Homebrew installation: `brew install basicmachines-co/basic-memory/basic-memory`
#### MCP Registry Publication
After PyPI release is published, update the MCP registry:
1. **Verify PyPI Release**
- Confirm package is live: https://pypi.org/project/basic-memory/<version>/
- The `server.json` version was auto-updated by `just release`
2. **Publish to MCP Registry**
```bash
cd /Users/drew/code/basic-memory
mcp-publisher publish
```
If not authenticated:
```bash
mcp-publisher login github
# Follow device authentication flow
mcp-publisher publish
```
3. **Verify Publication**
```bash
curl "https://registry.modelcontextprotocol.io/v0.1/servers?search=basic-memory"
```
**Note:** The `mcp-publisher` CLI can be installed via Homebrew (`brew install mcp-publisher`) or from GitHub releases.
#### Website Updates
**1. basicmachines.co** (`/Users/drew/code/basicmachines.co`)
@@ -145,6 +173,7 @@ Before starting, verify:
📋 GitHub Release: https://github.com/basicmachines-co/basic-memory/releases/tag/v0.13.2
📦 PyPI: https://pypi.org/project/basic-memory/0.13.2/
🍺 Homebrew: https://github.com/basicmachines-co/homebrew-basic-memory
🔌 MCP Registry: https://registry.modelcontextprotocol.io
🚀 GitHub Actions: Completed
Install with pip/uv:
@@ -162,8 +191,9 @@ Users can now upgrade:
- This creates production releases used by end users
- Must pass all quality gates before proceeding
- Uses the automated justfile target for consistency
- Version is automatically updated in `__init__.py`
- Version is automatically updated in `__init__.py` and `server.json`
- Triggers automated GitHub release with changelog
- Package is published to PyPI for `pip` and `uv` users
- Homebrew formula is automatically updated for stable releases
- MCP Registry is updated manually via `mcp-publisher publish`
- Supports multiple installation methods (uv, pip, Homebrew)
+16 -20
View File
@@ -1,17 +1,19 @@
---
allowed-tools: mcp__basic-memory__write_note, mcp__basic-memory__read_note, mcp__basic-memory__search_notes, mcp__basic-memory__edit_note, Task
argument-hint: [create|status|implement|review] [spec-name]
allowed-tools: mcp__basic-memory__write_note, mcp__basic-memory__read_note, mcp__basic-memory__search_notes, mcp__basic-memory__edit_note
argument-hint: [create|status|show|review] [spec-name]
description: Manage specifications in our development process
---
## Context
You are managing specifications using our specification-driven development process defined in @docs/specs/SPEC-001.md.
Specifications are managed in the Basic Memory "specs" project. All specs live in a centralized location accessible across all repositories via MCP tools.
See SPEC-1 and SPEC-2 in the "specs" project for the full specification-driven development process.
Available commands:
- `create [name]` - Create new specification
- `status` - Show all spec statuses
- `implement [spec-name]` - Hand spec to appropriate agent
- `show [spec-name]` - Read a specific spec
- `review [spec-name]` - Review implementation against spec
## Your task
@@ -19,23 +21,19 @@ Available commands:
Execute the spec command: `/spec $ARGUMENTS`
### If command is "create":
1. Get next SPEC number by searching existing specs
2. Create new spec using template from @docs/specs/Slash\ Commands\ Reference.md
3. Place in `/specs` folder with title "SPEC-XXX: [name]"
1. Get next SPEC number by searching existing specs in "specs" project
2. Create new spec using template from SPEC-2
3. Use mcp__basic-memory__write_note with project="specs"
4. Include standard sections: Why, What, How, How to Evaluate
### If command is "status":
1. Search all notes in `/specs` folder
2. Display table with spec number, title, and status
3. Show any dependencies or assigned agents
1. Use mcp__basic-memory__search_notes with project="specs"
2. Display table with spec number, title, and progress
3. Show completion status from checkboxes in content
### If command is "implement":
1. Read the specified spec
2. Determine appropriate agent based on content:
- Frontend/UI → vue-developer
- Architecture/system → system-architect
- Backend/API → python-developer
3. Launch Task tool with appropriate agent and spec context
### If command is "show":
1. Use mcp__basic-memory__read_note with project="specs"
2. Display the full spec content
### If command is "review":
1. Read the specified spec and its "How to Evaluate" section
@@ -49,7 +47,5 @@ Execute the spec command: `/spec $ARGUMENTS`
- **Architecture compliance** - Component isolation, state management patterns
- **Documentation completeness** - Implementation matches specification
3. Provide honest, accurate assessment - do not overstate completeness
4. Document findings and update spec with review results
4. Document findings and update spec with review results using mcp__basic-memory__edit_note
5. If gaps found, clearly identify what still needs to be implemented/tested
Use the agent definitions from @docs/specs/Agent\ Definitions.md for implementation handoffs.
+3
View File
@@ -0,0 +1,3 @@
{
"enabledPlugins": {}
}
+28
View File
@@ -0,0 +1,28 @@
# Basic Memory Environment Variables Example
# Copy this file to .env and customize as needed
# Note: .env files are gitignored and should never be committed
# ============================================================================
# PostgreSQL Test Database Configuration
# ============================================================================
# These variables allow you to override the default test database credentials
# Default values match docker-compose-postgres.yml for local development
#
# Only needed if you want to use different credentials or a remote test database
# By default, tests use: postgresql://basic_memory_user:dev_password@localhost:5433/basic_memory_test
# Full PostgreSQL test database URL (used by tests and migrations)
# POSTGRES_TEST_URL=postgresql+asyncpg://basic_memory_user:dev_password@localhost:5433/basic_memory_test
# Individual components (used by justfile postgres-reset command)
# POSTGRES_USER=basic_memory_user
# POSTGRES_TEST_DB=basic_memory_test
# ============================================================================
# Production Database Configuration
# ============================================================================
# For production use, set these in your deployment environment
# DO NOT use the test credentials above in production!
# BASIC_MEMORY_DATABASE_BACKEND=postgres # or "sqlite"
# BASIC_MEMORY_DATABASE_URL=postgresql+asyncpg://user:password@host:port/database
+1
View File
@@ -54,6 +54,7 @@ jobs:
- [ ] Unit tests for new functions/methods
- [ ] Integration tests for new MCP tools
- [ ] Test coverage for edge cases
- [ ] **100% test coverage maintained** (use `# pragma: no cover` only for truly hard-to-test code)
- [ ] Documentation updated (README, docstrings)
- [ ] CLAUDE.md updated if conventions change
+228 -31
View File
@@ -1,24 +1,70 @@
name: Tests
concurrency:
group: bm-ci-${{ github.workflow }}-${{ github.repository }}-${{ github.head_ref || github.ref }}
cancel-in-progress: true
on:
push:
branches: [ "main" ]
pull_request:
branches: [ "main" ]
# pull_request_target runs on the BASE of the PR, not the merge result.
# It has write permissions and access to secrets.
# It's useful for PRs from forks or automated PRs but requires careful use for security reasons.
# See: https://docs.github.com/en/actions/using-workflows/events-that-trigger-workflows#pull_request_target
pull_request_target:
branches: [ "main" ]
jobs:
test:
static-checks:
name: Static Checks (Python 3.12)
timeout-minutes: 20
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
with:
submodules: true
- name: Set up Python 3.12
uses: actions/setup-python@v4
with:
python-version: "3.12"
cache: "pip"
- name: Install uv
run: |
pip install uv
- uses: extractions/setup-just@v3
- name: Create virtual env
run: |
uv venv
- name: Install dependencies
run: |
uv pip install -e ".[dev]"
- name: Run type checks
run: |
just typecheck
- name: Run linting
run: |
just lint
test-sqlite-unit:
name: Test SQLite Unit (${{ matrix.os }}, Python ${{ matrix.python-version }})
timeout-minutes: 30
needs: [static-checks]
strategy:
fail-fast: false
matrix:
os: [ubuntu-latest, windows-latest]
python-version: [ "3.12", "3.13" ]
include:
- os: ubuntu-latest
python-version: "3.12"
- os: ubuntu-latest
python-version: "3.13"
- os: ubuntu-latest
python-version: "3.14"
- os: windows-latest
python-version: "3.12"
runs-on: ${{ matrix.os }}
steps:
@@ -36,17 +82,7 @@ jobs:
run: |
pip install uv
- name: Install just (Linux/macOS)
if: runner.os != 'Windows'
run: |
curl --proto '=https' --tlsv1.2 -sSf https://just.systems/install.sh | bash -s -- --to /usr/local/bin
- name: Install just (Windows)
if: runner.os == 'Windows'
run: |
# Install just using Chocolatey (pre-installed on GitHub Actions Windows runners)
choco install just --yes
shell: pwsh
- uses: extractions/setup-just@v3
- name: Create virtual env
run: |
@@ -54,17 +90,178 @@ jobs:
- name: Install dependencies
run: |
uv pip install -e .[dev]
- name: Run type checks
run: |
just typecheck
- name: Run linting
run: |
just lint
uv pip install -e ".[dev]"
- name: Run tests
run: |
uv pip install pytest pytest-cov
just test
just test-unit-sqlite
test-sqlite-integration:
name: Test SQLite Integration (${{ matrix.os }}, Python ${{ matrix.python-version }})
timeout-minutes: 45
needs: [static-checks]
strategy:
fail-fast: false
matrix:
include:
- os: ubuntu-latest
python-version: "3.12"
- os: ubuntu-latest
python-version: "3.13"
- os: ubuntu-latest
python-version: "3.14"
- os: windows-latest
python-version: "3.12"
runs-on: ${{ matrix.os }}
steps:
- uses: actions/checkout@v4
with:
submodules: true
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
cache: 'pip'
- name: Install uv
run: |
pip install uv
- uses: extractions/setup-just@v3
- name: Create virtual env
run: |
uv venv
- name: Install dependencies
run: |
uv pip install -e ".[dev]"
- name: Run tests
run: |
just test-int-sqlite
test-postgres-unit:
name: Test Postgres Unit (Python ${{ matrix.python-version }})
timeout-minutes: 30
needs: [static-checks]
strategy:
fail-fast: false
matrix:
include:
- python-version: "3.12"
- python-version: "3.13"
- python-version: "3.14"
runs-on: ubuntu-latest
# Note: No services section needed - testcontainers handles Postgres in Docker
steps:
- uses: actions/checkout@v4
with:
submodules: true
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
cache: 'pip'
- name: Install uv
run: |
pip install uv
- uses: extractions/setup-just@v3
- name: Create virtual env
run: |
uv venv
- name: Install dependencies
run: |
uv pip install -e ".[dev]"
- name: Run tests
run: |
just test-unit-postgres
test-postgres-integration:
name: Test Postgres Integration (Python ${{ matrix.python-version }})
timeout-minutes: 45
needs: [static-checks]
strategy:
fail-fast: false
matrix:
include:
- python-version: "3.12"
- python-version: "3.13"
- python-version: "3.14"
runs-on: ubuntu-latest
# Note: No services section needed - testcontainers handles Postgres in Docker
steps:
- uses: actions/checkout@v4
with:
submodules: true
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
cache: 'pip'
- name: Install uv
run: |
pip install uv
- uses: extractions/setup-just@v3
- name: Create virtual env
run: |
uv venv
- name: Install dependencies
run: |
uv pip install -e ".[dev]"
- name: Run tests
run: |
just test-int-postgres
test-semantic:
name: Test Semantic (Python 3.12)
timeout-minutes: 45
needs: [static-checks]
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
with:
submodules: true
- name: Set up Python 3.12
uses: actions/setup-python@v4
with:
python-version: "3.12"
cache: "pip"
- name: Install uv
run: |
pip install uv
- uses: extractions/setup-just@v3
- name: Create virtual env
run: |
uv venv
- name: Install dependencies
run: |
uv pip install -e ".[dev]"
- name: Run tests
run: |
just test-semantic
+6 -1
View File
@@ -1,6 +1,7 @@
*.py[cod]
__pycache__/
.pytest_cache/
.testmondata*
.coverage
htmlcov/
@@ -52,4 +53,8 @@ ENV/
# claude action
claude-output
**/.claude/settings.local.json
**/.claude/settings.local.json
.mcp.json
.mcpregistry_*
/.testmondata
.benchmarks/
+1 -1
View File
@@ -1 +1 @@
3.12
3.14
+446
View File
@@ -0,0 +1,446 @@
# AGENTS.md - Basic Memory Project Guide
## Project Overview
Basic Memory is a local-first knowledge management system built on the Model Context Protocol (MCP). It enables
bidirectional communication between LLMs (like Claude) and markdown files, creating a personal knowledge graph that can
be traversed using links between documents.
## CODEBASE DEVELOPMENT
### Project information
See the [README.md](README.md) file for a project overview.
### Build and Test Commands
- Install: `just install` or `pip install -e ".[dev]"`
- Run all tests (SQLite + Postgres): `just test`
- Run all tests against SQLite: `just test-sqlite`
- Run all tests against Postgres: `just test-postgres` (uses testcontainers)
- Run unit tests (SQLite): `just test-unit-sqlite`
- Run unit tests (Postgres): `just test-unit-postgres`
- Run integration tests (SQLite): `just test-int-sqlite`
- Run integration tests (Postgres): `just test-int-postgres`
- Run impacted tests: `just testmon` (pytest-testmon)
- Run MCP smoke test: `just test-smoke`
- Fast local loop: `just fast-check`
- Local consistency check: `just doctor`
- Generate HTML coverage: `just coverage`
- Single test: `pytest tests/path/to/test_file.py::test_function_name`
- Run benchmarks: `pytest test-int/test_sync_performance_benchmark.py -v -m "benchmark and not slow"`
- Lint: `just lint` or `ruff check . --fix`
- Type check: `just typecheck` or `uv run pyright`
- Type check (supplemental): `just typecheck-ty` or `uv run ty check src/`
- Format: `just format` or `uv run ruff format .`
- Run all code checks: `just check` (runs lint, format, typecheck, test)
- Create db migration: `just migration "Your migration message"`
- Run development MCP Inspector: `just run-inspector`
**Note:** Project requires Python 3.12+ (uses type parameter syntax and `type` aliases introduced in 3.12)
**Postgres Testing:** Uses [testcontainers](https://testcontainers-python.readthedocs.io/) which automatically spins up a Postgres instance in Docker. No manual database setup required - just have Docker running.
**Doctor Note:** `just doctor` runs with a temporary HOME/config so it won't touch your local Basic Memory settings. It leaves temp dirs in `/tmp` (safe to ignore or remove).
**Testmon Note:** When no files have changed, `just testmon` may collect 0 tests. That's expected and means no impacted tests were detected.
### Code/Test/Verify Loop (fast path)
1) **Code:** make changes.
2) **Test:** `just fast-check` (lint/format/typecheck + impacted tests + MCP smoke).
3) **Verify:** `just doctor` (end-to-end file ↔ DB loop in a temp project).
4) **Full gate (when needed):** `just test` or `just check` for SQLite + Postgres.
If testmon is “cold,” the first run may be long. Subsequent runs get much faster.
### Test Structure
- `tests/` - Unit tests for individual components (mocked, fast)
- `test-int/` - Integration tests for real-world scenarios (no mocks, realistic)
- Both directories are covered by unified coverage reporting
- Benchmark tests in `test-int/` are marked with `@pytest.mark.benchmark`
- Slow tests are marked with `@pytest.mark.slow`
- Smoke tests are marked with `@pytest.mark.smoke`
### Code Style Guidelines
- Line length: 100 characters max
- Python 3.12+ with full type annotations (uses type parameters and type aliases)
- Format with ruff (consistent styling)
- Import order: standard lib, third-party, local imports
- Naming: snake_case for functions/variables, PascalCase for classes
- Prefer async patterns with SQLAlchemy 2.0
- Use Pydantic v2 for data validation and schemas
- CLI uses Typer for command structure
- API uses FastAPI for endpoints
- Follow the repository pattern for data access
- Tools communicate to api routers via the httpx ASGI client (in process)
### Code Change Guidelines
- **Full file read before edits**: Before editing any file, read it in full first to ensure complete context; partial reads lead to corrupted edits
- **Minimize diffs**: Prefer the smallest change that satisfies the request. Avoid unrelated refactors or style rewrites unless necessary for correctness
- **No speculative getattr**: Never use `getattr(obj, "attr", default)` when unsure about attribute names. Check the class definition or source code first
- **Fail fast**: Write code with fail-fast logic by default. Do not swallow exceptions with errors or warnings
- **No fallback logic**: Do not add fallback logic unless explicitly told to and agreed with the user
- **No guessing**: Do not say "The issue is..." before you actually know what the issue is. Investigate first.
### Literate Programming Style
Code should tell a story. Comments must explain the "why" and narrative flow, not just the "what".
**Section Headers:**
For files with multiple phases of logic, add section headers so the control flow reads like chapters:
```python
# --- Authentication ---
# ... auth logic ...
# --- Data Validation ---
# ... validation logic ...
# --- Business Logic ---
# ... core logic ...
```
**Decision Point Comments:**
For conditionals that materially change behavior (gates, fallbacks, retries, feature flags), add comments with:
- **Trigger**: what condition causes this branch
- **Why**: the rationale (cost, correctness, UX, determinism)
- **Outcome**: what changes downstream
```python
# Trigger: project has no active sync watcher
# Why: avoid duplicate file system watchers consuming resources
# Outcome: starts new watcher, registers in active_watchers dict
if project_id not in active_watchers:
start_watcher(project_id)
```
**Constraint Comments:**
If code exists because of a constraint (async requirements, rate limits, schema compatibility), explain the constraint near the code:
```python
# SQLite requires WAL mode for concurrent read/write access
connection.execute("PRAGMA journal_mode=WAL")
```
**What NOT to Comment:**
Avoid comments that restate obvious code:
```python
# Bad - restates code
counter += 1 # increment counter
# Good - explains why
counter += 1 # track retries for backoff calculation
```
### Codebase Architecture
See [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md) for detailed architecture documentation.
**Directory Structure:**
- `/alembic` - Alembic db migrations
- `/api` - FastAPI REST endpoints + `container.py` composition root
- `/cli` - Typer CLI + `container.py` composition root
- `/deps` - Feature-scoped FastAPI dependencies (config, db, projects, repositories, services, importers)
- `/importers` - Import functionality for Claude, ChatGPT, and other sources
- `/markdown` - Markdown parsing and processing
- `/mcp` - MCP server + `container.py` composition root + `clients/` typed API clients
- `/models` - SQLAlchemy ORM models
- `/repository` - Data access layer
- `/schemas` - Pydantic models for validation
- `/services` - Business logic layer
- `/sync` - File synchronization services + `coordinator.py` for lifecycle management
**Composition Roots:**
Each entrypoint (API, MCP, CLI) has a composition root that:
- Reads `ConfigManager` (the only place that reads global config)
- Resolves runtime mode via `RuntimeMode` enum (TEST > CLOUD > LOCAL)
- Provides dependencies to downstream code explicitly
**Typed API Clients (MCP):**
MCP tools use typed clients in `mcp/clients/` to communicate with the API:
- `KnowledgeClient` - Entity CRUD operations
- `SearchClient` - Search operations
- `MemoryClient` - Context building
- `DirectoryClient` - Directory listing
- `ResourceClient` - Resource reading
- `ProjectClient` - Project management
Flow: MCP Tool → Typed Client → HTTP API → Router → Service → Repository
### Development Notes
- MCP tools are defined in src/basic_memory/mcp/tools/
- MCP prompts are defined in src/basic_memory/mcp/prompts/
- MCP tools should be atomic, composable operations
- Use `textwrap.dedent()` for multi-line string formatting in prompts and tools
- MCP Prompts are used to invoke tools and format content with instructions for an LLM
- Schema changes require Alembic migrations
- SQLite is used for indexing and full text search, files are source of truth
- Testing uses pytest with asyncio support (strict mode)
- Unit tests (`tests/`) use mocks when necessary; integration tests (`test-int/`) use real implementations
- By default, tests run against SQLite (fast, no Docker needed)
- Set `BASIC_MEMORY_TEST_POSTGRES=1` to run against Postgres (uses testcontainers - Docker required)
- Each test runs in a standalone environment with isolated database and tmp_path directory
- CI runs SQLite and Postgres tests in parallel for faster feedback
- Performance benchmarks are in `test-int/test_sync_performance_benchmark.py`
- Use pytest markers: `@pytest.mark.benchmark` for benchmarks, `@pytest.mark.slow` for slow tests
- **Coverage must stay at 100%**: Write tests for new code. Only use `# pragma: no cover` when tests would require excessive mocking (e.g., TYPE_CHECKING blocks, error handlers that need failure injection, runtime-mode-dependent code paths)
### Async Client Pattern (Important!)
**MCP tools use `get_project_client()` for per-project routing:**
```python
from basic_memory.mcp.project_context import get_project_client
@mcp.tool()
async def my_tool(project: str | None = None, context: Context | None = None):
async with get_project_client(project, context) as (client, active_project):
# client is routed based on project's mode (local ASGI or cloud HTTP)
response = await call_get(client, "/path")
return response
```
**CLI commands and non-project-scoped code use `get_client()` directly:**
```python
from basic_memory.mcp.async_client import get_client
async def my_cli_command():
async with get_client() as client:
response = await call_get(client, "/path")
return response
# Per-project routing (when project name is known):
async with get_client(project_name="research") as client:
...
```
**Do NOT use:**
-`from basic_memory.mcp.async_client import client` (deprecated module-level client)
- ❌ Manual auth header management
-`inject_auth_header()` (deleted)
- ❌ Separate `get_client()` + `get_active_project()` in MCP tools (use `get_project_client()` instead)
**Key principles:**
- Auth happens at client creation, not per-request
- Proper resource management via context managers
- Per-project routing: each project can be LOCAL or CLOUD independently
- Cloud projects use API key (`cloud_api_key` in config) as Bearer token
- Routing priority: factory injection > force-local > per-project cloud > global cloud > local ASGI
- Factory pattern enables dependency injection for cloud consolidation
**For cloud app integration:**
```python
from basic_memory.mcp import async_client
# Set custom factory before importing tools
async_client.set_client_factory(your_custom_factory)
```
See SPEC-16 for full context manager refactor details.
## BASIC MEMORY PRODUCT USAGE
### Knowledge Structure
- Entity: Any concept, document, or idea represented as a markdown file
- Observation: A categorized fact about an entity (`- [category] content`)
- Relation: A directional link between entities (`- relation_type [[Target]]`)
- Frontmatter: YAML metadata at the top of markdown files
- Knowledge representation follows precise markdown format:
- Observations with [category] prefixes
- Relations with WikiLinks [[Entity]]
- Frontmatter with metadata
### Basic Memory Commands
**Local Commands:**
- Check sync status: `basic-memory status`
- Doctor check (file <-> DB loop): `basic-memory doctor`
- Import from Claude: `basic-memory import claude conversations`
- Import from ChatGPT: `basic-memory import chatgpt`
- Import from Memory JSON: `basic-memory import memory-json`
- Tool access: `basic-memory tool` (provides CLI access to MCP tools)
- Continue: `basic-memory tool continue-conversation --topic="search"`
**Project Management:**
- List projects: `basic-memory project list`
- Add project: `basic-memory project add "name" ~/path`
- Project info: `basic-memory project info`
- Set cloud mode: `basic-memory project set-cloud "name"`
- Set local mode: `basic-memory project set-local "name"`
- One-way sync (local -> cloud): `basic-memory project sync`
- Bidirectional sync: `basic-memory project bisync`
- Integrity check: `basic-memory project check`
**Cloud Commands (requires subscription):**
- Authenticate (global): `basic-memory cloud login`
- Logout (global): `basic-memory cloud logout`
- Check cloud status: `basic-memory cloud status`
- Setup cloud sync: `basic-memory cloud setup`
- Save API key: `basic-memory cloud set-key bmc_...`
- Create API key: `basic-memory cloud create-key "name"`
- Manage snapshots: `basic-memory cloud snapshot [create|list|delete|show|browse]`
- Restore from snapshot: `basic-memory cloud restore <path> --snapshot <id>`
### MCP Capabilities
- Basic Memory exposes these MCP tools to LLMs:
**Content Management:**
- `write_note(title, content, directory, tags)` - Create/update markdown notes with semantic observations and relations
- `read_note(identifier, page, page_size)` - Read notes by title, permalink, or memory:// URL with knowledge graph awareness
- `read_content(path)` - Read raw file content (text, images, binaries) without knowledge graph processing
- `view_note(identifier, page, page_size)` - View notes as formatted artifacts for better readability
- `edit_note(identifier, operation, content)` - Edit notes incrementally (append, prepend, find/replace, replace_section)
- `move_note(identifier, destination_path, is_directory)` - Move notes or directories to new locations, updating database and maintaining links
- `delete_note(identifier, is_directory)` - Delete notes or directories from the knowledge base
**Knowledge Graph Navigation:**
- `build_context(url, depth, timeframe)` - Navigate the knowledge graph via memory:// URLs for conversation continuity
- `recent_activity(type, depth, timeframe)` - Get recently updated information with specified timeframe (e.g., "1d", "1 week")
- `list_directory(dir_name, depth, file_name_glob)` - Browse directory contents with filtering and depth control
**Search & Discovery:**
- `search_notes(query, page, page_size, search_type, types, entity_types, after_date)` - Full-text search across all content with advanced filtering options
**Project Management:**
- `list_memory_projects()` - List all available projects with their status
- `create_memory_project(project_name, project_path, set_default)` - Create new Basic Memory projects
- `delete_project(project_name)` - Delete a project from configuration
**Visualization:**
- `canvas(nodes, edges, title, directory)` - Generate Obsidian canvas files for knowledge graph visualization
**ChatGPT-Compatible Tools:**
- `search(query)` - Search across knowledge base (OpenAI actions compatible)
- `fetch(id)` - Fetch full content of a search result document
- MCP Prompts for better AI interaction:
- `ai_assistant_guide()` - Guidance on effectively using Basic Memory tools for AI assistants
- `continue_conversation(topic, timeframe)` - Continue previous conversations with relevant historical context
- `search(query, after_date)` - Search with detailed, formatted results for better context understanding
- `recent_activity(timeframe)` - View recently changed items with formatted output
### Cloud Features (v0.15.0+)
Basic Memory now supports cloud synchronization and storage (requires active subscription):
**Authentication:**
- JWT-based authentication with subscription validation
- Secure session management with token refresh
- Support for multiple cloud projects
**Bidirectional Sync:**
- rclone bisync integration for two-way synchronization
- Conflict resolution and integrity verification
- Real-time sync with change detection
- Mount/unmount cloud storage for direct file access
**Cloud Project Management:**
- Create and manage projects in the cloud
- Toggle between local and cloud modes
- Per-project sync configuration
- Subscription-based access control
**Security & Performance:**
- Removed .env file loading for improved security
- .gitignore integration (respects gitignored files)
- WAL mode for SQLite performance
- Background relation resolution (non-blocking startup)
- API performance optimizations (SPEC-11)
**Per-Project Cloud Routing:**
Individual projects can be routed through the cloud while others stay local, using an API key:
```bash
# Save API key and set project to cloud mode
basic-memory cloud set-key bmc_abc123...
basic-memory project set-cloud research # route through cloud
basic-memory project set-local research # revert to local
```
MCP tools use `get_project_client()` which automatically routes based on the project's mode. Cloud projects use the `cloud_api_key` from config as Bearer token.
**CLI Routing Flags (Global Cloud Mode):**
When global cloud mode is enabled, CLI commands route to the cloud API by default. Use `--local` and `--cloud` flags to override:
```bash
# Force local routing (ignore cloud mode)
basic-memory status --local
basic-memory project list --local
# Force cloud routing (when cloud mode is disabled)
basic-memory status --cloud
basic-memory project info my-project --cloud
```
Key behaviors:
- The local MCP server (`basic-memory mcp`) automatically uses local routing
- This allows simultaneous use of local Claude Desktop and cloud-based clients
- Some commands (like `project default`, `project sync-config`, `project move`) require `--local` in cloud mode since they modify local configuration
- Environment variable `BASIC_MEMORY_FORCE_LOCAL=true` forces local routing globally
- Per-project cloud routing via API key works independently of global cloud mode
## AI-Human Collaborative Development
Basic Memory emerged from and enables a new kind of development process that combines human and AI capabilities. Instead
of using AI just for code generation, we've developed a true collaborative workflow:
1. AI (LLM) writes initial implementation based on specifications and context
2. Human reviews, runs tests, and commits code with any necessary adjustments
3. Knowledge persists across conversations using Basic Memory's knowledge graph
4. Development continues seamlessly across different AI sessions with consistent context
5. Results improve through iterative collaboration and shared understanding
This approach has allowed us to tackle more complex challenges and build a more robust system than either humans or AI
could achieve independently.
**Problem-Solving Guidance:**
- If a solution isn't working after reasonable effort, suggest alternative approaches
- Don't persist with a problematic library or pattern when better alternatives exist
- Example: When py-pglite caused cascading test failures, switching to testcontainers-postgres was the right call
## GitHub Integration
Basic Memory has taken AI-Human collaboration to the next level by integrating Claude directly into the development workflow through GitHub:
### GitHub MCP Tools
Using the GitHub Model Context Protocol server, Claude can now:
- **Repository Management**:
- View repository files and structure
- Read file contents
- Create new branches
- Create and update files
- **Issue Management**:
- Create new issues
- Comment on existing issues
- Close and update issues
- Search across issues
- **Pull Request Workflow**:
- Create pull requests
- Review code changes
- Add comments to PRs
This integration enables Claude to participate as a full team member in the development process, not just as a code generation tool. Claude's GitHub account ([bm-claudeai](https://github.com/bm-claudeai)) is a member of the Basic Machines organization with direct contributor access to the codebase.
### Collaborative Development Process
With GitHub integration, the development workflow includes:
1. **Direct code review** - Claude can analyze PRs and provide detailed feedback
2. **Contribution tracking** - All of Claude's contributions are properly attributed in the Git history
3. **Branch management** - Claude can create feature branches for implementations
4. **Documentation maintenance** - Claude can keep documentation updated as the code evolves
5. **Code Commits**: ALWAYS sign off commits with `git commit -s`
This level of integration represents a new paradigm in AI-human collaboration, where the AI assistant becomes a full-fledged team member rather than just a tool for generating code snippets.
+638
View File
@@ -1,5 +1,643 @@
# CHANGELOG
## Unreleased
## v0.20.3 (2026-03-26)
### Bug Fixes
- **#698**: CLI cloud commands now use API key when configured
- `get_authenticated_headers()` only checked OAuth tokens, ignoring `config.cloud_api_key`
- All CLI cloud commands (`upload`, `status`, `snapshot`, `restore`, etc.) failed for API-key-only users while MCP tools worked fine
- Now mirrors the same credential priority as MCP: API key first, OAuth fallback
- Fixes `bm cloud upload --project` returning "project does not exist" when authenticated with `bmc_*` API key
## v0.20.2 (2026-03-10)
### Bug Fixes
- Fix auto-update Homebrew detection: `brew outdated` exits 1 when a formula is outdated, not on error
- Previously treated exit code 1 as a failure, causing "Automatic update check failed" instead of detecting the available update
## v0.20.1 (2026-03-10)
### Bug Fixes
- **#661**: Fix `bm project list` MCP column to show transport type (stdio/https) instead of DB presence
- Renamed "MCP (stdio)" column to "MCP"
- Shows actual routing mode: `stdio` for local, `https` for cloud projects
- Clears local path display for cloud-mode projects
- **#662**: Invalidate config cache when file is modified by another process
- Adds mtime-based cache validation to `ConfigManager.load_config()`
- Long-lived processes (MCP stdio server) now detect external config changes
- Fixes `bm project set-cloud` having no effect on running MCP server
## v0.20.0 (2026-03-10)
### Features
- **#643**: Default-on auto-update system and `bm update` command
- Automatic background update checks for CLI installs (uv tool, Homebrew)
- Install-source detection (homebrew, uv_tool, uvx, unknown) with uvx skip behavior
- Periodic check gating via `auto_update_last_checked_at` + `update_check_interval` config
- Manager-specific update flows: Homebrew (`brew upgrade`) and uv tool (`uv tool upgrade`)
- Silent, non-blocking MCP behavior via daemon thread before server run
- Manual commands: `bm update` (force check + apply) and `bm update --check` (check only)
- New config fields: `auto_update`, `update_check_interval`, `auto_update_last_checked_at`
## v0.19.2 (2026-03-09)
### Bug Fixes
- **#657**: Coerce string params to list/dict in MCP tools
- MCP clients that serialize `list`/`dict` arguments as JSON strings no longer fail Pydantic validation
- Adds `BeforeValidator` coercion to `search_notes` (`entity_types`, `note_types`, `tags`, `metadata_filters`), `write_note` (`metadata`), and `canvas` (`nodes`, `edges`)
- **#655**: Handle SQLite and Windows semantic search regressions
- Fix embedding status query for non-semantic SQLite databases
- Windows-safe log file rotation with per-process log filenames
- Robust `setup_logging` that handles all environments cleanly
## v0.19.1 (2026-03-08)
### Bug Fixes
- **#649**: Enforce strict entity resolution in destructive MCP tools (`edit_note`, `move_note`, `delete_note`)
- Prevents fuzzy-match fallback from silently editing/moving/deleting the wrong note
- DST-related timeframe validation fix (round instead of truncate days)
### Features
- **#648**: Add `insert_before_section` and `insert_after_section` edit operations
- Add `GET /knowledge/graph` endpoint for full graph visualization
### Dependencies
- Bump authlib from 1.6.6 to 1.6.7
## v0.19.0 (2026-03-07)
### Highlights
- **Semantic vector search** for SQLite and Postgres with FastEmbed embeddings
- **Schema system** for validating and inferring knowledge base structure
- **Per-project cloud routing** with API key authentication
- **Upgraded to FastMCP 3.0** with tool annotations
- **CLI overhaul** with JSON output, workspace awareness, and project dashboard
### Features
- **#550**: Add semantic vector search for SQLite and Postgres
- FastEmbed-based embeddings with automatic backfill
- Hybrid search combining full-text and vector similarity
- Score-based fusion replacing RRF for better ranking
- `min_similarity` override for tuning search precision
- Semantic dependencies are now default, with optional extras fallback
- **#549**: Schema system for Basic Memory
- `schema_infer` — infer schema from existing notes
- `schema_validate` — validate notes against a schema definition
- `schema_diff` — compare schemas across projects
- Frontmatter validation support (#597)
- Read schema definitions from file instead of stale DB metadata (#635)
- **#555**: Per-project local/cloud routing with API key auth
- Individual projects route through cloud while others stay local
- `basic-memory cloud set-key` and `basic-memory project set-cloud/set-local`
- Stdio MCP honors per-project cloud routing (#590)
- **#598**: Upgrade FastMCP 2.12.3 to 3.0.1 with tool annotations
- **#585**: Add JSON output mode for MCP tools (default text)
- `--json` output for CLI commands for scripting and CI
- **#576**: Add workspace selection flow for MCP and CLI
- Workspace-aware cloud project listing
- CLI refactoring for workspace support
- **#544**: Project-prefixed permalinks and memory URL routing
- **#632**: Add overwrite guard to `write_note` tool
- **#614**: `edit_note` append/prepend auto-creates note if not found
- **#609**: Richer content context in search results
- Return matched chunk text in search results (#601)
- Improved content hit rate
- **#602**: Add `created_by` and `last_updated_by` user tracking to Entity
- **#600**: Rename `entity_type` to `note_type` across codebase
- **#574**: Add `display_name` and `is_private` to ProjectItem
- **#569**: Expose `external_id` in EntityResponse and link resolver
- **#567**: Isolate default SQLite DB by config dir
- **#560**: Enable `default_project_mode` by default
- **#559**: Add `basic-memory watch` CLI command
- **#546**: Add cloud discovery touchpoints to CLI and MCP
- **#572**: CLI analytics via Umami event collector
- Replace project info with htop-inspired dashboard
- Merge `search_by_metadata` into `search_notes` with optional query
- Add `--strip-frontmatter` to `basic-memory tool read-note`
- Add `destination_folder` parameter to `move_note` tool
### Bug Fixes
- **#644**: Fix default project resolution in cloud mode
- ChatGPT search/fetch tools broken in cloud mode
- `resolve_project_parameter` falls back to projects API
- **#638**: Restore API backward compatibility for v0.18.x clients
- **#637**: Create backup before config migration overwrites old format
- **#636**: `list_workspaces` bypasses factory pattern on cloud MCP server
- **#631**: `build_context` related_results schema validation failure
- **#613**: Reduce excessive log volume by demoting per-request noise to DEBUG
- **#612**: Handle quoted picoschema enum strings in YAML frontmatter
- **#607**: Guard against closed streams in promo and missing vector tables
- **#606**: Accept null for `expected_replacements` in `edit_note`
- **#595**: `recent_activity` dedup and pagination across MCP tools
- **#593**: Backend-specific distance-to-similarity conversion
- **#582**: Use LinkResolver fallback in `build_context` for flexible identifier matching
- **#577**: Replace RRF with score-based fusion in hybrid search
- **#575**: Remove hardcoded "main" default from `default_project`
- **#534**: Speed up `bm --version` startup
- Fix semantic embeddings not generated on fresh DB or upgrade
- Clarify `search_notes` parameter naming and fix `note_types` case sensitivity
- Parse `tag:` prefix at MCP tool level to avoid hybrid search failure
- Cap sqlite-vec knn k parameter at 4096 limit
- Parameterize SQL queries in search repository type filters
- Coerce list frontmatter values to strings for title and type fields
- Avoid `Post(**metadata)` crash when frontmatter contains 'content' or 'handler' keys
- Upgrade cryptography and python-multipart for security advisories
### Internal
- **#594**: Add `ty` as supplemental type checker
- Batched vector sync orchestration across repositories
- FastEmbed parallel guardrails and provider caching
- Improved cloud CLI status and error messages
- CI coverage and Postgres test fixes
## v0.18.5 (2026-02-13)
### Bug Fixes
- Strip NUL bytes from content before PostgreSQL search indexing
([`ec9b2c4`](https://github.com/basicmachines-co/basic-memory/commit/ec9b2c4))
## v0.18.4 (2026-02-12)
### Bug Fixes
- Use global `--header` flag for Tigris consistency on all rclone transactions
([`0eae0e1`](https://github.com/basicmachines-co/basic-memory/commit/0eae0e1))
- `--header-download` / `--header-upload` only apply to GET/PUT requests, missing S3
ListObjectsV2 calls that bisync issues first. Non-US users saw stale edge-cached metadata.
- `--header` applies to ALL HTTP transactions (list, download, upload), fixing bisync for
users outside the Tigris origin region.
## v0.18.2 (2026-02-11)
### Bug Fixes
- **#562**: Use VIRTUAL instead of STORED columns in SQLite migration
([`344e651`](https://github.com/basicmachines-co/basic-memory/commit/344e651))
- Fixes compatibility issue with SQLite STORED generated columns
## v0.18.1 (2026-02-11)
### Features
- **#552**: Add `--format json` to CLI tool commands
([`a47c9c0`](https://github.com/basicmachines-co/basic-memory/commit/a47c9c0))
- CLI tool commands now support `--format json` for machine-readable output
- **#535**: Support `tag:` query shorthand in search
([`f1d50c2`](https://github.com/basicmachines-co/basic-memory/commit/f1d50c2))
- Use `tag:mytag` as a convenient shorthand in search queries
- **#532**: Fast edit entities, refactors for webui, enhanced search
([`530cbac`](https://github.com/basicmachines-co/basic-memory/commit/530cbac))
- Performance improvements for entity editing and search operations
### Bug Fixes
- **#558**: Add X-Tigris-Consistent headers to all rclone commands
([`8489a3d`](https://github.com/basicmachines-co/basic-memory/commit/8489a3d))
- Ensures consistent reads from Tigris object storage during sync
- **#541**: Handle EntityCreationError as conflict
([`343a6e1`](https://github.com/basicmachines-co/basic-memory/commit/343a6e1))
- **#536**: Stabilize metadata filters on Postgres
([`009e849`](https://github.com/basicmachines-co/basic-memory/commit/009e849))
- **#533**: Fix recent_activity prompt defaults
([`24ca5f6`](https://github.com/basicmachines-co/basic-memory/commit/24ca5f6))
- **#530**: Prevent spurious `metadata: {}` in frontmatter output
([`e3ced49`](https://github.com/basicmachines-co/basic-memory/commit/e3ced49))
- Add POST legacy compat routes for v0.18.0 CLI
([`c46d7a6`](https://github.com/basicmachines-co/basic-memory/commit/c46d7a6))
- Restore legacy `/projects/projects` endpoint for older CLI versions
([`a0e754b`](https://github.com/basicmachines-co/basic-memory/commit/a0e754b))
### Internal
- **#538**: Add fast feedback loop tooling (`just fast-check`, `just doctor`, `just testmon`)
([`8072449`](https://github.com/basicmachines-co/basic-memory/commit/8072449))
## v0.18.0 (2026-01-28)
### Features
- **#527**: Add context-aware wiki link resolution with source_path support
([`0023e73`](https://github.com/basicmachines-co/basic-memory/commit/0023e73))
- Add `source_path` parameter to `resolve_link()` for context-aware resolution
- Relative path resolution: `[[nested/note]]` from `folder/file.md``folder/nested/note.md`
- Proximity-based resolution for duplicate titles (prefers notes in same folder)
- Strict mode to disable fuzzy search fallback for wiki links
- **#518**: Add directory support to move_note and delete_note tools
([`0b20801`](https://github.com/basicmachines-co/basic-memory/commit/0b20801))
- Add `is_directory` parameter to `move_note` and `delete_note` MCP tools
- New `POST /move-directory` and delete directory API endpoints
- Rename `folder``directory` parameter across codebase for consistency
- **#522**: Local MCP cloud mode routing
([`8730067`](https://github.com/basicmachines-co/basic-memory/commit/8730067))
- Add `--local` and `--cloud` CLI routing flags
- Local MCP server (`basic-memory mcp`) automatically uses local routing
- Enables simultaneous use of local Claude Desktop and cloud-based clients
- Environment variable `BASIC_MEMORY_FORCE_LOCAL=true` for global override
### Bug Fixes
- **#524**: Fix MCP prompt rendering errors
([`e14ba92`](https://github.com/basicmachines-co/basic-memory/commit/e14ba92))
- Fix "Error rendering prompt recent_activity" error
- Change `TimeFrame` to `str` in prompt type annotations for FastMCP compatibility
## v0.17.9 (2026-01-24)
### Bug Fixes
- **#523**: Fix `remove_project()` checking stale config in cloud mode
([`17c0e0a`](https://github.com/basicmachines-co/basic-memory/commit/17c0e0a))
- In cloud mode, only check database `is_default` field (source of truth)
- Config file can become stale when users set default project via v2 API
## v0.17.8 (2026-01-24)
### Bug Fixes
- **#521**: Fix `get_default_project()` returning multiple results
([`6888eff`](https://github.com/basicmachines-co/basic-memory/commit/6888eff))
- Query incorrectly matched any project with non-NULL `is_default` (both True and False)
- Now correctly checks for `is_default=True` only
## v0.17.7 (2026-01-24)
### Features
- **#476**: Add SPEC-29 Phase 3 bucket snapshot CLI commands
([`369ad37`](https://github.com/basicmachines-co/basic-memory/commit/369ad37))
- New `basic-memory cloud snapshot` commands for managing cloud snapshots
- Commands: `create`, `list`, `delete`, `show`, `browse`
- **#515**: Add MCP registry publication files
([`7a502e6`](https://github.com/basicmachines-co/basic-memory/commit/7a502e6))
### Bug Fixes
- **#520**: Read default project from database in cloud mode
([`38616c3`](https://github.com/basicmachines-co/basic-memory/commit/38616c3))
- **#513**: Ensure external_id is set on entity creation
([`c7835a9`](https://github.com/basicmachines-co/basic-memory/commit/c7835a9))
### Internal
- **#514**: Remove OpenPanel telemetry
([`85835ae`](https://github.com/basicmachines-co/basic-memory/commit/85835ae))
- Update README links to point to basicmemory.com
([`2aaee73`](https://github.com/basicmachines-co/basic-memory/commit/2aaee73))
## v0.17.6 (2026-01-17)
### Bug Fixes
- **#510**: Fix Docker container Python symlink broken at runtime
([`1799c94`](https://github.com/basicmachines-co/basic-memory/commit/1799c94))
### Internal
- Remove logfire config and specs docs, reduce lifespan and sync logging to debug level
([`d1d433d`](https://github.com/basicmachines-co/basic-memory/commit/d1d433d),
[`803f3ef`](https://github.com/basicmachines-co/basic-memory/commit/803f3ef))
## v0.17.5 (2026-01-11)
### Bug Fixes
- **#505**: Prevent CLI commands from hanging on exit (Python 3.14 compatibility)
([`863e0a4`](https://github.com/basicmachines-co/basic-memory/commit/863e0a4))
- Skip `nest_asyncio` on Python 3.14+ where it causes event loop issues
- Simplify CLI test infrastructure for cross-version compatibility
- Update pyright to 1.1.408 for Python 3.14 support
- Fix SQLAlchemy rowcount typing for Python 3.14
## v0.17.4 (2026-01-05)
### Bug Fixes
- **#503**: Preserve search index across server restarts
([`26f7e98`](https://github.com/basicmachines-co/basic-memory/commit/26f7e98))
- Fixes critical bug where search index was wiped on every MCP server restart
- Bug was introduced in v0.16.3, affecting v0.16.3-v0.17.3
- **User action**: Run `basic-memory reset` once after updating to rebuild search index
### Internal
- **#502**: Major architecture refactor with composition roots and typed API clients
([`5947f04`](https://github.com/basicmachines-co/basic-memory/commit/5947f04))
- Add composition roots for API, MCP, and CLI entrypoints
- Split deps.py into feature-scoped modules (config, db, projects, repositories, services, importers)
- Add ProjectResolver for unified project selection
- Add SyncCoordinator for centralized sync/watch lifecycle
- Introduce typed API clients for MCP tools (KnowledgeClient, SearchClient, MemoryClient, etc.)
## v0.17.3 (2026-01-03)
### Features
- **#485**: Add stable external_id (UUID) to Project and Entity models
([`a4000f6`](https://github.com/basicmachines-co/basic-memory/commit/a4000f6))
- Projects and entities now have immutable UUID identifiers
- API v2 endpoints use external_id for stable references
- Directory responses include external_id for entities
### Bug Fixes
- **#501**: Update mcp dependency to support protocol version 2025-11-25
([`c6baf58`](https://github.com/basicmachines-co/basic-memory/commit/c6baf58))
- Fixes "Unsupported protocol version" error when using Claude Code
- Bump mcp from >=1.2.0 to >=1.23.1
- **#499**: Fix route ordering for cloud deployments
([`53c4c20`](https://github.com/basicmachines-co/basic-memory/commit/53c4c20))
- **#486**: Skip config file update for set_default_project in cloud mode
([`fd732aa`](https://github.com/basicmachines-co/basic-memory/commit/fd732aa))
- **#484**: Make RelationResponse.from_id optional to handle null permalinks
([`537e58a`](https://github.com/basicmachines-co/basic-memory/commit/537e58a))
- Use upsert to prevent IntegrityError during parallel search indexing
([`4ce2198`](https://github.com/basicmachines-co/basic-memory/commit/4ce2198))
- Use relative file paths in importers for cloud storage compatibility
([`8adf1f4`](https://github.com/basicmachines-co/basic-memory/commit/8adf1f4))
### Internal
- Refactor importers to use FileService for cloud compatibility
([`45ce181`](https://github.com/basicmachines-co/basic-memory/commit/45ce181))
- Strengthen integration test coverage, remove stdlib mocks
([`b4486d2`](https://github.com/basicmachines-co/basic-memory/commit/b4486d2))
## v0.17.2 (2025-12-29)
### Bug Fixes
- Allow recent_activity discovery mode in cloud mode
([`0bcda4a`](https://github.com/basicmachines-co/basic-memory/commit/0bcda4a))
- Add `allow_discovery` parameter to `resolve_project_parameter()`
- Tools like `recent_activity` can now work across all projects in cloud mode
- Fix circular import in project_context module
### Internal
- Optimize release workflow by running lint/typecheck only (skip full tests)
([`0b5425f`](https://github.com/basicmachines-co/basic-memory/commit/0b5425f))
## v0.17.1 (2025-12-29)
### Bug Fixes
- **#482**: Only set BASIC_MEMORY_ENV=test during pytest runs
([`98fbd60`](https://github.com/basicmachines-co/basic-memory/commit/98fbd60))
- Fixes environment variable pollution affecting alembic migrations
- Test environment detection now scoped to pytest execution only
## v0.17.0 (2025-12-28)
### Features
- **#478**: Add anonymous usage telemetry with Homebrew-style opt-out
([`856737f`](https://github.com/basicmachines-co/basic-memory/commit/856737f))
- Privacy-respecting anonymous usage analytics
- Easy opt-out via `BASIC_MEMORY_NO_ANALYTICS=1` environment variable
- Helps improve Basic Memory based on real usage patterns
- **#474**: Add auto-format files on save with built-in Python formatter
([`1fd680c`](https://github.com/basicmachines-co/basic-memory/commit/1fd680c))
- Automatic markdown formatting on file save
- Built-in Python formatter for consistent code style
- Configurable formatting options
- **#447**: Complete Phase 2 of API v2 migration - MCP tools use v2 endpoints
([`1a74d85`](https://github.com/basicmachines-co/basic-memory/commit/1a74d85))
- All MCP tools now use optimized v2 API endpoints
- Improved performance for knowledge graph operations
- Foundation for future API enhancements
### Bug Fixes
- Fix UTF-8 BOM handling in frontmatter parsing
([`85684f8`](https://github.com/basicmachines-co/basic-memory/commit/85684f8))
- Handles files with UTF-8 byte order marks correctly
- Prevents frontmatter parsing failures
- **#475**: Handle null titles in ChatGPT import
([`14ce5a3`](https://github.com/basicmachines-co/basic-memory/commit/14ce5a3))
- Gracefully handles conversations without titles
- Improved import robustness
- Remove MaxLen constraint from observation content
([`45d6caf`](https://github.com/basicmachines-co/basic-memory/commit/45d6caf))
- Allows longer observation content without truncation
- Removes arbitrary 2000 character limit
- Handle FileNotFoundError gracefully during sync
([`1652f86`](https://github.com/basicmachines-co/basic-memory/commit/1652f86))
- Prevents sync failures when files are deleted during sync
- More resilient file watching
- Use canonical project names in API response messages
([`c23927d`](https://github.com/basicmachines-co/basic-memory/commit/c23927d))
- Consistent project name formatting in all responses
- Suppress CLI warnings for cleaner output
([`d71c6e8`](https://github.com/basicmachines-co/basic-memory/commit/d71c6e8))
- Cleaner terminal output without spurious warnings
- Prevent DEBUG logs from appearing on CLI stdout
([`63b9849`](https://github.com/basicmachines-co/basic-memory/commit/63b9849))
- Debug logging no longer pollutes CLI output
- **#473**: Detect rclone version for --create-empty-src-dirs support
([`622d37e`](https://github.com/basicmachines-co/basic-memory/commit/622d37e))
- Automatic rclone version detection for compatibility
- Prevents errors on older rclone versions
- **#471**: Prevent CLI commands from hanging on exit
([`916baf8`](https://github.com/basicmachines-co/basic-memory/commit/916baf8))
- Fixes CLI hang on shutdown
- Proper async cleanup
- Add cloud_mode check to initialize_app()
([`ef7adb7`](https://github.com/basicmachines-co/basic-memory/commit/ef7adb7))
- Correct initialization for cloud deployments
### Internal
- Centralize test environment detection in config.is_test_env
([`3cd9178`](https://github.com/basicmachines-co/basic-memory/commit/3cd9178))
- Unified test environment detection
- Disables analytics in test environments
- Make test-int-postgres compatible with macOS
([`95937c6`](https://github.com/basicmachines-co/basic-memory/commit/95937c6))
- Cross-platform PostgreSQL testing support
## v0.16.3 (2025-12-20)
### Features
- **#439**: Add PostgreSQL database backend support
([`fb5e9e1`](https://github.com/basicmachines-co/basic-memory/commit/fb5e9e1))
- Full PostgreSQL/Neon database support as alternative to SQLite
- Async connection pooling with asyncpg
- Alembic migrations support for both backends
- Configurable via `BASIC_MEMORY_DATABASE_BACKEND` environment variable
- **#441**: Implement API v2 with ID-based endpoints (Phase 1)
([`28cc522`](https://github.com/basicmachines-co/basic-memory/commit/28cc522))
- New ID-based API endpoints for improved performance
- Foundation for future API enhancements
- Backward compatible with existing endpoints
- Add project_id to Relation and Observation for efficient project-scoped queries
([`a920a9f`](https://github.com/basicmachines-co/basic-memory/commit/a920a9f))
- Enables faster queries in multi-project environments
- Improved database schema for cloud deployments
- Add bulk insert with ON CONFLICT handling for relations
([`0818bda`](https://github.com/basicmachines-co/basic-memory/commit/0818bda))
- Faster relation creation during sync operations
- Handles duplicate relations gracefully
### Performance
- Lightweight permalink resolution to avoid eager loading
([`6f99d2e`](https://github.com/basicmachines-co/basic-memory/commit/6f99d2e))
- Reduces database queries during entity lookups
- Improved response times for read operations
### Bug Fixes
- **#464**: Pin FastMCP to 2.12.3 to fix MCP tools visibility
([`f227ef6`](https://github.com/basicmachines-co/basic-memory/commit/f227ef6))
- Fixes issue where MCP tools were not visible to Claude
- Reverts to last known working FastMCP version
- **#458**: Reduce watch service CPU usage by increasing reload interval
([`897b1ed`](https://github.com/basicmachines-co/basic-memory/commit/897b1ed))
- Lowers CPU usage during file watching
- More efficient resource utilization
- **#456**: Await background sync task cancellation in lifespan shutdown
([`efbc758`](https://github.com/basicmachines-co/basic-memory/commit/efbc758))
- Prevents hanging on shutdown
- Clean async task cleanup
- **#434**: Respect --project flag in background sync
([`70bb10b`](https://github.com/basicmachines-co/basic-memory/commit/70bb10b))
- Background sync now correctly uses specified project
- Fixes multi-project sync issues
- **#446**: Fix observation parsing and permalink limits
([`73d940e`](https://github.com/basicmachines-co/basic-memory/commit/73d940e))
- Handles edge cases in observation content
- Prevents permalink truncation issues
- **#424**: Handle periods in kebab_filenames mode
([`b004565`](https://github.com/basicmachines-co/basic-memory/commit/b004565))
- Fixes filename handling for files with multiple periods
- Improved kebab-case conversion
- Fix Postgres/Neon connection settings and search index dedupe
([`b5d4fb5`](https://github.com/basicmachines-co/basic-memory/commit/b5d4fb5))
- Optimized connection pooling for Postgres
- Prevents duplicate search index entries
### Testing & CI
- Replace py-pglite with testcontainers for Postgres testing
([`c462faf`](https://github.com/basicmachines-co/basic-memory/commit/c462faf))
- More reliable Postgres testing infrastructure
- Uses Docker-based test containers
- Add PostgreSQL testing to GitHub Actions workflow
([`66b91b2`](https://github.com/basicmachines-co/basic-memory/commit/66b91b2))
- CI now tests both SQLite and PostgreSQL backends
- Ensures cross-database compatibility
- **#416**: Add integration test for read_note with underscored folders
([`0c12a39`](https://github.com/basicmachines-co/basic-memory/commit/0c12a39))
- Verifies folder name handling edge cases
### Internal
- Cloud compatibility fixes and performance improvements (#454)
- Remove logfire instrumentation for cleaner production deployments
- Truncate content_stems to fix Postgres 8KB index row limit
## v0.16.2 (2025-11-16)
### Bug Fixes
-268
View File
@@ -1,268 +0,0 @@
# CLAUDE.md - Basic Memory Project Guide
## Project Overview
Basic Memory is a local-first knowledge management system built on the Model Context Protocol (MCP). It enables
bidirectional communication between LLMs (like Claude) and markdown files, creating a personal knowledge graph that can
be traversed using links between documents.
## CODEBASE DEVELOPMENT
### Project information
See the [README.md](README.md) file for a project overview.
### Build and Test Commands
- Install: `just install` or `pip install -e ".[dev]"`
- Run all tests (with coverage): `just test` - Runs both unit and integration tests with unified coverage
- Run unit tests only: `just test-unit` - Fast, no coverage
- Run integration tests only: `just test-int` - Fast, no coverage
- Generate HTML coverage: `just coverage` - Opens in browser
- Single test: `pytest tests/path/to/test_file.py::test_function_name`
- Run benchmarks: `pytest test-int/test_sync_performance_benchmark.py -v -m "benchmark and not slow"`
- Lint: `just lint` or `ruff check . --fix`
- Type check: `just typecheck` or `uv run pyright`
- Format: `just format` or `uv run ruff format .`
- Run all code checks: `just check` (runs lint, format, typecheck, test)
- Create db migration: `just migration "Your migration message"`
- Run development MCP Inspector: `just run-inspector`
**Note:** Project requires Python 3.12+ (uses type parameter syntax and `type` aliases introduced in 3.12)
### Test Structure
- `tests/` - Unit tests for individual components (mocked, fast)
- `test-int/` - Integration tests for real-world scenarios (no mocks, realistic)
- Both directories are covered by unified coverage reporting
- Benchmark tests in `test-int/` are marked with `@pytest.mark.benchmark`
- Slow tests are marked with `@pytest.mark.slow`
### Code Style Guidelines
- Line length: 100 characters max
- Python 3.12+ with full type annotations (uses type parameters and type aliases)
- Format with ruff (consistent styling)
- Import order: standard lib, third-party, local imports
- Naming: snake_case for functions/variables, PascalCase for classes
- Prefer async patterns with SQLAlchemy 2.0
- Use Pydantic v2 for data validation and schemas
- CLI uses Typer for command structure
- API uses FastAPI for endpoints
- Follow the repository pattern for data access
- Tools communicate to api routers via the httpx ASGI client (in process)
### Codebase Architecture
- `/alembic` - Alembic db migrations
- `/api` - FastAPI implementation of REST endpoints
- `/cli` - Typer command-line interface
- `/markdown` - Markdown parsing and processing
- `/mcp` - Model Context Protocol server implementation
- `/models` - SQLAlchemy ORM models
- `/repository` - Data access layer
- `/schemas` - Pydantic models for validation
- `/services` - Business logic layer
- `/sync` - File synchronization services
### Development Notes
- MCP tools are defined in src/basic_memory/mcp/tools/
- MCP prompts are defined in src/basic_memory/mcp/prompts/
- MCP tools should be atomic, composable operations
- Use `textwrap.dedent()` for multi-line string formatting in prompts and tools
- MCP Prompts are used to invoke tools and format content with instructions for an LLM
- Schema changes require Alembic migrations
- SQLite is used for indexing and full text search, files are source of truth
- Testing uses pytest with asyncio support (strict mode)
- Unit tests (`tests/`) use mocks when necessary; integration tests (`test-int/`) use real implementations
- Test database uses in-memory SQLite
- Each test runs in a standalone environment with in-memory SQLite and tmp_file directory
- Performance benchmarks are in `test-int/test_sync_performance_benchmark.py`
- Use pytest markers: `@pytest.mark.benchmark` for benchmarks, `@pytest.mark.slow` for slow tests
### Async Client Pattern (Important!)
**All MCP tools and CLI commands use the context manager pattern for HTTP clients:**
```python
from basic_memory.mcp.async_client import get_client
async def my_mcp_tool():
async with get_client() as client:
# Use client for API calls
response = await call_get(client, "/path")
return response
```
**Do NOT use:**
-`from basic_memory.mcp.async_client import client` (deprecated module-level client)
- ❌ Manual auth header management
-`inject_auth_header()` (deleted)
**Key principles:**
- Auth happens at client creation, not per-request
- Proper resource management via context managers
- Supports three modes: Local (ASGI), CLI cloud (HTTP + auth), Cloud app (factory injection)
- Factory pattern enables dependency injection for cloud consolidation
**For cloud app integration:**
```python
from basic_memory.mcp import async_client
# Set custom factory before importing tools
async_client.set_client_factory(your_custom_factory)
```
See SPEC-16 for full context manager refactor details.
## BASIC MEMORY PRODUCT USAGE
### Knowledge Structure
- Entity: Any concept, document, or idea represented as a markdown file
- Observation: A categorized fact about an entity (`- [category] content`)
- Relation: A directional link between entities (`- relation_type [[Target]]`)
- Frontmatter: YAML metadata at the top of markdown files
- Knowledge representation follows precise markdown format:
- Observations with [category] prefixes
- Relations with WikiLinks [[Entity]]
- Frontmatter with metadata
### Basic Memory Commands
**Local Commands:**
- Sync knowledge: `basic-memory sync` or `basic-memory sync --watch`
- Import from Claude: `basic-memory import claude conversations`
- Import from ChatGPT: `basic-memory import chatgpt`
- Import from Memory JSON: `basic-memory import memory-json`
- Check sync status: `basic-memory status`
- Tool access: `basic-memory tools` (provides CLI access to MCP tools)
- Guide: `basic-memory tools basic-memory-guide`
- Continue: `basic-memory tools continue-conversation --topic="search"`
**Cloud Commands (requires subscription):**
- Authenticate: `basic-memory cloud login`
- Logout: `basic-memory cloud logout`
- Bidirectional sync: `basic-memory cloud sync`
- Integrity check: `basic-memory cloud check`
- Mount cloud storage: `basic-memory cloud mount`
- Unmount cloud storage: `basic-memory cloud unmount`
### MCP Capabilities
- Basic Memory exposes these MCP tools to LLMs:
**Content Management:**
- `write_note(title, content, folder, tags)` - Create/update markdown notes with semantic observations and relations
- `read_note(identifier, page, page_size)` - Read notes by title, permalink, or memory:// URL with knowledge graph awareness
- `read_content(path)` - Read raw file content (text, images, binaries) without knowledge graph processing
- `view_note(identifier, page, page_size)` - View notes as formatted artifacts for better readability
- `edit_note(identifier, operation, content)` - Edit notes incrementally (append, prepend, find/replace, replace_section)
- `move_note(identifier, destination_path)` - Move notes to new locations, updating database and maintaining links
- `delete_note(identifier)` - Delete notes from the knowledge base
**Knowledge Graph Navigation:**
- `build_context(url, depth, timeframe)` - Navigate the knowledge graph via memory:// URLs for conversation continuity
- `recent_activity(type, depth, timeframe)` - Get recently updated information with specified timeframe (e.g., "1d", "1 week")
- `list_directory(dir_name, depth, file_name_glob)` - Browse directory contents with filtering and depth control
**Search & Discovery:**
- `search_notes(query, page, page_size, search_type, types, entity_types, after_date)` - Full-text search across all content with advanced filtering options
**Project Management:**
- `list_memory_projects()` - List all available projects with their status
- `create_memory_project(project_name, project_path, set_default)` - Create new Basic Memory projects
- `delete_project(project_name)` - Delete a project from configuration
- `get_current_project()` - Get current project information and stats
- `sync_status()` - Check file synchronization and background operation status
**Visualization:**
- `canvas(nodes, edges, title, folder)` - Generate Obsidian canvas files for knowledge graph visualization
- MCP Prompts for better AI interaction:
- `ai_assistant_guide()` - Guidance on effectively using Basic Memory tools for AI assistants
- `continue_conversation(topic, timeframe)` - Continue previous conversations with relevant historical context
- `search(query, after_date)` - Search with detailed, formatted results for better context understanding
- `recent_activity(timeframe)` - View recently changed items with formatted output
- `json_canvas_spec()` - Full JSON Canvas specification for Obsidian visualization
### Cloud Features (v0.15.0+)
Basic Memory now supports cloud synchronization and storage (requires active subscription):
**Authentication:**
- JWT-based authentication with subscription validation
- Secure session management with token refresh
- Support for multiple cloud projects
**Bidirectional Sync:**
- rclone bisync integration for two-way synchronization
- Conflict resolution and integrity verification
- Real-time sync with change detection
- Mount/unmount cloud storage for direct file access
**Cloud Project Management:**
- Create and manage projects in the cloud
- Toggle between local and cloud modes
- Per-project sync configuration
- Subscription-based access control
**Security & Performance:**
- Removed .env file loading for improved security
- .gitignore integration (respects gitignored files)
- WAL mode for SQLite performance
- Background relation resolution (non-blocking startup)
- API performance optimizations (SPEC-11)
## AI-Human Collaborative Development
Basic Memory emerged from and enables a new kind of development process that combines human and AI capabilities. Instead
of using AI just for code generation, we've developed a true collaborative workflow:
1. AI (LLM) writes initial implementation based on specifications and context
2. Human reviews, runs tests, and commits code with any necessary adjustments
3. Knowledge persists across conversations using Basic Memory's knowledge graph
4. Development continues seamlessly across different AI sessions with consistent context
5. Results improve through iterative collaboration and shared understanding
This approach has allowed us to tackle more complex challenges and build a more robust system than either humans or AI
could achieve independently.
## GitHub Integration
Basic Memory has taken AI-Human collaboration to the next level by integrating Claude directly into the development workflow through GitHub:
### GitHub MCP Tools
Using the GitHub Model Context Protocol server, Claude can now:
- **Repository Management**:
- View repository files and structure
- Read file contents
- Create new branches
- Create and update files
- **Issue Management**:
- Create new issues
- Comment on existing issues
- Close and update issues
- Search across issues
- **Pull Request Workflow**:
- Create pull requests
- Review code changes
- Add comments to PRs
This integration enables Claude to participate as a full team member in the development process, not just as a code generation tool. Claude's GitHub account ([bm-claudeai](https://github.com/bm-claudeai)) is a member of the Basic Machines organization with direct contributor access to the codebase.
### Collaborative Development Process
With GitHub integration, the development workflow includes:
1. **Direct code review** - Claude can analyze PRs and provide detailed feedback
2. **Contribution tracking** - All of Claude's contributions are properly attributed in the Git history
3. **Branch management** - Claude can create feature branches for implementations
4. **Documentation maintenance** - Claude can keep documentation updated as the code evolves
This level of integration represents a new paradigm in AI-human collaboration, where the AI assistant becomes a full-fledged team member rather than just a tool for generating code snippets.
Symlink
+1
View File
@@ -0,0 +1 @@
AGENTS.md
+10 -4
View File
@@ -8,8 +8,13 @@ ARG GID=1000
COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
# Set environment variables
# UV_PYTHON_INSTALL_DIR ensures Python is installed to a persistent location
# that survives in the final image (not in /root/.local which gets lost)
# UV_PYTHON_PREFERENCE=only-managed tells uv to use its managed Python version
ENV PYTHONUNBUFFERED=1 \
PYTHONDONTWRITEBYTECODE=1
PYTHONDONTWRITEBYTECODE=1 \
UV_PYTHON_INSTALL_DIR=/python \
UV_PYTHON_PREFERENCE=only-managed
# Create a group and user with the provided UID/GID
# Check if the GID already exists, if not create appgroup
@@ -19,9 +24,10 @@ RUN (getent group ${GID} || groupadd --gid ${GID} appgroup) && \
# Copy the project into the image
ADD . /app
# Sync the project into a new environment, asserting the lockfile is up to date
# Install Python 3.13 explicitly and sync the project
WORKDIR /app
RUN uv sync --locked
RUN uv python install 3.13
RUN uv sync --locked --python 3.13
# Create necessary directories and set ownership
RUN mkdir -p /app/data/basic-memory /app/.basic-memory && \
@@ -43,4 +49,4 @@ HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
CMD basic-memory --version || exit 1
# Use the basic-memory entrypoint to run the MCP server with default SSE transport
CMD ["basic-memory", "mcp", "--transport", "sse", "--host", "0.0.0.0", "--port", "8000"]
CMD ["basic-memory", "mcp", "--transport", "sse", "--host", "0.0.0.0", "--port", "8000"]
+494
View File
@@ -0,0 +1,494 @@
# Note Format Reference
Every document in Basic Memory is a plain Markdown file. Files are the source of truth — changes to files automatically update the knowledge graph in the database. You maintain complete ownership, files work with git, and knowledge persists independently of any AI conversation.
## Document Structure
A note has three parts: YAML frontmatter, content (observations), and relations.
```markdown
---
title: Coffee Brewing Methods
type: note
tags: [coffee, brewing]
permalink: coffee-brewing-methods
---
# Coffee Brewing Methods
## Observations
- [method] Pour over provides more flavor clarity than French press
- [technique] Water temperature at 205°F extracts optimal compounds #brewing
- [preference] Ethiopian beans work well with lighter roasts (personal experience)
## Relations
- relates_to [[Coffee Bean Origins]]
- requires [[Proper Grinding Technique]]
- contrasts_with [[Tea Brewing Methods]]
```
The `## Observations` and `## Relations` headings are conventional but not required — the parser detects observations and relations by their syntax patterns anywhere in the document.
## Frontmatter
YAML metadata between `---` fences at the top of the file.
| Field | Required | Default | Description |
|-------|----------|---------|-------------|
| `title` | No | filename stem | Used for linking and references. Auto-set from filename if missing. |
| `type` | No | `note` | Entity type. Used for schema resolution and filtering. |
| `tags` | No | `[]` | List or comma-separated string. Used for organization and search. |
| `permalink` | No | generated from title | Stable identifier. Persists even if the file moves. |
| `schema` | No | none | Schema attachment — dict (inline), string (reference), or omitted (implicit). |
Custom fields are allowed. Any key not in the standard set is stored as `entity_metadata` and indexed for search and filtering.
```yaml
---
title: Paul Graham
type: Person
tags: [startups, essays, lisp]
permalink: paul-graham
status: active
source: wikipedia
---
```
Here `status` and `source` are custom fields stored in `entity_metadata`.
### Frontmatter Value Handling
YAML automatically converts some values to native types. Basic Memory normalizes them:
- Date strings (`2025-10-24`) → kept as ISO format strings
- Numbers (`1.0`) → converted to strings
- Booleans (`true`) → converted to strings (`"True"`)
- Lists and dicts → preserved, items normalized recursively
This prevents errors when downstream code expects string values.
## Observations
An observation is a categorized fact about the entity. Written as a Markdown list item.
**Syntax:**
```
- [category] content text #tag1 #tag2 (context)
```
| Part | Required | Description |
|------|----------|-------------|
| `[category]` | Yes | Classification in square brackets. Any text except `[]()` chars. |
| content | Yes | The fact or statement. |
| `#tags` | No | Inline tags. Space-separated, each starting with `#`. |
| `(context)` | No | Parenthesized text at end of line. Supporting details or source. |
### Examples
```markdown
- [tech] Uses SQLite for storage #database
- [design] Follows local-first architecture #architecture
- [decision] Selected bcrypt for passwords #security (based on OWASP audit)
- [name] Paul Graham
- [expertise] Startups
- [expertise] Lisp
- [expertise] Essay writing
```
Array-like fields use repeated categories — multiple `[expertise]` observations above.
### What Is Not an Observation
The parser excludes these list item patterns:
| Pattern | Example | Reason |
|---------|---------|--------|
| Checkboxes | `- [ ] Todo item`, `- [x] Done`, `- [-] Cancelled` | Task list syntax |
| Markdown links | `- [text](url)` | URL link syntax |
| Bare wiki links | `- [[Target]]` | Treated as a relation instead |
A list item with `#tags` but no `[category]` is still parsed — the tags are extracted and the category defaults to `Note`.
## Relations
Relations connect documents to form the knowledge graph. There are two kinds.
### Explicit Relations
Written as list items with a relation type and a `[[wiki link]]` target.
**Syntax:**
```
- relation_type [[Target Entity]] (context)
```
| Part | Required | Description |
|------|----------|-------------|
| `relation_type` | No | Text before `[[`. Defaults to `relates_to` if omitted. |
| `[[Target]]` | Yes | Wiki link to the target entity. Matched by title or permalink. |
| `(context)` | No | Parenthesized text after `]]`. Supporting details. |
### Examples
```markdown
- implements [[Search Design]]
- depends_on [[Database Schema]]
- works_at [[Y Combinator]] (co-founder)
- [[Some Entity]]
```
The last example — a bare `[[wiki link]]` in a list item — gets relation type `relates_to`.
Common relation types:
- `implements`, `depends_on`, `relates_to`, `inspired_by`
- `extends`, `part_of`, `contains`, `pairs_with`
- `works_at`, `authored`, `collaborated_with`
Any text works as a relation type. These are conventions, not a fixed set.
### Inline References
Wiki links appearing in regular prose (not as list items) create implicit `links_to` relations.
```markdown
This builds on [[Core Design]] and uses [[Utility Functions]].
```
This creates two relations: `links_to [[Core Design]]` and `links_to [[Utility Functions]]`.
### Forward References
Relations can link to entities that don't exist yet. Basic Memory resolves them when the target is created.
## Permalinks and memory:// URLs
Every document has a unique **permalink** — a stable identifier derived from its title. You can set one explicitly in frontmatter, or let the system generate it.
```yaml
permalink: auth-approaches-2024
```
Permalinks form the basis of `memory://` URLs:
```
memory://auth-approaches-2024 # By permalink
memory://Authentication Approaches # By title (auto-resolves)
memory://project/auth-approaches # By path
```
Pattern matching is supported:
```
memory://auth* # Starts with "auth"
memory://*/approaches # Ends with "approaches"
memory://project/*/requirements # Nested wildcard
```
## Schemas
Schemas declare the expected structure of a note — which observation categories and relation types a well-formed note should have. They use Picoschema, a compact notation from Google's Dotprompt that fits naturally in YAML frontmatter.
### Picoschema Syntax
```yaml
schema:
name: string, full name # required field with description
email?: string, contact email # ? = optional
role?: string, job title
works_at?: Organization, employer # capitalized type = entity reference
tags?(array): string, categories # array of type
status?(enum): [active, inactive] # enum with allowed values
metadata?(object): # nested object
updated_at?: string
source?: string
```
| Notation | Meaning | Example |
|----------|---------|---------|
| `field: type` | Required field | `name: string` |
| `field?: type` | Optional field | `role?: string` |
| `field(array): type` | Array of values | `expertise(array): string` |
| `field?(enum): [vals]` | Enum with allowed values | `status?(enum): [active, inactive]` |
| `field?(object):` | Nested object with sub-fields | `metadata?(object):` |
| `, description` | Description after comma | `name: string, full name` |
| `EntityName` | Capitalized type = entity reference | `works_at?: Organization` |
**Scalar types:** `string`, `integer`, `number`, `boolean`, `any`
Any type not in that set whose first letter is uppercase is treated as an entity reference (a relation target).
### Schema-to-Note Mapping
Schemas validate against existing observation/relation syntax. Note authors don't learn new syntax.
| Schema Declaration | Maps To | Example in Note |
|--------------------|---------|-----------------|
| `field: string` | Observation `[field] value` | `- [name] Paul Graham` |
| `field?(array): string` | Multiple `[field]` observations | `- [expertise] Lisp` (repeated) |
| `field?: EntityType` | Relation `field [[Target]]` | `- works_at [[Y Combinator]]` |
| `field?(array): EntityType` | Multiple `field` relations | `- authored [[Book]]` (repeated) |
| `tags` | Frontmatter `tags` array | `tags: [startups, essays]` |
| `field?(enum): [vals]` | Observation `[field] value` where value is in the set | `- [status] active` |
Observations and relations not covered by the schema are valid — schemas describe a subset, not a straitjacket.
### Schema Attachment
Three ways to attach a schema to a note, resolved in priority order:
**1. Inline schema**`schema` is a dict in frontmatter:
```yaml
---
title: Team Standup 2024-01-15
type: meeting
schema:
attendees(array): string, who was there
decisions(array): string, what was decided
action_items(array): string, follow-ups
blockers?(array): string, anything stuck
---
```
Good for one-off structured notes or prototyping a schema before extracting it.
**2. Explicit reference**`schema` is a string naming a schema note:
```yaml
---
title: Basic Memory
schema: SoftwareProject
---
```
or by permalink:
```yaml
---
title: LLM Memory Patterns
schema: schema/research-project
---
```
Use when the note's `type` differs from the schema it should validate against, or when multiple schema variants exist.
**3. Implicit by type** — no `schema` field, resolved by matching `type`:
```yaml
---
title: Paul Graham
type: Person
---
```
The system looks up a schema note where `entity: Person`. If found, it applies. If not, no validation occurs.
**4. No schema** — perfectly fine. Most notes don't need one.
### Schema Notes
A schema is itself a Basic Memory note with `type: schema`. It lives anywhere (though `schema/` is the conventional directory).
```yaml
# schema/Person.md
---
title: Person
type: schema
entity: Person
version: 1
schema:
name: string, full name
role?: string, job title or position
works_at?: Organization, employer
expertise?(array): string, areas of knowledge
email?: string, contact email
settings:
validation: warn
---
# Person
A human individual in the knowledge graph.
```
| Field | Required | Description |
|-------|----------|-------------|
| `type` | Yes | Must be `schema` |
| `entity` | Yes | The entity type this schema describes (e.g., `Person`) |
| `version` | No | Schema version number (default: `1`) |
| `schema` | Yes | Picoschema dict defining the fields |
| `settings.validation` | No | Validation mode (default: `warn`) |
Schema notes are regular notes — they show up in search, can have observations and relations, and participate in the knowledge graph.
### Validation Modes
| Mode | Behavior |
|------|----------|
| `warn` | Warnings in output, doesn't block (default) |
| `strict` | Errors that block sync, for CI/CD enforcement |
| `off` | No validation |
### Validation Output
```
$ bm schema validate people/ada-lovelace.md
⚠ Person schema validation:
- Missing required field: name (expected [name] observation)
- Missing optional field: role
- Missing optional field: works_at (no relation found)
Unmatched observations: [fact] ×2, [born] ×1
Unmatched relations: collaborated_with
```
"Unmatched" items are informational — observations and relations the schema doesn't cover.
### Schema Inference
Generate schemas from existing notes by analyzing observation and relation frequency:
```
$ bm schema infer Person
Analyzing 30 notes with type: Person...
Observations found:
[name] 30/30 100% → name: string
[role] 27/30 90% → role?: string
[expertise] 18/30 60% → expertise?(array): string
[email] 8/30 27% → email?: string
Relations found:
works_at 22/30 73% → works_at?: Organization
Suggested schema:
name: string, full name
role?: string, job title
expertise?(array): string, areas of knowledge
email?: string, contact email
works_at?: Organization, employer
Save to schema/Person.md? [y/n]
```
Frequency thresholds:
- **100% present** → required field
- **25%+ present** → optional field
- **Below 25%** → excluded from suggestion
### Schema Drift Detection
Track how usage patterns shift over time:
```
$ bm schema diff Person
Schema drift detected:
+ expertise: now in 81% of notes (was 12%)
- department: dropped to 3% of notes
~ works_at: cardinality changed (one → many)
Update schema? [y/n/review]
```
## Complete Examples
### Simple Note (No Schema)
```markdown
---
title: Project Ideas
type: note
tags: [ideas, brainstorm]
---
# Project Ideas
## Observations
- [idea] Build a CLI tool for markdown linting #tooling
- [idea] Create a recipe knowledge base #cooking
- [priority] Focus on developer tools first (Q1 goal)
## Relations
- inspired_by [[Developer Workflow Research]]
- part_of [[Q1 Planning]]
```
### Schema-Validated Note
Schema at `schema/Person.md`:
```yaml
---
title: Person
type: schema
entity: Person
version: 1
schema:
name: string, full name
role?: string, job title or position
works_at?: Organization, employer
expertise?(array): string, areas of knowledge
email?: string, contact email
settings:
validation: warn
---
# Person
A human individual in the knowledge graph.
```
Note at `people/paul-graham.md`:
```markdown
---
title: Paul Graham
type: Person
tags: [startups, essays, lisp]
---
# Paul Graham
## Observations
- [name] Paul Graham
- [role] Essayist and investor
- [expertise] Startups
- [expertise] Lisp
- [expertise] Essay writing
- [fact] Created Viaweb, the first web app
## Relations
- works_at [[Y Combinator]]
- authored [[Hackers and Painters]]
```
The `[fact]` observation and `authored` relation are not in the schema — they're valid, just unmatched. The schema only checks that `[name]` exists (required) and looks for optional fields like `[role]`, `[expertise]`, and `works_at`.
### Inline Schema Note
```markdown
---
title: Team Standup 2024-01-15
type: meeting
schema:
attendees(array): string, who was there
decisions(array): string, what was decided
action_items(array): string, follow-ups
blockers?(array): string, anything stuck
---
# Team Standup 2024-01-15
## Observations
- [attendees] Paul
- [attendees] Sarah
- [decisions] Ship v2 by Friday
- [action_items] Paul to review PR #42
- [blockers] Waiting on API credentials
```
+267 -40
View File
@@ -1,3 +1,4 @@
<!-- mcp-name: io.github.basicmachines-co/basic-memory -->
[![License: AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)
[![PyPI version](https://badge.fury.io/py/basic-memory.svg)](https://badge.fury.io/py/basic-memory)
[![Python 3.12+](https://img.shields.io/badge/python-3.12+-blue.svg)](https://www.python.org/downloads/)
@@ -5,15 +6,14 @@
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
![](https://badge.mcpx.dev?type=server 'MCP Server')
![](https://badge.mcpx.dev?type=dev 'MCP Dev')
[![smithery badge](https://smithery.ai/badge/@basicmachines-co/basic-memory)](https://smithery.ai/server/@basicmachines-co/basic-memory)
## 🚀 Basic Memory Cloud is Live!
- **Cross-device and multi-platform support is here.** Your knowledge graph now works on desktop, web, and mobile - seamlessly synced across all your AI tools (Claude, ChatGPT, Gemini, Claude Code, and Codex)
- **Early Supporter Pricing:** Early users get 25% off forever.
The open source project continues as always. Cloud just makes it work everywhere.
- **Cross-device and multi-platform support is here.** Your knowledge graph now works on desktop, web, and mobile.
- **Cloud is optional.** The local-first open-source workflow continues as always.
- **OSS discount:** use code `BMFOSS` for 20% off for 3 months.
[Sign up now →](https://basicmemory.com/beta)
[Sign up now →](https://basicmemory.com?utm_source=github&utm_medium=referral&utm_campaign=readme)
with a 7 day free trial
@@ -23,8 +23,21 @@ Basic Memory lets you build persistent knowledge through natural conversations w
Claude, while keeping everything in simple Markdown files on your computer. It uses the Model Context Protocol (MCP) to
enable any compatible LLM to read and write to your local knowledge base.
- Website: https://basicmachines.co
- Documentation: https://memory.basicmachines.co
## What's New in v0.19.0
- **Semantic Vector Search** — find notes by meaning, not just keywords. Combines full-text and vector similarity for hybrid search with FastEmbed embeddings.
- **Schema System** — infer, validate, and diff the structure of your knowledge base with `schema_infer`, `schema_validate`, and `schema_diff` tools.
- **Per-Project Cloud Routing** — route individual projects through the cloud while others stay local, using API key authentication (`basic-memory project set-cloud`).
- **FastMCP 3.0** — upgraded to FastMCP 3.0 with tool annotations for better client integration.
- **CLI Overhaul** — JSON output mode (`--json`) for scripting, workspace-aware commands, and an htop-inspired project dashboard.
- **Smarter Editing** — `edit_note` append/prepend auto-creates notes if they don't exist; `write_note` has an overwrite guard to prevent accidental data loss.
- **Richer Search Results** — matched chunk text returned in search results for better context.
See the full [CHANGELOG](CHANGELOG.md) for details.
- Website: [basicmemory.com](https://basicmemory.com?utm_source=github&utm_medium=referral&utm_campaign=readme)
- Documentation: [docs.basicmemory.com](https://docs.basicmemory.com?utm_source=github&utm_medium=referral&utm_campaign=readme)
- Community: [Discord](https://discord.gg/tyvKNccgqN?utm_source=github&utm_medium=referral&utm_campaign=readme)
## Pick up your conversation right where you left off
@@ -62,23 +75,35 @@ uv tool install basic-memory
You can view shared context via files in `~/basic-memory` (default directory location).
### Alternative Installation via Smithery
## Automatic Updates
You can use [Smithery](https://smithery.ai/server/@basicmachines-co/basic-memory) to automatically configure Basic
Memory for Claude Desktop:
Basic Memory includes a default-on auto-update flow for CLI installs.
- **Auto-install supported:** `uv tool` and Homebrew installs
- **Default check interval:** every 24 hours (`86400` seconds)
- **MCP-safe behavior:** update checks run silently in `basic-memory mcp` mode
- **`uvx` behavior:** skipped (runtime is ephemeral and managed by `uvx`)
Manual update commands:
```bash
npx -y @smithery/cli install @basicmachines-co/basic-memory --client claude
# Check now and install if supported
bm update
# Check only, do not install
bm update --check
```
This installs and configures Basic Memory without requiring manual edits to the Claude Desktop configuration file. The
Smithery server hosts the MCP server component, while your data remains stored locally as Markdown files.
Config options in `~/.basic-memory/config.json`:
### Glama.ai
```json
{
"auto_update": true,
"update_check_interval": 86400
}
```
<a href="https://glama.ai/mcp/servers/o90kttu9ym">
<img width="380" height="200" src="https://glama.ai/mcp/servers/o90kttu9ym/badge" alt="basic-memory MCP server" />
</a>
To disable automatic updates, set `"auto_update": false`.
## Why Basic Memory?
@@ -362,52 +387,120 @@ basic-memory sync --watch
3. Cloud features (optional, requires subscription):
```bash
# Authenticate with cloud
# Authenticate with cloud (stores OAuth token locally)
basic-memory cloud login
# Bidirectional sync with cloud
basic-memory cloud sync
# (Optional) install/configure rclone for file sync commands
basic-memory cloud setup
# Verify cloud integrity
basic-memory cloud check
# Check cloud auth + health
basic-memory cloud status
```
# Mount cloud storage
basic-memory cloud mount
**Per-Project Cloud Routing** (API key based):
Individual projects can be routed through the cloud while others stay local. This uses an API key for routed
project calls:
```bash
# Save an API key (create one in the web app or via CLI)
basic-memory cloud set-key bmc_abc123...
# Or create one via CLI (requires OAuth login first)
basic-memory cloud create-key "my-laptop"
# Set a project to route through cloud
basic-memory project set-cloud research
# Revert a project to local mode
basic-memory project set-local research
# List projects and route metadata
basic-memory project list
```
`basic-memory cloud login` / `basic-memory cloud logout` are authentication commands. They do not change default CLI
routing behavior.
**Routing Flags**:
Use routing flags to disambiguate command targets:
```bash
# Force local routing for this command
basic-memory status --local
basic-memory project list --local
basic-memory project ls --name main --local
# Force cloud routing for this command
basic-memory status --cloud
basic-memory project info my-project --cloud
basic-memory project ls --name main --cloud
```
No-flag behavior defaults to local when no project context is present.
The local MCP server routes per transport: `--transport stdio` honors per-project routing
(local or cloud), while `--transport streamable-http` and `--transport sse` always route locally.
**CLI Note Editing (`tool edit-note`):**
```bash
# Append content
basic-memory tool edit-note project-plan --operation append --content $'\n## Next Steps\n- Finalize rollout'
# Find/replace with replacement count validation
basic-memory tool edit-note docs/api --operation find_replace --find-text "v0.14.0" --content "v0.15.0" --expected-replacements 2
# Replace a section body
basic-memory tool edit-note docs/setup --operation replace_section --section "## Installation" --content $'Updated install steps\n- Run just install'
# JSON metadata output for integrations
basic-memory tool edit-note docs/setup --operation append --content $'\n- Added note' --format json
```
4. In Claude Desktop, the LLM can now use these tools:
**Content Management:**
```
write_note(title, content, folder, tags) - Create or update notes
read_note(identifier, page, page_size) - Read notes by title or permalink
write_note(title, content, folder, tags, output_format="text"|"json") - Create or update notes
read_note(identifier, page, page_size, output_format="text"|"json") - Read notes by title or permalink
read_content(path) - Read raw file content (text, images, binaries)
view_note(identifier) - View notes as formatted artifacts
edit_note(identifier, operation, content) - Edit notes incrementally
move_note(identifier, destination_path) - Move notes with database consistency
delete_note(identifier) - Delete notes from knowledge base
edit_note(identifier, operation, content, output_format="text"|"json") - Edit notes incrementally
move_note(identifier, destination_path, output_format="text"|"json") - Move notes with database consistency
delete_note(identifier, output_format="text"|"json") - Delete notes from knowledge base
```
**Knowledge Graph Navigation:**
```
build_context(url, depth, timeframe) - Navigate knowledge graph via memory:// URLs
recent_activity(type, depth, timeframe) - Find recently updated information
build_context(url, depth, timeframe, output_format="json"|"text") - Navigate knowledge graph via memory:// URLs
recent_activity(type, depth, timeframe, output_format="text"|"json") - Find recently updated information
list_directory(dir_name, depth) - Browse directory contents with filtering
```
**Search & Discovery:**
```
search(query, page, page_size) - Search across your knowledge base
search_notes(query, page, page_size, search_type, types, entity_types, after_date, metadata_filters, tags, status, project) - Search with filters (query is optional for filter-only searches)
```
**Project Management:**
```
list_memory_projects() - List all available projects
create_memory_project(project_name, project_path) - Create new projects
list_memory_projects(output_format="text"|"json") - List all available projects
create_memory_project(project_name, project_path, output_format="text"|"json") - Create new projects
get_current_project() - Show current project stats
sync_status() - Check synchronization status
```
`output_format` defaults to `"text"` for these tools, preserving current human-readable responses.
`build_context` defaults to `"json"` and can be switched to `"text"` when compact markdown output is preferred.
**Cloud Discovery (opt-in):**
```
cloud_info() - Show optional Cloud overview and setup guidance
release_notes() - Show latest release notes
```
**Visualization:**
```
canvas(nodes, edges, title, folder) - Generate knowledge visualizations
@@ -425,13 +518,147 @@ canvas(nodes, edges, title, folder) - Generate knowledge visualizations
## Futher info
See the [Documentation](https://memory.basicmachines.co/) for more info, including:
See the [Documentation](https://docs.basicmemory.com?utm_source=github&utm_medium=referral&utm_campaign=readme) for more info, including:
- [Complete User Guide](https://docs.basicmemory.com/user-guide/)
- [CLI tools](https://docs.basicmemory.com/guides/cli-reference/)
- [Cloud CLI and Sync](https://docs.basicmemory.com/guides/cloud-cli/)
- [Managing multiple Projects](https://docs.basicmemory.com/guides/cli-reference/#project)
- [Importing data from OpenAI/Claude Projects](https://docs.basicmemory.com/guides/cli-reference/#import)
- [Complete User Guide](https://docs.basicmemory.com/user-guide/?utm_source=github&utm_medium=referral&utm_campaign=readme)
- [CLI tools](https://docs.basicmemory.com/guides/cli-reference/?utm_source=github&utm_medium=referral&utm_campaign=readme)
- [Cloud CLI and Sync](https://docs.basicmemory.com/guides/cloud-cli/?utm_source=github&utm_medium=referral&utm_campaign=readme)
- [Managing multiple Projects](https://docs.basicmemory.com/guides/cli-reference/?utm_source=github&utm_medium=referral&utm_campaign=readme#project)
- [Importing data from OpenAI/Claude Projects](https://docs.basicmemory.com/guides/cli-reference/?utm_source=github&utm_medium=referral&utm_campaign=readme#import)
## Telemetry
Basic Memory collects anonymous, minimal usage events to understand how the CLI-to-cloud conversion funnel performs. This helps us prioritize features and improve the product.
**What we collect:**
- Cloud promo impressions (when the promo banner is shown)
- Cloud login attempts and outcomes
- Promo opt-out events
**What we do NOT collect:**
- No file contents, note titles, or knowledge base data
- No personally identifiable information (PII)
- No IP address tracking or fingerprinting
- No per-command or per-tool-call tracking
Events are sent to our [Umami Cloud](https://umami.is) instance, an open-source, privacy-focused analytics platform. Events are fire-and-forget on a background thread — analytics never blocks or slows the CLI.
**Opt out** by setting the environment variable:
```bash
export BASIC_MEMORY_NO_PROMOS=1
```
This disables both promo messages and all telemetry events.
## Logging
Basic Memory uses [Loguru](https://github.com/Delgan/loguru) for logging. The logging behavior varies by entry point:
| Entry Point | Default Behavior | Use Case |
|-------------|------------------|----------|
| CLI commands | File only | Prevents log output from interfering with command output |
| MCP server | File only | Stdout would corrupt the JSON-RPC protocol |
| API server | File (local) or stdout (cloud) | Docker/cloud deployments use stdout |
**Log file location:** `~/.basic-memory/basic-memory.log` (10MB rotation, 10 days retention)
### Environment Variables
| Variable | Default | Description |
|----------|---------|-------------|
| `BASIC_MEMORY_LOG_LEVEL` | `INFO` | Log level: DEBUG, INFO, WARNING, ERROR |
| `BASIC_MEMORY_CLOUD_MODE` | `false` | When `true`, API logs to stdout with structured context |
| `BASIC_MEMORY_FORCE_LOCAL` | `false` | When `true`, forces local API routing |
| `BASIC_MEMORY_FORCE_CLOUD` | `false` | When `true`, forces cloud API routing |
| `BASIC_MEMORY_EXPLICIT_ROUTING` | `false` | When `true`, marks route selection as explicit (`--local`/`--cloud`) |
| `BASIC_MEMORY_ENV` | `dev` | Set to `test` for test mode (stderr only) |
| `BASIC_MEMORY_NO_PROMOS` | `false` | When `true`, disables cloud promo messages and telemetry |
### Examples
```bash
# Enable debug logging
BASIC_MEMORY_LOG_LEVEL=DEBUG basic-memory sync
# View logs
tail -f ~/.basic-memory/basic-memory.log
# Cloud/Docker mode (stdout logging with structured context)
BASIC_MEMORY_CLOUD_MODE=true uvicorn basic_memory.api.app:app
```
## Development
### Running Tests
Basic Memory supports dual database backends (SQLite and Postgres). By default, tests run against SQLite. Set `BASIC_MEMORY_TEST_POSTGRES=1` to run against Postgres (uses testcontainers - Docker required).
**Quick Start:**
```bash
# Run all tests against SQLite (default, fast)
just test-sqlite
# Run all tests against Postgres (uses testcontainers)
just test-postgres
# Run both SQLite and Postgres tests
just test
```
**Available Test Commands:**
- `just test` - Run all tests against both SQLite and Postgres
- `just test-sqlite` - Run all tests against SQLite (fast, no Docker needed)
- `just test-postgres` - Run all tests against Postgres (uses testcontainers)
- `just test-unit-sqlite` - Run unit tests against SQLite
- `just test-unit-postgres` - Run unit tests against Postgres
- `just test-int-sqlite` - Run integration tests against SQLite
- `just test-int-postgres` - Run integration tests against Postgres
- `just test-windows` - Run Windows-specific tests (auto-skips on other platforms)
- `just test-benchmark` - Run performance benchmark tests
- `just testmon` - Run tests impacted by recent changes (pytest-testmon)
- `just test-smoke` - Run fast MCP end-to-end smoke test
- `just fast-check` - Run fix/format/typecheck + impacted tests + smoke test
- `just doctor` - Run local file <-> DB consistency checks with temp config
**Postgres Testing:**
Postgres tests use [testcontainers](https://testcontainers-python.readthedocs.io/) which automatically spins up a Postgres instance in Docker. No manual database setup required - just have Docker running.
**Testmon Note:** When no files have changed, `just testmon` may collect 0 tests. That's expected and means no impacted tests were detected.
**Test Markers:**
Tests use pytest markers for selective execution:
- `windows` - Windows-specific database optimizations
- `benchmark` - Performance tests (excluded from default runs)
- `smoke` - Fast MCP end-to-end smoke tests
**Other Development Commands:**
```bash
just install # Install with dev dependencies
just lint # Run linting checks
just typecheck # Run type checking
just typecheck-ty # Run ty type checking (incremental supplement to pyright)
just format # Format code with ruff
just fast-check # Fast local loop (fix/format/typecheck + testmon + smoke)
just doctor # Local consistency check (temp config)
just check # Run all quality checks
just migration "msg" # Create database migration
```
**Type Checking Strategy:**
- `just typecheck` (Pyright) remains the primary, blocking type checker.
- `just typecheck-ty` (Astral `ty`) is available as a supplemental checker while rules are adopted incrementally.
- We recommend running both locally while reducing `ty` diagnostics over time.
**Local Consistency Check:**
```bash
basic-memory doctor # Verifies file <-> database sync in a temp project
```
See the [justfile](justfile) for the complete list of development commands.
## License
@@ -450,4 +677,4 @@ and submitting PRs.
</picture>
</a>
Built with ♥️ by Basic Machines
Built with ♥️ by [Basic Machines](https://basicmachines.co?utm_source=github&utm_medium=referral&utm_campaign=readme)
+42
View File
@@ -0,0 +1,42 @@
# Docker Compose configuration for Basic Memory with PostgreSQL
# Use this for local development and testing with Postgres backend
#
# Usage:
# docker-compose -f docker-compose-postgres.yml up -d
# docker-compose -f docker-compose-postgres.yml down
services:
postgres:
image: postgres:17
container_name: basic-memory-postgres
environment:
# Local development/test credentials - NOT for production
# These values are referenced by tests and justfile commands
POSTGRES_DB: basic_memory
POSTGRES_USER: basic_memory_user
POSTGRES_PASSWORD: dev_password # Simple password for local testing only
ports:
- "5433:5432"
volumes:
- postgres_data:/var/lib/postgresql/data
healthcheck:
test: ["CMD-SHELL", "pg_isready -U basic_memory_user -d basic_memory"]
interval: 10s
timeout: 5s
retries: 5
restart: unless-stopped
volumes:
# Named volume for Postgres data
postgres_data:
driver: local
# Named volume for persistent configuration
# Database will be stored in Postgres, not in this volume
basic-memory-config:
driver: local
# Network configuration (optional)
# networks:
# basic-memory-net:
# driver: bridge
+442
View File
@@ -0,0 +1,442 @@
# Basic Memory Architecture
This document describes the architectural patterns and composition structure of Basic Memory.
## Overview
Basic Memory is a local-first knowledge management system with three entrypoints:
- **API** - FastAPI REST server for HTTP access
- **MCP** - Model Context Protocol server for LLM integration
- **CLI** - Typer command-line interface
Each entrypoint uses a **composition root** pattern to manage configuration and dependencies.
## Composition Roots
### What is a Composition Root?
A composition root is the single place in an application where dependencies are wired together. In Basic Memory, each entrypoint has its own composition root that:
1. Reads configuration from `ConfigManager`
2. Resolves runtime mode (local/test)
3. Creates and provides dependencies to downstream code
**Key principle**: Only composition roots read global configuration. All other modules receive configuration explicitly.
### Container Structure
Each entrypoint has a container dataclass in its package:
```
src/basic_memory/
├── api/
│ └── container.py # ApiContainer
├── mcp/
│ └── container.py # McpContainer
├── cli/
│ └── container.py # CliContainer
└── runtime.py # RuntimeMode enum and resolver
```
### Container Pattern
All containers follow the same structure:
```python
@dataclass
class Container:
config: BasicMemoryConfig
mode: RuntimeMode
@classmethod
def create(cls) -> "Container":
"""Create container by reading ConfigManager."""
config = ConfigManager().config
mode = resolve_runtime_mode(is_test_env=config.is_test_env)
return cls(config=config, mode=mode)
@property
def some_computed_property(self) -> bool:
"""Derived values based on config and mode."""
return self.mode.is_local and self.config.some_setting
# Module-level singleton
_container: Container | None = None
def get_container() -> Container:
if _container is None:
raise RuntimeError("Container not initialized")
return _container
def set_container(container: Container) -> None:
global _container
_container = container
```
### Runtime Mode Resolution
The `RuntimeMode` enum centralizes mode detection:
```python
class RuntimeMode(Enum):
LOCAL = "local"
CLOUD = "cloud"
TEST = "test"
@property
def is_cloud(self) -> bool:
return self == RuntimeMode.CLOUD
@property
def is_local(self) -> bool:
return self == RuntimeMode.LOCAL
@property
def is_test(self) -> bool:
return self == RuntimeMode.TEST
```
Resolution follows this precedence in local app flows: **TEST > LOCAL**
```python
def resolve_runtime_mode(is_test_env: bool) -> RuntimeMode:
if is_test_env:
return RuntimeMode.TEST
return RuntimeMode.LOCAL
```
**Note**: `RuntimeMode` determines global behavior (e.g., whether to start file sync).
Per-project routing is orthogonal: individual projects can be set to `cloud` mode via `ProjectMode`,
which affects client routing in `get_client(project_name=...)` without changing global runtime mode.
`RuntimeMode.CLOUD` may remain for compatibility, but standard local runtime resolution does not select it.
## Dependencies Package
### Structure
The `deps/` package provides FastAPI dependencies organized by feature:
```
src/basic_memory/deps/
├── __init__.py # Re-exports for backwards compatibility
├── config.py # Configuration access
├── db.py # Database/session management
├── projects.py # Project resolution
├── repositories.py # Data access layer
├── services.py # Business logic layer
└── importers.py # Import functionality
```
### Usage in Routers
```python
from basic_memory.deps.services import get_entity_service
from basic_memory.deps.projects import get_project_config
@router.get("/entities/{id}")
async def get_entity(
id: int,
entity_service: EntityService = Depends(get_entity_service),
project: ProjectConfig = Depends(get_project_config),
):
return await entity_service.get(id)
```
### Backwards Compatibility
The old `deps.py` file still exists as a thin re-export shim:
```python
# deps.py - backwards compatibility shim
from basic_memory.deps import *
```
New code should import from specific submodules (`basic_memory.deps.services`) for clarity.
## MCP Tools Architecture
### Typed API Clients
MCP tools communicate with the API through typed clients that encapsulate HTTP paths and response validation:
```
src/basic_memory/mcp/clients/
├── __init__.py # Re-exports all clients
├── base.py # BaseClient with common logic
├── knowledge.py # KnowledgeClient - entity CRUD
├── search.py # SearchClient - search operations
├── memory.py # MemoryClient - context building
├── directory.py # DirectoryClient - directory listing
├── resource.py # ResourceClient - resource reading
└── project.py # ProjectClient - project management
```
### Client Pattern
Each client encapsulates API paths and validates responses:
```python
class KnowledgeClient(BaseClient):
"""Client for knowledge/entity operations."""
async def resolve_entity(self, identifier: str) -> int:
"""Resolve identifier to entity ID."""
response = await call_get(
self.http_client,
f"{self._base_path}/resolve/{identifier}",
)
return int(response.text)
async def get_entity(self, entity_id: int) -> EntityResponse:
"""Get entity by ID."""
response = await call_get(
self.http_client,
f"{self._base_path}/entities/{entity_id}",
)
return EntityResponse.model_validate(response.json())
```
### Tool → Client → API Flow
```
MCP Tool (thin adapter)
Typed Client (encapsulates paths, validates responses)
HTTP API (FastAPI router)
Service Layer (business logic)
Repository Layer (data access)
```
Example tool using typed client:
```python
@mcp.tool()
async def search_notes(
query: str,
project: str | None = None,
metadata_filters: dict | None = None,
tags: list[str] | None = None,
status: str | None = None,
) -> SearchResponse:
async with get_project_client(project, context) as (client, active_project):
# Import client inside function to avoid circular imports
from basic_memory.mcp.clients import SearchClient
from basic_memory.schemas.search import SearchQuery
search_query = SearchQuery(
text=query,
metadata_filters=metadata_filters,
tags=tags,
status=status,
)
search_client = SearchClient(client, active_project.external_id)
return await search_client.search(search_query.model_dump())
```
### Per-Project Client Routing
`get_project_client()` from `mcp/project_context.py` is an async context manager that:
1. Resolves the project name from config (no network call)
2. Creates the correctly-routed client based on the project's mode (local ASGI or cloud HTTP with API key)
3. Validates the project via the API
4. Yields `(client, active_project)` tuple
This solves the bootstrap problem: you need the project name to choose the right client (local vs cloud), but you need the client to validate the project exists.
```python
from basic_memory.mcp.project_context import get_project_client
async with get_project_client(project, context) as (client, active_project):
# client is routed based on project's mode (local or cloud)
# active_project is validated via the API
...
```
## Sync Coordination
### SyncCoordinator
The `SyncCoordinator` centralizes sync/watch lifecycle management:
```python
@dataclass
class SyncCoordinator:
"""Coordinates file sync and watch operations."""
status: SyncStatus = SyncStatus.NOT_STARTED
sync_task: asyncio.Task | None = None
watch_service: WatchService | None = None
async def start(self, ...):
"""Start sync and watch operations."""
async def stop(self):
"""Stop all sync operations gracefully."""
def get_status_info(self) -> dict:
"""Get current sync status for observability."""
```
### Status Enum
```python
class SyncStatus(Enum):
NOT_STARTED = "not_started"
STARTING = "starting"
RUNNING = "running"
STOPPING = "stopping"
STOPPED = "stopped"
ERROR = "error"
```
## Project Resolution
### ProjectResolver
Unified project selection across all entrypoints:
```python
class ProjectResolver:
"""Resolves which project to use based on context."""
def resolve(
self,
explicit_project: str | None = None,
) -> ResolvedProject:
"""Resolve project using three-tier hierarchy:
1. Explicit project parameter
2. Default project from config
3. Single available project
"""
```
### Resolution Modes
```python
class ResolutionMode(Enum):
EXPLICIT = "explicit" # User specified project
DEFAULT = "default" # Using configured default
SINGLE_PROJECT = "single" # Only one project exists
FALLBACK = "fallback" # Using first available
```
## Testing Patterns
### Container Testing
Each container has corresponding tests:
```
tests/
├── api/test_api_container.py
├── mcp/test_mcp_container.py
└── cli/test_cli_container.py
```
Tests verify:
- Container creation from config
- Runtime mode properties
- Container accessor functions (get/set)
### Mocking Typed Clients
When testing MCP tools, mock at the client level:
```python
def test_search_notes(monkeypatch):
import basic_memory.mcp.clients as clients_mod
class MockSearchClient:
async def search(self, query):
return SearchResponse(results=[...])
monkeypatch.setattr(clients_mod, "SearchClient", MockSearchClient)
```
## Design Principles
### 1. Explicit Dependencies
Modules receive configuration explicitly rather than reading globals:
```python
# Good - explicit injection
async def sync_files(config: BasicMemoryConfig):
...
# Avoid - hidden global access
async def sync_files():
config = ConfigManager().config # Hidden coupling
```
### 2. Single Responsibility
Each layer has a clear responsibility:
- **Containers**: Wire dependencies
- **Clients**: Encapsulate HTTP communication
- **Services**: Business logic
- **Repositories**: Data access
- **Tools/Routers**: Thin adapters
### 3. Deferred Imports
To avoid circular imports, typed clients are imported inside functions:
```python
async def my_tool():
async with get_client() as client:
# Import here to avoid circular dependency
from basic_memory.mcp.clients import KnowledgeClient
knowledge_client = KnowledgeClient(client, project_id)
```
### 4. Backwards Compatibility
When refactoring, maintain backwards compatibility via shims:
```python
# Old module becomes a shim
from basic_memory.new_location import *
# Docstring explains migration path
"""
DEPRECATED: Import from basic_memory.new_location instead.
This shim will be removed in a future version.
"""
```
## File Organization
```
src/basic_memory/
├── api/
│ ├── container.py # API composition root
│ ├── routers/ # FastAPI routers
│ └── ...
├── mcp/
│ ├── container.py # MCP composition root
│ ├── clients/ # Typed API clients
│ ├── tools/ # MCP tool definitions
│ └── server.py # MCP server setup
├── cli/
│ ├── container.py # CLI composition root
│ ├── app.py # Typer app
│ └── commands/ # CLI command groups
├── deps/
│ ├── config.py # Config dependencies
│ ├── db.py # Database dependencies
│ ├── projects.py # Project dependencies
│ ├── repositories.py # Repository dependencies
│ ├── services.py # Service dependencies
│ └── importers.py # Importer dependencies
├── sync/
│ ├── coordinator.py # SyncCoordinator
│ └── ...
├── runtime.py # RuntimeMode resolution
├── project_resolver.py # Unified project selection
└── config.py # Configuration management
```
+494
View File
@@ -0,0 +1,494 @@
# Note Format Reference
Every document in Basic Memory is a plain Markdown file. Files are the source of truth — changes to files automatically update the knowledge graph in the database. You maintain complete ownership, files work with git, and knowledge persists independently of any AI conversation.
## Document Structure
A note has three parts: YAML frontmatter, content (observations), and relations.
```markdown
---
title: Coffee Brewing Methods
type: note
tags: [coffee, brewing]
permalink: coffee-brewing-methods
---
# Coffee Brewing Methods
## Observations
- [method] Pour over provides more flavor clarity than French press
- [technique] Water temperature at 205°F extracts optimal compounds #brewing
- [preference] Ethiopian beans work well with lighter roasts (personal experience)
## Relations
- relates_to [[Coffee Bean Origins]]
- requires [[Proper Grinding Technique]]
- contrasts_with [[Tea Brewing Methods]]
```
The `## Observations` and `## Relations` headings are conventional but not required — the parser detects observations and relations by their syntax patterns anywhere in the document.
## Frontmatter
YAML metadata between `---` fences at the top of the file.
| Field | Required | Default | Description |
|-------|----------|---------|-------------|
| `title` | No | filename stem | Used for linking and references. Auto-set from filename if missing. |
| `type` | No | `note` | Entity type. Used for schema resolution and filtering. |
| `tags` | No | `[]` | List or comma-separated string. Used for organization and search. |
| `permalink` | No | generated from title | Stable identifier. Persists even if the file moves. |
| `schema` | No | none | Schema attachment — dict (inline), string (reference), or omitted (implicit). |
Custom fields are allowed. Any key not in the standard set is stored as `entity_metadata` and indexed for search and filtering.
```yaml
---
title: Paul Graham
type: Person
tags: [startups, essays, lisp]
permalink: paul-graham
status: active
source: wikipedia
---
```
Here `status` and `source` are custom fields stored in `entity_metadata`.
### Frontmatter Value Handling
YAML automatically converts some values to native types. Basic Memory normalizes them:
- Date strings (`2025-10-24`) → kept as ISO format strings
- Numbers (`1.0`) → converted to strings
- Booleans (`true`) → converted to strings (`"True"`)
- Lists and dicts → preserved, items normalized recursively
This prevents errors when downstream code expects string values.
## Observations
An observation is a categorized fact about the entity. Written as a Markdown list item.
**Syntax:**
```
- [category] content text #tag1 #tag2 (context)
```
| Part | Required | Description |
|------|----------|-------------|
| `[category]` | Yes | Classification in square brackets. Any text except `[]()` chars. |
| content | Yes | The fact or statement. |
| `#tags` | No | Inline tags. Space-separated, each starting with `#`. |
| `(context)` | No | Parenthesized text at end of line. Supporting details or source. |
### Examples
```markdown
- [tech] Uses SQLite for storage #database
- [design] Follows local-first architecture #architecture
- [decision] Selected bcrypt for passwords #security (based on OWASP audit)
- [name] Paul Graham
- [expertise] Startups
- [expertise] Lisp
- [expertise] Essay writing
```
Array-like fields use repeated categories — multiple `[expertise]` observations above.
### What Is Not an Observation
The parser excludes these list item patterns:
| Pattern | Example | Reason |
|---------|---------|--------|
| Checkboxes | `- [ ] Todo item`, `- [x] Done`, `- [-] Cancelled` | Task list syntax |
| Markdown links | `- [text](url)` | URL link syntax |
| Bare wiki links | `- [[Target]]` | Treated as a relation instead |
A list item with `#tags` but no `[category]` is still parsed — the tags are extracted and the category defaults to `Note`.
## Relations
Relations connect documents to form the knowledge graph. There are two kinds.
### Explicit Relations
Written as list items with a relation type and a `[[wiki link]]` target.
**Syntax:**
```
- relation_type [[Target Entity]] (context)
```
| Part | Required | Description |
|------|----------|-------------|
| `relation_type` | No | Text before `[[`. Defaults to `relates_to` if omitted. |
| `[[Target]]` | Yes | Wiki link to the target entity. Matched by title or permalink. |
| `(context)` | No | Parenthesized text after `]]`. Supporting details. |
### Examples
```markdown
- implements [[Search Design]]
- depends_on [[Database Schema]]
- works_at [[Y Combinator]] (co-founder)
- [[Some Entity]]
```
The last example — a bare `[[wiki link]]` in a list item — gets relation type `relates_to`.
Common relation types:
- `implements`, `depends_on`, `relates_to`, `inspired_by`
- `extends`, `part_of`, `contains`, `pairs_with`
- `works_at`, `authored`, `collaborated_with`
Any text works as a relation type. These are conventions, not a fixed set.
### Inline References
Wiki links appearing in regular prose (not as list items) create implicit `links_to` relations.
```markdown
This builds on [[Core Design]] and uses [[Utility Functions]].
```
This creates two relations: `links_to [[Core Design]]` and `links_to [[Utility Functions]]`.
### Forward References
Relations can link to entities that don't exist yet. Basic Memory resolves them when the target is created.
## Permalinks and memory:// URLs
Every document has a unique **permalink** — a stable identifier derived from its title. You can set one explicitly in frontmatter, or let the system generate it.
```yaml
permalink: auth-approaches-2024
```
Permalinks form the basis of `memory://` URLs:
```
memory://auth-approaches-2024 # By permalink
memory://Authentication Approaches # By title (auto-resolves)
memory://project/auth-approaches # By path
```
Pattern matching is supported:
```
memory://auth* # Starts with "auth"
memory://*/approaches # Ends with "approaches"
memory://project/*/requirements # Nested wildcard
```
## Schemas
Schemas declare the expected structure of a note — which observation categories and relation types a well-formed note should have. They use Picoschema, a compact notation from Google's Dotprompt that fits naturally in YAML frontmatter.
### Picoschema Syntax
```yaml
schema:
name: string, full name # required field with description
email?: string, contact email # ? = optional
role?: string, job title
works_at?: Organization, employer # capitalized type = entity reference
tags?(array): string, categories # array of type
status?(enum): [active, inactive] # enum with allowed values
metadata?(object): # nested object
updated_at?: string
source?: string
```
| Notation | Meaning | Example |
|----------|---------|---------|
| `field: type` | Required field | `name: string` |
| `field?: type` | Optional field | `role?: string` |
| `field(array): type` | Array of values | `expertise(array): string` |
| `field?(enum): [vals]` | Enum with allowed values | `status?(enum): [active, inactive]` |
| `field?(object):` | Nested object with sub-fields | `metadata?(object):` |
| `, description` | Description after comma | `name: string, full name` |
| `EntityName` | Capitalized type = entity reference | `works_at?: Organization` |
**Scalar types:** `string`, `integer`, `number`, `boolean`, `any`
Any type not in that set whose first letter is uppercase is treated as an entity reference (a relation target).
### Schema-to-Note Mapping
Schemas validate against existing observation/relation syntax. Note authors don't learn new syntax.
| Schema Declaration | Maps To | Example in Note |
|--------------------|---------|-----------------|
| `field: string` | Observation `[field] value` | `- [name] Paul Graham` |
| `field?(array): string` | Multiple `[field]` observations | `- [expertise] Lisp` (repeated) |
| `field?: EntityType` | Relation `field [[Target]]` | `- works_at [[Y Combinator]]` |
| `field?(array): EntityType` | Multiple `field` relations | `- authored [[Book]]` (repeated) |
| `tags` | Frontmatter `tags` array | `tags: [startups, essays]` |
| `field?(enum): [vals]` | Observation `[field] value` where value is in the set | `- [status] active` |
Observations and relations not covered by the schema are valid — schemas describe a subset, not a straitjacket.
### Schema Attachment
Three ways to attach a schema to a note, resolved in priority order:
**1. Inline schema**`schema` is a dict in frontmatter:
```yaml
---
title: Team Standup 2024-01-15
type: meeting
schema:
attendees(array): string, who was there
decisions(array): string, what was decided
action_items(array): string, follow-ups
blockers?(array): string, anything stuck
---
```
Good for one-off structured notes or prototyping a schema before extracting it.
**2. Explicit reference**`schema` is a string naming a schema note:
```yaml
---
title: Basic Memory
schema: SoftwareProject
---
```
or by permalink:
```yaml
---
title: LLM Memory Patterns
schema: schema/research-project
---
```
Use when the note's `type` differs from the schema it should validate against, or when multiple schema variants exist.
**3. Implicit by type** — no `schema` field, resolved by matching `type`:
```yaml
---
title: Paul Graham
type: Person
---
```
The system looks up a schema note where `entity: Person`. If found, it applies. If not, no validation occurs.
**4. No schema** — perfectly fine. Most notes don't need one.
### Schema Notes
A schema is itself a Basic Memory note with `type: schema`. It lives anywhere (though `schema/` is the conventional directory).
```yaml
# schema/Person.md
---
title: Person
type: schema
entity: Person
version: 1
schema:
name: string, full name
role?: string, job title or position
works_at?: Organization, employer
expertise?(array): string, areas of knowledge
email?: string, contact email
settings:
validation: warn
---
# Person
A human individual in the knowledge graph.
```
| Field | Required | Description |
|-------|----------|-------------|
| `type` | Yes | Must be `schema` |
| `entity` | Yes | The entity type this schema describes (e.g., `Person`) |
| `version` | No | Schema version number (default: `1`) |
| `schema` | Yes | Picoschema dict defining the fields |
| `settings.validation` | No | Validation mode (default: `warn`) |
Schema notes are regular notes — they show up in search, can have observations and relations, and participate in the knowledge graph.
### Validation Modes
| Mode | Behavior |
|------|----------|
| `warn` | Warnings in output, doesn't block (default) |
| `strict` | Errors that block sync, for CI/CD enforcement |
| `off` | No validation |
### Validation Output
```
$ bm schema validate people/ada-lovelace.md
⚠ Person schema validation:
- Missing required field: name (expected [name] observation)
- Missing optional field: role
- Missing optional field: works_at (no relation found)
Unmatched observations: [fact] ×2, [born] ×1
Unmatched relations: collaborated_with
```
"Unmatched" items are informational — observations and relations the schema doesn't cover.
### Schema Inference
Generate schemas from existing notes by analyzing observation and relation frequency:
```
$ bm schema infer Person
Analyzing 30 notes with type: Person...
Observations found:
[name] 30/30 100% → name: string
[role] 27/30 90% → role?: string
[expertise] 18/30 60% → expertise?(array): string
[email] 8/30 27% → email?: string
Relations found:
works_at 22/30 73% → works_at?: Organization
Suggested schema:
name: string, full name
role?: string, job title
expertise?(array): string, areas of knowledge
email?: string, contact email
works_at?: Organization, employer
Save to schema/Person.md? [y/n]
```
Frequency thresholds:
- **100% present** → required field
- **25%+ present** → optional field
- **Below 25%** → excluded from suggestion
### Schema Drift Detection
Track how usage patterns shift over time:
```
$ bm schema diff Person
Schema drift detected:
+ expertise: now in 81% of notes (was 12%)
- department: dropped to 3% of notes
~ works_at: cardinality changed (one → many)
Update schema? [y/n/review]
```
## Complete Examples
### Simple Note (No Schema)
```markdown
---
title: Project Ideas
type: note
tags: [ideas, brainstorm]
---
# Project Ideas
## Observations
- [idea] Build a CLI tool for markdown linting #tooling
- [idea] Create a recipe knowledge base #cooking
- [priority] Focus on developer tools first (Q1 goal)
## Relations
- inspired_by [[Developer Workflow Research]]
- part_of [[Q1 Planning]]
```
### Schema-Validated Note
Schema at `schema/Person.md`:
```yaml
---
title: Person
type: schema
entity: Person
version: 1
schema:
name: string, full name
role?: string, job title or position
works_at?: Organization, employer
expertise?(array): string, areas of knowledge
email?: string, contact email
settings:
validation: warn
---
# Person
A human individual in the knowledge graph.
```
Note at `people/paul-graham.md`:
```markdown
---
title: Paul Graham
type: Person
tags: [startups, essays, lisp]
---
# Paul Graham
## Observations
- [name] Paul Graham
- [role] Essayist and investor
- [expertise] Startups
- [expertise] Lisp
- [expertise] Essay writing
- [fact] Created Viaweb, the first web app
## Relations
- works_at [[Y Combinator]]
- authored [[Hackers and Painters]]
```
The `[fact]` observation and `authored` relation are not in the schema — they're valid, just unmatched. The schema only checks that `[name]` exists (required) and looks for optional fields like `[role]`, `[expertise]`, and `works_at`.
### Inline Schema Note
```markdown
---
title: Team Standup 2024-01-15
type: meeting
schema:
attendees(array): string, who was there
decisions(array): string, what was decided
action_items(array): string, follow-ups
blockers?(array): string, anything stuck
---
# Team Standup 2024-01-15
## Observations
- [attendees] Paul
- [attendees] Sarah
- [decisions] Ship v2 by Friday
- [action_items] Paul to review PR #42
- [blockers] Waiting on API credentials
```
+147
View File
@@ -0,0 +1,147 @@
# Simplified Local/Cloud Routing
## Context
Basic Memory now uses explicit, project-aware routing without a global cloud-mode toggle.
Routing is determined by command-level flags and project mode, not by a global `cloud_mode` state.
This document is the canonical contract for local/cloud routing behavior in CLI, MCP, and API-adjacent clients.
## Goals
1. Remove global `cloud_mode` from runtime/routing semantics.
2. Keep MCP HTTP/SSE local-only; let stdio honor per-project routing.
3. Make CLI routing explicit and easy to reason about.
4. Support projects that exist in both local and cloud without ambiguity.
## Routing Contract
Routing is resolved in this order:
1. Injected client factory (for composition/integration contexts)
2. Explicit routing override (`--local` / `--cloud` or env vars below)
3. Project-scoped routing (`project.mode`) when a project is known
4. Default local routing
### Routing Environment Variables
- `BASIC_MEMORY_FORCE_LOCAL=true`: force local transport
- `BASIC_MEMORY_FORCE_CLOUD=true`: force cloud proxy transport
- `BASIC_MEMORY_EXPLICIT_ROUTING=true`: marks routing as explicitly chosen for this command
When explicit routing is active, project mode does not override the selected route.
## Config Semantics
- `project.mode` is the only config-based routing signal for project-scoped operations.
- Legacy `cloud_mode` values may be encountered during migration/loading but are not used for routing behavior.
- Normalization saves remove stale `cloud_mode` from `~/.basic-memory/config.json`.
### Example Config
```json
{
"projects": {
"main": {
"path": "/Users/me/basic-memory",
"mode": "local",
"local_sync_path": null,
"bisync_initialized": false,
"last_sync": null
},
"specs": {
"path": "specs",
"mode": "cloud",
"local_sync_path": "/Users/me/dev/specs",
"bisync_initialized": true,
"last_sync": "2026-02-06T17:36:38.544153"
}
},
"default_project": "main",
"cloud_api_key": "bmc_abc123...",
"cloud_host": "https://cloud.basicmemory.com"
}
```
## Cloud Commands Are Auth-Only
`bm cloud login`, `bm cloud logout`, and `bm cloud status` manage authentication state.
- `bm cloud login`
- performs OAuth device flow
- stores/refreshes token material
- may verify cloud health/subscription
- does not change routing defaults
- `bm cloud logout`
- removes stored OAuth session tokens
- does not change routing defaults
- `bm cloud status`
- reports auth state (API key, OAuth token validity)
- runs health checks only when credentials are available
## MCP Transport Routing
### Stdio (default)
`bm mcp --transport stdio` uses natural per-project routing.
- Local-mode projects route through the in-process ASGI transport.
- Cloud-mode projects route to the cloud proxy with Bearer auth (API key).
- No explicit routing env vars are injected by the CLI command.
- Externally-set env vars are honored (e.g. `BASIC_MEMORY_FORCE_CLOUD=true` for cloud deployments).
- Users who need all projects forced local can set `BASIC_MEMORY_FORCE_LOCAL=true` externally.
### HTTP and SSE Transports
`bm mcp --transport streamable-http` and `bm mcp --transport sse` always route locally.
These transports set explicit local routing (`BASIC_MEMORY_FORCE_LOCAL=true` and
`BASIC_MEMORY_EXPLICIT_ROUTING=true`) before starting the server. This prevents cloud
routing regardless of project mode, since HTTP/SSE serve as local API endpoints.
## Project List UX for Dual Presence
Projects may exist in both local and cloud. `bm project list` should display that clearly in one row per logical
project identity, with explicit source/target signals.
Recommended display contract:
1. Keep one row per normalized project name/permalink.
2. Show both local and cloud presence as separate columns/indicators.
3. Show an explicit `MCP (stdio)` target column that always resolves to `local`.
4. Keep CLI route semantics explicit:
- no flags: default local for non-project commands
- `--cloud`: force cloud
- `--local`: force local
## Project LS Targeting
`bm project ls` should clearly identify which project instance is being listed.
Targeting rules:
1. No routing flags: list local project files.
2. `--cloud`: list cloud project files.
3. `--local`: list local project files (explicit override).
4. Output should label the active target (`LOCAL` or `CLOUD`) in heading or status line.
## Runtime Mode
Runtime mode is no longer a cloud/local routing switch for local app flows.
- `resolve_runtime_mode(is_test_env)` resolves to:
- `TEST` when running in test environment
- `LOCAL` otherwise
- `RuntimeMode.CLOUD` may remain for compatibility with existing tests/call sites but is not selected by normal local
runtime resolution.
## Verification Checklist
1. Loading config with legacy `cloud_mode` succeeds.
2. Saving config strips legacy `cloud_mode`.
3. `--local/--cloud` always override per-project mode for that command.
4. No-project + no-flags commands route local by default.
5. `bm cloud login/logout` do not toggle routing behavior.
6. `bm mcp` stdio routes per-project mode; HTTP/SSE remain local-forced.
7. `bm project list` communicates dual local/cloud presence without ambiguity.
8. `bm project ls` output identifies route target explicitly.
+130 -10
View File
@@ -427,6 +427,8 @@ await write_note(
)
```
> **Important**: `write_note` errors if the note already exists. Use `edit_note` for incremental changes, or pass `overwrite=True` to replace.
**Well-structured note**:
```python
@@ -760,6 +762,9 @@ notes = await read_note(
identifier="memory://specs/*",
project="main"
)
# Cross-project URL (auto-routes to the correct project)
note = await read_note(identifier="memory://research/specs/api-design")
```
```python
@@ -1038,9 +1043,41 @@ recent_decisions = await search_notes(
)
```
**Structured frontmatter filters**:
```python
# Filter by tags and status
results = await search_notes(
query="authentication",
tags=["security"],
status="in-progress",
project="main"
)
# Complex metadata filters (supports $in, $gt, $gte, $lt, $lte, $between)
results = await search_notes(
query="api design",
metadata_filters={
"type": "spec",
"priority": {"$in": ["high", "critical"]},
"tags": ["architecture"]
},
project="main"
)
# Metadata-only search (no query needed)
results = await search_notes(
metadata_filters={"type": "spec", "status": "in-progress"},
project="main"
)
```
### Search Types
**Text search (default)**:
Available types: `"text"`, `"title"`, `"permalink"`, `"vector"`/`"semantic"`, `"hybrid"`.
Default is `"hybrid"` when semantic search is enabled, `"text"` otherwise.
**Text search**:
```python
# Full-text search across all content
@@ -1051,15 +1088,50 @@ results = await search_notes(
)
```
**Semantic search**:
**Title and permalink search**:
```python
# Search by title only
results = await search_notes(query="API Design", search_type="title", project="main")
# Search by permalink
results = await search_notes(query="specs/api-design", search_type="permalink", project="main")
```
**Semantic/vector search**:
```python
# Semantic/vector search (if enabled)
results = await search_notes(
query="user login security",
search_type="semantic",
search_type="semantic", # or "vector"
project="main"
)
# Override similarity threshold
results = await search_notes(
query="user login security",
search_type="semantic",
min_similarity=0.5,
project="main"
)
```
**Hybrid search** (combines text + semantic):
```python
results = await search_notes(
query="authentication best practices",
search_type="hybrid",
project="main"
)
```
**Tag shorthand in query**:
```python
# Use tag: prefix as shorthand
results = await search_notes(query="tag:security", project="main")
```
### Search Response
@@ -2132,6 +2204,31 @@ active_project = projects[0]["name"]
results = await search_notes(query="test", project=active_project)
```
### Note Already Exists
**Error**: `write_note` called for a note that already exists
**Solution**:
```python
# Preferred: use edit_note for incremental updates
await edit_note(
identifier="Existing Topic",
operation="append",
content="\n- [update] new information",
project="main"
)
# Alternative: replace the entire note
await write_note(
title="Existing Topic",
content="# Existing Topic\n...",
folder="notes",
overwrite=True,
project="main"
)
```
### Entity Not Found
**Error**: Note doesn't exist
@@ -2687,14 +2784,15 @@ await write_note(
### Content Management
**write_note(title, content, folder, tags, note_type, project)**
- Create or update markdown notes
**write_note(title, content, folder, tags, note_type, overwrite, project)**
- Create new markdown notes (errors if note already exists unless overwrite=True)
- Parameters:
- `title` (required): Note title
- `content` (required): Markdown content
- `folder` (required): Destination folder
- `tags` (optional): List of tags
- `note_type` (optional): Type of note (stored in frontmatter). Can be "note", "person", "meeting", "guide", etc.
- `overwrite` (optional): Set to True to replace an existing note (default: error if exists)
- `project` (required unless default_project_mode): Target project
- Returns: Created/updated entity with permalink
- Example:
@@ -2861,16 +2959,20 @@ contents = await list_directory(
### Search & Discovery
**search_notes(query, page, page_size, search_type, types, entity_types, after_date, project)**
**search_notes(query, page, page_size, search_type, types, entity_types, after_date, metadata_filters, tags, status, min_similarity, project)**
- Search across knowledge base
- Parameters:
- `query` (required): Search query
- `query` (optional): Search query (not required for filter-only searches)
- `page` (optional): Page number (default: 1)
- `page_size` (optional): Results per page (default: 10)
- `search_type` (optional): "text" or "semantic"
- `search_type` (optional): "text", "title", "permalink", "vector"/"semantic", "hybrid" (default: "hybrid" when semantic enabled, "text" otherwise)
- `types` (optional): Entity type filter
- `entity_types` (optional): Observation category filter
- `after_date` (optional): Date filter (ISO format)
- `metadata_filters` (optional): Structured frontmatter filters (dict, supports `$in`, `$gt`, `$gte`, `$lt`, `$lte`, `$between` operators)
- `tags` (optional): Frontmatter tags filter (list); also available via `tag:` query shorthand
- `status` (optional): Frontmatter status filter (string)
- `min_similarity` (optional): Override similarity threshold for vector/hybrid search
- `project` (required unless default_project_mode): Target project
- Returns: Matching entities with scores
- Example:
@@ -2883,6 +2985,15 @@ results = await search_notes(
)
```
**Metadata-only search (via search_notes)**
- Use `search_notes` with `metadata_filters` and no `query` for metadata-only searches:
```python
results = await search_notes(
metadata_filters={"type": "spec", "status": "in-progress"},
project="main"
)
```
### Project Management
**list_memory_projects()**
@@ -2930,6 +3041,15 @@ await delete_project(project_name="old-project")
status = await sync_status(project="main")
```
**list_workspaces()**
- List available workspaces (cloud)
- Parameters: None
- Returns: List of workspaces with metadata
- Example:
```python
workspaces = await list_workspaces()
```
### Visualization
**canvas(nodes, edges, title, folder, project)**
@@ -3198,8 +3318,8 @@ await edit_note(
project="main"
)
# Avoid: Complete rewrite
# (unless necessary for major restructuring)
# When full rewrite is needed, use overwrite=True
await write_note(title="Note", content="...", folder="notes", overwrite=True)
```
### 14. Tagging Strategy
+190 -61
View File
@@ -5,7 +5,7 @@ The Basic Memory Cloud CLI provides seamless integration between local and cloud
## Overview
The cloud CLI enables you to:
- **Toggle cloud mode** - All regular `bm` commands work with cloud when enabled
- **Authenticate cloud access** - OAuth/API key credentials are stored locally for cloud operations
- **Project-scoped sync** - Each project independently manages its sync configuration
- **Explicit operations** - Sync only what you want, when you want
- **Bidirectional sync** - Keep local and cloud in sync with rclone bisync
@@ -17,6 +17,8 @@ Before using Basic Memory Cloud, you need:
- **Active Subscription**: An active Basic Memory Cloud subscription is required to access cloud features
- **Subscribe**: Visit [https://basicmemory.com/subscribe](https://basicmemory.com/subscribe) to sign up
- **Optional**: Cloud is optional. Local-first open-source usage continues without cloud.
- **OSS Discount**: Use code `{{OSS_DISCOUNT_CODE}}` for 20% off for 3 months.
If you attempt to log in without an active subscription, you'll receive a "Subscription Required" error with a link to subscribe.
@@ -38,7 +40,7 @@ If you attempt to log in without an active subscription, you'll receive a "Subsc
1. **Cloud-only** - Project exists on cloud, no local copy
2. **Cloud + Local (synced)** - Project has a local working directory that syncs
3. **Local-only** - Project exists locally (when cloud mode is disabled)
3. **Local-only** - Project exists locally and is not routed to cloud
**Example:**
@@ -48,9 +50,9 @@ If you attempt to log in without an active subscription, you'll receive a "Subsc
# - work: wants local sync at ~/work-notes
# - temp: cloud-only, no local sync needed
bm project add research --local-path ~/Documents/research
bm project add work --local-path ~/work-notes
bm project add temp # No local sync
bm project add research --cloud --local-path ~/Documents/research
bm project add work --cloud --local-path ~/work-notes
bm project add temp --cloud # No local sync
# Now you can sync individually (after initial --resync):
bm project bisync --name research
@@ -66,9 +68,9 @@ bm project bisync --name work
## Quick Start
### 1. Enable Cloud Mode
### 1. Authenticate Cloud Access
Authenticate and enable cloud mode:
Authenticate with cloud:
```bash
bm cloud login
@@ -76,11 +78,12 @@ bm cloud login
**What this does:**
1. Opens browser to Basic Memory Cloud authentication page
2. Stores authentication token in `~/.basic-memory/auth/token`
3. **Enables cloud mode** - all CLI commands now work against cloud
4. Validates your subscription status
2. Stores authentication tokens in `~/.basic-memory/basic-memory-cloud.json`
3. Validates your subscription status
4. Leaves routing behavior unchanged (auth only)
**Result:** All `bm project`, `bm tools` commands now work with cloud.
**Result:** Cloud credentials are available for cloud-routed commands.
Apply OSS discount code `{{OSS_DISCOUNT_CODE}}` during checkout to receive 20% off for 3 months.
### 2. Set Up Sync
@@ -104,10 +107,10 @@ Create projects with optional local sync paths:
```bash
# Create cloud project without local sync
bm project add research
bm project add research --cloud
# Create cloud project WITH local sync
bm project add research --local-path ~/Documents/research
bm project add research --cloud --local-path ~/Documents/research
# Or configure sync for existing project
bm project sync-setup research ~/Documents/research
@@ -117,7 +120,7 @@ bm project sync-setup research ~/Documents/research
When you add a project with `--local-path`:
1. Project created on cloud at `/app/data/research`
2. Local path stored in config: `cloud_projects.research.local_path = "~/Documents/research"`
2. Local path stored in config for that project (`local_sync_path`)
3. Local directory created if it doesn't exist
4. Bisync state directory created at `~/.basic-memory/bisync-state/research/`
@@ -179,7 +182,8 @@ bm cloud status
```
You should see:
- `Mode: Cloud (enabled)`
- `OAuth: token valid` (or missing/expired)
- `API Key: configured` (or not set)
- `Cloud instance is healthy`
- Instructions for project sync commands
@@ -187,16 +191,16 @@ You should see:
### Understanding Project Commands
**Key concept:** When cloud mode is enabled, use regular `bm project` commands (not `bm cloud project`).
**Key concept:** Use regular `bm project` commands (not `bm cloud project`).
```bash
# In cloud mode:
bm project list # Lists cloud projects
bm project add research # Creates cloud project
# Local route
bm project list --local
bm project add research ~/Documents/research
# In local mode:
bm project list # Lists local projects
bm project add research ~/Documents/research # Creates local project
# Cloud route
bm project list --cloud
bm project add research --cloud
```
### Creating Projects
@@ -204,7 +208,7 @@ bm project add research ~/Documents/research # Creates local project
**Use case 1: Cloud-only project (no local sync)**
```bash
bm project add temp-notes
bm project add temp-notes --cloud
```
**What this does:**
@@ -217,7 +221,7 @@ bm project add temp-notes
**Use case 2: Cloud project with local sync**
```bash
bm project add research --local-path ~/Documents/research
bm project add research --cloud --local-path ~/Documents/research
```
**What this does:**
@@ -251,11 +255,35 @@ bm project list
```
**What you see:**
- All projects in cloud (when cloud mode enabled)
- Local projects always
- Cloud projects when credentials are available
- Default project marked
- Project paths shown
- Route-related metadata (for example, local/cloud presence and sync info)
**Future:** Will show sync status (synced/not synced, last sync time).
Example shape (single row for dual-presence projects):
```text
Name Path Local Path Cloud Path CLI Default MCP (stdio)
main /basic-memory ~/basic-memory /basic-memory local local
specs /specs ~/dev/specs /specs cloud local
```
### When a Project Exists in Both Local and Cloud
Use routing flags to disambiguate command targets:
```bash
# Force local target for this command
bm project info main --local
bm project ls --name main --local
# Force cloud target for this command
bm project info main --cloud
bm project ls --name main --cloud
```
Default behavior for no-project, no-flag commands is local.
For MCP stdio, routing is always local.
## File Synchronization
@@ -363,24 +391,28 @@ bm project bisync --name research --dry-run
**Result:** Safe preview of sync operations.
### Advanced: List Remote Files
### Advanced: List Project Files by Route
**Use case:** See what files exist on cloud without syncing.
**Use case:** Inspect local or cloud project files explicitly.
```bash
# List all files in project
# List local project files (default target when no route flag is given)
bm project ls --name research
bm project ls --name research --local
# List cloud project files
bm project ls --name research --cloud
# List files in subdirectory
bm project ls --name research --path subfolder
bm project ls --name research --cloud --path subfolder
```
**What happens:**
1. Connects to cloud via rclone
2. Lists files in remote project path
1. Resolves route from flags (or local default when no route is given)
2. Lists files for the chosen project instance
3. No files transferred
**Result:** See cloud file listing.
**Result:** See file listing for the target route.
## Multiple Projects
@@ -390,9 +422,9 @@ bm project ls --name research --path subfolder
```bash
# Setup multiple projects
bm project add research --local-path ~/Documents/research
bm project add work --local-path ~/work-notes
bm project add personal --local-path ~/personal
bm project add research --cloud --local-path ~/Documents/research
bm project add work --cloud --local-path ~/work-notes
bm project add personal --cloud --local-path ~/personal
# Establish baselines
bm project bisync --name research --resync
@@ -417,12 +449,12 @@ bm project bisync --all # Coming soon
```bash
# Projects with sync
bm project add research --local-path ~/Documents/research
bm project add work --local-path ~/work
bm project add research --cloud --local-path ~/Documents/research
bm project add work --cloud --local-path ~/work
# Cloud-only projects
bm project add archive
bm project add temp-notes
bm project add archive --cloud
bm project add temp-notes --cloud
# Sync only the configured ones
bm project bisync --name research
@@ -433,20 +465,101 @@ bm project bisync --name work
**Result:** Fine-grained control over what syncs.
## Disable Cloud Mode
## Per-Project Cloud Routing (API Key)
Return to local mode:
Route individual projects through cloud using an API key. This lets you keep some projects local while others route through cloud.
### Setting Up API Key Auth
**Option A: Create a key in the web app, then save it locally:**
```bash
bm cloud set-key bmc_abc123...
```
**Option B: Create a key via CLI (requires OAuth login first):**
```bash
bm cloud login # One-time OAuth login
bm cloud create-key "my-laptop" # Creates key and saves it locally
```
The API key is account-level — it grants access to all your cloud projects. It's stored in `~/.basic-memory/config.json` as `cloud_api_key`.
### Setting Project Modes
```bash
# Route a project through cloud
bm project set-cloud research
# Revert to local mode
bm project set-local research
# View project modes
bm project list
```
**What happens:**
- `set-cloud`: validates the API key exists, then sets the project mode to `cloud` in config
- `set-local`: reverts the project to local mode (removes the mode entry from config)
- MCP tools and CLI commands for that project will route to `cloud_host/proxy` with the API key as Bearer token
### How It Works
When an MCP tool or CLI command runs for a cloud-mode project:
1. `get_client(project_name="research")` checks the project's mode in config
2. If mode is `cloud`, creates an HTTP client pointed at `cloud_host/proxy` with `Authorization: Bearer bmc_...`
3. If mode is `local` (default), uses the in-process ASGI transport as usual
**Routing priority** (highest to lowest):
1. Factory injection (cloud app, tests)
2. Explicit route override (`--local` / `--cloud`)
3. Per-project cloud mode (API key)
4. Local ASGI transport (default)
Route override environment variables:
- `BASIC_MEMORY_FORCE_LOCAL=true`
- `BASIC_MEMORY_FORCE_CLOUD=true`
- `BASIC_MEMORY_EXPLICIT_ROUTING=true`
No-project, no-flag CLI commands default to local routing.
### Configuration Example
```json
{
"projects": {
"personal": "/Users/me/notes",
"research": "/Users/me/research"
},
"project_modes": {
"research": "cloud"
},
"cloud_api_key": "bmc_abc123...",
"cloud_host": "https://cloud.basicmemory.com",
"default_project": "personal"
}
```
In this example, `personal` stays local and `research` routes through cloud. Projects not listed in `project_modes` default to local.
### Sync Behavior
Cloud-mode projects are automatically skipped during local file sync (background sync and file watching). Their files live on the cloud instance, not locally.
## OAuth Logout
```bash
bm cloud logout
```
**What this does:**
1. Disables cloud mode in config
2. All commands now work locally
3. Auth token remains (can re-enable with login)
1. Removes stored OAuth token(s)
2. Does not change per-project route configuration
3. Does not change command routing defaults
**Result:** All `bm` commands work with local projects again.
**Result:** OAuth session is cleared. API-key-based routing still works if `cloud_api_key` is configured.
## Filter Configuration
@@ -653,12 +766,20 @@ If instance is down, wait a few minutes and retry.
## Command Reference
### Cloud Mode Management
### Cloud Authentication
```bash
bm cloud login # Authenticate and enable cloud mode
bm cloud logout # Disable cloud mode
bm cloud status # Check cloud mode and instance health
bm cloud login # Authenticate and store OAuth credentials
bm cloud logout # Remove stored OAuth credentials
bm cloud status # Check auth state and instance health
bm cloud promo --off # Disable CLI cloud promo notices
```
### API Key Management
```bash
bm cloud set-key <key> # Save a cloud API key (bmc_ prefixed)
bm cloud create-key <name> # Create API key via cloud API (requires OAuth login)
```
### Setup
@@ -669,16 +790,22 @@ bm cloud setup # Install rclone and configure credentials
### Project Management
When cloud mode is enabled:
```bash
bm project list # List cloud projects
bm project add <name> # Create cloud project (no sync)
bm project add <name> --local-path <path> # Create with local sync
bm project list --local # Local project list
bm project list --cloud # Cloud project list
bm project add <name> --cloud # Create cloud project (no sync)
bm project add <name> --cloud --local-path <path> # Create with local sync
bm project sync-setup <name> <path> # Add sync to existing project
bm project rm <name> # Delete project
```
### Per-Project Routing
```bash
bm project set-cloud <name> # Route project through cloud (requires API key)
bm project set-local <name> # Revert project to local mode
```
### File Synchronization
```bash
@@ -697,18 +824,20 @@ bm project bisync --name <project> --verbose
bm project check --name <project>
bm project check --name <project> --one-way
# List remote files
bm project ls --name <project>
bm project ls --name <project> --path <subpath>
# List project files by route
bm project ls --name <project> # Default target: local
bm project ls --name <project> --local
bm project ls --name <project> --cloud
bm project ls --name <project> --cloud --path <subpath>
```
## Summary
**Basic Memory Cloud uses project-scoped sync:**
1. **Enable cloud mode** - `bm cloud login`
1. **Authenticate cloud access** - `bm cloud login`
2. **Install rclone** - `bm cloud setup`
3. **Add projects with sync** - `bm project add research --local-path ~/Documents/research`
3. **Add projects with sync** - `bm project add research --cloud --local-path ~/Documents/research`
4. **Preview first sync** - `bm project bisync --name research --resync --dry-run`
5. **Establish baseline** - `bm project bisync --name research --resync`
6. **Daily workflow** - `bm project bisync --name research`
+91
View File
@@ -0,0 +1,91 @@
# Cloud Semantic Search Value (Customer-Facing Technical Story)
This document explains why teams should buy cloud semantic search even when local search exists.
## Core Promise
Markdown files remain the source of truth in both local and cloud modes.
- Files are portable.
- Search indexes are derived and rebuildable.
- You never get locked into proprietary document storage.
## The Customer Problem
Teams paying for cloud are usually not optimizing for "can this run locally." They are optimizing for:
- finding the right note the first time,
- keeping retrieval quality high as note volume grows,
- avoiding search slowdowns while content is actively changing,
- getting consistent results across users, agents, and sessions.
## Why Cloud Is the Aspirin
Cloud semantic search is the immediate pain reliever because it fixes the problems users feel right now.
### 1) Better hit rate on real queries
Cloud uses stronger managed embeddings than the default local model, which improves semantic recall for paraphrases and vague questions.
Customer outcome:
- fewer "I know this exists but search missed it" moments,
- less query rewording,
- faster time to answer.
### 2) Better behavior under active workloads
Cloud indexing runs out of band in workers, so indexing does not compete with interactive read/write traffic.
Customer outcome:
- stable search responsiveness during heavy updates,
- fresher semantic results shortly after edits,
- less user-visible performance variance.
### 3) Better consistency for shared knowledge
Cloud retrieval runs against a centralized tenant index, so teams and agents resolve against the same semantic state.
Customer outcome:
- fewer "works on my machine" search differences,
- more predictable agent behavior across environments,
- easier cross-user collaboration on large knowledge bases.
### 4) Better quality at higher scale
With Postgres + `pgvector` per tenant, cloud can sustain larger note collections and higher query volumes than typical local setups.
Customer outcome:
- confidence as repositories grow to tens of thousands of notes,
- less need for user-side tuning,
- fewer quality regressions as usage increases.
## Local Is the Vitamin
Local semantic search still matters and should stay strong.
- offline use,
- privacy-first operation,
- no cloud dependency,
- user-controlled runtime.
It compounds long-term ownership and resilience, but does not remove the immediate pain points cloud solves for teams at scale.
## Recommended Messaging
One-liner:
"Cloud semantic search is the aspirin: it fixes retrieval quality and performance pain now. Local semantic search is the vitamin: it builds long-term control and resilience."
Long form:
"Basic Memory keeps markdown as the source of truth everywhere. Local gives privacy and offline control. Cloud adds immediate, measurable improvements in search quality, consistency, and responsiveness for teams and agents running at scale."
## Packaging Guidance
- Base: local FTS plus optional local semantic search.
- Cloud value: higher semantic quality, stable performance under load, and consistent team-wide retrieval.
- Keep interfaces pluggable (`EmbeddingProvider`, vector backend protocol) so implementation can evolve without changing user workflows.
+499
View File
@@ -0,0 +1,499 @@
# Logfire Instrumentation Strategy
## Why
We want Logfire in Basic Memory for two specific use cases:
1. Local development and performance investigation
2. Cloud deployments where Basic Memory runs inside Basic Memory Cloud
This instrumentation must be:
- Disabled by default
- Useful when enabled
- Safe for local-first users
- Searchable in Logfire over time
The previous integration added telemetry, but it leaned too much on generic framework instrumentation. That created noisy spans with weak names and made the trace view harder to navigate. This strategy favors manual instrumentation around Basic Memory's real units of work.
## Core Principles
### 1. Default-off
Basic Memory should ship with Logfire disabled unless the operator explicitly enables it.
That means:
- no required token for normal local usage
- no surprise outbound telemetry
- no behavior change for existing users
### 2. Manual spans over automatic framework spans
We should not rely on broad auto-instrumentation for FastAPI, MCP, SQLAlchemy, or HTTP as the primary experience.
Why:
- auto-generated span names are often generic
- routes and middleware produce too many low-signal spans
- it becomes harder to answer product questions like "why was `write_note` slow?" or "where did sync time go?"
The preferred model is:
- one meaningful root span per high-level operation
- a small number of child spans for important phases
- optional targeted instrumentation only where it adds clear value
### 3. Logs must live inside traces
Basic Memory already uses `loguru` pervasively. The Logfire integration should preserve that and make those logs visible inside the active trace/span context.
If traces exist but the logs are detached from them, the integration is not doing its job.
### 4. Stable names, selective attributes
Span names should describe the operation class, not the specific input.
Good:
- `mcp.tool.write_note`
- `sync.project.scan`
- `search.execute`
- `routing.resolve_project`
Bad:
- `Searching for "foo bar baz"`
- `POST /v2/projects/123/search/`
- `write note to /specs/api.md`
Dynamic values belong in attributes, not in the span name.
## What We Should Not Do
### Avoid broad FastAPI auto-instrumentation
We should not turn on `instrument_fastapi()` and treat that as the main telemetry story.
It may still be useful in narrowly scoped debugging, but it should not define the production trace shape. The meaningful root spans should come from Basic Memory's own entrypoints and service boundaries.
### Avoid per-file spans by default
`sync` can process many files. A span per file will explode trace cardinality and make performance views noisy.
Default behavior should be:
- one span for the project sync
- child spans for scan, move handling, delete handling, markdown sync batch, relation resolution, embedding sync, watermark update
- per-file spans only for failures or very slow outliers
### Avoid high-cardinality attributes on every span
Do not attach large or highly variable values everywhere:
- raw note content
- file bodies
- long search text
- arbitrary metadata blobs
- unique IDs that make every span shape distinct
Prefer compact, queryable attributes:
- `project_name`
- `workspace_id`
- `route_mode`
- `scan_type`
- `file_count`
- `result_count`
- `search_type`
- `retrieval_mode`
- `duration_ms`
## Proposed Architecture
Add a dedicated telemetry module in core Basic Memory, separate from logging setup.
Suggested shape:
```python
# basic_memory/telemetry.py
def configure_telemetry(service_name: str, *, enable_logfire: bool) -> None: ...
def telemetry_enabled() -> bool: ...
def span(name: str, **attrs): ...
def bind_telemetry_context(**attrs): ...
```
This module should:
- configure Logfire only when explicitly enabled
- set up the Logfire `loguru` handler
- expose lightweight helpers so application code does not import `logfire` directly everywhere
- degrade cleanly to no-op behavior when disabled
This keeps the rest of the codebase readable and makes it easy to reason about what telemetry is doing.
## Logging Integration Strategy
### Goal
When a span is active, logs emitted through `loguru` during that operation should show up in the same trace.
### Preferred design
1. Configure Logfire once in the telemetry bootstrap
2. Add the Logfire `loguru` handler to the existing `loguru` configuration
3. At operation boundaries, bind stable contextual fields with `loguru`
4. Let logs emitted inside the span inherit the active trace context
### Context to bind
Bind only the fields that help correlate work across the system:
- `service_name`
- `entrypoint`
- `project_name`
- `workspace_id`
- `route_mode`
- `tool_name`
- `command_name`
This binding should happen at the root of an operation, not deep in leaf functions.
### Important nuance
We should not try to encode the entire trace model into logger extras. The logger context should be a human-meaningful slice of the active operation. Trace linkage comes from the active Logfire/OpenTelemetry context; logger extras are there to improve searchability and readability.
## Span Model
### Root spans
Each user-visible or system-visible operation should get one root span.
Examples:
- `cli.command.status`
- `cli.command.project_sync`
- `api.request.search`
- `mcp.tool.write_note`
- `mcp.tool.read_note`
- `mcp.tool.search_notes`
- `sync.project.run`
- `db.semantic_backfill`
### Child spans
Child spans should represent real phases whose duration we care about.
Examples:
- `routing.client_session`
- `routing.resolve_project`
- `routing.resolve_workspace`
- `api.search.execute`
- `sync.project.scan`
- `sync.project.detect_moves`
- `sync.project.apply_changes`
- `sync.project.resolve_relations`
- `sync.project.sync_embeddings`
- `sync.file.markdown`
- `sync.file.regular`
- `search.execute`
- `search.relaxed_fts_retry`
- `db.init`
- `db.migrate`
### Span naming rules
- Use dot-separated names
- Start with subsystem
- Keep the verb at the end
- Keep names stable across runs
- Never include request-specific text in the span name
## Attribute Taxonomy
### Required attributes on root spans
Every root span should have a small common set:
- `service_name`
- `entrypoint`
- `project_name` when applicable
- `workspace_id` when applicable
- `route_mode` with values like `local_asgi`, `cloud_proxy`, `factory`
### Operation-specific attributes
Examples:
For search:
- `search_type`
- `retrieval_mode`
- `page`
- `page_size`
- `result_count`
- `fallback_used`
For sync:
- `scan_type`
- `force_full`
- `new_count`
- `modified_count`
- `deleted_count`
- `move_count`
- `skipped_count`
- `embeddings_enabled`
For note operations:
- `tool_name`
- `note_type`
- `directory`
- `overwrite`
- `output_format`
### Attributes to avoid by default
- full `query.text`
- full note titles if they create privacy or cardinality issues
- file content
- raw frontmatter
- raw HTTP bodies
If we need richer payloads for a local debugging session, that should be an explicit temporary mode, not the default telemetry shape.
## Instrumentation Plan By Layer
### 1. Entrypoints
Instrument these first:
- `cli.app` callback and major commands
- API lifespan and selected routers
- MCP server lifespan
- MCP tool entrypoints
Why:
- this establishes clean root spans
- it gives us trace boundaries that match how users think about the product
### 2. Routing and context resolution
Instrument:
- client routing decisions
- workspace resolution
- project resolution
- default-project fallback
Why:
- Basic Memory has local/cloud/per-project routing logic
- when something is slow or surprising, we need to know which path was taken
### 3. Sync and indexing
This is the highest-value area to instrument deeply.
Instrument:
- sync root
- scan strategy decision
- filesystem scan
- move detection
- delete handling
- markdown sync phase
- relation resolution
- vector embedding sync
- scan watermark update
Why:
- this is where performance work will happen
- cloud and local both benefit from this visibility
### 4. Search
Instrument:
- search execution
- retrieval mode
- relaxed FTS fallback
- result shaping
Why:
- search is user-facing and latency-sensitive
- hybrid/vector/FTS paths need to be distinguishable
### 5. Database and initialization
Instrument selectively:
- DB init
- migrations
- semantic backfill
- connection mode selection
Avoid full automatic SQL span firehose by default.
## Recommended Rollout Phases
## Task List
- [x] Phase 1: Bootstrap and config gating
- [x] Phase 2: Root spans for entrypoints and primary operations
- [x] Phase 3: Child spans for sync, search, and routing
- [x] Phase 4: Failure-focused detail and final verification
- [x] Phase 5: Loguru context binding and scoped context inheritance
## Recommended Rollout Phases
### Phase 1: Bootstrap and config gating
Add:
- telemetry bootstrap module
- config/env gating
- `loguru` + Logfire handler integration
This gives immediate value with low noise.
### Phase 2: Root spans for entrypoints and primary operations
Add:
- root spans for CLI, API, MCP, and main MCP tools
- stable root attributes for project, workspace, route mode, and operation type
This gives us clean top-level traces that match how users think about the product.
### Phase 3: Child spans for sync, search, and routing
Add child spans to:
- sync
- search
- routing
This is the main performance-investigation layer.
### Phase 4: Failure-focused detail
Add selective deeper spans/log enrichment for:
- sync failures
- relation resolution failures
- slow file operations
- cloud routing/auth failures
This keeps normal traces clean while improving debuggability.
### Phase 5: Loguru context binding and scoped context inheritance
Add:
- context-local telemetry state in `basic_memory.telemetry`
- a shared `scope(...)` helper that opens a span and binds stable logger context together
- context inheritance for routing, sync, and search so downstream `loguru` logs carry the active operation fields
This makes the trace view and the log stream tell the same story without forcing logger rewrites across the codebase.
## Local Dev Playbook
The fastest way to sanity-check the current trace shape is:
```bash
LOGFIRE_TOKEN=lf_... just telemetry-smoke
```
What this does:
- creates an isolated temp home, config dir, and project path
- enables Logfire for the run
- automatically exports to Logfire when `LOGFIRE_TOKEN` is present
- defaults `BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=false` so the smoke run stays fast and trace-friendly
- disables promo telemetry so the trace is about Basic Memory work, not analytics noise
- runs a small CLI workflow:
- `project add`
- `tool write-note`
- `tool read-note`
- `tool edit-note`
- `tool build-context`
- `tool search-notes`
- `doctor`
If you want to exercise the instrumentation without exporting anything upstream:
```bash
BASIC_MEMORY_LOGFIRE_SEND_TO_LOGFIRE=false just telemetry-smoke
```
If you want the smoke run to include vector or hybrid retrieval spans too:
```bash
LOGFIRE_TOKEN=lf_... BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true just telemetry-smoke
```
The recipe sets `BASIC_MEMORY_LOGFIRE_ENVIRONMENT=telemetry-smoke` by default so these traces are easy to isolate in Logfire. Override it if you want the smoke traces grouped under a different environment name.
### What to look for
You should see a small set of comparable root spans rather than a framework-generated span forest:
- `cli.command.project`
- `cli.command.tool`
- `mcp.tool.write_note`
- `mcp.tool.read_note`
- `mcp.tool.edit_note`
- `mcp.tool.build_context`
- `mcp.tool.search_notes`
- `sync.project.run`
You should also see correlated logs under those traces with stable fields like:
- `project_name`
- `route_mode`
- `tool_name`
- `entrypoint`
### Expected nuance
`doctor` creates its own temporary project on purpose. That means the sync trace will usually show a different project name than the `telemetry-smoke` write/search traces. That is fine for smoke testing because the goal is to confirm:
- root span names are meaningful
- scoped logs stay attached to the active trace
- routing, tool, search, and sync phases are easy to distinguish
## Validation Checklist
We should consider the integration successful when the following are true:
1. With telemetry disabled, Basic Memory behaves exactly as it does today.
2. With telemetry enabled, one user action produces one obvious root span.
3. Logs emitted during that action are visible inside the same trace.
4. A search in Logfire for `mcp.tool.write_note` or `sync.project.run` returns comparable spans across runs.
5. Trace views show phase timing clearly without drowning in framework noise.
6. Sensitive payloads are not captured by default.
## Immediate Implementation Direction
When we start coding, the first pass should be:
1. Add `basic_memory.telemetry`
2. Add config/env switches for `enabled`, `send_to_logfire`, and service name
3. Wire telemetry bootstrap into CLI, API, and MCP entrypoints
4. Configure `loguru` to emit to both existing sinks and the Logfire handler when enabled
5. Add manual root spans around:
- CLI commands
- API request handlers we care about
- MCP tool entrypoints
- sync root
- search root
6. Add child spans to the sync and routing phases only after the root span model feels clean
That gives us a strong foundation without repeating the earlier "turn on instrumentation everywhere" approach.
+138
View File
@@ -0,0 +1,138 @@
# MCP UI Bakeoff - Instructions & Test Plan
Last updated: 2026-02-02
## Scope
Compare three presentation paths for Basic Memory MCP tools:
1. **ToolUI (React)** via MCP App resources.
2. **MCPUI Python SDK** embedded UI resources (legacy host path).
3. **ASCII/ANSI** output for TUI clients.
This doc is the running instruction set and test plan. Update as implementation progresses.
---
## Prerequisites
- Repo: `basic-memory` (worktree: `basic-memory-mcp-ui-poc`)
- Node for toolui build (already used for POC)
- Python 3.12+ with `uv`
Optional (for MCPUI Python SDK path):
- Local repo: `/Users/phernandez/dev/mcp-ui`
- Install the server SDK into the Basic Memory venv:
- `uv pip install -e /Users/phernandez/dev/mcp-ui/sdks/python/server`
---
## Build / Refresh Steps
### ToolUI React bundle
```bash
cd ui/tool-ui-react
npm install
npm run build
```
This regenerates:
- `src/basic_memory/mcp/ui/html/search-results-tool-ui.html`
- `src/basic_memory/mcp/ui/html/note-preview-tool-ui.html`
---
## How to Run the MCP Server
```bash
basic-memory mcp --transport stdio
```
Optional to pick UI variant for MCP App resources:
```bash
export BASIC_MEMORY_MCP_UI_VARIANT=tool-ui # or vanilla | mcp-ui
```
---
## Test Cases
### 1) MCP App Resource UI (toolui / vanilla / mcpui)
Tools:
- `search_notes`
- `read_note`
Expect:
- Tool meta points to `ui://basic-memory/search-results` and `ui://basic-memory/note-preview`
- Resource content differs by `BASIC_MEMORY_MCP_UI_VARIANT`
- Variantspecific URIs also available:
- `ui://basic-memory/search-results/vanilla`
- `ui://basic-memory/search-results/tool-ui`
- `ui://basic-memory/search-results/mcp-ui`
- `ui://basic-memory/note-preview/vanilla`
- `ui://basic-memory/note-preview/tool-ui`
- `ui://basic-memory/note-preview/mcp-ui`
Manual check:
- Trigger tool in MCPAppcapable host and confirm UI renders.
---
### 2) Text / JSON Output Modes
Tools:
- `search_notes(output_format="text" | "json")`
- `read_note(output_format="text" | "json")`
- `write_note(output_format="text" | "json")`
- `edit_note(output_format="text" | "json")`
- `recent_activity(output_format="text" | "json")`
- `list_memory_projects(output_format="text" | "json")`
- `create_memory_project(output_format="text" | "json")`
- `delete_note(output_format="text" | "json")`
- `move_note(output_format="text" | "json")`
- `build_context(output_format="json" | "text")`
Expect:
- `text` mode preserves existing human-readable responses.
- `json` mode returns structured dict/list payloads for machine-readable clients.
Automated:
- `uv run pytest test-int/mcp/test_output_format_json_integration.py`
---
### 3) MCPUI Python SDK (embedded UI resource)
Tools (embedded resource responses):
- `search_notes_ui` (MCPUI SDK)
- `read_note_ui` (MCPUI SDK)
Expected output:
- Tool response content contains an EmbeddedResource (`type: "resource"`)
- `mimeType` is `text/html`
- `_meta` includes:
- `mcpui.dev/ui-preferred-frame-size`
- `mcpui.dev/ui-initial-render-data`
Manual check:
- Render tool responses using `UIResourceRenderer` (legacy host flow).
Automated (if SDK installed):
- `uv run pytest test-int/mcp/test_ui_sdk_integration.py`
---
## Bakeoff Notes Template
Fill in after running:
- ToolUI (React): __
- MCPUI SDK (embedded): __
- Text/JSON modes: __
Decision + rationale: __
+260
View File
@@ -0,0 +1,260 @@
# Metadata Search Reference
Basic Memory automatically indexes custom frontmatter fields so you can query them with structured filters. Any YAML key in a note's frontmatter beyond the standard set (`title`, `type`, `tags`, `permalink`, `schema`) is stored as `entity_metadata` and becomes searchable.
## Querying with `search_notes`
`search_notes` is the single search tool for all queries — text, metadata filters, or both. The `query` parameter is optional, so you can use metadata filters alone without passing an empty string.
## Filter Syntax
Filters are a JSON dictionary where each key targets a frontmatter field and the value specifies the match condition. Multiple keys combine with **AND** logic — every filter must match.
### Equality
Match a single value exactly.
```json
{"status": "active"}
```
Finds notes whose frontmatter contains `status: active`.
### Array Contains (all)
Pass a list to require **all** listed values to be present in the field.
```json
{"tags": ["security", "oauth"]}
```
Finds notes tagged with both `security` and `oauth`.
### `$in` (any of)
Match if the field equals **any** value in the list.
```json
{"priority": {"$in": ["high", "critical"]}}
```
### `$gt`, `$gte`, `$lt`, `$lte`
Numeric and text comparisons. Numeric values use numeric comparison; strings use lexicographic comparison.
```json
{"confidence": {"$gt": 0.7}}
{"score": {"$lte": 100}}
```
### `$between`
Range filter (inclusive). Takes a `[min, max]` pair.
```json
{"score": {"$between": [0.3, 0.8]}}
```
### Nested Access (dot notation)
Access nested frontmatter values using dots.
```json
{"schema.version": "2"}
```
This queries the `version` key inside a `schema` object in frontmatter.
### Summary Table
| Operator | Syntax | Example |
|----------|--------|---------|
| Equality | `{"field": "value"}` | `{"status": "active"}` |
| Array contains (all) | `{"field": ["a", "b"]}` | `{"tags": ["security", "oauth"]}` |
| `$in` (any of) | `{"field": {"$in": [...]}}` | `{"priority": {"$in": ["high", "critical"]}}` |
| `$gt` / `$gte` | `{"field": {"$gt": N}}` | `{"confidence": {"$gt": 0.7}}` |
| `$lt` / `$lte` | `{"field": {"$lt": N}}` | `{"score": {"$lt": 0.5}}` |
| `$between` | `{"field": {"$between": [min, max]}}` | `{"score": {"$between": [0.3, 0.8]}}` |
| Nested access | `{"a.b": "value"}` | `{"schema.version": "2"}` |
**Key rules:**
- Filter keys must match `[A-Za-z0-9_-]+` (dots separate nesting levels).
- Each operator dict must contain exactly one operator.
- `$in` and array-contains require non-empty lists.
- `$between` requires exactly two values `[min, max]`.
## MCP Tool — `search_notes`
`search_notes` is the single search tool for text queries, metadata filters, or both. The `query` parameter is optional.
**Relevant parameters:**
| Parameter | Type | Description |
|-----------|------|-------------|
| `query` | string (optional) | Text search query. Omit for filter-only searches. |
| `metadata_filters` | dict | Structured filter dict (see syntax above) |
| `tags` | list[str] | Convenience shorthand — merged into `metadata_filters["tags"]` |
| `status` | string | Convenience shorthand — merged into `metadata_filters["status"]` |
**Merging rules:** `tags` and `status` are convenience shortcuts. They are merged into `metadata_filters` using `setdefault` — if the same key already exists in `metadata_filters`, the explicit filter wins.
**Examples:**
```python
# Text search filtered by metadata
await search_notes("authentication", metadata_filters={"status": "draft"})
# Filter-only search (no query needed)
await search_notes(metadata_filters={"type": "spec"})
# Combine text, tags shortcut, and metadata
await search_notes(
"oauth flow",
tags=["security"],
metadata_filters={"confidence": {"$gt": 0.7}},
)
# Convenience shortcuts
await search_notes("planning", status="active")
await search_notes(tags=["tier1", "alpha"])
```
## Tag Search Shortcuts
The `tag:` prefix in a search query is a shorthand for tag-based metadata filtering. When `search_notes` receives a query starting with `tag:`, it converts the query into a `tags` filter and clears the text query.
```python
# These are equivalent:
await search_notes("tag:tier1")
await search_notes("", tags=["tier1"])
# Multiple tags (comma or space separated) — all must be present:
await search_notes("tag:tier1,alpha")
await search_notes("tag:tier1 alpha")
```
## CLI Access
The `bm tool search-notes` command exposes metadata filtering via `--meta` and `--filter` flags.
### `--meta` — simple key=value filters
Repeatable flag for equality filters on frontmatter fields.
```bash
# Single filter
bm tool search-notes "my query" --meta status=draft
# Multiple filters (AND logic)
bm tool search-notes "" --meta status=active --meta priority=high
```
### `--filter` — advanced JSON filters
Pass a full JSON filter dictionary for operator-based queries.
```bash
# Range filter
bm tool search-notes "" --filter '{"score": {"$between": [0.3, 0.8]}}'
# $in filter
bm tool search-notes "" --filter '{"priority": {"$in": ["high", "critical"]}}'
```
### `--tag` and `--status` — convenience shortcuts
```bash
bm tool search-notes "query" --tag security --tag oauth
bm tool search-notes "" --status draft
```
### Combined example
```bash
bm tool search-notes "authentication" --tag security --meta status=draft --type spec
```
## Practical Examples
### Example notes with custom frontmatter
**`specs/auth-design.md`:**
```markdown
---
title: Auth Design
type: spec
tags: [security, oauth]
status: in-progress
priority: high
confidence: 0.85
---
# Auth Design
## Observations
- [decision] Use OAuth 2.1 with PKCE for all client types #security
- [requirement] Token refresh must be transparent to the user
## Relations
- implements [[Security Requirements]]
```
**`specs/search-redesign.md`:**
```markdown
---
title: Search Redesign
type: spec
tags: [search, performance]
status: draft
priority: medium
confidence: 0.6
---
# Search Redesign
## Observations
- [goal] Sub-100ms search response times #performance
- [approach] Hybrid FTS + vector retrieval
## Relations
- depends_on [[Database Schema]]
```
### Queries that find them
```python
# Find all in-progress specs
await search_notes(metadata_filters={"status": "in-progress", "type": "spec"})
# → Auth Design
# Find high-confidence specs
await search_notes(metadata_filters={"confidence": {"$gt": 0.7}})
# → Auth Design (confidence: 0.85)
# Find specs with priority high or medium
await search_notes(metadata_filters={"priority": {"$in": ["high", "medium"]}})
# → Auth Design, Search Redesign
# Find specs in a confidence range
await search_notes(metadata_filters={"confidence": {"$between": [0.5, 0.9]}})
# → Auth Design (0.85), Search Redesign (0.6)
# Find notes tagged with security
await search_notes("tag:security")
# → Auth Design
# Combined: text search + metadata filter
await search_notes("OAuth", metadata_filters={"status": "in-progress"})
# → Auth Design
```
### CLI equivalents
```bash
bm tool search-notes "" --meta status=in-progress --type spec
bm tool search-notes "" --filter '{"confidence": {"$gt": 0.7}}'
bm tool search-notes "OAuth" --meta status=in-progress
bm tool search-notes --tag security
```
+344
View File
@@ -0,0 +1,344 @@
# Post-v0.18.0 Test Plan and Acceptance Criteria
## Goal
Define a complete validation plan for all major features merged after `v0.18.0`, combining:
- Coverage-gap-driven automated tests
- Real MCP server integration tests (no mocks for target flows)
- Manual MCP verification via LLM-driven tool calls
This plan is based on commits in `v0.18.0..HEAD` and the latest `just check` coverage output.
## Scope Window
- Start tag: `v0.18.0` (2026-01-28)
- End: current `main`
- Change volume: 12 feature commits + 14 bug-fix commits (+ release chores/hotfixes)
## Execution Strategy
1. Stabilize all feature-level acceptance criteria in automated tests first.
2. Add black-box MCP integration tests for semantic search + schema (real server startup).
3. Run manual MCP tool-call verification to confirm real UX and routing behavior.
4. Re-run full gate: `just check` + targeted integration packs.
## Global Quality Gates
- Feature criteria below must all pass.
- No regressions in existing suites.
- Coverage improves in targeted low-coverage feature modules.
- SQLite and Postgres parity for search/semantic features.
## Priority Coverage Gaps (from latest run)
These are the most important post-`v0.18.0` feature modules currently under-covered:
- `src/basic_memory/mcp/tools/schema.py` (27%)
- `src/basic_memory/mcp/clients/schema.py` (36%)
- `src/basic_memory/mcp/tools/ui_sdk.py` (43%)
- `src/basic_memory/mcp/tools/search.py` (73%)
- `src/basic_memory/repository/postgres_search_repository.py` (63%)
- `src/basic_memory/mcp/async_client.py` (82%)
- `src/basic_memory/api/v2/routers/schema_router.py` (80%)
## Feature Acceptance Criteria and Test Plan
### 1) Schema System (`c97733d`) — DONE
### Acceptance criteria
- `schema_validate`, `schema_infer`, and `schema_diff` produce consistent outcomes across CLI/API/MCP for the same fixture set.
- Strict validation fails deterministically on required-field/type violations.
- Validation warnings are stable and machine-readable in non-strict mode.
- Inference output is deterministic for unchanged input corpus.
- Drift diff output is deterministic and identifies missing/extra/type-mismatch fields correctly.
### Existing coverage anchor points
- `tests/schema/*`
- `tests/api/v2/test_schema_router.py`
- `test-int/test_schema/*`
### Gaps to close — DONE
- ~~MCP schema tool branches (`src/basic_memory/mcp/tools/schema.py`)~~ — 18 tests in `tests/mcp/test_tool_schema.py`
- ~~MCP schema client behavior (`src/basic_memory/mcp/clients/schema.py`)~~ — `tests/mcp/test_client_schema.py`
- ~~Schema router error-path branches (`src/basic_memory/api/v2/routers/schema_router.py`)~~ — `tests/api/v2/test_schema_router.py`
### Planned additions — DONE
- ~~Add MCP tool tests for `schema_validate` strict + non-strict result shapes.~~ **DONE**
- ~~Add MCP tool tests for `schema_infer` with explicit `entity_type` and inferred type fallback.~~ **DONE**
- ~~Add MCP tool tests for `schema_diff` empty-diff and non-empty-diff paths.~~ **DONE**
- ~~Add API tests for schema router invalid payload/edge error handling.~~ **DONE**
- Add integration test that starts MCP server and calls schema tools end-to-end on fixture notes. — deferred to backlog item 4.
### 2) Semantic Search (`0777879`, `1428d18`, `344e651`) — DONE
### Acceptance criteria
- `search_type=text|vector|hybrid` returns expected ranked results on canonical semantic corpus.
- Missing semantic dependencies fail fast with actionable install guidance.
- Reindex and provider/model changes produce valid vectors without dimension mismatch.
- SQLite and Postgres produce equivalent behavior for semantic modes on the same dataset.
- Generated-column migration path is valid on SQLite environments in use.
### Existing coverage anchor points
- `tests/repository/test_sqlite_vector_search_repository.py`
- `tests/repository/test_postgres_search_repository.py`
- `tests/services/test_semantic_search.py`
- `tests/mcp/test_tool_search.py`
- `test-int/test_search_performance_benchmark.py`
### Gaps to close — DONE
- ~~Uncovered Postgres vector/hybrid branches~~ — 20 tests in `tests/repository/test_postgres_search_repository_unit.py` + 5 integration tests in `test-int/semantic/test_semantic_coverage.py`
- ~~MCP search semantic/output branches~~ — expanded `tests/mcp/test_tool_search.py`
### Planned additions — DONE
- ~~Expand Postgres repository tests for vector query composition edge cases.~~ **DONE**
- ~~Expand Postgres repository tests for hybrid fusion ranking and pagination branches.~~ **DONE**
- ~~Expand Postgres repository tests for embedding/provider error handling branches.~~ **DONE**
- ~~Expand MCP search tool tests for vector/hybrid output formatting branches.~~ **DONE**
- ~~Expand MCP search tool tests for semantic-disabled and missing-dependency failures.~~ **DONE**
- Add MCP integration tests that start server and execute semantic `search_notes` tool calls. — deferred to backlog item 4.
### Semantic search quality benchmarks (NEW)
Full benchmark suite in `test-int/semantic/` covering 5 backend×provider combinations:
- `sqlite-fts`, `sqlite-fastembed`, `postgres-fts`, `postgres-fastembed`, `postgres-openai`
- Quality metrics: hit@1, recall@5, MRR@10 with per-query timing
- Realistic corpus with cross-topic vocabulary overlap (240 notes, 4 topics)
- Rich CLI viewer: `just semantic-report`
- JSON artifact output: `just test-semantic-report`
Key finding: **FastEmbed (384-d local ONNX) matches or exceeds OpenAI (1536-d) quality at 30x lower latency.** Recommending FastEmbed as default for both local and cloud deployments.
### 3) Per-Project Local/Cloud Routing + API Key Auth (`d84708c`, `ed94877`, `312662f`) — DONE
### Acceptance criteria
- Project mode (`local`/`cloud`) persists and displays correctly.
- Routing selects ASGI for local projects and HTTP+Bearer for cloud projects.
- Cloud project without key fails with explicit remediation (`cloud set-key`/`cloud create-key`).
- Resolution precedence is correct (factory > force-local > per-project cloud > global fallback > local).
- Watch/sync only run for local projects.
### Existing coverage anchor points
- `tests/mcp/test_async_client_modes.py`
- `tests/cli/test_project_set_cloud_local.py`
- `tests/mcp/test_project_context.py`
- `tests/test_project_resolver.py`
- `tests/sync/test_watch_service_reload.py`
### Gaps to close — DONE
- ~~Cloud routing branch gaps in `src/basic_memory/mcp/async_client.py`~~ — expanded `tests/mcp/test_async_client_modes.py`
### Planned additions — DONE
- ~~Add branch-focused tests for all unresolved routing branches in `get_client()`.~~ **DONE**
- Add MCP integration scenario with mixed local/cloud project config — deferred to backlog item 4.
### 4) Project-Prefixed Permalinks + Memory URL Routing (`545804f`) — DONE
### Acceptance criteria
- Project-prefixed permalinks are generated consistently on create/update/import flows.
- Memory URLs resolve to the correct project/entity even with duplicate note titles.
- `read_note`, `search`, `build_context`, write/edit/move flows preserve project identity correctly.
- Link resolution remains correct for context-aware wikilinks.
### Existing coverage anchor points
- `tests/utils/test_permalink_formatting.py`
- `tests/mcp/test_tool_read_note.py`
- `tests/mcp/test_tool_search.py`
- `tests/services/test_context_service.py`
- `test-int/mcp/test_read_note_integration.py`
### Gaps to close
- No major coverage alarm in report, but keep as regression-critical due broad impact surface.
### Planned additions — DONE
- ~~Add one integration test with colliding titles across two projects and assert URL routing invariants.~~ **DONE**`test-int/mcp/test_permalink_collision_integration.py` (2 tests: collision across projects + memory:// URL routing with project prefix)
### 5) MCP UI Variants + TUI Output (`8bc03d1`) — DONE
### Acceptance criteria
- UI resource variant selection (`tool-ui`, `vanilla`, `mcp-ui`) follows env configuration.
- `search_notes` and `read_note` expose expected resource metadata for UI hosts.
- `ascii`/`ansi` outputs are deterministic and stable for terminal clients.
### Existing coverage anchor points
- `tests/mcp/test_tool_contracts.py`
- `test-int/mcp/test_output_format_json_integration.py`
- `test-int/mcp/test_ui_sdk_integration.py`
### Gaps to close — DONE
- ~~`src/basic_memory/mcp/tools/ui_sdk.py` branch coverage~~ — `tests/mcp/test_ui_sdk.py`
- ~~`src/basic_memory/mcp/ui/sdk.py` and `src/basic_memory/mcp/ui/templates.py` branch coverage~~ — `tests/mcp/test_ui_templates.py` + `tests/mcp/test_ui_resources.py`
### Planned additions — DONE
- ~~Add unit tests for UI SDK metadata generation and template selection branches.~~ **DONE** — 31 tests
- ~~Add integration assertion for variant-specific resource URIs and metadata payload shape.~~ **DONE**
### 6) Watch Command (`8df88e4`) — DONE
### Acceptance criteria
- `basic-memory watch` starts and processes create/update/delete events.
- Watch restart/reload path does not duplicate watchers.
- Cloud-mode projects are excluded from active watcher set.
### Existing coverage anchor points
- `tests/cli/test_watch.py`
- `tests/sync/test_coordinator.py`
- `tests/sync/test_watch_service_reload.py`
### Planned additions — DONE
- ~~Add one stress-style integration test for rapid file changes and watcher stability.~~ **DONE**`tests/sync/test_watch_service_stress.py` (3 tests: 50-file batch, mixed add/modify/delete batch, rapid modifications to same file)
### 7) CLI JSON Output (`a47c9c0`) — DONE
### Acceptance criteria
- `--format json` returns valid JSON with stable keys for success paths.
- Error paths also return JSON-shaped output with correct non-zero exits.
- Default human output remains unchanged.
### Existing coverage anchor points
- `tests/cli/test_cli_tool_json_output.py`
- `test-int/cli/test_cli_tool_json_integration.py`
### Planned additions — DONE
- ~~Add one failure-path integration test per high-use tool command.~~ **DONE**`test-int/cli/test_cli_tool_json_failure_integration.py` (4 tests: read-note not found, write-note missing content, write→read roundtrip, recent-activity empty project)
### 8) Search/Edit and Metadata Fixes (`530cbac`, `f1d50c2`, `8838571`, `009e849`) — DONE
### Acceptance criteria
- Metadata filters produce consistent results on SQLite and Postgres.
- `tag:` shorthand works alone and with mixed query terms.
- Fast write/edit paths preserve `external_id` and metadata integrity.
### Existing coverage anchor points
- `tests/repository/test_metadata_filters.py`
- `tests/repository/test_search_repository.py`
- `tests/services/test_search_service.py`
### Planned additions — DONE
- ~~Add Postgres-specific metadata filter edge-case tests to mirror SQLite assertions exactly.~~ **DONE**`tests/repository/test_metadata_filters_edge_cases.py` (6 tests: missing field, AND logic, contains single-element array, nested path missing intermediate, $gte/$lte boundaries, $between inclusive — all pass on both SQLite and Postgres)
### 9) Compatibility and Hotfix Regression Pack (`c46d7a6`, `a0e754b`, `343a6e1`, `24ca5f6`, `e3ced49`, `8489a3d`, `b609c4e`, `f6e0a5b`, `7624a20`)
### Acceptance criteria
- Legacy endpoints required by older CLI versions function without `405` (`GET /projects/projects`, `POST /projects/projects`, `POST /projects/config/sync`).
- Entity creation conflicts map to conflict status (not 500).
- `recent_activity` prompt defaults are correct.
- No spurious `metadata: {}` in serialized frontmatter.
- Tigris/rclone uses global consistency headers for all transaction types.
- `bm --version` fast path avoids heavy import path and remains responsive.
- Default SQLite DB path is isolated by config dir.
### Gaps to close
- ~~Commits with no direct tests added (`c46d7a6`, `344e651`, `f6e0a5b`) need explicit regression tests.~~ **DONE**
### Planned additions — DONE
- ~~Add API compat test covering all legacy endpoint methods and payloads.~~ **DONE**`test_legacy_v1_add_project_endpoint`, `test_legacy_v1_sync_config_endpoint`
- ~~Add CLI fast-path test for `--version` import behavior/performance guard.~~ **DONE**`test_bm_version_does_not_import_heavy_modules`
- ~~Add empty metadata serialization regression test.~~ **DONE**`test_schema_to_markdown_empty_metadata_no_metadata_key`
- Add migration safety test for SQLite generated columns (`VIRTUAL` expectation) — deferred, low risk.
## MCP Manual Verification Plan (LLM Tool Calls)
Run after automated tests pass.
### Setup
- Start MCP server: `basic-memory mcp --transport stdio`
- Use an MCP-capable client and issue tool calls directly.
### Manual scenarios
- Schema: call `schema_validate`, `schema_infer`, and `schema_diff` on known fixtures.
- Schema: verify error and success payloads match acceptance criteria.
- Semantic search: call `search_notes` with `search_type=text|vector|hybrid`.
- Semantic search: verify ranking relevance on semantic fixture queries.
- Routing: call tools with explicit project on mixed local/cloud setup.
- Routing: verify success/failure paths with and without API key.
- Permalink routing: read/write/search notes across projects with colliding titles.
- Permalink routing: verify memory URL routing correctness.
- UI/TUI: call `search_notes` and `read_note` with UI variants and `output_format=text|json`.
- UI/TUI: verify payload/resource format and metadata completeness.
## Implementation Backlog (Ordered)
1. ~~Fill schema MCP/client/router coverage gaps.~~ **DONE** — 18 tests in `test_tool_schema.py` + `test_client_schema.py`
2. ~~Fill semantic search MCP + Postgres repository gaps.~~ **DONE** — 20 tests in `test_postgres_search_repository_unit.py` + `test_tool_search.py`
3. ~~Add compatibility regression tests (legacy endpoints, migration, version fast path).~~ **DONE** — 5 tests across 3 files (see below)
4. ~~Add feature-level integration tests (permalinks, watch, CLI JSON, metadata filters).~~ **DONE** — 15 tests across 4 files (see items 4, 6, 7, 8 above)
5. ~~Expand UI SDK and template branch tests.~~ **DONE** — 31 tests in `test_ui_templates.py` + `test_ui_sdk.py` + `test_ui_resources.py`
6. ~~Run full gate and capture results in a short release readiness summary.~~ **DONE** — see results below
### Full Gate Results (`just check`)
| Phase | Result |
|-------|--------|
| lint | PASS |
| format | PASS |
| typecheck | PASS |
| Unit tests (SQLite) | 1788 passed, 15 skipped |
| Integration tests (SQLite) | 243 passed, 4 skipped, 10 deselected |
| Unit tests (Postgres) | 1760 passed, 28 skipped |
| Integration tests (Postgres) | 234 passed, 13 skipped, 10 deselected |
**0 failures. 10 deselected = semantic benchmark tests (run separately via `just test-semantic`).**
### Item 3 Details — Compatibility Regression Tests
| Test | File | What it covers |
|------|------|----------------|
| `test_legacy_v1_add_project_endpoint` | `tests/api/v2/test_project_router.py` | POST `/projects/projects` legacy route reachable (idempotent path) |
| `test_legacy_v1_sync_config_endpoint` | `tests/api/v2/test_project_router.py` | POST `/projects/config/sync` legacy route reachable |
| `test_bm_version_does_not_import_heavy_modules` | `tests/cli/test_cli_exit.py` | `bm --version` fast path does not load `basic_memory.mcp` |
| `test_schema_to_markdown_empty_metadata_no_metadata_key` | `tests/markdown/test_entity_parser_error_handling.py` | `schema_to_markdown()` with `entity_metadata={}` emits no `metadata:` key |
| `test_legacy_v1_list_projects_endpoint` | `tests/api/v2/test_project_router.py` | (pre-existing) GET `/projects/projects` legacy route |
**Suite totals after item 3: 1764 passed, 15 skipped, 0 failures.**
## Suggested Commands
- Full suite: `just check`
- Fast loop: `just fast-check`
- E2E consistency: `just doctor`
- SQLite focused: `just test-sqlite`
- Postgres focused: `just test-postgres`
- Schema integration: `pytest test-int/test_schema -q`
- Semantic + repo focus: `pytest tests/repository/test_postgres_search_repository.py tests/mcp/test_tool_search.py tests/services/test_semantic_search.py -q`
- MCP integration focus: `pytest test-int/mcp -q`
## Exit Criteria for This Plan
- All feature acceptance criteria above are validated.
- All identified high-priority coverage gaps are addressed or explicitly documented as intentional.
- Manual MCP verification scenarios complete with no P0/P1 findings.
+318
View File
@@ -0,0 +1,318 @@
# v0.19.0 Release Notes
## Overview
v0.19.0 is a major release that introduces semantic vector search, a schema validation system,
project-prefixed permalinks, per-project cloud routing, and a significant upgrade to FastMCP 3.0.
It includes 90+ commits since v0.18.0 spanning new features, architectural improvements, and
stability fixes across both SQLite and Postgres backends.
---
## Major Features
### Semantic Vector Search
Full vector and hybrid search for SQLite (via sqlite-vec) and Postgres (via pgvector).
- **Hybrid search mode** combines full-text search (FTS) with vector similarity for best results
- **Score-based fusion** replaces RRF for hybrid ranking — `max(vec, fts) + 0.3 * min(vec, fts)` preserves dominant signals and rewards dual-source agreement (#577)
- **Default search mode** is now `hybrid` when semantic search is enabled, `text` when disabled
- Embedding providers: FastEmbed (local, default) or OpenAI API
- Configurable similarity threshold via `semantic_min_similarity` (default 0.55)
- Per-query `min_similarity` override on `search_notes` tool
- Auto-backfill: existing entities get embeddings generated on first startup
- Backend-specific distance-to-similarity conversion (cosine for SQLite, inner product for Postgres)
- FTS fallback: if semantic dependencies are missing, search gracefully degrades to text-only
- sqlite-vec knn `k` parameter capped at 4096 to prevent backend errors
**Configuration:**
```json
{
"semantic_search_enabled": true,
"semantic_embedding_provider": "fastembed",
"semantic_embedding_model": "bge-small-en-v1.5",
"semantic_min_similarity": 0.55
}
```
**Usage:**
```
search_notes("machine learning concepts", search_type="hybrid")
search_notes("similar to my notes on coffee", search_type="vector")
search_notes("exact phrase match", search_type="text")
search_notes("broad search", min_similarity=0.3) # lower threshold for more results
```
### Schema System
Validate note structure against user-defined schemas with frontmatter-based rules.
- Define schemas as YAML in note frontmatter with field types, required fields, and constraints
- Frontmatter validation during sync — malformed notes get clear error messages
- Schema inference from existing notes to bootstrap schemas from your content
- Schema diff to compare two schemas and see changes
- Available via MCP tools and CLI
### Project-Prefixed Permalinks
Permalinks now include the project name for unambiguous cross-project references.
- Memory URLs like `memory://project-name/folder/note` route to the correct project
- Existing non-prefixed permalinks continue to work (backwards compatible)
- Controlled by `permalinks_include_project` config (default: true)
- `build_context` and `search_notes` auto-detect project from URL prefix
### Per-Project Cloud Routing
Individual projects can be routed through the cloud while others stay local.
- Set a project to cloud mode: `bm project set-cloud research`
- Revert to local: `bm project set-local research`
- Uses API key authentication: `bm cloud set-key bmc_abc123...`
- MCP tools automatically route based on each project's mode
- Local MCP server (`bm mcp`) still uses local routing for all projects by default
- `--local` and `--cloud` CLI flags override per-command
### Workspace Selection
Cloud projects can target specific workspaces for multi-tenant environments.
- `workspace` parameter on MCP tools for explicit workspace targeting
- CLI workspace-aware project listing with `bm project list`
- Spinner feedback while fetching cloud projects
---
## New Tools and Capabilities
### Dashboard (`bm project info`)
`bm project info` now displays an htop-inspired compact dashboard with:
- Horizontal bar charts for note types (top 5)
- Embedding coverage bar with Unicode block characters
- Colored status dots for at-a-glance health
- `EmbeddingStatus` schema and `get_embedding_status()` service method for programmatic access
### Unified Metadata Search
`search_by_metadata` has been merged into `search_notes` — one tool for all searches.
`query` is now optional, so you can search purely by frontmatter metadata.
```
search_notes(metadata_filters={"status": "in-progress"})
search_notes(metadata_filters={"tags": ["security", "oauth"]})
search_notes(metadata_filters={"priority": {"$in": ["high", "critical"]}})
search_notes(metadata_filters={"schema.confidence": {"$gt": 0.7}})
search_notes(tags=["security"]) # convenience shorthand
search_notes(status="draft") # convenience shorthand
```
### JSON Output Mode
All MCP tools now support `output_format="json"` for machine-readable responses.
- Default remains `"text"` for human-readable output (no breaking changes)
- `build_context` defaults to `"json"` with slimmed payloads (redundant fields stripped)
- CLI tool commands support `--format json` flag
### `tag:` Search Shorthand
Search by tag using convenient shorthand syntax.
```
search_notes("tag:security")
search_notes("tag:coffee AND tag:brewing")
```
### Entity User Tracking
Entities now track `created_by` and `last_updated_by` fields for attribution.
### Improved Search Result Content (#609)
Search results now surface more relevant context:
- `matched_chunk_text` populated for FTS-only hybrid results (no more fallback to truncated content)
- `TOP_CHUNKS_PER_RESULT` increased from 3 to 5, catching answers deeper in large notes (~2700 → ~4500 chars)
- `CONTENT_DISPLAY_LIMIT` doubled from 2000 to 4000 chars for results without matched chunks
### `write_note` Overwrite Guard (#632)
`write_note` is now non-idempotent by default. If a note already exists, the tool returns an
error instead of silently overwriting. Pass `overwrite=True` to replace, or use `edit_note`
for incremental updates. Config option `write_note_overwrite_default` restores the old upsert
behavior.
---
## Architecture Changes
### Score-Based Hybrid Fusion (#577)
RRF (Reciprocal Rank Fusion) compressed all fused scores to ~0.016, destroying ranking
differentiation. The new formula `max(vec, fts) + FUSION_BONUS * min(vec, fts)` preserves
dominant signals and rewards dual-source agreement. Zero-score results now produce zero
fused score instead of receiving a 0.1 weight floor.
### FastMCP 3.0 Upgrade
Upgraded from FastMCP 2.12.3 to 3.0.1.
- Tool annotations (`readOnlyHint`, `openWorldHint`) for better client integration
- Improved MCP protocol compliance
- Better error handling and context management
### Prompts Call MCP Tools Directly
MCP prompts (`search`, `continue_conversation`) now call MCP tools directly instead of
going through API endpoints. This fixes empty results in discovery mode and ensures prompts
use the same resolution logic as tools (including LinkResolver fallback).
### build_context LinkResolver Fallback
`build_context` now falls back to LinkResolver when an exact permalink lookup returns empty.
This uses the same 7-strategy resolution pipeline as `read_note`, so callers no longer get
empty results for valid note identifiers that don't match exact permalinks.
### Sync Handles Semantic Dependency Errors Gracefully
When sqlite-vec or another embedding provider is unavailable, `sync_file` now catches
`SemanticDependenciesMissingError` separately. The entity is created and FTS-indexed
successfully — only vector embeddings are skipped, with a clear warning:
```
WARNING: Semantic search dependencies missing — vector embeddings skipped for path=note.md.
Run 'bm reindex --embeddings' after resolving the dependency issue.
```
### Unified Project Path
Cloud projects with bisync now store the local filesystem path in `path` (not the Docker
container path). Config migration automatically promotes `local_sync_path``path` for
existing configs.
---
## CLI Improvements
### Status and Doctor Default to Local Routing
`bm status` and `bm doctor` now default to local routing since they scan the local filesystem.
Previously, cloud-mode projects would route these commands to the cloud API, which returned
Docker-internal paths that don't exist locally.
### `--format json` for CLI Tool Commands
All `bm tool` subcommands support `--format json` for machine-readable output, enabling
integration with scripts and plugins.
### `--json` for Top-Level CLI Commands
Five additional CLI commands now support `--json` for machine-readable output:
- `bm status --json` — sync report with new/modified/deleted/moved files and skipped files
- `bm project list --json` — structured project list with name, paths, routing mode, and defaults
- `bm schema validate --json` — validation report with per-note pass/fail, warnings, and errors
- `bm schema infer --json` — field frequency analysis and suggested schema definition
- `bm schema diff --json` — drift report with new fields, dropped fields, and cardinality changes
This complements the existing `bm project info --json` and `bm tool --format json` support,
making all major CLI commands scriptable for CI pipelines and automation.
### Cloud Promo and Analytics
- Cloud promo panel shown on first run or version bump with OSS discount code
- Anonymous usage telemetry via Umami Cloud (promo/login funnel events only)
- Opt out with `BASIC_MEMORY_NO_PROMOS=1`
- No PII, no file contents, no per-command tracking
- See [Telemetry](https://github.com/basicmachines-co/basic-memory#telemetry) in README
---
## Bug Fixes
- **#577**: RRF fusion compressed all hybrid scores to ~0.016, destroying ranking differentiation
- **#582**: build_context returns empty results on valid note identifiers
- **#575**: Remove hardcoded "main" default from default_project
- **#595**: recent_activity dedup and pagination across MCP tools
- **#593**: Backend-specific distance-to-similarity conversion
- **#592**: Strip NUL bytes from content before PostgreSQL search indexing
- **#562**: Use VIRTUAL instead of STORED columns in SQLite migration
- **#558**: Add X-Tigris-Consistent headers to all rclone commands
- **#541**: Handle EntityCreationError as conflict
- **#536**: Stabilize metadata filters on Postgres
- **#533**: Fix recent_activity prompt defaults
- **#530**: Prevent spurious `metadata: {}` in frontmatter output
- **#601**: Return matched chunk text in search results
- **#606**: Accept `null` for `expected_replacements` in `edit_note`
- **#579, #607**: Guard against closed streams in promo panel and missing vector tables on shutdown
- **#609**: FTS-only hybrid results missing `matched_chunk_text`; content limits too conservative
- **#631**: `build_context` related_results schema validation failure — replaced fragile `_slim_context()` stripping with Pydantic `exclude=True` field config
- **#630**: Skip workspace resolution when client factory is active — prevents 401 errors in cloud MCP server mode
- **#30**: `tag:` prefix query fails with hybrid search — moved tag prefix parsing to MCP tool level so it works with all search modes
- **#31**: `search_notes` returns cluttered observation/relation-level results — now defaults to entity-level results
- **#28**: `schema_infer` and `schema_diff` return raw Pydantic models as "undefined" in LLM output — added markdown formatters
- Fix `schema_validate` identifier resolution (now uses LinkResolver) and text rendering (markdown formatter)
- **#634**: `schema_validate` and `schema_diff` use stale database metadata instead of reading schema definitions from file — now reads frontmatter directly from the file with fallback to database metadata
- Fix `Post(**metadata)` crash when frontmatter contains `content` or `handler` keys
- Fix list-valued frontmatter fields (`title`, `type`) crashing on `.strip()` — now coerced to strings
- Cap sqlite-vec knn `k` parameter at 4096 to prevent backend errors
- Parameterize SQL queries in search repository type filters
- Double-default display in project list
- `ensure_frontmatter_on_sync` default changed to `True`
- Status/doctor commands fail with cloud-mode projects (Docker path error)
- Prompts return "0 projects" in discovery mode
---
## Security
- Upgrade `cryptography` for CVE advisory
- Upgrade `python-multipart` for security advisory
---
## Internal / Developer
- **#598**: Upgrade FastMCP 2.12.3 → 3.0.1 with tool annotations
- **#594**: Add `ty` as supplemental type checker
- **#538**: Add fast feedback loop tooling (`just fast-check`, `just doctor`, `just testmon`)
- **#600**: Rename `entity_type` to `note_type` for consistency
- **#596**: Fix CLI runtime defects and audit regressions
- CLI refactoring and workspace-aware cloud project listing
- Split and speed up PR test matrix in CI
- Fix CI: collect coverage from test jobs instead of re-running all tests
- Create `search_vector_chunks` in test fixtures for Postgres compatibility
---
## Configuration Changes
| Setting | Old Default | New Default | Notes |
|---------|-------------|-------------|-------|
| `semantic_search_enabled` | `false` | `true` | Semantic search on by default |
| `ensure_frontmatter_on_sync` | `false` | `true` | Frontmatter added during sync |
| `permalinks_include_project` | `false` | `true` | Project prefix in permalinks |
---
## Upgrade Notes
- **Semantic search dependencies** are now included by default. If sqlite-vec fails to load,
search gracefully falls back to FTS. Run `bm reindex --embeddings` to generate embeddings
for existing content.
- **Hybrid search scoring** has changed from RRF to score-based fusion. Search result ordering
may differ — results should be more accurate with better score differentiation.
- **`search_by_metadata`** is removed as a standalone tool. Use `search_notes` with
`metadata_filters` instead (same parameters, same behavior).
- **Project-prefixed permalinks** are enabled by default. Existing notes keep their current
permalinks until modified. Set `permalinks_include_project: false` to disable.
- **Frontmatter on sync** is now enabled by default. Files without frontmatter will have it
added on next sync. Set `ensure_frontmatter_on_sync: false` to preserve old behavior.
- **Config migration** runs automatically for cloud projects with bisync — `local_sync_path`
is promoted to `path` so filesystem operations work correctly.
- **`write_note` is no longer idempotent** — calls to `write_note` for existing notes now
return an error unless `overwrite=True` is passed. Use `edit_note` for incremental changes,
or set `write_note_overwrite_default: true` in config to restore the old behavior.
+209
View File
@@ -0,0 +1,209 @@
# Semantic Search Manual Test Log
## Overview
Manual test session for semantic (vector) search on the main project.
- Date: 2026-02-15
- Database: ~/.basic-memory/memory.db (SQLite)
- Entities: 456 embedded, 2714 vector chunks
- Search index: 2390 FTS entries
- Embedding model: default (384-dim, sqlite-vec)
## Test Plan
1. **Search Type Routing** — verify vector/hybrid/text dispatch, invalid search_type handling
2. **Conceptual Queries** — natural language where vector should beat FTS
3. **Keyword Queries** — exact terms where FTS should be strong
4. **Hybrid Ranking** — queries where both FTS and vector contribute
5. **Result Types** — entities, observations, relations in vector results
6. **Filters + Vector** — combine vector with types/entity_types/after_date
7. **Edge Cases** — short queries, long queries, empty, special chars, no-match
8. **Pagination** — page > 1, page_size respected
---
## Test Results
### Test 1: Search Type Routing
#### 1a: search_type="semantic" (invalid value)
- **Input:** query="how does the knowledge graph work", search_type="semantic"
- **Expected:** error or explicit fallback
- **Actual:** Silently falls through to text search (else branch in search.py:430)
- **Verdict:** BUG — should either be a recognized alias for "vector" or return an error
#### 1b: search_type="vector"
- **Input:** query="keeping AI context between sessions", search_type="vector"
- **Actual:** 5 results, scores ~0.58-0.59, found "Maintaining context across conversation boundaries" observation
- **Verdict:** PASS
#### 1c: search_type="text" with conceptual query
- **Input:** query="keeping AI context between sessions", search_type="text"
- **Actual:** 0 results (no exact keyword match)
- **Verdict:** PASS (expected — FTS requires token overlap)
#### 1d: search_type="hybrid" with conceptual query
- **Input:** query="keeping AI context between sessions", search_type="hybrid"
- **Actual:** 5 results, same ranking as vector (FTS contributed nothing here)
- **Verdict:** PASS
#### 1e: search_type="text" with keyword query
- **Input:** query="OAuth authentication", search_type="text"
- **Actual:** 3 results — AUTH.md Supabase OAuth, OAuth Rip-and-Replace, OAuth Integration Analysis
- **Verdict:** PASS
#### 1f: search_type="vector" with keyword query
- **Input:** query="OAuth authentication", search_type="vector"
- **Actual:** Same top results as text (keyword-rich content also scores well in vector space)
- **Verdict:** PASS
---
### Test 2: Conceptual Queries (vector advantage)
#### 2a: Natural language question
- **Input:** query="why do AI assistants forget things", search_type="vector"
- **Actual:** 5 results — Manual Testing Session, "Balance security and usability" observation, "Tools should match thought patterns" observation. Scores ~0.56-0.57
- **Vector advantage:** Found conceptually related content despite no exact keyword overlap
- **Verdict:** PASS
#### 2b: Same query, text search
- **Input:** query="why do AI assistants forget things", search_type="text"
- **Actual:** 1 result — "What is Basic Memory?" (likely matched on "AI" token)
- **Verdict:** PASS (demonstrates vector advantage — text barely matched)
#### 2c: Domain concept with no jargon
- **Input:** query="pricing strategy for cloud product", search_type="vector"
- **Actual:** 3 results — SPEC-16 MCP Cloud Service Consolidation, knowledge architecture observation, Visual Knowledge Spaces relation. Scores ~0.56-0.57
- **Verdict:** PASS (found cloud-related content conceptually)
#### 2d: Technical concept, long query
- **Input:** query="SQLite performance optimization WAL mode concurrent writes", search_type="vector"
- **Actual:** 3 results — SPEC-11 API Performance Optimization, Real-Time Updates with WebSockets, marketing status update. Scores ~0.55-0.58
- **Verdict:** PASS (found performance-related content)
---
### Test 3: Keyword Queries (FTS strength)
#### 3a: Exact term match — "OAuth authentication"
- **Text:** 3 results with high relevance (exact matches in titles)
- **Vector:** Same top results (keyword overlap helps vector too)
- **Verdict:** PASS — FTS and vector converge on keyword-rich queries
#### 3b: "OAuth" single keyword, hybrid mode
- **Input:** query="OAuth", search_type="hybrid"
- **Actual:** 5 results — Basic Memory Coding Guide, AI Collaboration Examples, SPEC-18, daily note, Manual Testing Session. FTS + vector blended. Scores ~0.016-0.032
- **Note:** Top hybrid result is "Basic Memory Coding Guide" not an OAuth-specific doc — suggests hybrid scoring may dilute strong FTS matches
- **Verdict:** PASS but hybrid ranking questionable for single-keyword queries
---
### Test 4: Hybrid Ranking
#### 4a: Hybrid vs vector on "OAuth authentication"
- **Hybrid with entity_types=["entity"]:** 5 results — RLS Implementation Lessons, Cloud Readiness Assessment, AUTH.md OAuth, Core Service Implementation, OAuth Rip-and-Replace. Scores ~0.016-0.023
- **Vector with entity_types=["entity"]:** 5 results — Core Service Implementation, SPEC-13 CLI Auth, Coding Guide, Authentication Service, ADR Production Auth. Scores ~0.55-0.60
- **Observation:** Hybrid surfaces different top results than vector-only. Hybrid found RLS and Cloud Readiness docs that vector didn't prioritize. Different ranking is expected from RRF fusion.
- **Verdict:** PASS — hybrid produces meaningfully different ranking
---
### Test 5: Result Types
#### 5a: Vector returns all result types
- **Input:** query="keeping AI context between sessions", search_type="vector"
- **Entities:** SPEC-18 AI Memory Management Tool (type=entity)
- **Relations:** Prompt Builder integrates_with (type=relation)
- **Observations:** "Translation layer is key" (type=observation), "Maintaining context across conversation boundaries" (type=observation)
- **Verdict:** PASS — all three types appear in vector results
#### 5b: Observations carry metadata
- **Observation result:** category="challenge", content="Maintaining context across conversation boundaries", from_entity="research/ai-knowledge-management-research"
- **Verdict:** PASS — category, content, from_entity, tags all present
#### 5c: Relations carry link info
- **Relation result:** relation_type="integrates_with", from_entity="development/features/prompt-builder...", to_entity (present but truncated in some)
- **Verdict:** PASS — relation metadata present
---
### Test 6: Filters + Vector Search
#### 6a: entity_types=["entity"] with vector
- **Input:** query="OAuth authentication", search_type="vector", entity_types=["entity"]
- **Actual:** 5 results, all type="entity" (Core Service Implementation, SPEC-13, Coding Guide, Authentication Service, ADR Auth)
- **Verdict:** PASS — filter correctly restricts to entities only
#### 6b: types=["note"] with vector
- **Input:** query="OAuth authentication", search_type="vector", types=["note"]
- **Actual:** Same 5 results (all have entity_type="note" in metadata)
- **Verdict:** PASS — types filter works with vector search
#### 6c: after_date with vector
- **Input:** query="OAuth authentication", search_type="vector", after_date="2025-06-01"
- **Actual:** 3 results — Core Service Implementation, Cloud Web App analysis observation, SPEC-13. Filtered out older OAuth docs.
- **Verdict:** PASS — date filter applied correctly
#### 6d: entity_types=["entity"] with hybrid
- **Input:** query="OAuth authentication", search_type="hybrid", entity_types=["entity"]
- **Actual:** 5 results, all type="entity" — RLS lessons, Cloud Readiness, AUTH.md OAuth, Core Service, OAuth Rip-and-Replace
- **Verdict:** PASS — filter works with hybrid mode too
#### 6e: types=["entity"] with vector (WRONG filter name)
- **Input:** query="OAuth authentication", search_type="vector", types=["entity"]
- **Actual:** 0 results
- **Note:** `types` filters by entity_type metadata (e.g., "note", "person"), NOT by SearchItemType. Using types=["entity"] looks for entity_type="entity" which few/no notes have. This is a UX confusion point — the param names are ambiguous.
- **Verdict:** PASS (correct behavior) but USABILITY ISSUE — easy to confuse types vs entity_types
---
### Test 7: Edge Cases
#### 7a: Single character query
- **Input:** query="x", search_type="vector"
- **Actual:** 3 results — "Self-contained application bundle" observation, Non-Markdown File Support relation, quick-win-tools entity. Scores ~0.57-0.59
- **Note:** Single character still produces an embedding and returns results. Quality is low/random as expected.
- **Verdict:** PASS (no crash, returns results)
#### 7b: Whitespace-only query
- **Input:** query=" ", search_type="vector"
- **Actual:** 0 results
- **Verdict:** PASS (handled gracefully — _check_vector_eligible strips and rejects empty)
#### 7c: Query with no relevant content
- **Input:** query="quantum computing blockchain", search_type="vector"
- **Actual:** 3 results — Inter-Agent Communication relation, Self-contained bundle observation, JSON-LD interop observation. Scores ~0.54
- **Note:** Still returns results because vector search always finds nearest neighbors. Scores are lower (~0.54) than relevant queries (~0.58-0.60). No relevance threshold applied.
- **Verdict:** PASS (expected behavior) but NOTE — no relevance cutoff means irrelevant queries always return something
---
### Test 8: Pagination
#### 8a: Vector search page 2
- **Input:** query="keeping AI context between sessions", search_type="vector", page=2, page_size=3
- **Actual:** 3 results on page 2, current_page=2. Different results from page 1. Top: "Maintaining context across conversation boundaries" observation (score 0.587)
- **Note:** Interestingly, page 2 had a higher-scoring result than some page 1 results. This may indicate pagination doesn't sort globally — it might be paginating within a pre-scored set.
- **Verdict:** PASS (pagination works) but POSSIBLE ISSUE — result ordering across pages needs investigation
---
## Summary
### Passing Tests: 20/21
### Bugs Found
1. **search_type="semantic" silently falls through** (Test 1a) — Invalid search_type values fall to the `else` branch and default to text search without any warning. Should either alias "semantic" to "vector" or raise an error.
### Usability Issues
2. **types vs entity_types confusion** (Test 6e) — `types` filters by entity_type metadata (note, person, etc.) while `entity_types` filters by SearchItemType (entity, observation, relation). The naming is ambiguous and easy to mix up.
3. **No relevance threshold** (Test 7c) — Vector search always returns nearest neighbors even for completely irrelevant queries. Consider adding a minimum score threshold or at least documenting expected score ranges.
4. **Hybrid ranking for single keywords** (Test 3b) — Hybrid mode on simple keyword queries produced less intuitive rankings than pure FTS or pure vector. The RRF fusion may dilute strong FTS signals.
### Observations
- Vector search successfully finds conceptually related content that FTS misses entirely
- Score ranges: relevant queries ~0.56-0.60, irrelevant queries ~0.54 (narrow spread)
- All three result types (entity, observation, relation) appear correctly in vector results
- Filters (entity_types, types, after_date) all work correctly with vector and hybrid modes
- Pagination works but cross-page ordering may need investigation
+270
View File
@@ -0,0 +1,270 @@
# Semantic Search
This guide covers Basic Memory's semantic (vector) search feature, which adds meaning-based retrieval alongside the existing full-text search.
## Overview
Basic Memory's search supports both full-text search (FTS) and semantic retrieval. Semantic search adds vector embeddings that capture the *meaning* of your content, enabling:
- **Paraphrase matching**: Find "authentication flow" when searching for "login process"
- **Conceptual queries**: Search for "ways to improve performance" and find notes about caching, indexing, and optimization
- **Hybrid retrieval**: Combine the precision of keyword search with the recall of semantic similarity
Semantic search is enabled by default when semantic dependencies are available at runtime. It works on both SQLite (local) and Postgres (cloud) backends.
## Installation
Semantic search dependencies (fastembed, sqlite-vec, openai) are included in the default `basic-memory` install.
```bash
pip install basic-memory
```
You can always override with `BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true|false`.
### Platform Compatibility
| Platform | FastEmbed (local) | OpenAI (API) |
|---|---|---|
| macOS ARM64 (Apple Silicon) | Yes | Yes |
| macOS x86_64 (Intel Mac) | No — see workaround below | Yes |
| Linux x86_64 | Yes | Yes |
| Linux ARM64 | Yes | Yes |
| Windows x86_64 | Yes | Yes |
#### Intel Mac Workaround
The default install includes FastEmbed, which depends on ONNX Runtime. ONNX Runtime dropped Intel Mac (x86_64) wheels starting in v1.24, so install with a compatible ONNX Runtime pin first:
```bash
pip install basic-memory 'onnxruntime<1.24'
```
After installation, Intel Mac users have two runtime options:
**Option 1: Use OpenAI embeddings (recommended)**
```bash
export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=openai
export OPENAI_API_KEY=sk-...
```
**Option 2: Use FastEmbed locally**
Keep the same pinned installation and use FastEmbed (default provider):
```bash
export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=fastembed
```
## Quick Start
1. Install Basic Memory:
```bash
pip install basic-memory
```
2. (Optional) Explicitly enable semantic search:
```bash
export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
```
3. Build vector embeddings for your existing content:
```bash
bm reindex --embeddings
```
4. Search using semantic modes:
```python
# Pure vector similarity
search_notes("login process", search_type="vector")
# Hybrid: combines FTS precision with vector recall (recommended)
search_notes("login process", search_type="hybrid")
# Explicit full-text search
search_notes("login process", search_type="text")
```
## Configuration Reference
All settings are fields on `BasicMemoryConfig` and can be set via environment variables (prefixed with `BASIC_MEMORY_`).
| Config Field | Env Var | Default | Description |
|---|---|---|---|
| `semantic_search_enabled` | `BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED` | Auto (`true` when semantic deps are available) | Enable semantic search. Required before vector/hybrid modes work. |
| `semantic_embedding_provider` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER` | `"fastembed"` | Embedding provider: `"fastembed"` (local) or `"openai"` (API). |
| `semantic_embedding_model` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL` | `"bge-small-en-v1.5"` | Model identifier. Auto-adjusted per provider if left at default. |
| `semantic_embedding_dimensions` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_DIMENSIONS` | Auto-detected | Vector dimensions. 384 for FastEmbed, 1536 for OpenAI. Override only if using a non-default model. |
| `semantic_embedding_batch_size` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_BATCH_SIZE` | `64` | Number of texts to embed per batch. |
| `semantic_vector_k` | `BASIC_MEMORY_SEMANTIC_VECTOR_K` | `100` | Candidate count for vector nearest-neighbour retrieval. Higher values improve recall at the cost of latency. |
## Embedding Providers
### FastEmbed (default)
FastEmbed runs entirely locally using ONNX models — no API key, no network calls, no cost.
- **Model**: `BAAI/bge-small-en-v1.5`
- **Dimensions**: 384
- **Tradeoff**: Smaller model, fast inference, good quality for most use cases
```bash
# Install basic-memory and enable semantic search
pip install basic-memory
export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
```
### OpenAI
Uses OpenAI's embeddings API for higher-dimensional vectors. Requires an API key.
- **Model**: `text-embedding-3-small`
- **Dimensions**: 1536
- **Tradeoff**: Higher quality embeddings, requires API calls and an OpenAI key
```bash
export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=openai
export OPENAI_API_KEY=sk-...
```
When switching from FastEmbed to OpenAI (or vice versa), you must rebuild embeddings since the vector dimensions differ:
```bash
bm reindex --embeddings
```
## Search Modes
### `text` (default)
Full-text keyword search using FTS5 (SQLite) or tsvector (Postgres). Supports boolean operators (`AND`, `OR`, `NOT`), phrase matching, and prefix wildcards.
```python
search_notes("project AND planning", search_type="text")
```
This is the existing default and does not require semantic search to be enabled.
### `vector`
Pure semantic similarity search. Embeds your query and finds the nearest content vectors. Good for conceptual or paraphrase queries where exact keywords may not appear in the content.
```python
search_notes("how to speed up the app", search_type="vector")
```
Returns results ranked by cosine similarity. Individual observations and relations surface as first-class results, not collapsed into parent entities.
### `hybrid`
Combines FTS and vector results using score-based fusion. This is generally the best mode when you want both keyword precision and semantic recall.
```python
search_notes("authentication security", search_type="hybrid")
```
Score-based fusion uses the formula `max(vec, fts) + bonus * min(vec, fts)` to preserve the dominant signal while rewarding results found by both methods.
### When to Use Which
| Mode | Best For |
|---|---|
| `text` | Exact keyword matching, boolean queries, tag/category searches |
| `vector` | Conceptual queries, paraphrase matching, exploratory searches |
| `hybrid` | General-purpose search combining precision and recall |
## The Reindex Command
The `bm reindex` command rebuilds search indexes without dropping the database.
```bash
# Rebuild everything (FTS + embeddings if semantic is enabled)
bm reindex
# Only rebuild vector embeddings
bm reindex --embeddings
# Only rebuild the full-text search index
bm reindex --search
# Target a specific project
bm reindex -p my-project
```
### When You Need to Reindex
- **Upgrade note**: Migration now performs a one-time automatic embedding backfill on upgrade.
- **Manual enable case**: If you explicitly had `semantic_search_enabled=false` and then turn it on
- **Provider change**: After switching between `fastembed` and `openai`
- **Model change**: After changing `semantic_embedding_model`
- **Dimension change**: After changing `semantic_embedding_dimensions`
The reindex command shows progress with embedded/skipped/error counts:
```
Project: main
Building vector embeddings...
✓ Embeddings complete: 142 entities embedded, 0 skipped, 0 errors
Reindex complete!
```
## How It Works
### Chunking
Each entity in the search index is split into semantic chunks before embedding:
- **Headers**: Markdown headers (`#`, `##`, etc.) start new chunks
- **Bullets**: Each bullet item (`-`, `*`) becomes its own chunk for granular fact retrieval
- **Prose sections**: Non-bullet text is merged up to ~900 characters per chunk
- **Long sections**: Oversized content is split with ~120 character overlap to preserve context at boundaries
Each search index item type (entity, observation, relation) is chunked independently, so observations and relations are embeddable as discrete facts.
### Deduplication
Each chunk has a `source_hash` (SHA-256 of the chunk text). On re-sync, unchanged chunks skip re-embedding entirely. This makes incremental updates fast — only modified content triggers API calls or model inference.
### Hybrid Fusion
Hybrid search uses score-based fusion to merge FTS and vector results:
1. Run FTS search to get keyword-ranked results; normalize scores to [0, 1]
2. Run vector search to get similarity-ranked results (already [0, 1])
3. For each result, compute: `fused = max(vec_score, fts_score) + 0.3 * min(vec_score, fts_score)`
4. Sort by fused score
The dominant signal (whichever source scored higher) is preserved, and dual-source agreement adds a bonus. Unlike rank-based fusion, this approach retains score magnitude — a strong vector match stays strong even without an FTS hit.
### Observation-Level Results
Vector and hybrid modes return individual observations and relations as first-class search results, not just parent entities. This means a search for "water temperature for brewing" can surface the specific observation about 205°F without returning the entire "Coffee Brewing Methods" entity.
## Database Backends
### SQLite (local)
- **Vector storage**: [sqlite-vec](https://github.com/asg017/sqlite-vec) virtual table
- **Table creation**: At runtime when semantic search is first used — no migration needed
- **Embedding table**: `search_vector_embeddings` using `vec0(embedding float[N])` where N is the configured dimensions
- **Chunk metadata**: `search_vector_chunks` table stores chunk text, keys, and source hashes
The sqlite-vec extension is loaded per-connection. Vector tables are created lazily on first use.
### Postgres (cloud)
- **Vector storage**: [pgvector](https://github.com/pgvector/pgvector) with HNSW indexing
- **Chunk metadata table**: Created via Alembic migration (`search_vector_chunks` with `BIGSERIAL` primary key)
- **Embedding table**: `search_vector_embeddings` created at runtime (dimension-dependent, same pattern as SQLite)
- **Index**: HNSW index on the embedding column for fast approximate nearest-neighbour queries
The Alembic migration creates the dimension-independent chunks table. The embeddings table and HNSW index are deferred to runtime because they depend on the configured vector dimensions.
+225
View File
@@ -0,0 +1,225 @@
# SPEC-LOCAL-PLUS-PUBLISH: Local+ Published Notes and Privacy Tiers
**Status:** Draft
**Date:** 2026-02-14
**Owner:** Basic Memory
## Summary
Add a paid Local+ feature that lets users publish selected notes to shareable URLs while keeping the
main knowledge base local-first. Use this as a product wedge for users who do not want full cloud
hosting but do want collaboration and distribution features.
This spec also captures a practical position on "zero knowledge" for Local+.
## Context
Basic Memory already has strong local-first primitives and optional cloud routing/sync. A recurring
request is:
- keep knowledge local by default,
- pay for selective value-add,
- share specific outputs externally.
Published Notes fits this model: explicit per-note opt-in, reversible, and easy to understand.
## Goals
1. Provide an Obsidian Publish-style sharing experience for selected notes.
2. Keep local markdown files as source of truth.
3. Make sharing compatible with current cloud/auth/billing primitives.
4. Define clear Local+ packaging that does not degrade OSS local workflows.
5. Document zero-knowledge constraints so product decisions are explicit.
## Non-Goals
1. Full hosted editing for all notes (Cloud Full remains separate).
2. Public website builder/CMS features.
3. Strict cryptographic zero-knowledge server processing for MCP/search in v1.
## Local+ Feature Catalog (Sellable)
Core Local+ candidates:
1. Published Notes (share URL, revoke, expiry, password).
2. Snapshot Time Machine (point-in-time restore for local projects).
3. Recovery Drill Reports (automated restore verification).
4. Device/API Key Governance (per-device keys, revocation, audit trail).
5. BYO Storage Orchestration (managed setup for user-owned object storage).
6. Semantic Boost Add-on (higher quality retrieval options while files remain source-of-truth).
Team-oriented add-ons:
1. Team-owned shared links and domain branding.
2. Role-based publish permissions.
3. Shared workspace policies for what can be published.
## Proposed MVP: Published Notes
### User Experience
Per note actions:
1. Publish.
2. Unpublish.
3. Copy URL.
4. Regenerate URL.
5. Set visibility and controls.
Controls:
1. Visibility: `unlisted` (default) or `public`.
2. Optional password gate.
3. Optional expiration datetime.
4. Optional "disable indexing" flag for public mode.
Behavior:
1. Source note remains local markdown.
2. Publish is explicit opt-in per note.
3. Unpublish removes public access immediately.
4. Republish creates a new URL token unless user chooses to keep current URL.
### URL Model
1. Unlisted share URL: high-entropy token path.
2. Public URL: slug path (optional, later phase).
3. Team plans can support custom domain mapping in later phase.
### Content Model
v1 published page includes:
1. Rendered markdown body.
2. Optional metadata (title, updated_at).
v1 excludes:
1. Full graph traversal expansion.
2. Related note auto-discovery on public pages.
### Sync Model
1. Local file remains canonical.
2. Publish stores a rendered snapshot plus metadata in cloud.
3. Update path:
- manual "update published version", or
- optional auto-update on note change (plan-gated).
## Architecture (v1)
### High-Level Flow
1. Client selects a note to publish.
2. Client sends publish request with note identifier and policy.
3. Service resolves note content (local sync artifact or explicit upload payload).
4. Service stores published artifact and returns share URL.
### Data Model
`published_notes`
1. `id` (uuid)
2. `tenant_id` or `workspace_id`
3. `project_id`
4. `entity_permalink` (or stable external_id)
5. `share_token` (hashed in DB)
6. `visibility` (`unlisted`|`public`)
7. `password_hash` (nullable)
8. `expires_at` (nullable)
9. `is_active`
10. `published_content` (rendered snapshot or reference)
11. `published_at`
12. `updated_at`
### API Shape (Draft)
1. `POST /api/published-notes`
2. `GET /api/published-notes`
3. `GET /api/published-notes/{id}`
4. `PATCH /api/published-notes/{id}`
5. `DELETE /api/published-notes/{id}` (unpublish)
6. `POST /api/published-notes/{id}/regenerate-url`
7. `GET /p/{token}` (public resolver)
### CLI Shape (Draft)
1. `bm cloud publish <identifier>`
2. `bm cloud publish list`
3. `bm cloud publish update <id>`
4. `bm cloud publish unpublish <id>`
5. `bm cloud publish rotate-url <id>`
### Security
1. Default to unlisted URLs.
2. Store only hashed share tokens.
3. Passwords hashed server-side.
4. Enforce expiration at request time.
5. Log publish/unpublish/rotate events for auditability.
## Packaging and Pricing Direction
Suggested split:
1. OSS Local: no publish URLs.
2. Local+ Solo: publish URLs + snapshots + recovery.
3. Local+ Team: solo features + team governance and branding.
4. Cloud Full: hosted app + full cloud workflows.
Key message:
"Keep everything local. Publish only what you choose."
## Rollout Plan
1. Phase 1: Unlisted publish URLs + unpublish + regenerate URL.
2. Phase 2: Password/expiry controls.
3. Phase 3: Auto-update on note change and basic analytics.
4. Phase 4: Team branding/domains/policies.
## Zero-Knowledge Position
### Strict Zero-Knowledge Definition
Strict zero-knowledge means the server cannot decrypt note content at all.
### Why This Conflicts with MCP and Search
If server cannot decrypt:
1. MCP tool execution against cloud content cannot read/write semantic content.
2. Full-text search cannot index plaintext content.
3. Semantic/vector search cannot generate or query embeddings on plaintext.
4. Server-side relation resolution and context building become severely limited.
This matches earlier findings: strict zero-knowledge materially handicaps MCP-driven behavior and
search quality.
### Viable Alternatives (Not Strict Zero-Knowledge)
1. Encryption at rest/in transit with server-side decrypt in trusted runtime.
- Preserves MCP/search quality.
- Not zero-knowledge cryptographically.
2. Client-side retrieval mode.
- Keep MCP/search local; cloud is sync/share/backup relay.
- Best for privacy-first users.
- Requires local agent availability for advanced retrieval.
3. Limited encrypted indexing.
- Blind indexes for exact keywords only.
- No high-quality semantic search.
- Usually poor UX for natural-language memory recall.
### Recommendation
For Local+:
1. Do not promise strict zero-knowledge for cloud MCP/search paths.
2. Offer a privacy-first local mode where advanced retrieval stays local.
3. Clearly label tradeoffs:
- "Local private mode" (best privacy, best local retrieval).
- "Cloud-assisted mode" (best cross-device/MCP consistency, trusted-runtime decrypt).
This keeps messaging honest and avoids repeating the known incompatibility.
+368
View File
@@ -0,0 +1,368 @@
# SPEC-SCHEMA-IMPL: Schema System Implementation Plan
**Status:** Draft
**Created:** 2025-02-06
**Branch:** `feature/schema-system`
**Depends on:** [SPEC-SCHEMA](SPEC-SCHEMA.md)
## Overview
Implementation plan for the Basic Memory Schema System. The system is entirely programmatic —
no LLM agent runtime or API key required. The LLM already in the user's session (Claude Code,
Claude Desktop, etc.) provides the intelligence layer by reading schema notes via existing
MCP tools.
## Architecture
```
┌─────────────────────────────────────────────────┐
│ Entry Points │
│ CLI (bm schema ...) │ MCP (schema_validate) │
└──────────┬────────────┴──────────┬──────────────┘
│ │
▼ ▼
┌─────────────────────────────────────────────────┐
│ Schema Service Layer │
│ resolve_schema · validate · infer · diff │
└──────────┬────────────────────────┬──────────────┘
│ │
▼ ▼
┌──────────────────────┐ ┌────────────────────────┐
│ Picoschema Parser │ │ Note/Entity Access │
│ YAML → SchemaModel │ │ (existing repository) │
└──────────────────────┘ └────────────────────────┘
```
No new database tables. Schemas are notes with `type: schema` — they're already indexed.
Validation reads observations and relations from existing data.
## Components
### 1. Picoschema Parser
**Location:** `src/basic_memory/schema/parser.py`
Parses Picoschema YAML into an internal representation.
```python
@dataclass
class SchemaField:
name: str
type: str # string, integer, number, boolean, any, or EntityName
required: bool # True unless field name ends with ?
is_array: bool # True if (array) notation
is_enum: bool # True if (enum) notation
enum_values: list[str] # Populated for enums
description: str | None # Text after comma
is_entity_ref: bool # True if type is capitalized (entity reference)
children: list[SchemaField] # For (object) types
@dataclass
class SchemaDefinition:
entity: str # The entity type this schema describes
version: int # Schema version
fields: list[SchemaField] # Parsed fields
validation_mode: str # "warn" | "strict" | "off"
frontmatter_fields: list[SchemaField] # From settings.frontmatter (default: [])
def parse_picoschema(yaml_dict: dict) -> list[SchemaField]:
"""Parse a Picoschema YAML dict into a list of SchemaField objects."""
def parse_schema_note(frontmatter: dict) -> SchemaDefinition:
"""Parse a full schema note's frontmatter into a SchemaDefinition."""
```
**Input/Output:**
```yaml
# Input (YAML dict from frontmatter)
schema:
name: string, full name
role?: string, job title
works_at?: Organization, employer
expertise?(array): string, areas of knowledge
```
```python
# Output
[
SchemaField(name="name", type="string", required=True, description="full name", ...),
SchemaField(name="role", type="string", required=False, description="job title", ...),
SchemaField(name="works_at", type="Organization", required=False, is_entity_ref=True, ...),
SchemaField(name="expertise", type="string", required=False, is_array=True, ...),
]
```
### 2. Schema Resolver
**Location:** `src/basic_memory/schema/resolver.py`
Finds the applicable schema for a note using the resolution order.
```python
async def resolve_schema(
note_frontmatter: dict,
search_fn: Callable, # injected search capability
) -> SchemaDefinition | None:
"""Resolve schema for a note.
Resolution order:
1. Inline schema (frontmatter['schema'] is a dict)
2. Explicit reference (frontmatter['schema'] is a string)
3. Implicit by type (frontmatter['type'] → schema note with matching entity)
4. No schema (returns None)
"""
```
### 3. Schema Validator
**Location:** `src/basic_memory/schema/validator.py`
Validates a note's observations and relations against a resolved schema.
```python
@dataclass
class FieldResult:
field: SchemaField
status: str # "present" | "missing" | "type_mismatch"
values: list[str] # Matched observation values or relation targets
message: str | None # Human-readable detail
@dataclass
class ValidationResult:
note_identifier: str
schema_entity: str
passed: bool # True if no errors (warnings are OK)
field_results: list[FieldResult]
unmatched_observations: dict[str, int] # category → count
unmatched_relations: list[str] # relation types not in schema
warnings: list[str]
errors: list[str]
async def validate_note(
note: Note,
schema: SchemaDefinition,
frontmatter: dict | None = None,
) -> ValidationResult:
"""Validate a note against a schema definition.
Mapping rules:
- field: string → observation [field] exists
- field?(array): type → multiple [field] observations
- field?: EntityType → relation 'field [[...]]' exists
- field?(enum): [v] → observation [field] value ∈ enum values
- settings.frontmatter field → frontmatter key presence/value
"""
```
### 4. Schema Inference Engine
**Location:** `src/basic_memory/schema/inference.py`
Analyzes notes of a given type and suggests a schema based on usage frequency.
```python
@dataclass
class FieldFrequency:
name: str
source: str # "observation" | "relation"
count: int # notes containing this field
total: int # total notes analyzed
percentage: float
sample_values: list[str] # representative values
is_array: bool # True if typically appears multiple times per note
target_type: str | None # For relations, the most common target entity type
@dataclass
class InferenceResult:
entity_type: str
notes_analyzed: int
field_frequencies: list[FieldFrequency]
suggested_schema: dict # Ready-to-use Picoschema YAML dict
suggested_required: list[str]
suggested_optional: list[str]
excluded: list[str] # Below threshold
async def infer_schema(
entity_type: str,
notes: list[Note],
required_threshold: float = 0.95, # 95%+ = required
optional_threshold: float = 0.25, # 25%+ = optional
) -> InferenceResult:
"""Analyze notes and suggest a Picoschema definition."""
```
### 5. Schema Diff
**Location:** `src/basic_memory/schema/diff.py`
Compares current note usage against an existing schema definition.
```python
@dataclass
class SchemaDrift:
new_fields: list[FieldFrequency] # Fields not in schema but common in notes
dropped_fields: list[FieldFrequency] # Fields in schema but rare in notes
cardinality_changes: list[str] # one → many or many → one
type_mismatches: list[str] # observation values don't match declared type
async def diff_schema(
schema: SchemaDefinition,
notes: list[Note],
) -> SchemaDrift:
"""Compare a schema against actual note usage to detect drift."""
```
## Entry Points
### CLI Commands
**Location:** `src/basic_memory/cli/schema.py`
```python
import typer
schema_app = typer.Typer(name="schema", help="Schema management commands")
@schema_app.command()
async def validate(
target: str = typer.Argument(None, help="Note path or entity type"),
strict: bool = typer.Option(False, help="Override to strict mode"),
):
"""Validate notes against their schemas."""
@schema_app.command()
async def infer(
entity_type: str = typer.Argument(..., help="Entity type to analyze"),
threshold: float = typer.Option(0.25, help="Minimum frequency for optional fields"),
save: bool = typer.Option(False, help="Save to schema/ directory"),
):
"""Infer schema from existing notes of a type."""
@schema_app.command()
async def diff(
entity_type: str = typer.Argument(..., help="Entity type to diff"),
):
"""Show drift between schema and actual usage."""
```
Registered as subcommand: `bm schema validate`, `bm schema infer`, `bm schema diff`.
### MCP Tools
**Location:** `src/basic_memory/mcp/tools/schema.py`
```python
@mcp_tool
async def schema_validate(
entity_type: str | None = None,
identifier: str | None = None,
project: str | None = None,
) -> str:
"""Validate notes against their resolved schema."""
@mcp_tool
async def schema_infer(
entity_type: str,
threshold: float = 0.25,
project: str | None = None,
) -> str:
"""Analyze existing notes and suggest a schema definition."""
```
### API Endpoints
**Location:** `src/basic_memory/api/schema_router.py`
```python
router = APIRouter(prefix="/schema", tags=["schema"])
@router.post("/validate")
async def validate_schema(...) -> ValidationReport: ...
@router.post("/infer")
async def infer_schema(...) -> InferenceResult: ...
@router.get("/diff/{entity_type}")
async def diff_schema(...) -> SchemaDrift: ...
```
MCP tools call these endpoints via the typed client pattern (consistent with existing
architecture).
## Implementation Phases
### Phase 1: Parser + Resolver
Build the foundation — can parse Picoschema and find schemas for notes.
**Deliverables:**
- `schema/parser.py` — Picoschema YAML → `SchemaDefinition`
- `schema/resolver.py` — Resolution order (inline → explicit ref → implicit by type → none)
- Unit tests for all Picoschema syntax variations
- Unit tests for resolution order
**No external dependencies.** Pure Python parsing of YAML dicts. Can develop and test
in isolation.
### Phase 2: Validator
Connect schemas to notes and produce validation results.
**Deliverables:**
- `schema/validator.py` — Validate note observations/relations against schema fields
- API endpoint: `POST /schema/validate`
- MCP tool: `schema_validate`
- CLI command: `bm schema validate`
- Integration tests with real notes and schemas
**Depends on:** Phase 1 (parser + resolver)
### Phase 3: Inference
Analyze existing notes to suggest schemas.
**Deliverables:**
- `schema/inference.py` — Frequency analysis across notes of a type
- API endpoint: `POST /schema/infer`
- MCP tool: `schema_infer`
- CLI command: `bm schema infer`
- Option to save inferred schema as a note via `write_note`
**Depends on:** Phase 1 (parser for output format)
### Phase 4: Diff
Compare schemas against current usage.
**Deliverables:**
- `schema/diff.py` — Drift detection between schema and actual notes
- API endpoint: `GET /schema/diff/{entity_type}`
- CLI command: `bm schema diff`
**Depends on:** Phase 1 (parser), Phase 3 (inference, for frequency analysis)
## Testing Strategy
- **Unit tests** (`tests/schema/`): Parser edge cases, resolution logic, validation mapping,
inference thresholds
- **Integration tests** (`test-int/schema/`): End-to-end with real markdown files, schema notes
on disk, CLI invocation
- Coverage target: 100% (consistent with project standard)
## What This Does NOT Include
- No new database tables or migrations
- No new markdown syntax (schemas validate existing observations/relations)
- No LLM agent runtime or API key management
- No hook integration (deferred)
- No schema composition/inheritance (deferred)
- No OWL/RDF export (deferred)
- No built-in templates (deferred)
+492
View File
@@ -0,0 +1,492 @@
# SPEC-SCHEMA: Basic Memory Schema System
**Status:** Draft
**Created:** 2025-02-06
**Branch:** `feature/schema-system`
## Summary
A schema system for Basic Memory that uses [Picoschema](https://genkit.dev/docs/dotprompt/)
syntax in YAML frontmatter. Schemas validate notes against their existing observation/relation
structure — no new data model, no migration, just a declarative lens over what's already there.
## Core Principles
1. **Schemas are just notes** — A schema is a note with `type: schema`, lives anywhere
2. **Use prior art** — Picoschema syntax in YAML frontmatter, no custom notation
3. **Validation maps to existing format** — Observations and relations, not a parallel data model
4. **Validation is soft** — Warnings by default, not blocking errors
5. **Inference over prescription** — Schemas describe reality, emerge from usage
6. **No built-in agent** — Programmatic core; the LLM already in the session provides intelligence
## Picoschema Syntax
Picoschema is a compact schema notation from Google's Dotprompt that fits naturally in YAML
frontmatter.
### Supported Types
| Type | Description |
|------|-------------|
| `string` | Text value |
| `integer` | Whole number |
| `number` | Decimal number |
| `boolean` | True/false |
| `any` | Any scalar type |
| `EntityName` | Reference to another entity (capitalized = entity reference) |
### Syntax Rules
```yaml
schema:
name: string, full name # required field with description
email?: string, contact email # ? = optional
role?: string, job title
works_at?: Organization, employer # capitalized type = entity reference
tags?(array): string, categories # array of type
status?(enum): [active, inactive] # enum with allowed values
metadata?(object): # nested object
updated_at?: string
source?: string
```
- `field: type` — required field
- `field?: type` — optional field
- `field(array): type` — array of values
- `field?(enum): [values]` — enumeration
- `field?(object):` — nested object with sub-fields
- `, description` — description after comma
- `EntityName` as type (capitalized) — reference to another entity
## Schema-to-Note Mapping
Schemas validate against the existing Basic Memory note format. No new syntax for note
authors to learn.
### Mapping Rules
| Schema Declaration | Grounded In | Example Match |
|--------------------|-------------|---------------|
| `field: string` | Observation `[field] value` | `- [name] Paul Graham` |
| `field?(array): string` | Multiple `[field]` observations | `- [expertise] Lisp` (×N) |
| `field?: EntityType` | Relation `field [[Target]]` | `- works_at [[Y Combinator]]` |
| `field?(array): EntityType` | Multiple `field` relations | `- authored [[Book]]` (×N) |
| `tags` | Frontmatter `tags` array | `tags: [startups, essays]` |
| `field?(enum): [values]` | Observation `[field] value` where value ∈ set | `- [status] active` |
| `settings.frontmatter` field | Frontmatter key presence/value | `tags: [python, ai]` |
### Key Insight
Schemas don't introduce a new way to store data. They describe the patterns already present
in observations and relations. A note doesn't have to change how it's written — the schema
just says "a good Person note has a `[name]` observation and a `works_at` relation."
## Schema Definition
### As a Dedicated Schema Note
```yaml
# schema/Person.md
---
title: Person
type: schema
entity: Person
version: 1
schema:
name: string, full name
email?: string, contact email
role?: string, job title
works_at?: Organization, employer
expertise?(array): string, areas of knowledge
settings:
validation: warn # warn | strict | off
frontmatter:
tags?(array): string, note categories
status?(enum): [draft, review, published]
---
# Person
A human individual in the knowledge graph.
Any documentation about this entity type goes here as prose.
```
Schema notes are regular Basic Memory notes. They show up in search, can have their own
observations and relations, and can be organized in any folder (though `schema/` is
the suggested convention).
### Inline Schema in a Note
Notes can carry their own schema directly:
```yaml
# meetings/2024-01-15-standup.md
---
title: Team Standup 2024-01-15
type: meeting
schema:
attendees(array): string, who was there
decisions(array): string, what was decided
action_items(array): string, follow-ups
blockers?(array): string, anything stuck
---
# Team Standup 2024-01-15
## Observations
- [attendees] Paul
- [attendees] Sarah
- [decisions] Ship v2 by Friday
- [action_items] Paul to review PR #42
- [blockers] Waiting on API credentials
```
Good for one-off structured notes or prototyping a schema before extracting it.
### Explicit Schema Reference
A note can reference a schema by entity name or permalink:
```yaml
# projects/basic-memory.md
---
title: Basic Memory
schema: SoftwareProject # by entity name
---
# research/llm-memory-patterns.md
---
title: LLM Memory Patterns
schema: schema/research-project # by permalink
---
```
Use cases:
- Note's `type` differs from the schema it should validate against
- Multiple schema variants exist for the same domain
- Applying structure to existing notes without changing their type
## Schema Resolution
When validating a note, schemas resolve in priority order:
```
1. Inline schema → schema: { ... } (dict in frontmatter)
2. Explicit ref → schema: Person (string in frontmatter)
3. Implicit by type → type: Person (lookup schema note with entity: Person)
4. No schema → no validation (perfectly fine)
```
```python
async def resolve_schema(note: Note) -> Schema | None:
schema_value = note.frontmatter.get('schema')
# 1. Inline schema (dict)
if isinstance(schema_value, dict):
return parse_picoschema(schema_value)
# 2. Explicit reference (string)
if isinstance(schema_value, str):
schema_note = await find_schema_note(schema_value)
if schema_note:
return parse_picoschema(schema_note.frontmatter['schema'])
# 3. Implicit by type
note_type = note.frontmatter.get('type')
if note_type:
results = await search_notes(f"type:schema entity:{note_type}")
if results:
return parse_picoschema(results[0].frontmatter['schema'])
# 4. No schema
return None
```
## Validation
### Modes
Configured in the schema's `settings.validation`:
| Mode | Behavior |
|------|----------|
| `off` | No validation |
| `warn` | Warnings in output, doesn't block (default) |
| `strict` | Errors that block sync, for CI/CD enforcement |
### Validation Output
For a note missing required fields:
```
$ bm schema validate people/ada-lovelace.md
⚠ Person schema validation:
- Missing required field: name (expected [name] observation)
- Missing optional field: role
- Missing optional field: works_at (no relation found)
Unmatched observations: [fact] ×2, [born] ×1
Unmatched relations: collaborated_with
```
"Unmatched" items are informational — observations and relations the schema doesn't cover.
They're valid. Schemas are a subset, not a straitjacket.
### Frontmatter Validation
Schema notes can declare validation rules for frontmatter keys under `settings.frontmatter`
using the same Picoschema syntax as the `schema` block:
```yaml
settings:
validation: warn
frontmatter:
tags?(array): string
status?(enum): [draft, review, published]
```
- Frontmatter rules use the same Picoschema key syntax (`?` for optional, `(enum)`, `(array)`)
- Only available on schema notes (inline schemas skip frontmatter validation)
- Checks key presence (required vs optional) and enum value membership
- Unmatched frontmatter keys not in the schema are silently ignored
- Missing required frontmatter keys produce a warning (or error in strict mode)
Example output for a missing required frontmatter key:
```
⚠ Person schema validation:
- Missing required frontmatter key: status
```
### Batch Validation
```
$ bm schema validate Person
Validating 30 notes against Person schema...
✓ people/paul-graham.md — all fields present
✓ people/rich-hickey.md — all fields present
⚠ people/ada-lovelace.md — missing: name
⚠ people/alan-kay.md — missing: name, role
✓ people/linus-torvalds.md — all fields present
...
Summary: 22/30 valid, 8 warnings, 0 errors
```
## Emerging Schemas
### The Problem with Traditional Schemas
Most schema systems require: define schema → create conforming content → fight the schema
when reality doesn't match. This is backwards. Knowledge grows organically.
### The Basic Memory Approach
```
Write notes freely → Patterns emerge → Crystallize into schema → Validate future notes
```
### Schema Inference
Generate schemas from existing notes by analyzing observation and relation frequency:
```
$ bm schema infer Person
Analyzing 30 notes with type: Person...
Observations found:
[name] 30/30 100% → name: string
[role] 27/30 90% → role?: string
[fact] 25/30 83% (generic — no single field)
[expertise] 18/30 60% → expertise?(array): string
[email] 8/30 27% → email?: string
[born] 6/30 20% (below threshold)
Relations found:
works_at 22/30 73% → works_at?: Organization
authored 11/30 37% → authored?(array): string
Suggested schema:
name: string, full name
role?: string, job title
expertise?(array): string, areas of knowledge
email?: string, contact email
works_at?: Organization, employer
Save to schema/Person.md? [y/n]
```
Frequency thresholds:
- 100% present → required field
- 25%+ present → optional field
- Below 25% → excluded from suggestion (but noted)
### Schema Drift Detection
Track how usage patterns shift over time:
```
$ bm schema diff Person
Schema drift detected:
+ expertise: now in 81% of notes (was 12%)
- department: dropped to 3% of notes
~ works_at: cardinality changed (one → many)
Update schema? [y/n/review]
```
## LLM Integration (AI Guidance)
No agent runtime or API key required. The LLM already in the session uses schemas as
context for note creation.
### Flow
1. User asks LLM to "write a note about Rich Hickey"
2. LLM determines `type: Person` is appropriate
3. LLM calls `search_notes("type:schema entity:Person")` → finds schema
4. LLM reads schema fields: required `name`, optional `role`, `works_at`, `expertise`
5. LLM calls `write_note` with observations and relations that satisfy the schema
The schema acts as a creation template. The LLM knows what a "complete" note looks like
without any custom agent infrastructure.
### MCP Tools
```python
@mcp_tool
async def schema_validate(
entity_type: str | None = None,
identifier: str | None = None,
project: str | None = None,
) -> ValidationReport:
"""Validate notes against their resolved schema.
Validates a specific note (by identifier) or all notes of a given type.
Returns warnings/errors based on the schema's validation mode.
"""
@mcp_tool
async def schema_infer(
entity_type: str,
threshold: float = 0.25,
project: str | None = None,
) -> SuggestedSchema:
"""Analyze existing notes and suggest a schema definition.
Examines observation categories and relation types across all notes
of the given type. Returns frequency analysis and suggested Picoschema.
"""
```
## CLI Commands
```bash
# Validate a specific note
bm schema validate people/ada-lovelace.md
# Validate all notes of a type
bm schema validate Person
# Validate everything with a schema
bm schema validate
# Infer schema from existing notes
bm schema infer Person
# Show schema drift from current definition
bm schema diff Person
# List all schema notes
bm search "type:schema"
```
## Examples
### Complete Person Workflow
**Schema:**
```yaml
# schema/Person.md
---
title: Person
type: schema
entity: Person
version: 1
schema:
name: string, full name
role?: string, job title or position
works_at?: Organization, employer
expertise?(array): string, areas of knowledge
email?: string, contact email
settings:
validation: warn
---
# Person
A human individual in the knowledge graph.
```
**Valid note:**
```yaml
# people/paul-graham.md
---
title: Paul Graham
type: Person
tags: [startups, essays, lisp]
---
# Paul Graham
## Observations
- [name] Paul Graham
- [role] Essayist and investor
- [expertise] Startups
- [expertise] Lisp
- [expertise] Essay writing
- [fact] Created Viaweb, the first web app
## Relations
- works_at [[Y Combinator]]
- authored [[Hackers and Painters]]
```
**Note with warnings:**
```yaml
# people/ada-lovelace.md
---
title: Ada Lovelace
type: Person
---
# Ada Lovelace
## Observations
- [fact] Wrote the first computer program
- [born] 1815
## Relations
- collaborated_with [[Charles Babbage]]
```
Validation: warns about missing required `[name]` observation. Everything else is optional
or unmatched (which is fine).
## Future Considerations (Deferred)
These are interesting but out of scope for the initial implementation:
- **Multiple schema inheritance** — `schema: [Person, Author]`
- **Hook integration** — Pre-write validation via the hooks system
- **OWL/RDF export** — `bm schema export --format owl`
- **SPARQL queries** — Schema-aware graph queries
- **Built-in templates** — `bm schema use gtd`, `bm schema use zettelkasten`
- **Schema versioning/migration** — Tracking breaking changes across versions
+28
View File
@@ -0,0 +1,28 @@
## Coverage policy (practical 100%)
Basic Memorys test suite intentionally mixes:
- unit tests (fast, deterministic)
- integration tests (real filesystem + real DB via `test-int/`)
To keep the default CI signal **stable and meaningful**, the default `pytest` coverage report targets **core library logic** and **excludes** a small set of modules that are either:
- highly environment-dependent (OS/DB tuning)
- inherently interactive (CLI)
- background-task orchestration (watchers/sync runners)
### What's excluded (and why)
Coverage excludes are configured in `pyproject.toml` under `[tool.coverage.report].omit`.
Current exclusions include:
- `src/basic_memory/cli/**`: interactive wrappers; behavior is validated via higher-level tests and smoke tests.
- `src/basic_memory/db.py`: platform/backend tuning paths (SQLite/Postgres/Windows), covered by integration tests and targeted runs.
- `src/basic_memory/services/initialization.py`: startup orchestration/background tasks; covered indirectly by app/MCP entrypoints.
- `src/basic_memory/sync/sync_service.py`: heavy filesystem↔DB integration; validated in integration suite (not enforced in unit coverage).
### Recommended additional runs
If you want extra confidence locally/CI:
- **Postgres backend**: run tests with `BASIC_MEMORY_TEST_POSTGRES=1`.
- **Strict backend-complete coverage**: run coverage on SQLite + Postgres and combine the results (recommended).
+246 -27
View File
@@ -2,26 +2,166 @@
# Install dependencies
install:
uv pip install -e ".[dev]"
uv sync
@echo ""
@echo "💡 Remember to activate the virtual environment by running: source .venv/bin/activate"
# Run unit tests only (fast, no coverage)
test-unit:
uv run pytest -p pytest_mock -v --no-cov -n auto tests
# ==============================================================================
# DATABASE BACKEND TESTING
# ==============================================================================
# Basic Memory supports dual database backends (SQLite and Postgres).
# By default, tests run against SQLite (fast, no dependencies).
# Set BASIC_MEMORY_TEST_POSTGRES=1 to run against Postgres (uses testcontainers).
#
# Quick Start:
# just test # Run all tests against SQLite (default)
# just test-sqlite # Run all tests against SQLite
# just test-postgres # Run all tests against Postgres (testcontainers)
# just test-unit-sqlite # Run unit tests against SQLite
# just test-unit-postgres # Run unit tests against Postgres
# just test-int-sqlite # Run integration tests against SQLite
# just test-int-postgres # Run integration tests against Postgres
#
# CI runs both in parallel for faster feedback.
# ==============================================================================
# Run integration tests only (fast, no coverage)
test-int:
uv run pytest -p pytest_mock -v --no-cov -n auto test-int
# Run all tests against SQLite and Postgres
test: test-sqlite test-postgres
# Run all tests with unified coverage report
test: test-unit test-int
# Run all tests against SQLite
test-sqlite: test-unit-sqlite test-int-sqlite
# Run all tests against Postgres (uses testcontainers)
test-postgres: test-unit-postgres test-int-postgres
# Run unit tests against SQLite
test-unit-sqlite:
BASIC_MEMORY_ENV=test uv run pytest -p pytest_mock -v --no-cov tests
# Run unit tests against Postgres
test-unit-postgres:
BASIC_MEMORY_ENV=test BASIC_MEMORY_TEST_POSTGRES=1 uv run pytest -p pytest_mock -v --no-cov tests
# Run integration tests against SQLite (excludes semantic benchmarks — use just test-semantic)
test-int-sqlite:
BASIC_MEMORY_ENV=test uv run pytest -p pytest_mock -v --no-cov -m "not semantic" test-int
# Run integration tests against Postgres
# Note: Uses timeout due to FastMCP Client + asyncpg cleanup hang (tests pass, process hangs on exit)
# See: https://github.com/jlowin/fastmcp/issues/1311
test-int-postgres:
#!/usr/bin/env bash
set -euo pipefail
# Use gtimeout (macOS/Homebrew) or timeout (Linux)
TIMEOUT_CMD=$(command -v gtimeout || command -v timeout || echo "")
if [[ -n "$TIMEOUT_CMD" ]]; then
$TIMEOUT_CMD --signal=KILL 600 bash -c 'BASIC_MEMORY_ENV=test BASIC_MEMORY_TEST_POSTGRES=1 uv run pytest -p pytest_mock -v --no-cov -m "not semantic" test-int' || test $? -eq 137
else
echo "⚠️ No timeout command found, running without timeout..."
BASIC_MEMORY_ENV=test BASIC_MEMORY_TEST_POSTGRES=1 uv run pytest -p pytest_mock -v --no-cov -m "not semantic" test-int
fi
# Run tests impacted by recent changes (requires pytest-testmon)
testmon *args:
BASIC_MEMORY_ENV=test uv run pytest -p pytest_mock -v --no-cov --testmon --testmon-forceselect {{args}}
# Run MCP smoke test (fast end-to-end loop)
test-smoke:
BASIC_MEMORY_ENV=test uv run pytest -p pytest_mock -v --no-cov -m smoke test-int/mcp/test_smoke_integration.py
# Fast local loop: lint, format, typecheck, impacted tests
fast-check:
just fix
just format
just typecheck
just testmon
just test-smoke
# Reset Postgres test database (drops and recreates schema)
# Useful when Alembic migration state gets out of sync during development
# Uses credentials from docker-compose-postgres.yml
postgres-reset:
docker exec basic-memory-postgres psql -U ${POSTGRES_USER:-basic_memory_user} -d ${POSTGRES_TEST_DB:-basic_memory_test} -c "DROP SCHEMA public CASCADE; CREATE SCHEMA public;"
@echo "✅ Postgres test database reset"
# Run Alembic migrations manually against Postgres test database
# Useful for debugging migration issues
# Uses credentials from docker-compose-postgres.yml (can override with env vars)
postgres-migrate:
@cd src/basic_memory/alembic && \
BASIC_MEMORY_DATABASE_BACKEND=postgres \
BASIC_MEMORY_DATABASE_URL=${POSTGRES_TEST_URL:-postgresql+asyncpg://basic_memory_user:dev_password@localhost:5433/basic_memory_test} \
uv run alembic upgrade head
@echo "✅ Migrations applied to Postgres test database"
# Run Windows-specific tests only (only works on Windows platform)
# These tests verify Windows-specific database optimizations (locking mode, NullPool)
# Will be skipped automatically on non-Windows platforms
test-windows:
BASIC_MEMORY_ENV=test uv run pytest -p pytest_mock -v --no-cov -m windows tests test-int
# Run benchmark tests only (performance testing)
# These are slow tests that measure sync performance with various file counts
# Excluded from default test runs to keep CI fast
test-benchmark:
BASIC_MEMORY_ENV=test uv run pytest -p pytest_mock -v --no-cov -m benchmark tests test-int
# Run semantic search quality benchmarks (all combos)
test-semantic:
BASIC_MEMORY_ENV=test uv run pytest -p pytest_mock -v --no-cov -m semantic test-int/semantic/
# Run semantic benchmarks with JSON artifact output, then show report
test-semantic-report:
BASIC_MEMORY_ENV=test BASIC_MEMORY_BENCHMARK_OUTPUT=.benchmarks/semantic-quality.jsonl uv run pytest -p pytest_mock -v -s --no-cov -m semantic test-int/semantic/
uv run python test-int/semantic/report.py .benchmarks/semantic-quality.jsonl
# Run semantic benchmarks (Postgres combos only)
test-semantic-postgres:
BASIC_MEMORY_ENV=test uv run pytest -p pytest_mock -v --no-cov -m semantic -k postgres test-int/semantic/
# View semantic benchmark results (rich formatted table)
# Usage: just semantic-report [--filter-combo sqlite] [--filter-suite paraphrase] [--sort-by avg_latency_ms]
semantic-report *args:
uv run python test-int/semantic/report.py .benchmarks/semantic-quality.jsonl {{args}}
# Compare two search benchmark JSONL outputs
# Usage:
# just benchmark-compare .benchmarks/search-baseline.jsonl .benchmarks/search-candidate.jsonl
# just benchmark-compare .benchmarks/search-baseline.jsonl .benchmarks/search-candidate.jsonl --format markdown --show-missing
benchmark-compare baseline candidate *args:
uv run python test-int/compare_search_benchmarks.py "{{baseline}}" "{{candidate}}" --format table {{args}}
# Run all tests including Windows, Postgres, and Benchmarks (for CI/comprehensive testing)
# Use this before releasing to ensure everything works across all backends and platforms
test-all:
BASIC_MEMORY_ENV=test uv run pytest -p pytest_mock -v --no-cov tests test-int
# Generate HTML coverage report
coverage:
uv run pytest -p pytest_mock -v -n auto tests test-int --cov-report=html
@echo "Coverage report generated in htmlcov/index.html"
#!/usr/bin/env bash
set -euo pipefail
uv run coverage erase
echo "🔎 Coverage (SQLite)..."
BASIC_MEMORY_ENV=test uv run coverage run --source=basic_memory -m pytest -p pytest_mock -v --no-cov tests test-int
echo "🔎 Coverage (Postgres via testcontainers)..."
# Note: Uses timeout due to FastMCP Client + asyncpg cleanup hang (tests pass, process hangs on exit)
# See: https://github.com/jlowin/fastmcp/issues/1311
TIMEOUT_CMD=$(command -v gtimeout || command -v timeout || echo "")
if [[ -n "$TIMEOUT_CMD" ]]; then
$TIMEOUT_CMD --signal=KILL 600 bash -c 'BASIC_MEMORY_ENV=test BASIC_MEMORY_TEST_POSTGRES=1 uv run coverage run --source=basic_memory -m pytest -p pytest_mock -v --no-cov -m postgres tests test-int' || test $? -eq 137
else
echo "⚠️ No timeout command found, running without timeout..."
BASIC_MEMORY_ENV=test BASIC_MEMORY_TEST_POSTGRES=1 uv run coverage run --source=basic_memory -m pytest -p pytest_mock -v --no-cov -m postgres tests test-int
fi
echo "🧩 Combining coverage data..."
uv run coverage combine
uv run coverage report -m
uv run coverage html
echo "Coverage report generated in htmlcov/index.html"
# Lint and fix code (calls fix)
lint: fix
@@ -30,10 +170,14 @@ lint: fix
fix:
uv run ruff check --fix --unsafe-fixes src tests test-int
# Type check code
# Type check code (pyright)
typecheck:
uv run pyright
# Type check code (ty)
typecheck-ty:
uv run ty check src/
# Clean build artifacts and cache files
clean:
find . -type f -name '*.pyc' -delete
@@ -49,14 +193,63 @@ format:
run-inspector:
npx @modelcontextprotocol/inspector
# Build macOS installer
installer-mac:
cd installer && chmod +x make_icons.sh && ./make_icons.sh
cd installer && uv run python setup.py bdist_mac
# Run doctor checks in an isolated temp home/config
doctor:
#!/usr/bin/env bash
set -euo pipefail
TMP_HOME=$(mktemp -d)
TMP_CONFIG=$(mktemp -d)
HOME="$TMP_HOME" \
BASIC_MEMORY_ENV=test \
BASIC_MEMORY_HOME="$TMP_HOME/basic-memory" \
BASIC_MEMORY_CONFIG_DIR="$TMP_CONFIG" \
./.venv/bin/python -m basic_memory.cli.main doctor --local
# Run an isolated Logfire smoke workflow for local trace inspection
telemetry-smoke:
#!/usr/bin/env bash
set -euo pipefail
TMP_HOME=$(mktemp -d)
TMP_CONFIG=$(mktemp -d)
TMP_PROJECT=$(mktemp -d)
export HOME="$TMP_HOME"
export BASIC_MEMORY_ENV="${BASIC_MEMORY_ENV:-dev}"
export BASIC_MEMORY_HOME="$TMP_PROJECT/home-root"
export BASIC_MEMORY_CONFIG_DIR="$TMP_CONFIG"
export BASIC_MEMORY_NO_PROMOS=1
export BASIC_MEMORY_LOG_LEVEL="${BASIC_MEMORY_LOG_LEVEL:-INFO}"
export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED="${BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED:-false}"
export BASIC_MEMORY_LOGFIRE_ENABLED="${BASIC_MEMORY_LOGFIRE_ENABLED:-true}"
export BASIC_MEMORY_LOGFIRE_ENVIRONMENT="${BASIC_MEMORY_LOGFIRE_ENVIRONMENT:-telemetry-smoke}"
if [[ -z "${BASIC_MEMORY_LOGFIRE_SEND_TO_LOGFIRE:-}" ]]; then
if [[ -n "${LOGFIRE_TOKEN:-}" ]]; then
export BASIC_MEMORY_LOGFIRE_SEND_TO_LOGFIRE=true
else
export BASIC_MEMORY_LOGFIRE_SEND_TO_LOGFIRE=false
fi
fi
mkdir -p "$BASIC_MEMORY_HOME"
echo "Telemetry smoke setup:"
echo " logfire_enabled=$BASIC_MEMORY_LOGFIRE_ENABLED"
echo " send_to_logfire=$BASIC_MEMORY_LOGFIRE_SEND_TO_LOGFIRE"
echo " log_level=$BASIC_MEMORY_LOG_LEVEL"
echo " semantic_search_enabled=$BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED"
echo " logfire_environment=$BASIC_MEMORY_LOGFIRE_ENVIRONMENT"
echo " project_path=$TMP_PROJECT"
./.venv/bin/python -m basic_memory.cli.main project add telemetry-smoke "$TMP_PROJECT" --default --local
./.venv/bin/python -m basic_memory.cli.main tool write-note --title "Telemetry Smoke" --folder notes --content "hello from smoke" --project telemetry-smoke --local
./.venv/bin/python -m basic_memory.cli.main tool read-note notes/telemetry-smoke --project telemetry-smoke --local
./.venv/bin/python -m basic_memory.cli.main tool edit-note notes/telemetry-smoke --operation append --content $'\n\nsmoke edit line' --project telemetry-smoke --local
./.venv/bin/python -m basic_memory.cli.main tool build-context notes/telemetry-smoke --project telemetry-smoke --local --page-size 5 --max-related 5
./.venv/bin/python -m basic_memory.cli.main tool search-notes telemetry --project telemetry-smoke --local
./.venv/bin/python -m basic_memory.cli.main doctor --local
echo ""
echo "Telemetry smoke complete."
echo "Search Logfire for:"
echo " service_name: basic-memory-cli"
echo " environment: $BASIC_MEMORY_LOGFIRE_ENVIRONMENT"
echo " span names: mcp.tool.write_note, mcp.tool.read_note, mcp.tool.edit_note, mcp.tool.build_context, mcp.tool.search_notes, sync.project.run"
# Build Windows installer
installer-win:
cd installer && uv run python setup.py bdist_win32
# Update all dependencies to latest versions
update-deps:
@@ -65,6 +258,9 @@ update-deps:
# Run all code quality checks and tests
check: lint format typecheck test
# Run all code quality checks and all test suites, including semantic benchmarks
check-all: lint format typecheck test test-semantic
# Generate Alembic migration with descriptive message
migration message:
cd src/basic_memory/alembic && alembic revision --autogenerate -m "{{message}}"
@@ -104,16 +300,22 @@ release version:
fi
# Run quality checks
echo "🔍 Running quality checks..."
just check
echo "🔍 Running lint checks..."
just lint
just typecheck
# Update version in __init__.py
echo "📝 Updating version in __init__.py..."
sed -i.bak "s/__version__ = \".*\"/__version__ = \"$VERSION_NUM\"/" src/basic_memory/__init__.py
rm -f src/basic_memory/__init__.py.bak
# Update version in server.json (MCP registry metadata)
echo "📝 Updating version in server.json..."
sed -i.bak "s/\"version\": \"[^\"]*\"/\"version\": \"$VERSION_NUM\"/g" server.json
rm -f server.json.bak
# Commit version update
git add src/basic_memory/__init__.py
git add src/basic_memory/__init__.py server.json
git commit -m "chore: update version to $VERSION_NUM for {{version}} release"
# Create and push tag
@@ -127,6 +329,12 @@ release version:
echo "✅ Release {{version}} created successfully!"
echo "📦 GitHub Actions will build and publish to PyPI"
echo "🔗 Monitor at: https://github.com/basicmachines-co/basic-memory/actions"
echo ""
echo "📝 REMINDER: Post-release tasks:"
echo " 1. docs.basicmemory.com - Add release notes to src/pages/latest-releases.mdx"
echo " 2. basicmachines.co - Update version in src/components/sections/hero.tsx"
echo " 3. MCP Registry - Run: mcp-publisher publish"
echo " See: .claude/commands/release/release.md for detailed instructions"
# Create a beta release (e.g., just beta v0.13.2b1)
beta version:
@@ -163,16 +371,22 @@ beta version:
fi
# Run quality checks
echo "🔍 Running quality checks..."
just check
echo "🔍 Running lint checks..."
just lint
just typecheck
# Update version in __init__.py
echo "📝 Updating version in __init__.py..."
sed -i.bak "s/__version__ = \".*\"/__version__ = \"$VERSION_NUM\"/" src/basic_memory/__init__.py
rm -f src/basic_memory/__init__.py.bak
# Update version in server.json (MCP registry metadata)
echo "📝 Updating version in server.json..."
sed -i.bak "s/\"version\": \"[^\"]*\"/\"version\": \"$VERSION_NUM\"/g" server.json
rm -f server.json.bak
# Commit version update
git add src/basic_memory/__init__.py
git add src/basic_memory/__init__.py server.json
git commit -m "chore: update version to $VERSION_NUM for {{version}} beta release"
# Create and push tag
@@ -187,6 +401,11 @@ beta version:
echo "📦 GitHub Actions will build and publish to PyPI as pre-release"
echo "🔗 Monitor at: https://github.com/basicmachines-co/basic-memory/actions"
echo "📥 Install with: uv tool install basic-memory --pre"
echo ""
echo "📝 REMINDER: For stable releases, update documentation sites:"
echo " 1. docs.basicmemory.com - Add release notes to src/pages/latest-releases.mdx"
echo " 2. basicmachines.co - Update version in src/components/sections/hero.tsx"
echo " See: .claude/commands/release/release.md for detailed instructions"
# List all available recipes
default:
+17 -1
View File
@@ -54,6 +54,22 @@ Or for a one-time sync:
basic-memory sync
```
### 4. Updating Basic Memory
Basic Memory supports automatic updates by default for `uv tool` and Homebrew installs.
For manual checks and upgrades:
```bash
# Check now and install if supported
bm update
# Check only, do not install
bm update --check
```
To disable automatic updates, set `"auto_update": false` in `~/.basic-memory/config.json`.
## Configuration Options
### Custom Directory
@@ -125,4 +141,4 @@ If you encounter issues:
cat ~/.basic-memory/basic-memory.log
```
For more detailed information, refer to the [full documentation](https://memory.basicmachines.co/).
For more detailed information, refer to the [full documentation](https://docs.basicmemory.com/).
+39 -13
View File
@@ -14,9 +14,8 @@ dependencies = [
"typer>=0.9.0",
"aiosqlite>=0.20.0",
"greenlet>=3.1.1",
"pydantic[email,timezone]>=2.10.3",
"icecream>=2.1.3",
"mcp>=1.2.0",
"pydantic[email,timezone]>=2.12.0",
"mcp>=1.23.1",
"pydantic-settings>=2.6.1",
"loguru>=0.7.3",
"pyright>=1.1.390",
@@ -30,15 +29,26 @@ dependencies = [
"alembic>=1.14.1",
"pillow>=11.1.0",
"pybars3>=0.9.7",
"fastmcp>=2.10.2",
"fastmcp>=3.0.1,<4",
"pyjwt>=2.10.1",
"python-dotenv>=1.1.0",
"pytest-aio>=1.9.0",
"aiofiles>=24.1.0", # Async file I/O
"logfire>=0.73.0", # Optional observability (disabled by default via config)
"aiofiles>=24.1.0", # Optional observability (disabled by default via config)
"asyncpg>=0.30.0",
"nest-asyncio>=1.6.0", # For Alembic migrations with Postgres
"pytest-asyncio>=1.2.0",
"psycopg==3.3.1",
"mdformat>=0.7.22",
"mdformat-gfm>=0.3.7",
"mdformat-frontmatter>=2.0.8",
"sniffio>=1.3.1",
"anyio>=4.10.0",
"httpx>=0.28.0",
"fastembed>=0.7.4",
"sqlite-vec>=0.1.6",
"openai>=1.100.2",
]
[project.urls]
Homepage = "https://github.com/basicmachines-co/basic-memory"
Repository = "https://github.com/basicmachines-co/basic-memory"
@@ -48,6 +58,9 @@ Documentation = "https://github.com/basicmachines-co/basic-memory#readme"
basic-memory = "basic_memory.cli.main:app"
bm = "basic_memory.cli.main:app"
[project.optional-dependencies]
telemetry = ["logfire>=4.19.0"]
[build-system]
requires = ["hatchling", "uv-dynamic-versioning>=0.7.0"]
build-backend = "hatchling.build"
@@ -61,6 +74,10 @@ asyncio_default_fixture_loop_scope = "function"
markers = [
"benchmark: Performance benchmark tests (deselect with '-m \"not benchmark\"')",
"slow: Slow-running tests (deselect with '-m \"not slow\"')",
"postgres: Tests that run against Postgres backend (deselect with '-m \"not postgres\"')",
"windows: Windows-specific tests (deselect with '-m \"not windows\"')",
"smoke: Fast end-to-end smoke tests for MCP flows",
"semantic: Tests requiring semantic dependencies (fastembed, sqlite-vec, openai)",
]
[tool.ruff]
@@ -69,6 +86,7 @@ target-version = "py312"
[dependency-groups]
dev = [
"logfire>=4.19.0",
"gevent>=24.11.1",
"icecream>=2.1.3",
"pytest>=8.3.4",
@@ -78,6 +96,13 @@ dev = [
"pytest-xdist>=3.0.0",
"ruff>=0.1.6",
"freezegun>=1.5.5",
"testcontainers[postgres]>=4.0.0",
"psycopg>=3.2.0",
"pyright>=1.1.408",
"pytest-testmon>=2.2.0",
"ty>=0.0.18",
"cst-lsp>=0.1.3",
"libcst>=1.8.6",
]
[tool.hatch.version]
@@ -96,12 +121,15 @@ ignore = ["test/"]
defineConstant = { DEBUG = true }
reportMissingImports = "error"
reportMissingTypeStubs = false
reportUnusedImport = "none"
pythonVersion = "3.12"
[tool.coverage.run]
concurrency = ["thread", "gevent"]
parallel = true
source = ["basic_memory"]
[tool.coverage.report]
exclude_lines = [
@@ -123,11 +151,9 @@ omit = [
"*/supabase_auth_provider.py", # External HTTP calls to Supabase APIs
"*/watch_service.py", # File system watching - complex integration testing
"*/background_sync.py", # Background processes
"*/cli/main.py", # CLI entry point
"*/mcp/tools/project_management.py", # Covered by integration tests
"*/mcp/tools/sync_status.py", # Covered by integration tests
"*/cli/**", # CLI is an interactive wrapper; core logic is covered via API/MCP/service tests
"*/db.py", # Backend/runtime-dependent (sqlite/postgres/windows tuning); validated via integration tests
"*/services/initialization.py", # Startup orchestration + background tasks (watchers); exercised indirectly in entrypoints
"*/sync/sync_service.py", # Heavy filesystem/db integration; covered by integration suite, not enforced in unit coverage
"*/services/migration_service.py", # Complex migration scenarios
]
[tool.logfire]
ignore_no_config = true
+25
View File
@@ -0,0 +1,25 @@
{
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
"name": "io.github.basicmachines-co/basic-memory",
"description": "Local-first knowledge management with bi-directional LLM sync via Markdown files.",
"repository": {
"url": "https://github.com/basicmachines-co/basic-memory.git",
"source": "github"
},
"version": "0.20.3",
"packages": [
{
"registryType": "pypi",
"identifier": "basic-memory",
"version": "0.20.3",
"runtimeHint": "uvx",
"runtimeArguments": [
{"type": "positional", "value": "basic-memory"},
{"type": "positional", "value": "mcp"}
],
"transport": {
"type": "stdio"
}
}
]
}
@@ -1,156 +0,0 @@
---
title: 'SPEC-1: Specification-Driven Development Process'
type: spec
permalink: specs/spec-1-specification-driven-development-process
tags:
- process
- specification
- development
- meta
---
# SPEC-1: Specification-Driven Development Process
## Why
We're implementing specification-driven development to solve the complexity and circular refactoring issues in our web development process.
Instead of getting lost in framework details and type gymnastics, we start with clear specifications that drive implementation.
The default approach of adhoc development with AI agents tends to result in:
- Circular refactoring cycles
- Fighting framework complexity
- Lost context between sessions
- Unclear requirements and scope
## What
This spec defines our process for using basic-memory as the specification engine to build basic-memory-cloud.
We're creating a recursive development pattern where basic-memory manages the specs that drive the development of basic-memory-cloud.
**Affected Areas:**
- All future component development
- Architecture decisions
- Agent collaboration workflows
- Knowledge management and context preservation
## How (High Level)
### Specification Structure
Name: Spec names should be numbered sequentially, followed by a description eg. `SPEC-X - Simple Description.md`.
See: [[Spec-2: Slash Commands Reference]]
Every spec is a complete thought containing:
- **Why**: The reasoning and problem being solved
- **What**: What is affected or changed
- **How**: High-level approach to implementation
- **How to Evaluate**: Testing/validation procedure
- Additional context as needed
### Living Specification Format
Specifications are **living documents** that evolve throughout implementation:
**Progress Tracking:**
- **Completed items**: Use ✅ checkmark emoji for implemented features
- **Pending items**: Use `- [ ]` GitHub-style checkboxes for remaining tasks
- **In-progress items**: Use `- [x]` when work is actively underway
**Status Philosophy:**
- **Avoid static status headers** like "COMPLETE" or "IN PROGRESS" that become stale
- **Use checklists within content** to show granular implementation progress
- **Keep specs informative** while providing clear progress visibility
- **Update continuously** as understanding and implementation evolve
**Example Format:**
```markdown
### ComponentName
- ✅ Basic functionality implemented
- ✅ Props and events defined
- - [ ] Add sorting controls
- - [ ] Improve accessibility
- - [x] Currently implementing responsive design
```
This creates **git-friendly progress tracking** where `[ ]` easily becomes `[x]` or ✅ when completed, and specs remain valuable throughout the development lifecycle.
## Claude Code
We will leverage Claude Code capabilities to make the process semi-automated.
- Slash commands: define repeatable steps in the process (create spec, implement, review, etc)
- Agents: define roles to carry out instructions (front end developer, baskend developer, etc)
- MCP tools: enable agents to implement specs via actions (write code, test, etc)
### Workflow
1. **Create**: Write spec as complete thought in `/specs` folder
2. **Discuss**: Iterate and refine through agent collaboration
3. **Implement**: Hand spec to appropriate specialist agent
4. **Validate**: Review implementation against spec criteria
5. **Document**: Update spec with learnings and decisions
### Slash Commands
Claude slash commands are used to manage the flow.
These are simple instructions to help make the process uniform.
They can be updated and refined as needed.
- `/spec create [name]` - Create new specification
- `/spec status` - Show current spec states
- `/spec implement [name]` - Hand to appropriate agent
- `/spec review [name]` - Validate implementation
### Agent Orchestration
Agents are defined with clear roles, for instance:
- **system-architect**: Creates high-level specs, ADRs, architectural decisions
- **vue-developer**: Component specs, UI patterns, frontend architecture
- **python-developer**: Implementation specs, technical details, backend logic
-
- Each agent reads/updates specs through basic-memory tools.
## How to Evaluate
### Success Criteria
- Specs provide clear, actionable guidance for implementation
- Reduced circular refactoring and scope creep
- Persistent context across development sessions
- Clean separation between "what/why" and implementation details
- Specs record a history of what happened and why for historical context
### Testing Procedure
1. Create a spec for an existing problematic component
2. Have an agent implement following only the spec
3. Compare result quality and development speed vs. ad-hoc approach
4. Measure context preservation across sessions
5. Evaluate spec clarity and completeness
### Metrics
- Time from spec to working implementation
- Number of refactoring cycles required
- Agent understanding of requirements
- Spec reusability for similar components
## Notes
- Start simple: specs are just complete thoughts, not heavy processes
- Use basic-memory's knowledge graph to link specs, decisions, components
- Let the process evolve naturally based on what works
- Focus on solving the actual problem: Manage complexity in development
## Observations
- [problem] Web development without clear goals and documentation circular refactoring cycles #complexity
- [solution] Specification-driven development reduces scope creep and context loss #process-improvement
- [pattern] basic-memory as specification engine creates recursive development loop #meta-development
- [workflow] Five-step process: Create → Discuss → Implement → Validate → Document #methodology
- [tool] Slash commands provide uniform process automation #automation
- [agent-pattern] Three specialized agents handle different implementation domains #specialization
- [success-metric] Time from spec to working implementation measures process efficiency #measurement
- [learning] Process should evolve naturally based on what works in practice #adaptation
- [format] Living specifications use checklists for progress tracking instead of static status headers #documentation
- [evolution] Specs evolve throughout implementation maintaining value as working documents #continuous-improvement
## Relations
- spec [[Spec-2: Slash Commands Reference]]
- spec [[Spec-3: Agent Definitions]]
@@ -1,569 +0,0 @@
---
title: 'SPEC-10: Unified Deployment Workflow and Event Tracking'
type: spec
permalink: specs/spec-10-unified-deployment-workflow-event-tracking
tags:
- workflow
- deployment
- event-sourcing
- architecture
- simplification
---
# SPEC-10: Unified Deployment Workflow and Event Tracking
## Why
We replaced a complex multi-workflow system with DBOS orchestration that was proving to be more trouble than it was worth. The previous architecture had four separate workflows (`tenant_provisioning`, `tenant_update`, `tenant_deployment`, `tenant_undeploy`) with overlapping logic, complex state management, and fragmented event tracking. DBOS added unnecessary complexity without providing sufficient value, leading to harder debugging and maintenance.
**Problems Solved:**
- **Framework Complexity**: DBOS configuration overhead and fighting framework limitations
- **Code Duplication**: Multiple workflows implementing similar operations with duplicate logic
- **Poor Observability**: Fragmented event tracking across workflow boundaries
- **Maintenance Overhead**: Complex orchestration for fundamentally simple operations
- **Debugging Difficulty**: Framework abstractions hiding simple Python stack traces
## What
This spec documents the architectural simplification that consolidates tenant lifecycle management into a unified system with comprehensive event tracking.
**Affected Areas:**
- Tenant deployment workflows (provisioning, updates, undeploying)
- Event sourcing and workflow tracking infrastructure
- API endpoints for tenant operations
- Database schema for workflow and event correlation
- Integration testing for tenant lifecycle operations
**Key Changes:**
- **Removed DBOS entirely** - eliminated framework dependency and complexity
- **Consolidated 4 workflows → 2 unified deployment workflows (deploy/undeploy)**
- **Added workflow tracking system** with complete event correlation
- **Simplified API surface** - single `/deploy` endpoint handles all scenarios
- **Enhanced observability** through event sourcing with workflow grouping
## How (High Level)
### Architectural Philosophy
**Embrace simplicity over framework complexity** - use well-structured Python with proper database design instead of complex orchestration frameworks.
### Core Components
#### 1. Unified Deployment Workflow
```python
class TenantDeploymentWorkflow:
async def deploy_tenant_workflow(self, tenant_id: str, workflow_id: UUID, image_tag: str = None):
# Single workflow handles both initial provisioning AND updates
# Each step is idempotent and handles its own error recovery
# Database transactions provide the durability we need
await self.start_deployment_step(workflow_id, tenant_uuid, image_tag)
await self.create_fly_app_step(workflow_id, tenant_uuid)
await self.create_bucket_step(workflow_id, tenant_uuid)
await self.deploy_machine_step(workflow_id, tenant_uuid, image_tag)
await self.complete_deployment_step(workflow_id, tenant_uuid, image_tag, deployment_time)
```
**Key Benefits:**
- **Handles both provisioning and updates** in single workflow
- **Idempotent operations** - safe to retry any step
- **Clean error handling** via simple Python exceptions
- **Resumable** - can restart from any failed step
#### 2. Workflow Tracking System
**Database Schema:**
```sql
CREATE TABLE workflow (
id UUID PRIMARY KEY,
workflow_type VARCHAR(50) NOT NULL, -- 'tenant_deployment', 'tenant_undeploy'
tenant_id UUID REFERENCES tenant(id),
status VARCHAR(20) DEFAULT 'running', -- 'running', 'completed', 'failed'
workflow_metadata JSONB DEFAULT '{}' -- image_tag, etc.
);
ALTER TABLE event ADD COLUMN workflow_id UUID REFERENCES workflow(id);
```
**Event Correlation:**
- Every workflow operation generates events tagged with `workflow_id`
- Complete audit trail from workflow start to completion
- Events grouped by workflow for easy reconstruction of operations
#### 3. Parameter Standardization
All workflow methods follow consistent signature pattern:
```python
async def method_name(self, session: AsyncSession, workflow_id: UUID | None, tenant_id: UUID, ...)
```
**Benefits:**
- **Consistent event tagging** - all events properly correlated
- **Clear method contracts** - workflow_id always first parameter
- **Type safety** - proper UUID handling throughout
### Implementation Strategy
#### Phase 1: Workflow Consolidation ✅ COMPLETED
- [x] **Remove DBOS dependency** - eliminated dbos_config.py and all DBOS imports
- [x] **Create unified TenantDeploymentWorkflow** - handles both provisioning and updates
- [x] **Remove legacy workflows** - deleted tenant_provisioning.py, tenant_update.py
- [x] **Simplify API endpoints** - consolidated to single `/deploy` endpoint
- [x] **Update integration tests** - comprehensive edge case testing
#### Phase 2: Workflow Tracking System ✅ COMPLETED
- [x] **Database migration** - added workflow table and event.workflow_id foreign key
- [x] **Workflow repository** - CRUD operations for workflow records
- [x] **Event correlation** - all workflow events tagged with workflow_id
- [x] **Comprehensive testing** - workflow lifecycle and event grouping tests
#### Phase 3: Parameter Standardization ✅ COMPLETED
- [x] **Standardize method signatures** - workflow_id as first parameter pattern
- [x] **Fix event tagging** - ensure all workflow events properly correlated
- [x] **Update service methods** - consistent parameter order across tenant_service
- [x] **Integration test validation** - verify complete event sequences
### Architectural Benefits
#### Code Simplification
- **39 files changed**: 2,247 additions, 3,256 deletions (net -1,009 lines)
- **Eliminated framework complexity** - no more DBOS configuration or abstractions
- **Consolidated logic** - single deployment workflow vs 4 separate workflows
- **Cleaner API surface** - unified endpoint vs multiple workflow-specific endpoints
#### Enhanced Observability
- **Complete event correlation** - every workflow event tagged with workflow_id
- **Audit trail reconstruction** - can trace entire tenant lifecycle through events
- **Workflow status tracking** - running/completed/failed states in database
- **Comprehensive testing** - edge cases covered with real infrastructure
#### Operational Benefits
- **Simpler debugging** - plain Python stack traces vs framework abstractions
- **Reduced dependencies** - one less complex framework to maintain
- **Better error handling** - explicit exception handling vs framework magic
- **Easier maintenance** - straightforward Python code vs orchestration complexity
## How to Evaluate
### Success Criteria
#### Functional Completeness ✅ VERIFIED
- [x] **Unified deployment workflow** handles both initial provisioning and updates
- [x] **Undeploy workflow** properly integrated with event tracking
- [x] **All operations idempotent** - safe to retry any step without duplication
- [x] **Complete tenant lifecycle** - provision → active → update → undeploy
#### Event Tracking and Correlation ✅ VERIFIED
- [x] **All workflow events tagged** with proper workflow_id
- [x] **Event sequence verification** - tests assert exact event order and content
- [x] **Workflow grouping** - events can be queried by workflow_id for complete audit trail
- [x] **Cross-workflow isolation** - deployment vs undeploy events properly separated
#### Database Schema and Performance ✅ VERIFIED
- [x] **Migration applied** - workflow table and event.workflow_id column created
- [x] **Proper indexing** - performance optimized queries on workflow_type, tenant_id, status
- [x] **Foreign key constraints** - referential integrity between workflows and events
- [x] **Database triggers** - updated_at timestamp automation
#### Test Coverage ✅ COMPREHENSIVE
- [x] **Unit tests**: 4 workflow tracking tests covering lifecycle and event grouping
- [x] **Integration tests**: Real infrastructure testing with Fly.io resources
- [x] **Edge case coverage**: Failed deployments, partial state recovery, resource conflicts
- [x] **Event sequence verification**: Exact event order and content validation
### Testing Procedure
#### Unit Test Validation ✅ PASSING
```bash
cd apps/cloud && pytest tests/test_workflow_tracking.py -v
# 4/4 tests passing - workflow lifecycle and event grouping
```
#### Integration Test Validation ✅ PASSING
```bash
cd apps/cloud && pytest tests/integration/test_tenant_workflow_deployment_integration.py -v
cd apps/cloud && pytest tests/integration/test_tenant_workflow_undeploy_integration.py -v
# Comprehensive real infrastructure testing with actual Fly.io resources
# Tests provision → deploy → update → undeploy → cleanup cycles
```
### Performance Metrics
#### Code Metrics ✅ ACHIEVED
- **Net code reduction**: -1,009 lines (3,256 deletions, 2,247 additions)
- **Workflow consolidation**: 4 workflows → 1 unified deployment workflow
- **Dependency reduction**: Removed DBOS framework dependency entirely
- **API simplification**: Multiple endpoints → single `/deploy` endpoint
#### Operational Metrics ✅ VERIFIED
- **Event correlation**: 100% of workflow events properly tagged with workflow_id
- **Audit trail completeness**: Full tenant lifecycle traceable through event sequences
- **Error handling**: Clean Python exceptions vs framework abstractions
- **Debugging simplicity**: Direct stack traces vs orchestration complexity
### Implementation Status: ✅ COMPLETE
All phases completed successfully with comprehensive testing and verification:
**Phase 1 - Workflow Consolidation**: ✅ COMPLETE
- Removed DBOS dependency and consolidated workflows
- Unified deployment workflow handles all scenarios
- Comprehensive integration testing with real infrastructure
**Phase 2 - Workflow Tracking**: ✅ COMPLETE
- Database schema implemented with proper indexing
- Event correlation system fully functional
- Complete audit trail capability verified
**Phase 3 - Parameter Standardization**: ✅ COMPLETE
- Consistent method signatures across all workflow methods
- All events properly tagged with workflow_id
- Type safety verified across entire codebase
**Phase 4 - Asynchronous Job Queuing**:
**Goal**: Transform synchronous deployment workflows into background jobs for better user experience and system reliability.
**Current Problem**:
- Deployment API calls are synchronous - users wait for entire tenant provisioning (30-60 seconds)
- No retry mechanism for failed operations
- HTTP timeouts on long-running deployments
- Poor user experience during infrastructure provisioning
**Solution**: Redis-backed job queue with arq for reliable background processing
#### Architecture Overview
```python
# API Layer: Return immediately with job tracking
@router.post("/{tenant_id}/deploy")
async def deploy_tenant(tenant_id: UUID):
# Create workflow record in Postgres
workflow = await workflow_repo.create_workflow("tenant_deployment", tenant_id)
# Enqueue job in Redis
job = await arq_pool.enqueue_job('deploy_tenant_task', tenant_id, workflow.id)
# Return job ID immediately
return {"job_id": job.job_id, "workflow_id": workflow.id, "status": "queued"}
# Background Worker: Process via existing unified workflow
async def deploy_tenant_task(ctx, tenant_id: str, workflow_id: str):
# Existing workflow logic - zero changes needed!
await workflow_manager.deploy_tenant(UUID(tenant_id), workflow_id=UUID(workflow_id))
```
#### Implementation Tasks
**Phase 4.1: Core Job Queue Setup** ✅ COMPLETED
- [x] **Add arq dependency** - integrated Redis job queue with existing infrastructure
- [x] **Create job definitions** - wrapped existing deployment/undeploy workflows as arq tasks
- [x] **Update API endpoints** - updated provisioning endpoints to return job IDs instead of waiting for completion
- [x] **JobQueueService implementation** - service layer for job enqueueing and status tracking
- [x] **Job status tracking** - integrated with existing workflow table for status updates
- [x] **Comprehensive testing** - 18 tests covering positive, negative, and edge cases
**Phase 4.2: Background Worker Implementation** ✅ COMPLETED
- [x] **Job status API** - GET /jobs/{job_id}/status endpoint integrated with JobQueueService
- [x] **Background worker process** - arq worker to process queued jobs with proper settings and Redis configuration
- [x] **Worker settings and configuration** - WorkerSettings class with proper timeouts, max jobs, and error handling
- [x] **Fix API endpoints** - updated job status API to use JobQueueService instead of direct Redis access
- [x] **Integration testing** - comprehensive end-to-end testing with real ARQ workers and Fly.io infrastructure
- [x] **Worker entry points** - dual-purpose entrypoint.sh script and __main__.py module support for both API and worker processes
- [x] **Test fixture updates** - fixed all API and service test fixtures to work with job queue dependencies
- [x] **AsyncIO event loop fixes** - resolved event loop issues in integration tests for subprocess worker compatibility
- [x] **Complete test coverage** - all 46 tests passing across unit, integration, and API test suites
- [x] **Type safety verification** - 0 type checking errors across entire ARQ job queue implementation
#### Phase 4.2 Implementation Summary ✅ COMPLETE
**Core ARQ Job Queue System:**
- **JobQueueService** - Centralized service for job enqueueing, status tracking, and Redis pool management
- **deployment_jobs.py** - ARQ job functions that wrap existing deployment/undeploy workflows
- **Worker Settings** - Production-ready ARQ configuration with proper timeouts and error handling
- **Dual-Process Architecture** - Single Docker image with entrypoint.sh supporting both API and worker modes
**Key Files Added:**
- `apps/cloud/src/basic_memory_cloud/jobs/` - Complete job queue implementation (7 files)
- `apps/cloud/entrypoint.sh` - Dual-purpose Docker container entry point
- `apps/cloud/tests/integration/test_worker_integration.py` - Real infrastructure integration tests
- `apps/cloud/src/basic_memory_cloud/schemas/job_responses.py` - API response schemas
**API Integration:**
- Provisioning endpoints return job IDs immediately instead of blocking for 60+ seconds
- Job status API endpoints for real-time monitoring of deployment progress
- Proper error handling and job failure scenarios with detailed error messages
**Testing Achievement:**
- **46 total tests passing** across all test suites (unit, integration, API, services)
- **Real infrastructure testing** - ARQ workers process actual Fly.io deployments
- **Event loop safety** - Fixed asyncio issues for subprocess worker compatibility
- **Test fixture updates** - All fixtures properly support job queue dependencies
- **Type checking** - 0 errors across entire codebase
**Technical Metrics:**
- **38 files changed** - +1,736 insertions, -334 deletions
- **Integration test runtime** - ~18 seconds with real ARQ workers and Fly.io verification
- **Event loop isolation** - Proper async session management for subprocess compatibility
- **Redis integration** - Production-ready Redis configuration with connection pooling
**Phase 4.3: Production Hardening** ✅ COMPLETED
- [x] **Configure Upstash Redis** - production Redis setup on Fly.io
- [x] **Retry logic for external APIs** - exponential backoff for flaky Tigris IAM operations
- [x] **Monitoring and observability** - comprehensive Redis queue monitoring with CLI tools
- [x] **Error handling improvements** - graceful handling of expected API errors with appropriate log levels
- [x] **CLI tooling enhancements** - bulk update commands for CI/CD automation
- [x] **Documentation improvements** - comprehensive monitoring guide with Redis patterns
- [x] **Job uniqueness** - ARQ-based duplicate prevention for tenant operations
- [ ] **Worker scaling** - multiple arq workers for parallel job processing
- [ ] **Job persistence** - ensure jobs survive Redis/worker restarts
- [ ] **Error alerting** - notifications for failed deployment jobs
**Phase 4.4: Advanced Features** (Future)
- [ ] **Job scheduling** - deploy tenants at specific times
- [ ] **Priority queues** - urgent deployments processed first
- [ ] **Batch operations** - bulk tenant deployments
- [ ] **Job dependencies** - deployment → configuration → activation chains
#### Benefits Achieved ✅ REALIZED
**User Experience Improvements:**
- **Immediate API responses** - users get job ID instantly vs waiting 60+ seconds for deployment completion
- **Real-time job tracking** - status API provides live updates on deployment progress
- **Better error visibility** - detailed error messages and job failure tracking
- **CI/CD automation ready** - bulk update commands for automated tenant deployments
**System Reliability:**
- **Redis persistence** - jobs survive Redis/worker restarts with proper queue durability
- **Idempotent job processing** - jobs can be safely retried without side effects
- **Event loop isolation** - worker processes operate independently from API server
- **Retry resilience** - exponential backoff for flaky external API calls (3 attempts, 1s/2s delays)
- **Graceful error handling** - expected API errors logged at INFO level, unexpected at ERROR level
- **Job uniqueness** - prevent duplicate tenant operations with ARQ's built-in uniqueness feature
**Operational Benefits:**
- **Horizontal scaling ready** - architecture supports adding more workers for parallel processing
- **Comprehensive testing** - real infrastructure integration tests ensure production reliability
- **Type safety** - full type checking prevents runtime errors in job processing
- **Clean separation** - API and worker processes use same codebase with different entry points
- **Queue monitoring** - Redis CLI integration for real-time queue activity monitoring
- **Comprehensive documentation** - detailed monitoring guide with Redis pattern explanations
**Development Benefits:**
- **Zero workflow changes** - existing deployment/undeploy workflows work unchanged as background jobs
- **Async/await native** - modern Python asyncio patterns throughout the implementation
- **Event correlation preserved** - all existing workflow tracking and event sourcing continues to work
- **Enhanced CLI tooling** - unified tenant commands with proper endpoint routing
- **Database integrity** - proper foreign key constraint handling in tenant deletion
#### Infrastructure Requirements
- **Local**: Redis via docker-compose (already exists) ✅
- **Production**: Upstash Redis on Fly.io (already configured) ✅
- **Workers**: arq worker processes (new deployment target)
- **Monitoring**: Job status dashboard (simple web interface)
#### API Evolution
```python
# Before: Synchronous (blocks for 60+ seconds)
POST /tenant/{id}/deploy {status: "active", machine_id: "..."}
# After: Asynchronous (returns immediately)
POST /tenant/{id}/deploy {job_id: "uuid", workflow_id: "uuid", status: "queued"}
GET /jobs/{job_id}/status {status: "running", progress: "deploying_machine", workflow_id: "uuid"}
GET /workflows/{workflow_id}/events [...] # Existing event tracking works unchanged
```
**Technology Choice**: **arq (Redis)** over pgqueuer
- **Existing Redis infrastructure** - Upstash + docker-compose already configured
- **Better ecosystem** - monitoring tools, documentation, community
- **Made by pydantic team** - aligns with existing Python stack
- **Hybrid approach** - Redis for queue operations + Postgres for workflow state
#### Job Uniqueness Implementation
**Problem**: Multiple concurrent deployment requests for the same tenant could create duplicate jobs, wasting resources and potentially causing conflicts.
**Solution**: Leverage ARQ's built-in job uniqueness feature using predictable job IDs:
```python
# JobQueueService implementation
async def enqueue_deploy_job(self, tenant_id: UUID, image_tag: str | None = None) -> str:
unique_job_id = f"deploy-{tenant_id}"
job = await self.redis_pool.enqueue_job(
"deploy_tenant_job",
str(tenant_id),
image_tag,
_job_id=unique_job_id, # ARQ prevents duplicates
)
if job is None:
# Job already exists - return existing job ID
return unique_job_id
else:
# New job created - return ARQ job ID
return job.job_id
```
**Key Features:**
- **Predictable Job IDs**: `deploy-{tenant_id}`, `undeploy-{tenant_id}`
- **Duplicate Prevention**: ARQ returns `None` for duplicate job IDs
- **Graceful Handling**: Return existing job ID instead of raising errors
- **Idempotent Operations**: Safe to retry deployment requests
- **Clear Logging**: Distinguish "Enqueued new" vs "Found existing" jobs
**Benefits:**
- Prevents resource waste from duplicate deployments
- Eliminates race conditions from concurrent requests
- Makes job monitoring more predictable with consistent IDs
- Provides natural deduplication without complex locking mechanisms
## Notes
### Design Philosophy Lessons
- **Simplicity beats framework magic** - removing DBOS made the system more reliable and debuggable
- **Event sourcing > complex orchestration** - database-backed event tracking provides better observability than framework abstractions
- **Idempotent operations > resumable workflows** - each step handling its own retry logic is simpler than framework-managed resumability
- **Explicit error handling > framework exception handling** - Python exceptions are clearer than orchestration framework error states
### Future Considerations
- **Monitoring integration** - workflow tracking events could feed into observability systems
- **Performance optimization** - event querying patterns may benefit from additional indexing
- **Audit compliance** - complete event trail supports regulatory requirements
- **Operational dashboards** - workflow status could drive tenant health monitoring
### Related Specifications
- **SPEC-8**: TigrisFS Integration - bucket provisioning integrated with deployment workflow
- **SPEC-1**: Specification-Driven Development Process - this spec follows the established format
## Observations
- [architecture] Removing framework complexity led to more maintainable system #simplification
- [workflow] Single unified deployment workflow handles both provisioning and updates #consolidation
- [observability] Event sourcing with workflow correlation provides complete audit trail #event-tracking
- [database] Foreign key relationships between workflows and events enable powerful queries #schema-design
- [testing] Integration tests with real infrastructure catch edge cases that unit tests miss #testing-strategy
- [parameters] Consistent method signatures (workflow_id first) reduce cognitive overhead #api-design
- [maintenance] Fewer workflows and dependencies reduce long-term maintenance burden #operational-excellence
- [debugging] Plain Python exceptions are clearer than framework abstraction layers #developer-experience
- [resilience] Exponential backoff retry patterns handle flaky external API calls gracefully #error-handling
- [monitoring] Redis queue monitoring provides real-time operational visibility #observability
- [ci-cd] Bulk update commands enable automated tenant deployments in continuous delivery pipelines #automation
- [documentation] Comprehensive monitoring guides reduce operational learning curve #knowledge-management
- [error-logging] Context-aware log levels (INFO for expected errors, ERROR for unexpected) improve signal-to-noise ratio #logging-strategy
- [job-uniqueness] ARQ job uniqueness with predictable tenant-based IDs prevents duplicate operations and resource waste #deduplication
## Implementation Notes
### Configuration Integration
- **Redis Configuration**: Add Redis settings to existing `apps/cloud/src/basic_memory_cloud/config.py`
- **Local Development**: Leverage existing Redis setup from `docker-compose.yml`
- **Production**: Use Upstash Redis configuration for production environments
### Docker Entrypoint Strategy
Create `entrypoint.sh` script to toggle between API server and worker processes using single Docker image:
```bash
#!/bin/bash
# Entrypoint script for Basic Memory Cloud service
# Supports multiple process types: api, worker
set -e
case "$1" in
"api")
echo "Starting Basic Memory Cloud API server..."
exec uvicorn basic_memory_cloud.main:app \
--host 0.0.0.0 \
--port 8000 \
--log-level info
;;
"worker")
echo "Starting Basic Memory Cloud ARQ worker..."
# For ARQ worker implementation
exec python -m arq basic_memory_cloud.jobs.settings.WorkerSettings
;;
*)
echo "Usage: $0 {api|worker}"
echo " api - Start the FastAPI server"
echo " worker - Start the ARQ worker"
exit 1
;;
esac
```
### Fly.io Process Groups Configuration
Use separate machine groups for API and worker processes with independent scaling:
```toml
# fly.toml app configuration for basic-memory-cloud
app = 'basic-memory-cloud-dev-basic-machines'
primary_region = 'dfw'
org = 'basic-machines'
kill_signal = 'SIGINT'
kill_timeout = '5s'
[build]
# Process groups for API server and worker
[processes]
api = "api"
worker = "worker"
# Machine scaling configuration
[[machine]]
size = 'shared-cpu-1x'
processes = ['api']
min_machines_running = 1
auto_stop_machines = false
auto_start_machines = true
[[machine]]
size = 'shared-cpu-1x'
processes = ['worker']
min_machines_running = 1
auto_stop_machines = false
auto_start_machines = true
[env]
# Python configuration
PYTHONUNBUFFERED = '1'
PYTHONPATH = '/app'
# Logging configuration
LOG_LEVEL = 'DEBUG'
# Redis configuration for ARQ
REDIS_URL = 'redis://basic-memory-cloud-redis.upstash.io'
# Database configuration
DATABASE_HOST = 'basic-memory-cloud-db-dev-basic-machines.internal'
DATABASE_PORT = '5432'
DATABASE_NAME = 'basic_memory_cloud'
DATABASE_USER = 'postgres'
DATABASE_SSL = 'true'
# Worker configuration
ARQ_MAX_JOBS = '10'
ARQ_KEEP_RESULT = '3600'
# Fly.io configuration
FLY_ORG = 'basic-machines'
FLY_REGION = 'dfw'
# Internal service - no external HTTP exposure for worker
# API accessible via basic-memory-cloud-dev-basic-machines.flycast:8000
[[vm]]
size = 'shared-cpu-1x'
```
### Benefits of This Architecture
- **Single Docker Image**: Both API and worker use same container with different entrypoints
- **Independent Scaling**: Scale API and worker processes separately based on demand
- **Clean Separation**: Web traffic handling separate from background job processing
- **Existing Infrastructure**: Leverages current PostgreSQL + Redis setup without complexity
- **Hybrid State Management**: Redis for queue operations, PostgreSQL for persistent workflow tracking
## Relations
- implements [[SPEC-8 TigrisFS Integration]]
- follows [[SPEC-1 Specification-Driven Development Process]]
- supersedes previous multi-workflow architecture
@@ -1,186 +0,0 @@
---
title: 'SPEC-11: Basic Memory API Performance Optimization'
type: spec
permalink: specs/spec-11-basic-memory-api-performance-optimization
tags:
- performance
- api
- mcp
- database
- cloud
---
# SPEC-11: Basic Memory API Performance Optimization
## Why
The Basic Memory API experiences significant performance issues in cloud environments due to expensive per-request initialization. MCP tools making
HTTP requests to the API suffer from 350ms-2.6s latency overhead **before** any actual operation occurs.
**Root Cause Analysis:**
- GitHub Issue #82 shows repeated initialization sequences in logs (16:29:35 and 16:49:58)
- Each MCP tool call triggers full database initialization + project reconciliation
- `get_engine_factory()` dependency calls `db.get_or_create_db()` on every request
- `reconcile_projects_with_config()` runs expensive sync operations repeatedly
**Performance Impact:**
- Database connection setup: ~50-100ms per request
- Migration checks: ~100-500ms per request
- Project reconciliation: ~200ms-2s per request
- **Total overhead**: ~350ms-2.6s per MCP tool call
This creates compounding effects with tenant auto-start delays and increases timeout risk in cloud deployments.
## What
This optimization affects the **core basic-memory repository** components:
1. **API Lifespan Management** (`src/basic_memory/api/app.py`)
- Cache database connections in app state during startup
- Avoid repeated expensive initialization
2. **Dependency Injection** (`src/basic_memory/deps.py`)
- Modify `get_engine_factory()` to use cached connections
- Eliminate per-request database setup
3. **Initialization Service** (`src/basic_memory/services/initialization.py`)
- Add caching/throttling to project reconciliation
- Skip expensive operations when appropriate
4. **Configuration** (`src/basic_memory/config.py`)
- Add optional performance flags for cloud environments
**Backwards Compatibility**: All changes must be backwards compatible with existing CLI and non-cloud usage.
## How (High Level)
### Phase 1: Cache Database Connections (Critical - 80% of gains)
**Problem**: `get_engine_factory()` calls `db.get_or_create_db()` per request
**Solution**: Cache database engine/session in app state during lifespan
1. **Modify API Lifespan** (`api/app.py`):
```python
@asynccontextmanager
async def lifespan(app: FastAPI):
app_config = ConfigManager().config
await initialize_app(app_config)
# Cache database connection in app state
engine, session_maker = await db.get_or_create_db(app_config.database_path)
app.state.engine = engine
app.state.session_maker = session_maker
# ... rest of startup logic
```
2. Modify Dependency Injection (deps.py):
```python
async def get_engine_factory(
request: Request
) -> tuple[AsyncEngine, async_sessionmaker[AsyncSession]]:
"""Get cached engine and session maker from app state."""
return request.app.state.engine, request.app.state.session_maker
```
Phase 2: Optimize Project Reconciliation (Secondary - 20% of gains)
Problem: reconcile_projects_with_config() runs expensive sync repeatedly
Solution: Add module-level caching with time-based throttling
1. Add Reconciliation Cache (services/initialization.py):
```ptyhon
_project_reconciliation_completed = False
_last_reconciliation_time = 0
async def reconcile_projects_with_config(app_config, force=False):
# Skip if recently completed (within 60 seconds) unless forced
if recently_completed and not force:
return
# ... existing logic
```
Phase 3: Cloud Environment Flags (Optional)
Problem: Force expensive initialization in production environments
Solution: Add skip flags for cloud/stateless deployments
1. Add Config Flag (config.py):
skip_initialization_sync: bool = Field(default=False)
2. Configure in Cloud (basic-memory-cloud integration):
BASIC_MEMORY_SKIP_INITIALIZATION_SYNC=true
How to Evaluate
Success Criteria
1. Performance Metrics (Primary):
- MCP tool response time reduced by 50%+ (measure before/after)
- Database connection overhead eliminated (0ms vs 50-100ms)
- Migration check overhead eliminated (0ms vs 100-500ms)
- Project reconciliation overhead reduced by 90%+
2. Load Testing:
- Concurrent MCP tool calls maintain performance
- No memory leaks in cached connections
- Database connection pool behaves correctly
3. Functional Correctness:
- All existing API endpoints work identically
- MCP tools maintain full functionality
- CLI operations unaffected
- Database migrations still execute properly
4. Backwards Compatibility:
- No breaking changes to existing APIs
- Config changes are optional with safe defaults
- Non-cloud deployments work unchanged
Testing Strategy
Performance Testing:
# Before optimization
time basic-memory-mcp-tools write_note "test" "content" "folder"
# Measure: ~1-3 seconds
# After optimization
time basic-memory-mcp-tools write_note "test" "content" "folder"
# Target: <500ms
Load Testing:
# Multiple concurrent MCP tool calls
for i in {1..10}; do
basic-memory-mcp-tools search "test" &
done
wait
# Verify: No degradation, consistent response times
Regression Testing:
# Full basic-memory test suite
just test
# All tests must pass
# Integration tests with cloud deployment
# Verify MCP gateway → API → database flow works
Validation Checklist
- Phase 1 Complete: Database connections cached, dependency injection optimized
- Performance Benchmark: 50%+ improvement in MCP tool response times
- Memory Usage: No leaks in cached connections over 24h+ periods
- Stress Testing: 100+ concurrent requests maintain performance
- Backwards Compatibility: All existing functionality preserved
- Documentation: Performance optimization documented in README
- Cloud Integration: basic-memory-cloud sees performance benefits
Notes
Implementation Priority:
- Phase 1 provides 80% of performance gains and should be implemented first
- Phase 2 provides remaining 20% and addresses edge cases
- Phase 3 is optional for maximum cloud optimization
Risk Mitigation:
- All changes backwards compatible
- Gradual rollout possible (Phase 1 → 2 → 3)
- Easy rollback via configuration flags
Cloud Integration:
- This optimization directly addresses basic-memory-cloud issue #82
- Changes in core basic-memory will benefit all cloud tenants
- No changes needed in basic-memory-cloud itself
@@ -1,182 +0,0 @@
# SPEC-12: OpenTelemetry Observability
## Why
We need comprehensive observability for basic-memory-cloud to:
- Track request flows across our multi-tenant architecture (MCP → Cloud → API services)
- Debug performance issues and errors in production
- Understand user behavior and system usage patterns
- Correlate issues to specific tenants for targeted debugging
- Monitor service health and latency across the distributed system
Currently, we only have basic logging without request correlation or distributed tracing capabilities.
## What
Implement OpenTelemetry instrumentation across all basic-memory-cloud services with:
### Core Requirements
1. **Distributed Tracing**: End-to-end request tracing from MCP gateway through to tenant API instances
2. **Tenant Correlation**: All traces tagged with tenant_id, user_id, and workos_user_id
3. **Service Identification**: Clear service naming and namespace separation
4. **Auto-instrumentation**: Automatic tracing for FastAPI, SQLAlchemy, HTTP clients
5. **Grafana Cloud Integration**: Direct OTLP export to Grafana Cloud Tempo
### Services to Instrument
- **MCP Gateway** (basic-memory-mcp): Entry point with JWT extraction
- **Cloud Service** (basic-memory-cloud): Provisioning and management operations
- **API Service** (basic-memory-api): Tenant-specific instances
- **Worker Processes** (ARQ workers): Background job processing
### Key Trace Attributes
- `tenant.id`: UUID from UserProfile.tenant_id
- `user.id`: WorkOS user identifier
- `user.email`: User email for debugging
- `service.name`: Specific service identifier
- `service.namespace`: Environment (development/production)
- `operation.type`: Business operation (provision/update/delete)
- `tenant.app_name`: Fly.io app name for tenant instances
## How
### Phase 1: Setup OpenTelemetry SDK
1. Add OpenTelemetry dependencies to each service's pyproject.toml:
```python
"opentelemetry-distro[otlp]>=1.29.0",
"opentelemetry-instrumentation-fastapi>=0.50b0",
"opentelemetry-instrumentation-httpx>=0.50b0",
"opentelemetry-instrumentation-sqlalchemy>=0.50b0",
"opentelemetry-instrumentation-logging>=0.50b0",
```
2. Create shared telemetry initialization module (`apps/shared/telemetry.py`)
3. Configure Grafana Cloud OTLP endpoint via environment variables:
```bash
OTEL_EXPORTER_OTLP_ENDPOINT=https://otlp-gateway-prod-us-east-2.grafana.net/otlp
OTEL_EXPORTER_OTLP_HEADERS=Authorization=Basic[token]
OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf
```
### Phase 2: Instrument MCP Gateway
1. Extract tenant context from AuthKit JWT in middleware
2. Create root span with tenant attributes
3. Propagate trace context to downstream services via headers
### Phase 3: Instrument Cloud Service
1. Continue trace from MCP gateway
2. Add operation-specific attributes (provisioning events)
3. Instrument ARQ worker jobs for async operations
4. Track Fly.io API calls and latency
### Phase 4: Instrument API Service
1. Extract tenant context from JWT
2. Add machine-specific metadata (instance ID, region)
3. Instrument database operations with SQLAlchemy
4. Track MCP protocol operations
### Phase 5: Configure and Deploy
1. Add OTLP configuration to `.env.example` and `.env.example.secrets`
2. Set Fly.io secrets for production deployment
3. Update Dockerfiles to use `opentelemetry-instrument` wrapper
4. Deploy to development environment first for testing
## How to Evaluate
### Success Criteria
1. **End-to-end traces visible in Grafana Cloud** showing complete request flow
2. **Tenant filtering works** - Can filter traces by tenant_id to see all requests for a user
3. **Service maps accurate** - Grafana shows correct service dependencies
4. **Performance overhead < 5%** - Minimal latency impact from instrumentation
5. **Error correlation** - Can trace errors back to specific tenant and operation
### Testing Checklist
- [x] Single request creates connected trace across all services
- [x] Tenant attributes present on all spans
- [x] Background jobs (ARQ) appear in traces
- [x] Database queries show in trace timeline
- [x] HTTP calls to Fly.io API tracked
- [x] Traces exported successfully to Grafana Cloud
- [x] Can search traces by tenant_id in Grafana
- [x] Service dependency graph shows correct flow
### Monitoring Success
- All services reporting traces to Grafana Cloud
- No OTLP export errors in logs
- Trace sampling working correctly (if implemented)
- Resource usage acceptable (CPU/memory)
## Dependencies
- Grafana Cloud account with OTLP endpoint configured
- OpenTelemetry Python SDK v1.29.0+
- FastAPI instrumentation compatibility
- Network access from Fly.io to Grafana Cloud
## Implementation Assignment
**Recommended Agent**: python-developer
- Requires Python/FastAPI expertise
- Needs understanding of distributed systems
- Must implement middleware and context propagation
- Should understand OpenTelemetry SDK and instrumentation
## Follow-up Tasks
### Enhanced Log Correlation
While basic trace-to-log correlation works automatically via OpenTelemetry logging instrumentation, consider adding structured logging for improved log filtering:
1. **Structured Logging Context**: Add `logger.bind()` calls to inject tenant/user context directly into log records
2. **Custom Loguru Formatter**: Extract OpenTelemetry span attributes for better log readability
3. **Direct Log Filtering**: Enable searching logs directly by tenant_id, workflow_id without going through traces
This would complement the existing automatic trace correlation and provide better log search capabilities.
## Alternative Solution: Logfire
After implementing OpenTelemetry with Grafana Cloud, we discovered limitations in the observability experience:
- Traces work but lack useful context without correlated logs
- Setting up log correlation with Grafana is complex and requires additional infrastructure
- The developer experience for Python observability is suboptimal
### Logfire Evaluation
**Pydantic Logfire** offers a compelling alternative that addresses your specific requirements:
#### Core Requirements Match
- ✅ **User Activity Tracking**: Automatic request tracing with business context
- ✅ **Error Monitoring**: Built-in exception tracking with full context
- ✅ **Performance Metrics**: Automatic latency and performance monitoring
- ✅ **Request Tracing**: Native distributed tracing across services
- ✅ **Log Correlation**: Seamless trace-to-log correlation without setup
#### Key Advantages
1. **Python-First Design**: Built specifically for Python/FastAPI applications by the Pydantic team
2. **Simple Integration**: `pip install logfire` + `logfire.configure()` vs complex OTLP setup
3. **Automatic Correlation**: Logs automatically include trace context without manual configuration
4. **Real-time SQL Interface**: Query spans and logs using SQL with auto-completion
5. **Better Developer UX**: Purpose-built observability UI vs generic Grafana dashboards
6. **Loguru Integration**: `logger.configure(handlers=[logfire.loguru_handler()])` maintains existing logging
#### Pricing Assessment
- **Free Tier**: 10M spans/month (suitable for development and small production workloads)
- **Transparent Pricing**: $1 per million spans/metrics after free tier
- **No Hidden Costs**: No per-host fees, only usage-based metering
- **Production Ready**: Recently exited beta, enterprise features available
#### Migration Path
The existing OpenTelemetry instrumentation is compatible - Logfire uses OpenTelemetry under the hood, so the current spans and attributes would work unchanged.
### Recommendation
**Consider migrating to Logfire** for the following reasons:
1. It directly addresses the "next to useless" traces problem by providing integrated logs
2. Dramatically simpler setup and maintenance compared to Grafana Cloud + custom log correlation
3. Better ROI on observability investment with purpose-built Python tooling
4. Free tier sufficient for current development needs with clear scaling path
The current Grafana Cloud implementation provides a solid foundation and could remain as a backup/export target, while Logfire becomes the primary observability platform.
## Status
**Created**: 2024-01-28
**Status**: Completed (OpenTelemetry + Grafana Cloud)
**Next Phase**: Evaluate Logfire migration
**Priority**: High - Critical for production observability
@@ -1,917 +0,0 @@
---
title: 'SPEC-13: CLI Authentication with Subscription Validation'
type: spec
permalink: specs/spec-12-cli-auth-subscription-validation
tags:
- authentication
- security
- cli
- subscription
status: draft
created: 2025-10-02
---
# SPEC-13: CLI Authentication with Subscription Validation
## Why
The Basic Memory Cloud CLI currently has a security gap in authentication that allows unauthorized access:
**Current Web Flow (Secure)**:
1. User signs up via WorkOS AuthKit
2. User creates Polar subscription
3. Web app validates subscription before calling `POST /tenants/setup`
4. Tenant provisioned only after subscription validation ✅
**Current CLI Flow (Insecure)**:
1. User signs up via WorkOS AuthKit (OAuth device flow)
2. User runs `bm cloud login`
3. CLI receives JWT token from WorkOS
4. CLI can access all cloud endpoints without subscription check ❌
**Problem**: Anyone can sign up with WorkOS and immediately access cloud infrastructure via CLI without having an active Polar subscription. This creates:
- Revenue loss (free resource consumption)
- Security risk (unauthorized data access)
- Support burden (users accessing features they haven't paid for)
**Root Cause**: The CLI authentication flow validates JWT tokens but doesn't verify subscription status before granting access to cloud resources.
## What
Add subscription validation to authentication flow to ensure only users with active Polar subscriptions can access cloud resources across all access methods (CLI, MCP, Web App, Direct API).
**Affected Components**:
### basic-memory-cloud (Cloud Service)
- `apps/cloud/src/basic_memory_cloud/deps.py` - Add subscription validation dependency
- `apps/cloud/src/basic_memory_cloud/services/subscription_service.py` - Add subscription check method
- `apps/cloud/src/basic_memory_cloud/api/tenant_mount.py` - Protect mount endpoints
- `apps/cloud/src/basic_memory_cloud/api/proxy.py` - Protect proxy endpoints
### basic-memory (CLI)
- `src/basic_memory/cli/commands/cloud/core_commands.py` - Handle 403 errors
- `src/basic_memory/cli/commands/cloud/api_client.py` - Parse subscription errors
- `docs/cloud-cli.md` - Document subscription requirement
**Endpoints to Protect**:
- `GET /tenant/mount/info` - Used by CLI bisync setup
- `POST /tenant/mount/credentials` - Used by CLI bisync credentials
- `GET /proxy/{path:path}` - Used by Web App, MCP tools, CLI tools, Direct API
- All other `/proxy/*` endpoints - Centralized access point for all user operations
## Complete Authentication Flow Analysis
### Overview of All Access Flows
Basic Memory Cloud has **7 distinct authentication flows**. This spec closes subscription validation gaps in flows 2-4 and 6, which all converge on the `/proxy/*` endpoints.
### Flow 1: Polar Webhook → Registration ✅ SECURE
```
Polar webhook → POST /api/webhooks/polar
→ Validates Polar webhook signature
→ Creates/updates subscription in database
→ No direct user access - webhook only
```
**Auth**: Polar webhook signature validation
**Subscription Check**: N/A (webhook creates subscriptions)
**Status**: ✅ Secure - webhook validated, no user JWT involved
### Flow 2: Web App Login ❌ NEEDS FIX
```
User → apps/web (Vue.js/Nuxt)
→ WorkOS AuthKit magic link authentication
→ JWT stored in browser session
→ Web app calls /proxy/{project}/... endpoints (memory, directory, projects)
→ proxy.py validates JWT but does NOT check subscription
→ Access granted without subscription ❌
```
**Auth**: WorkOS JWT via `CurrentUserProfileHybridJwtDep`
**Subscription Check**: ❌ Missing
**Fixed By**: Task 1.4 (protect `/proxy/*` endpoints)
### Flow 3: MCP (Model Context Protocol) ❌ NEEDS FIX
```
AI Agent (Claude, Cursor, etc.) → https://mcp.basicmemory.com
→ AuthKit OAuth device flow
→ JWT stored in AI agent
→ MCP tools call {cloud_host}/proxy/{endpoint} with Authorization header
→ proxy.py validates JWT but does NOT check subscription
→ MCP tools can access all cloud resources without subscription ❌
```
**Auth**: AuthKit JWT via `CurrentUserProfileHybridJwtDep`
**Subscription Check**: ❌ Missing
**Fixed By**: Task 1.4 (protect `/proxy/*` endpoints)
### Flow 4: CLI Auth (basic-memory) ❌ NEEDS FIX
```
User → bm cloud login
→ AuthKit OAuth device flow
→ JWT stored in ~/.basic-memory/tokens.json
→ CLI calls:
- {cloud_host}/tenant/mount/info (for bisync setup)
- {cloud_host}/tenant/mount/credentials (for bisync credentials)
- {cloud_host}/proxy/{endpoint} (for all MCP tools)
→ tenant_mount.py and proxy.py validate JWT but do NOT check subscription
→ Access granted without subscription ❌
```
**Auth**: AuthKit JWT via `CurrentUserProfileHybridJwtDep`
**Subscription Check**: ❌ Missing
**Fixed By**: Task 1.3 (protect `/tenant/mount/*`) + Task 1.4 (protect `/proxy/*`)
### Flow 5: Cloud CLI (Admin Tasks) ✅ SECURE
```
Admin → python -m basic_memory_cloud.cli.tenant_cli
→ Uses CLIAuth with admin WorkOS OAuth client
→ Gets JWT token with admin org membership
→ Calls /tenants/* endpoints (create, list, delete tenants)
→ tenants.py validates JWT AND admin org membership via AdminUserHybridDep
→ Access granted only to admin organization members ✅
```
**Auth**: AuthKit JWT + Admin org validation via `AdminUserHybridDep`
**Subscription Check**: N/A (admins bypass subscription requirement)
**Status**: ✅ Secure - admin-only endpoints, separate from user flows
### Flow 6: Direct API Calls ❌ NEEDS FIX
```
Any HTTP client → {cloud_host}/proxy/{endpoint}
→ Sends Authorization: Bearer {jwt} header
→ proxy.py validates JWT but does NOT check subscription
→ Direct API access without subscription ❌
```
**Auth**: WorkOS or AuthKit JWT via `CurrentUserProfileHybridJwtDep`
**Subscription Check**: ❌ Missing
**Fixed By**: Task 1.4 (protect `/proxy/*` endpoints)
### Flow 7: Tenant API Instance (Internal) ✅ SECURE
```
/proxy/* → Tenant API (basic-memory-{tenant_id}.fly.dev)
→ Validates signed header from proxy (tenant_id + signature)
→ Direct external access will be disabled in production
→ Only accessible via /proxy endpoints
```
**Auth**: Signed header validation from proxy
**Subscription Check**: N/A (internal only, validated at proxy layer)
**Status**: ✅ Secure - validates proxy signature, not directly accessible
### Authentication Flow Summary Matrix
| Flow | Access Method | Current Auth | Subscription Check | Fixed By SPEC-13 |
|------|---------------|--------------|-------------------|------------------|
| 1. Polar Webhook | Polar webhook → `/api/webhooks/polar` | Polar signature | N/A (webhook) | N/A |
| 2. Web App | Browser → `/proxy/*` | WorkOS JWT ✅ | ❌ Missing | ✅ Task 1.4 |
| 3. MCP | AI Agent → `/proxy/*` | AuthKit JWT ✅ | ❌ Missing | ✅ Task 1.4 |
| 4. CLI | `bm cloud``/tenant/mount/*` + `/proxy/*` | AuthKit JWT ✅ | ❌ Missing | ✅ Task 1.3 + 1.4 |
| 5. Cloud CLI (Admin) | `tenant_cli``/tenants/*` | AuthKit JWT ✅ + Admin org | N/A (admin) | N/A (admin bypass) |
| 6. Direct API | HTTP client → `/proxy/*` | WorkOS/AuthKit JWT ✅ | ❌ Missing | ✅ Task 1.4 |
| 7. Tenant API | Proxy → tenant instance | Proxy signature ✅ | N/A (internal) | N/A |
### Key Insights
1. **Single Point of Failure**: All user access (Web, MCP, CLI, Direct API) converges on `/proxy/*` endpoints
2. **Centralized Fix**: Protecting `/proxy/*` with subscription validation closes gaps in flows 2, 3, 4, and 6 simultaneously
3. **Admin Bypass**: Cloud CLI admin tasks use separate `/tenants/*` endpoints with admin-only access (no subscription needed)
4. **Defense in Depth**: `/tenant/mount/*` endpoints also protected for CLI bisync operations
### Architecture Benefits
The `/proxy` layer serves as the **single centralized authorization point** for all user access:
- ✅ One place to validate JWT tokens
- ✅ One place to check subscription status
- ✅ One place to handle tenant routing
- ✅ Protects Web App, MCP, CLI, and Direct API simultaneously
This architecture makes the fix comprehensive and maintainable.
## How (High Level)
### Option A: Database Subscription Check (Recommended)
**Approach**: Add FastAPI dependency that validates subscription status from database before allowing access.
**Implementation**:
1. **Create Subscription Validation Dependency** (`deps.py`)
```python
async def get_authorized_cli_user_profile(
credentials: Annotated[HTTPAuthorizationCredentials, Depends(security)],
session: DatabaseSessionDep,
user_profile_repo: UserProfileRepositoryDep,
subscription_service: SubscriptionServiceDep,
) -> UserProfile:
"""
Hybrid authentication with subscription validation for CLI access.
Validates JWT (WorkOS or AuthKit) and checks for active subscription.
Returns UserProfile if both checks pass.
"""
# Try WorkOS JWT first (faster validation path)
try:
user_context = await validate_workos_jwt(credentials.credentials)
except HTTPException:
# Fall back to AuthKit JWT validation
try:
user_context = await validate_authkit_jwt(credentials.credentials)
except HTTPException as e:
raise HTTPException(
status_code=401,
detail="Invalid JWT token. Authentication required.",
) from e
# Check subscription status
has_subscription = await subscription_service.check_user_has_active_subscription(
session, user_context.workos_user_id
)
if not has_subscription:
raise HTTPException(
status_code=403,
detail={
"error": "subscription_required",
"message": "Active subscription required for CLI access",
"subscribe_url": "https://basicmemory.com/subscribe"
}
)
# Look up and return user profile
user_profile = await user_profile_repo.get_user_profile_by_workos_user_id(
session, user_context.workos_user_id
)
if not user_profile:
raise HTTPException(401, detail="User profile not found")
return user_profile
```
```python
AuthorizedCLIUserProfileDep = Annotated[UserProfile, Depends(get_authorized_cli_user_profile)]
```
2. **Add Subscription Check Method** (`subscription_service.py`)
```python
async def check_user_has_active_subscription(
self, session: AsyncSession, workos_user_id: str
) -> bool:
"""Check if user has active subscription."""
# Use existing repository method to get subscription by workos_user_id
# This joins UserProfile -> Subscription in a single query
subscription = await self.subscription_repository.get_subscription_by_workos_user_id(
session, workos_user_id
)
return subscription is not None and subscription.status == "active"
```
3. **Protect Endpoints** (Replace `CurrentUserProfileHybridJwtDep` with `AuthorizedCLIUserProfileDep`)
```python
# Before
@router.get("/mount/info")
async def get_mount_info(
user_profile: CurrentUserProfileHybridJwtDep,
session: DatabaseSessionDep,
):
tenant_id = user_profile.tenant_id
...
# After
@router.get("/mount/info")
async def get_mount_info(
user_profile: AuthorizedCLIUserProfileDep, # Now includes subscription check
session: DatabaseSessionDep,
):
tenant_id = user_profile.tenant_id # No changes needed to endpoint logic
...
```
4. **Update CLI Error Handling**
```python
# In core_commands.py login()
try:
success = await auth.login()
if success:
# Test subscription by calling protected endpoint
await make_api_request("GET", f"{host_url}/tenant/mount/info")
except CloudAPIError as e:
if e.status_code == 403 and e.detail.get("error") == "subscription_required":
console.print("[red]Subscription required[/red]")
console.print(f"Subscribe at: {e.detail['subscribe_url']}")
raise typer.Exit(1)
```
**Pros**:
- Simple to implement
- Fast (single database query)
- Clear error messages
- Works with existing subscription flow
**Cons**:
- Database is source of truth (could get out of sync with Polar)
- Adds one extra subscription lookup query per request (lightweight JOIN query)
### Option B: WorkOS Organizations
**Approach**: Add users to "beta-users" organization in WorkOS after subscription creation, validate org membership via JWT claims.
**Implementation**:
1. After Polar subscription webhook, add user to WorkOS org via API
2. Validate `org_id` claim in JWT matches authorized org
3. Use existing `get_admin_workos_jwt` pattern
**Pros**:
- WorkOS as single source of truth
- No database queries needed
- More secure (harder to bypass)
**Cons**:
- More complex (requires WorkOS API integration)
- Requires managing WorkOS org membership
- Less control over error messages
- Additional API calls during registration
### Recommendation
**Start with Option A (Database Check)** for:
- Faster implementation
- Clearer error messages
- Easier testing
- Existing subscription infrastructure
**Consider Option B later** if:
- Need tighter security
- Want to reduce database dependency
- Scale requires fewer database queries
## How to Evaluate
### Success Criteria
**1. Unauthorized Users Blocked**
- [ ] User without subscription cannot complete `bm cloud login`
- [ ] User without subscription receives clear error with subscribe link
- [ ] User without subscription cannot run `bm cloud setup`
- [ ] User without subscription cannot run `bm sync` in cloud mode
**2. Authorized Users Work**
- [ ] User with active subscription can login successfully
- [ ] User with active subscription can setup bisync
- [ ] User with active subscription can sync files
- [ ] User with active subscription can use all MCP tools via proxy
**3. Subscription State Changes**
- [ ] Expired subscription blocks access with clear error
- [ ] Renewed subscription immediately restores access
- [ ] Cancelled subscription blocks access after grace period
**4. Error Messages**
- [ ] 403 errors include "subscription_required" error code
- [ ] Error messages include subscribe URL
- [ ] CLI displays user-friendly messages
- [ ] Errors logged appropriately for debugging
**5. No Regressions**
- [ ] Web app login/subscription flow unaffected
- [ ] Admin endpoints still work (bypass check)
- [ ] Tenant provisioning workflow unchanged
- [ ] Performance not degraded
### Test Cases
**Manual Testing**:
```bash
# Test 1: Unauthorized user
1. Create new WorkOS account (no subscription)
2. Run `bm cloud login`
3. Verify: Login succeeds but shows subscription required error
4. Verify: Cannot run `bm cloud setup`
5. Verify: Clear error message with subscribe link
# Test 2: Authorized user
1. Use account with active Polar subscription
2. Run `bm cloud login`
3. Verify: Login succeeds without errors
4. Run `bm cloud setup`
5. Verify: Setup completes successfully
6. Run `bm sync`
7. Verify: Sync works normally
# Test 3: Subscription expiration
1. Use account with active subscription
2. Manually expire subscription in database
3. Run `bm cloud login`
4. Verify: Blocked with clear error
5. Renew subscription
6. Run `bm cloud login` again
7. Verify: Access restored
```
**Automated Tests**:
```python
# Test subscription validation dependency
async def test_authorized_user_allowed(
db_session,
user_profile_repo,
subscription_service,
mock_jwt_credentials
):
# Create user with active subscription
user_profile = await create_user_with_subscription(db_session, status="active")
# Mock JWT credentials for the user
credentials = mock_jwt_credentials(user_profile.workos_user_id)
# Should not raise exception
result = await get_authorized_cli_user_profile(
credentials, db_session, user_profile_repo, subscription_service
)
assert result.id == user_profile.id
assert result.workos_user_id == user_profile.workos_user_id
async def test_unauthorized_user_blocked(
db_session,
user_profile_repo,
subscription_service,
mock_jwt_credentials
):
# Create user without subscription
user_profile = await create_user_without_subscription(db_session)
credentials = mock_jwt_credentials(user_profile.workos_user_id)
# Should raise 403
with pytest.raises(HTTPException) as exc:
await get_authorized_cli_user_profile(
credentials, db_session, user_profile_repo, subscription_service
)
assert exc.value.status_code == 403
assert exc.value.detail["error"] == "subscription_required"
async def test_inactive_subscription_blocked(
db_session,
user_profile_repo,
subscription_service,
mock_jwt_credentials
):
# Create user with cancelled/inactive subscription
user_profile = await create_user_with_subscription(db_session, status="cancelled")
credentials = mock_jwt_credentials(user_profile.workos_user_id)
# Should raise 403
with pytest.raises(HTTPException) as exc:
await get_authorized_cli_user_profile(
credentials, db_session, user_profile_repo, subscription_service
)
assert exc.value.status_code == 403
assert exc.value.detail["error"] == "subscription_required"
```
## Implementation Tasks
### Phase 1: Cloud Service (basic-memory-cloud)
#### Task 1.1: Add subscription check method to SubscriptionService ✅
**File**: `apps/cloud/src/basic_memory_cloud/services/subscription_service.py`
- [x] Add method `check_subscription(session: AsyncSession, workos_user_id: str) -> bool`
- [x] Use existing `self.subscription_repository.get_subscription_by_workos_user_id(session, workos_user_id)`
- [x] Check both `status == "active"` AND `current_period_end >= now()`
- [x] Log both values when check fails
- [x] Add docstring explaining the method
- [x] Run `just typecheck` to verify types
**Actual implementation**:
```python
async def check_subscription(
self, session: AsyncSession, workos_user_id: str
) -> bool:
"""Check if user has active subscription with valid period."""
subscription = await self.subscription_repository.get_subscription_by_workos_user_id(
session, workos_user_id
)
if subscription is None:
return False
if subscription.status != "active":
logger.warning("Subscription inactive", workos_user_id=workos_user_id,
status=subscription.status, current_period_end=subscription.current_period_end)
return False
now = datetime.now(timezone.utc)
if subscription.current_period_end is None or subscription.current_period_end < now:
logger.warning("Subscription expired", workos_user_id=workos_user_id,
status=subscription.status, current_period_end=subscription.current_period_end)
return False
return True
```
#### Task 1.2: Add subscription validation dependency ✅
**File**: `apps/cloud/src/basic_memory_cloud/deps.py`
- [x] Import necessary types at top of file (if not already present)
- [x] Add `authorized_user_profile()` async function
- [x] Implement hybrid JWT validation (WorkOS first, AuthKit fallback)
- [x] Add subscription check using `subscription_service.check_subscription()`
- [x] Raise `HTTPException(403)` with structured error detail if no active subscription
- [x] Look up and return `UserProfile` after validation
- [x] Add `AuthorizedUserProfileDep` type annotation
- [x] Use `settings.subscription_url` from config (env var)
- [x] Run `just typecheck` to verify types
**Expected code**:
```python
async def get_authorized_cli_user_profile(
credentials: Annotated[HTTPAuthorizationCredentials, Depends(security)],
session: DatabaseSessionDep,
user_profile_repo: UserProfileRepositoryDep,
subscription_service: SubscriptionServiceDep,
) -> UserProfile:
"""
Hybrid authentication with subscription validation for CLI access.
Validates JWT (WorkOS or AuthKit) and checks for active subscription.
Returns UserProfile if both checks pass.
Raises:
HTTPException(401): Invalid JWT token
HTTPException(403): No active subscription
"""
# Try WorkOS JWT first (faster validation path)
try:
user_context = await validate_workos_jwt(credentials.credentials)
except HTTPException:
# Fall back to AuthKit JWT validation
try:
user_context = await validate_authkit_jwt(credentials.credentials)
except HTTPException as e:
raise HTTPException(
status_code=401,
detail="Invalid JWT token. Authentication required.",
) from e
# Check subscription status
has_subscription = await subscription_service.check_user_has_active_subscription(
session, user_context.workos_user_id
)
if not has_subscription:
logger.warning(
"CLI access denied: no active subscription",
workos_user_id=user_context.workos_user_id,
)
raise HTTPException(
status_code=403,
detail={
"error": "subscription_required",
"message": "Active subscription required for CLI access",
"subscribe_url": "https://basicmemory.com/subscribe"
}
)
# Look up and return user profile
user_profile = await user_profile_repo.get_user_profile_by_workos_user_id(
session, user_context.workos_user_id
)
if not user_profile:
logger.error(
"User profile not found after successful auth",
workos_user_id=user_context.workos_user_id,
)
raise HTTPException(401, detail="User profile not found")
logger.info(
"CLI access granted",
workos_user_id=user_context.workos_user_id,
user_profile_id=str(user_profile.id),
)
return user_profile
AuthorizedCLIUserProfileDep = Annotated[UserProfile, Depends(get_authorized_cli_user_profile)]
```
#### Task 1.3: Protect tenant mount endpoints ✅
**File**: `apps/cloud/src/basic_memory_cloud/api/tenant_mount.py`
- [x] Update import: add `AuthorizedUserProfileDep` from `..deps`
- [x] Replace `user_profile: CurrentUserProfileHybridJwtDep` with `user_profile: AuthorizedUserProfileDep` in:
- [x] `get_tenant_mount_info()` (line ~23)
- [x] `create_tenant_mount_credentials()` (line ~88)
- [x] `revoke_tenant_mount_credentials()` (line ~244)
- [x] `list_tenant_mount_credentials()` (line ~326)
- [x] Verify no other code changes needed (parameter name and usage stays the same)
- [x] Run `just typecheck` to verify types
#### Task 1.4: Protect proxy endpoints ✅
**File**: `apps/cloud/src/basic_memory_cloud/api/proxy.py`
- [x] Update import: add `AuthorizedUserProfileDep` from `..deps`
- [x] Replace `user_profile: CurrentUserProfileHybridJwtDep` with `user_profile: AuthorizedUserProfileDep` in:
- [x] `check_tenant_health()` (line ~21)
- [x] `proxy_to_tenant()` (line ~63)
- [x] Verify no other code changes needed (parameter name and usage stays the same)
- [x] Run `just typecheck` to verify types
**Why Keep /proxy Architecture:**
The proxy layer is valuable because it:
1. **Centralizes authorization** - Single place for JWT + subscription validation (closes both CLI and MCP auth gaps)
2. **Handles tenant routing** - Maps tenant_id → fly_app_name without exposing infrastructure details
3. **Abstracts infrastructure** - MCP and CLI don't need to know about Fly.io naming conventions
4. **Enables features** - Can add rate limiting, caching, request logging, etc. at proxy layer
5. **Supports both flows** - CLI tools and MCP tools both use /proxy endpoints
The extra HTTP hop is minimal (< 10ms) and worth it for architectural benefits.
**Performance Note:** Cloud app has Redis available - can cache subscription status to reduce database queries if needed. Initial implementation uses direct database query (simple, acceptable performance ~5-10ms).
#### Task 1.5: Add unit tests for subscription service
**File**: `apps/cloud/tests/services/test_subscription_service.py` (create if doesn't exist)
- [ ] Create test file if it doesn't exist
- [ ] Add test: `test_check_user_has_active_subscription_returns_true_for_active()`
- Create user with active subscription
- Call `check_user_has_active_subscription()`
- Assert returns `True`
- [ ] Add test: `test_check_user_has_active_subscription_returns_false_for_pending()`
- Create user with pending subscription
- Assert returns `False`
- [ ] Add test: `test_check_user_has_active_subscription_returns_false_for_cancelled()`
- Create user with cancelled subscription
- Assert returns `False`
- [ ] Add test: `test_check_user_has_active_subscription_returns_false_for_no_subscription()`
- Create user without subscription
- Assert returns `False`
- [ ] Run `just test` to verify tests pass
#### Task 1.6: Add integration tests for dependency
**File**: `apps/cloud/tests/test_deps.py` (create if doesn't exist)
- [ ] Create test file if it doesn't exist
- [ ] Add fixtures for mocking JWT credentials
- [ ] Add test: `test_authorized_cli_user_profile_with_active_subscription()`
- Mock valid JWT + active subscription
- Call dependency
- Assert returns UserProfile
- [ ] Add test: `test_authorized_cli_user_profile_without_subscription_raises_403()`
- Mock valid JWT + no subscription
- Assert raises HTTPException(403) with correct error detail
- [ ] Add test: `test_authorized_cli_user_profile_with_inactive_subscription_raises_403()`
- Mock valid JWT + cancelled subscription
- Assert raises HTTPException(403)
- [ ] Add test: `test_authorized_cli_user_profile_with_invalid_jwt_raises_401()`
- Mock invalid JWT
- Assert raises HTTPException(401)
- [ ] Run `just test` to verify tests pass
#### Task 1.7: Deploy and verify cloud service
- [ ] Run `just check` to verify all quality checks pass
- [ ] Commit changes with message: "feat: add subscription validation to CLI endpoints"
- [ ] Deploy to preview environment: `flyctl deploy --config apps/cloud/fly.toml`
- [ ] Test manually:
- [ ] Call `/tenant/mount/info` with valid JWT but no subscription → expect 403
- [ ] Call `/tenant/mount/info` with valid JWT and active subscription → expect 200
- [ ] Verify error response structure matches spec
### Phase 2: CLI (basic-memory)
#### Task 2.1: Review and understand CLI authentication flow
**Files**: `src/basic_memory/cli/commands/cloud/`
- [ ] Read `core_commands.py` to understand current login flow
- [ ] Read `api_client.py` to understand current error handling
- [ ] Identify where 403 errors should be caught
- [ ] Identify what error messages should be displayed
- [ ] Document current behavior in spec if needed
#### Task 2.2: Update API client error handling
**File**: `src/basic_memory/cli/commands/cloud/api_client.py`
- [ ] Add custom exception class `SubscriptionRequiredError` (or similar)
- [ ] Update HTTP error handling to parse 403 responses
- [ ] Extract `error`, `message`, and `subscribe_url` from error detail
- [ ] Raise specific exception for subscription_required errors
- [ ] Run `just typecheck` in basic-memory repo to verify types
#### Task 2.3: Update CLI login command error handling
**File**: `src/basic_memory/cli/commands/cloud/core_commands.py`
- [ ] Import the subscription error exception
- [ ] Wrap login flow with try/except for subscription errors
- [ ] Display user-friendly error message with rich console
- [ ] Show subscribe URL prominently
- [ ] Provide actionable next steps
- [ ] Run `just typecheck` to verify types
**Expected error handling**:
```python
try:
# Existing login logic
success = await auth.login()
if success:
# Test access to protected endpoint
await api_client.test_connection()
except SubscriptionRequiredError as e:
console.print("\n[red]✗ Subscription Required[/red]\n")
console.print(f"[yellow]{e.message}[/yellow]\n")
console.print(f"Subscribe at: [blue underline]{e.subscribe_url}[/blue underline]\n")
console.print("[dim]Once you have an active subscription, run [bold]bm cloud login[/bold] again.[/dim]")
raise typer.Exit(1)
```
#### Task 2.4: Update CLI tests
**File**: `tests/cli/test_cloud_commands.py`
- [ ] Add test: `test_login_without_subscription_shows_error()`
- Mock 403 subscription_required response
- Call login command
- Assert error message displayed
- Assert subscribe URL shown
- [ ] Add test: `test_login_with_subscription_succeeds()`
- Mock successful authentication + subscription check
- Call login command
- Assert success message
- [ ] Run `just test` to verify tests pass
#### Task 2.5: Update CLI documentation
**File**: `docs/cloud-cli.md` (in basic-memory-docs repo)
- [ ] Add "Prerequisites" section if not present
- [ ] Document subscription requirement
- [ ] Add "Troubleshooting" section
- [ ] Document "Subscription Required" error
- [ ] Provide subscribe URL
- [ ] Add FAQ entry about subscription errors
- [ ] Build docs locally to verify formatting
### Phase 3: End-to-End Testing
#### Task 3.1: Create test user accounts
**Prerequisites**: Access to WorkOS admin and database
- [ ] Create test user WITHOUT subscription:
- [ ] Sign up via WorkOS AuthKit
- [ ] Get workos_user_id from database
- [ ] Verify no subscription record exists
- [ ] Save credentials for testing
- [ ] Create test user WITH active subscription:
- [ ] Sign up via WorkOS AuthKit
- [ ] Create subscription via Polar or dev endpoint
- [ ] Verify subscription.status = "active" in database
- [ ] Save credentials for testing
#### Task 3.2: Manual testing - User without subscription
**Environment**: Preview/staging deployment
- [ ] Run `bm cloud login` with no-subscription user
- [ ] Verify: Login shows "Subscription Required" error
- [ ] Verify: Subscribe URL is displayed
- [ ] Verify: Cannot run `bm cloud setup`
- [ ] Verify: Cannot call `/tenant/mount/info` directly via curl
- [ ] Document any issues found
#### Task 3.3: Manual testing - User with active subscription
**Environment**: Preview/staging deployment
- [ ] Run `bm cloud login` with active-subscription user
- [ ] Verify: Login succeeds without errors
- [ ] Verify: Can run `bm cloud setup`
- [ ] Verify: Can call `/tenant/mount/info` successfully
- [ ] Verify: Can call `/proxy/*` endpoints successfully
- [ ] Document any issues found
#### Task 3.4: Test subscription state transitions
**Environment**: Preview/staging deployment + database access
- [ ] Start with active subscription user
- [ ] Verify: All operations work
- [ ] Update subscription.status to "cancelled" in database
- [ ] Verify: Login now shows "Subscription Required" error
- [ ] Verify: Existing tokens are rejected with 403
- [ ] Update subscription.status back to "active"
- [ ] Verify: Access restored immediately
- [ ] Document any issues found
#### Task 3.5: Integration test suite
**File**: `apps/cloud/tests/integration/test_cli_subscription_flow.py` (create if doesn't exist)
- [ ] Create integration test file
- [ ] Add test: `test_cli_flow_without_subscription()`
- Simulate full CLI flow without subscription
- Assert 403 at appropriate points
- [ ] Add test: `test_cli_flow_with_active_subscription()`
- Simulate full CLI flow with active subscription
- Assert all operations succeed
- [ ] Add test: `test_subscription_expiration_blocks_access()`
- Start with active subscription
- Change status to cancelled
- Assert access denied
- [ ] Run tests in CI/CD pipeline
- [ ] Document test coverage
#### Task 3.6: Load/performance testing (optional)
**Environment**: Staging environment
- [ ] Test subscription check performance under load
- [ ] Measure latency added by subscription check
- [ ] Verify database query performance
- [ ] Document any performance concerns
- [ ] Optimize if needed
## Implementation Summary Checklist
Use this high-level checklist to track overall progress:
### Phase 1: Cloud Service 🔄
- [x] Add subscription check method to SubscriptionService
- [x] Add subscription validation dependency to deps.py
- [x] Add subscription_url config (env var)
- [x] Protect tenant mount endpoints (4 endpoints)
- [x] Protect proxy endpoints (2 endpoints)
- [ ] Add unit tests for subscription service
- [ ] Add integration tests for dependency
- [ ] Deploy and verify cloud service
### Phase 2: CLI Updates 🔄
- [ ] Review CLI authentication flow
- [ ] Update API client error handling
- [ ] Update CLI login command error handling
- [ ] Add CLI tests
- [ ] Update CLI documentation
### Phase 3: End-to-End Testing 🧪
- [ ] Create test user accounts
- [ ] Manual testing - user without subscription
- [ ] Manual testing - user with active subscription
- [ ] Test subscription state transitions
- [ ] Integration test suite
- [ ] Load/performance testing (optional)
## Questions to Resolve
### Resolved ✅
1. **Admin Access**
- ✅ **Decision**: Admin users bypass subscription check
- **Rationale**: Admin endpoints already use `AdminUserHybridDep`, which is separate from CLI user endpoints
- **Implementation**: No changes needed to admin endpoints
2. **Subscription Check Implementation**
- ✅ **Decision**: Use Option A (Database Check)
- **Rationale**: Simpler, faster to implement, works with existing infrastructure
- **Implementation**: Single JOIN query via `get_subscription_by_workos_user_id()`
3. **Dependency Return Type**
- ✅ **Decision**: Return `UserProfile` (not `UserContext`)
- **Rationale**: Drop-in compatibility with existing endpoints, no refactoring needed
- **Implementation**: `AuthorizedCLIUserProfileDep` returns `UserProfile`
### To Be Resolved ⏳
1. **Subscription Check Frequency**
- **Options**:
- Check on every API call (slower, more secure) ✅ **RECOMMENDED**
- Cache subscription status (faster, risk of stale data)
- Check only on login/setup (fast, but allows expired subscriptions temporarily)
- **Recommendation**: Check on every call via dependency injection (simple, secure, acceptable performance)
- **Impact**: ~5-10ms per request (single indexed JOIN query)
2. **Grace Period**
- **Options**:
- No grace period - immediate block when status != "active" ✅ **RECOMMENDED**
- 7-day grace period after period_end
- 14-day grace period after period_end
- **Recommendation**: No grace period initially, add later if needed based on customer feedback
- **Implementation**: Check `subscription.status == "active"` only (ignore period_end initially)
3. **Subscription Expiration Handling**
- **Question**: Should we check `current_period_end < now()` in addition to `status == "active"`?
- **Options**:
- Only check status field (rely on Polar webhooks to update status) ✅ **RECOMMENDED**
- Check both status and current_period_end (more defensive)
- **Recommendation**: Only check status field, assume Polar webhooks keep it current
- **Risk**: If webhooks fail, expired subscriptions might retain access until webhook succeeds
4. **Subscribe URL**
- **Question**: What's the actual subscription URL?
- **Current**: Spec uses `https://basicmemory.com/subscribe`
- **Action Required**: Verify correct URL before implementation
5. **Dev Mode / Testing Bypass**
- **Question**: Support bypass for development/testing?
- **Options**:
- Environment variable: `DISABLE_SUBSCRIPTION_CHECK=true`
- Always enforce (more realistic testing) ✅ **RECOMMENDED**
- **Recommendation**: No bypass - use test users with real subscriptions for realistic testing
- **Implementation**: Create dev endpoint to activate subscriptions for testing
## Related Specs
- SPEC-9: Multi-Project Bidirectional Sync Architecture (CLI affected by this change)
- SPEC-8: TigrisFS Integration (Mount endpoints protected)
## Notes
- This spec prioritizes security over convenience - better to block unauthorized access than risk revenue loss
- Clear error messages are critical - users should understand why they're blocked and how to resolve it
- Consider adding telemetry to track subscription_required errors for monitoring signup conversion
@@ -1,210 +0,0 @@
---
title: 'SPEC-14: Cloud Git Versioning & GitHub Backup'
type: spec
permalink: specs/spec-14-cloud-git-versioning
tags:
- git
- github
- backup
- versioning
- cloud
related:
- specs/spec-9-multi-project-bisync
- specs/spec-9-follow-ups-conflict-sync-and-observability
status: deferred
---
# SPEC-14: Cloud Git Versioning & GitHub Backup
**Status: DEFERRED** - Postponed until multi-user/teams feature development. Using S3 versioning (SPEC-9.1) for v1 instead.
## Why Deferred
**Original goals can be met with simpler solutions:**
- Version history → **S3 bucket versioning** (automatic, zero config)
- Offsite backup → **Tigris global replication** (built-in)
- Restore capability → **S3 version restore** (`bm cloud restore --version-id`)
- Collaboration → **Deferred to teams/multi-user feature** (not v1 requirement)
**Complexity vs value trade-off:**
- Git integration adds: committer service, puller service, webhooks, LFS, merge conflicts
- Risk: Loop detection between Git ↔ rclone bisync ↔ local edits
- S3 versioning gives 80% of value with 5% of complexity
**When to revisit:**
- Teams/multi-user features (PR-based collaboration workflow)
- User requests for commit messages and branch-based workflows
- Need for fine-grained audit trail beyond S3 object metadata
---
## Original Specification (for reference)
## Why
Early access users want **transparent version history**, easy **offsite backup**, and a familiar **restore/branching** workflow. Git/GitHub integration would provide:
- Auditable history of every change (who/when/why)
- Branches/PRs for review and collaboration
- Offsite private backup under the user's control
- Escape hatch: users can always `git clone` their knowledge base
**Note:** These goals are now addressed via S3 versioning (SPEC-9.1) for single-user use case.
## Goals
- **Transparent**: Users keep using Basic Memory; Git runs behind the scenes.
- **Private**: Push to a **private GitHub repo** that the user owns (or tenant org).
- **Reliable**: No data loss, deterministic mapping of filesystem ↔ Git.
- **Composable**: Plays nicely with SPEC9 bisync and upcoming conflict features (SPEC9 FollowUps).
**NonGoals (for v1):**
- Finegrained perfile encryption in Git history (can be layered later).
- Large media optimization beyond Git LFS defaults.
## User Stories
1. *As a user*, I connect my GitHub and choose a private backup repo.
2. *As a user*, every change I make in cloud (or via bisync) is **committed** and **pushed** automatically.
3. *As a user*, I can **restore** a file/folder/project to a prior version.
4. *As a power user*, I can **git pull/push** directly to collaborate outside the app.
5. *As an admin*, I can enforce repo ownership (tenant org) and leastprivilege scopes.
## Scope
- **In scope:** Full repo backup of `/app/data/` (all projects) with optional selective subpaths.
- **Out of scope (v1):** Partial shallow mirrors; encrypted Git; crossprovider SCM (GitLab/Bitbucket).
## Architecture
### Topology
- **Authoritative working tree**: `/app/data/` (bucket mount) remains the source of truth (SPEC9).
- **Bare repo** lives alongside: `/app/git/${tenant}/knowledge.git` (serverside).
- **Mirror remote**: `github.com/<owner>/<repo>.git` (private).
```mermaid
flowchart LR
A[/Users & Agents/] -->|writes/edits| B[/app/data/]
B -->|file events| C[Committer Service]
C -->|git commit| D[(Bare Repo)]
D -->|push| E[(GitHub Private Repo)]
E -->|webhook (push)| F[Puller Service]
F -->|git pull/merge| D
D -->|checkout/merge| B
```
### Services
- **Committer Service** (daemon):
- Watches `/app/data/` for changes (inotify/poll)
- Batches changes (debounce e.g. 25s)
- Writes `.bmmeta` (if present) into commit message trailer (see FollowUps)
- `git add -A && git commit -m "chore(sync): <summary>
BM-Meta: <json>"`
- Periodic `git push` to GitHub mirror (configurable interval)
- **Puller Service** (webhook target):
- Receives GitHub webhook (push) → `git fetch`
- **Fastforward** merges to `main` only; reject nonFF unless policy allows
- Applies changes back to `/app/data/` via clean checkout
- Emits sync events for Basic Memory indexers
### Auth & Security
- **GitHub App** (recommended): minimal scopes: `contents:read/write`, `metadata:read`, webhook.
- Tenantscoped installation; repo created in user account or tenant org.
- Tokens stored in KMS/secret manager; rotated automatically.
- Optional policy: allow only **FF merges** on `main`; nonFF requires PR.
### Repo Layout
- **Monorepo** (default): one repo per tenant mirrors `/app/data/` with subfolders per project.
- Optional multirepo mode (later): one repo per project.
### File Handling
- Honor `.gitignore` generated from `.bmignore.rclone` + BM defaults (cache, temp, state).
- **Git LFS** for large binaries (images, media) — auto track by extension/size threshold.
- Normalize newline + Unicode (aligns with FollowUps).
### Conflict Model
- **Primary concurrency**: SPEC9 FollowUps (`.bmmeta`, conflict copies) stays the first line of defense.
- **Git merges** are a **secondary** mechanism:
- Server only automerges **text** conflicts when trivial (FF or clean 3way).
- Otherwise, create `name (conflict from <branch>, <ts>).md` and surface via events.
### Data Flow vs Bisync
- Bisync (rclone) continues between local sync dir ↔ bucket.
- Git sits **cloudside** between bucket and GitHub.
- On **pull** from GitHub → files written to `/app/data/` → picked up by indexers & eventually by bisync back to users.
## CLI & UX
New commands (cloud mode):
- `bm cloud git connect` — Launch GitHub App installation; create private repo; store installation id.
- `bm cloud git status` — Show connected repo, last push time, last webhook delivery, pending commits.
- `bm cloud git push` — Manual push (rarely needed).
- `bm cloud git pull` — Manual pull/FF (admin only by default).
- `bm cloud snapshot -m "message"` — Create a tagged pointintime snapshot (git tag).
- `bm restore <path> --to <commit|tag>` — Restore file/folder/project to prior version.
Settings:
- `bm config set git.autoPushInterval=5s`
- `bm config set git.lfs.sizeThreshold=10MB`
- `bm config set git.allowNonFF=false`
## Migration & Backfill
- On connect, if repo empty: initial commit of entire `/app/data/`.
- If repo has content: require **onetime import** path (clone to staging, reconcile, choose direction).
## Edge Cases
- Massive deletes: gated by SPEC9 `max_delete` **and** Git prepush hook checks.
- Case changes and rename detection: rely on git rename heuristics + FollowUps move hints.
- Secrets: default ignore common secret patterns; allow custom deny list.
## Telemetry & Observability
- Emit `git_commit`, `git_push`, `git_pull`, `git_conflict` events with correlation IDs.
- `bm sync --report` extended with Git stats (commit count, delta bytes, push latency).
## Phased Plan
### Phase 0 — Prototype (1 sprint)
- Server: bare repo init + simple committer (batch every 10s) + manual GitHub token.
- CLI: `bm cloud git connect --token <PAT>` (devonly)
- Success: edits in `/app/data/` appear in GitHub within 30s.
### Phase 1 — GitHub App & Webhooks (12 sprints)
- Switch to GitHub App installs; create private repo; store installation id.
- Committer hardened (debounce 25s, backoff, retries).
- Puller service with webhook → FF merge → checkout to `/app/data/`.
- LFS autotrack + `.gitignore` generation.
- CLI surfaces status + logs.
### Phase 2 — Restore & Snapshots (1 sprint)
- `bm restore` for file/folder/project with dryrun.
- `bm cloud snapshot` tags + list/inspect.
- Policy: PRonly nonFF, admin override.
### Phase 3 — Selective & MultiRepo (nicetohave)
- Include/exclude projects; optional perproject repos.
- Advanced policies (branch protections, required reviews).
## Acceptance Criteria
- Changes to `/app/data/` are committed and pushed automatically within configurable interval (default ≤5s).
- GitHub webhook pull results in updated files in `/app/data/` (FFonly by default).
- LFS configured and functioning; large files don't bloat history.
- `bm cloud git status` shows connected repo and last push/pull times.
- `bm restore` restores a file/folder to a prior commit with a clear audit trail.
- Endtoend works alongside SPEC9 bisync without loops or data loss.
## Risks & Mitigations
- **Loop risk (Git ↔ Bisync)**: Writes to `/app/data/` → bisync → local → user edits → back again. *Mitigation*: Debounce, commit squashing, idempotent `.bmmeta` versioning, and watch exclusion windows during pull.
- **Repo bloat**: Lots of binary churn. *Mitigation*: default LFS, size threshold, optional mediaonly repo later.
- **Security**: Token leakage. *Mitigation*: GitHub App with shortlived tokens, KMS storage, scoped permissions.
- **Merge complexity**: Nontrivial conflicts. *Mitigation*: prefer FF; otherwise conflict copies + events; require PR for nonFF.
## Open Questions
- Do we default to **monorepo** per tenant, or offer projectperrepo at connect time?
- Should `restore` write to a branch and open a PR, or directly modify `main`?
- How do we expose Git history in UI (timeline view) without users dropping to CLI?
## Appendix: Sample Config
```json
{
"git": {
"enabled": true,
"repo": "https://github.com/<owner>/<repo>.git",
"autoPushInterval": "5s",
"allowNonFF": false,
"lfs": { "sizeThreshold": 10485760 }
}
}
```
@@ -1,210 +0,0 @@
---
title: 'SPEC-14: Cloud Git Versioning & GitHub Backup'
type: spec
permalink: specs/spec-14-cloud-git-versioning
tags:
- git
- github
- backup
- versioning
- cloud
related:
- specs/spec-9-multi-project-bisync
- specs/spec-9-follow-ups-conflict-sync-and-observability
status: deferred
---
# SPEC-14: Cloud Git Versioning & GitHub Backup
**Status: DEFERRED** - Postponed until multi-user/teams feature development. Using S3 versioning (SPEC-9.1) for v1 instead.
## Why Deferred
**Original goals can be met with simpler solutions:**
- Version history → **S3 bucket versioning** (automatic, zero config)
- Offsite backup → **Tigris global replication** (built-in)
- Restore capability → **S3 version restore** (`bm cloud restore --version-id`)
- Collaboration → **Deferred to teams/multi-user feature** (not v1 requirement)
**Complexity vs value trade-off:**
- Git integration adds: committer service, puller service, webhooks, LFS, merge conflicts
- Risk: Loop detection between Git ↔ rclone bisync ↔ local edits
- S3 versioning gives 80% of value with 5% of complexity
**When to revisit:**
- Teams/multi-user features (PR-based collaboration workflow)
- User requests for commit messages and branch-based workflows
- Need for fine-grained audit trail beyond S3 object metadata
---
## Original Specification (for reference)
## Why
Early access users want **transparent version history**, easy **offsite backup**, and a familiar **restore/branching** workflow. Git/GitHub integration would provide:
- Auditable history of every change (who/when/why)
- Branches/PRs for review and collaboration
- Offsite private backup under the user's control
- Escape hatch: users can always `git clone` their knowledge base
**Note:** These goals are now addressed via S3 versioning (SPEC-9.1) for single-user use case.
## Goals
- **Transparent**: Users keep using Basic Memory; Git runs behind the scenes.
- **Private**: Push to a **private GitHub repo** that the user owns (or tenant org).
- **Reliable**: No data loss, deterministic mapping of filesystem ↔ Git.
- **Composable**: Plays nicely with SPEC9 bisync and upcoming conflict features (SPEC9 FollowUps).
**NonGoals (for v1):**
- Finegrained perfile encryption in Git history (can be layered later).
- Large media optimization beyond Git LFS defaults.
## User Stories
1. *As a user*, I connect my GitHub and choose a private backup repo.
2. *As a user*, every change I make in cloud (or via bisync) is **committed** and **pushed** automatically.
3. *As a user*, I can **restore** a file/folder/project to a prior version.
4. *As a power user*, I can **git pull/push** directly to collaborate outside the app.
5. *As an admin*, I can enforce repo ownership (tenant org) and leastprivilege scopes.
## Scope
- **In scope:** Full repo backup of `/app/data/` (all projects) with optional selective subpaths.
- **Out of scope (v1):** Partial shallow mirrors; encrypted Git; crossprovider SCM (GitLab/Bitbucket).
## Architecture
### Topology
- **Authoritative working tree**: `/app/data/` (bucket mount) remains the source of truth (SPEC9).
- **Bare repo** lives alongside: `/app/git/${tenant}/knowledge.git` (serverside).
- **Mirror remote**: `github.com/<owner>/<repo>.git` (private).
```mermaid
flowchart LR
A[/Users & Agents/] -->|writes/edits| B[/app/data/]
B -->|file events| C[Committer Service]
C -->|git commit| D[(Bare Repo)]
D -->|push| E[(GitHub Private Repo)]
E -->|webhook (push)| F[Puller Service]
F -->|git pull/merge| D
D -->|checkout/merge| B
```
### Services
- **Committer Service** (daemon):
- Watches `/app/data/` for changes (inotify/poll)
- Batches changes (debounce e.g. 25s)
- Writes `.bmmeta` (if present) into commit message trailer (see FollowUps)
- `git add -A && git commit -m "chore(sync): <summary>
BM-Meta: <json>"`
- Periodic `git push` to GitHub mirror (configurable interval)
- **Puller Service** (webhook target):
- Receives GitHub webhook (push) → `git fetch`
- **Fastforward** merges to `main` only; reject nonFF unless policy allows
- Applies changes back to `/app/data/` via clean checkout
- Emits sync events for Basic Memory indexers
### Auth & Security
- **GitHub App** (recommended): minimal scopes: `contents:read/write`, `metadata:read`, webhook.
- Tenantscoped installation; repo created in user account or tenant org.
- Tokens stored in KMS/secret manager; rotated automatically.
- Optional policy: allow only **FF merges** on `main`; nonFF requires PR.
### Repo Layout
- **Monorepo** (default): one repo per tenant mirrors `/app/data/` with subfolders per project.
- Optional multirepo mode (later): one repo per project.
### File Handling
- Honor `.gitignore` generated from `.bmignore.rclone` + BM defaults (cache, temp, state).
- **Git LFS** for large binaries (images, media) — auto track by extension/size threshold.
- Normalize newline + Unicode (aligns with FollowUps).
### Conflict Model
- **Primary concurrency**: SPEC9 FollowUps (`.bmmeta`, conflict copies) stays the first line of defense.
- **Git merges** are a **secondary** mechanism:
- Server only automerges **text** conflicts when trivial (FF or clean 3way).
- Otherwise, create `name (conflict from <branch>, <ts>).md` and surface via events.
### Data Flow vs Bisync
- Bisync (rclone) continues between local sync dir ↔ bucket.
- Git sits **cloudside** between bucket and GitHub.
- On **pull** from GitHub → files written to `/app/data/` → picked up by indexers & eventually by bisync back to users.
## CLI & UX
New commands (cloud mode):
- `bm cloud git connect` — Launch GitHub App installation; create private repo; store installation id.
- `bm cloud git status` — Show connected repo, last push time, last webhook delivery, pending commits.
- `bm cloud git push` — Manual push (rarely needed).
- `bm cloud git pull` — Manual pull/FF (admin only by default).
- `bm cloud snapshot -m "message"` — Create a tagged pointintime snapshot (git tag).
- `bm restore <path> --to <commit|tag>` — Restore file/folder/project to prior version.
Settings:
- `bm config set git.autoPushInterval=5s`
- `bm config set git.lfs.sizeThreshold=10MB`
- `bm config set git.allowNonFF=false`
## Migration & Backfill
- On connect, if repo empty: initial commit of entire `/app/data/`.
- If repo has content: require **onetime import** path (clone to staging, reconcile, choose direction).
## Edge Cases
- Massive deletes: gated by SPEC9 `max_delete` **and** Git prepush hook checks.
- Case changes and rename detection: rely on git rename heuristics + FollowUps move hints.
- Secrets: default ignore common secret patterns; allow custom deny list.
## Telemetry & Observability
- Emit `git_commit`, `git_push`, `git_pull`, `git_conflict` events with correlation IDs.
- `bm sync --report` extended with Git stats (commit count, delta bytes, push latency).
## Phased Plan
### Phase 0 — Prototype (1 sprint)
- Server: bare repo init + simple committer (batch every 10s) + manual GitHub token.
- CLI: `bm cloud git connect --token <PAT>` (devonly)
- Success: edits in `/app/data/` appear in GitHub within 30s.
### Phase 1 — GitHub App & Webhooks (12 sprints)
- Switch to GitHub App installs; create private repo; store installation id.
- Committer hardened (debounce 25s, backoff, retries).
- Puller service with webhook → FF merge → checkout to `/app/data/`.
- LFS autotrack + `.gitignore` generation.
- CLI surfaces status + logs.
### Phase 2 — Restore & Snapshots (1 sprint)
- `bm restore` for file/folder/project with dryrun.
- `bm cloud snapshot` tags + list/inspect.
- Policy: PRonly nonFF, admin override.
### Phase 3 — Selective & MultiRepo (nicetohave)
- Include/exclude projects; optional perproject repos.
- Advanced policies (branch protections, required reviews).
## Acceptance Criteria
- Changes to `/app/data/` are committed and pushed automatically within configurable interval (default ≤5s).
- GitHub webhook pull results in updated files in `/app/data/` (FFonly by default).
- LFS configured and functioning; large files don't bloat history.
- `bm cloud git status` shows connected repo and last push/pull times.
- `bm restore` restores a file/folder to a prior commit with a clear audit trail.
- Endtoend works alongside SPEC9 bisync without loops or data loss.
## Risks & Mitigations
- **Loop risk (Git ↔ Bisync)**: Writes to `/app/data/` → bisync → local → user edits → back again. *Mitigation*: Debounce, commit squashing, idempotent `.bmmeta` versioning, and watch exclusion windows during pull.
- **Repo bloat**: Lots of binary churn. *Mitigation*: default LFS, size threshold, optional mediaonly repo later.
- **Security**: Token leakage. *Mitigation*: GitHub App with shortlived tokens, KMS storage, scoped permissions.
- **Merge complexity**: Nontrivial conflicts. *Mitigation*: prefer FF; otherwise conflict copies + events; require PR for nonFF.
## Open Questions
- Do we default to **monorepo** per tenant, or offer projectperrepo at connect time?
- Should `restore` write to a branch and open a PR, or directly modify `main`?
- How do we expose Git history in UI (timeline view) without users dropping to CLI?
## Appendix: Sample Config
```json
{
"git": {
"enabled": true,
"repo": "https://github.com/<owner>/<repo>.git",
"autoPushInterval": "5s",
"allowNonFF": false,
"lfs": { "sizeThreshold": 10485760 }
}
}
```
@@ -1,273 +0,0 @@
---
title: 'SPEC-15: Configuration Persistence via Tigris for Cloud Tenants'
type: spec
permalink: specs/spec-14-config-persistence-tigris
tags:
- persistence
- tigris
- multi-tenant
- infrastructure
- configuration
status: draft
---
# SPEC-15: Configuration Persistence via Tigris for Cloud Tenants
## Why
We need to persist Basic Memory configuration across Fly.io deployments without using persistent volumes or external databases.
**Current Problems:**
- `~/.basic-memory/config.json` lost on every deployment (project configuration)
- `~/.basic-memory/memory.db` lost on every deployment (search index)
- Persistent volumes break clean deployment workflow
- External databases (Turso) require per-tenant token management
**The Insight:**
The SQLite database is just an **index cache** of the markdown files. It can be rebuilt in seconds from the source markdown files in Tigris. Only the small `config.json` file needs true persistence.
**Solution:**
- Store `config.json` in Tigris bucket (persistent, small file)
- Rebuild `memory.db` on startup from markdown files (fast, ephemeral)
- No persistent volumes, no external databases, no token management
## What
Store Basic Memory configuration in the Tigris bucket and rebuild the database index on tenant machine startup.
**Affected Components:**
- `basic-memory/src/basic_memory/config.py` - Add configurable config directory
**Architecture:**
```bash
# Tigris Bucket (persistent, mounted at /app/data)
/app/data/
├── .basic-memory/
│ └── config.json # ← Project configuration (persistent, accessed via BASIC_MEMORY_CONFIG_DIR)
└── basic-memory/ # ← Markdown files (persistent, BASIC_MEMORY_HOME)
├── project1/
└── project2/
# Fly Machine (ephemeral)
/app/.basic-memory/
└── memory.db # ← Rebuilt on startup (fast local disk)
```
## How (High Level)
### 1. Add Configurable Config Directory to Basic Memory
Currently `ConfigManager` hardcodes `~/.basic-memory/config.json`. Add environment variable to override:
```python
# basic-memory/src/basic_memory/config.py
class ConfigManager:
"""Manages Basic Memory configuration."""
def __init__(self) -> None:
"""Initialize the configuration manager."""
home = os.getenv("HOME", Path.home())
if isinstance(home, str):
home = Path(home)
# Allow override via environment variable
if config_dir := os.getenv("BASIC_MEMORY_CONFIG_DIR"):
self.config_dir = Path(config_dir)
else:
self.config_dir = home / DATA_DIR_NAME
self.config_file = self.config_dir / CONFIG_FILE_NAME
# Ensure config directory exists
self.config_dir.mkdir(parents=True, exist_ok=True)
```
### 2. Rebuild Database on Startup
Basic Memory already has the sync functionality. Just ensure it runs on startup:
```python
# apps/api/src/basic_memory_cloud_api/main.py
@app.on_event("startup")
async def startup_sync():
"""Rebuild database index from Tigris markdown files."""
logger.info("Starting database rebuild from Tigris")
# Initialize file sync (rebuilds index from markdown files)
app_config = ConfigManager().config
await initialize_file_sync(app_config)
logger.info("Database rebuild complete")
```
### 3. Environment Configuration
```bash
# Machine environment variables
BASIC_MEMORY_CONFIG_DIR=/app/data/.basic-memory # Config read/written directly to Tigris
# memory.db stays in default location: /app/.basic-memory/memory.db (local ephemeral disk)
```
## Implementation Task List
### Phase 1: Basic Memory Changes ✅
- [x] Add `BASIC_MEMORY_CONFIG_DIR` environment variable support to `ConfigManager.__init__()`
- [x] Test config loading from custom directory
- [x] Update tests to verify custom config dir works
### Phase 2: Tigris Bucket Structure ✅
- [x] Ensure `.basic-memory/` directory exists in Tigris bucket on tenant creation
- ✅ ConfigManager auto-creates on first run, no explicit provisioning needed
- [x] Initialize `config.json` in Tigris on first tenant deployment
- ✅ ConfigManager creates config.json automatically in BASIC_MEMORY_CONFIG_DIR
- [x] Verify TigrisFS handles hidden directories correctly
- ✅ TigrisFS supports hidden directories (verified in SPEC-8)
### Phase 3: Deployment Integration ✅
- [x] Set `BASIC_MEMORY_CONFIG_DIR` environment variable in machine deployment
- ✅ Added to BasicMemoryMachineConfigBuilder in fly_schemas.py
- [x] Ensure database rebuild runs on machine startup via initialization sync
- ✅ sync_worker.py runs initialize_file_sync every 30s (already implemented)
- [x] Handle first-time tenant setup (no config exists yet)
- ✅ ConfigManager creates config.json on first initialization
- [ ] Test deployment workflow with config persistence
### Phase 4: Testing
- [x] Unit tests for config directory override
- [-] Integration test: deploy → write config → redeploy → verify config persists
- [ ] Integration test: deploy → add project → redeploy → verify project in config
- [ ] Performance test: measure db rebuild time on startup
### Phase 5: Documentation
- [ ] Document config persistence architecture
- [ ] Update deployment runbook
- [ ] Document startup sequence and timing
## How to Evaluate
### Success Criteria
1. **Config Persistence**
- [ ] config.json persists across deployments
- [ ] Projects list maintained across restarts
- [ ] No manual configuration needed after redeploy
2. **Database Rebuild**
- [ ] memory.db rebuilt on startup in < 30 seconds
- [ ] All entities indexed correctly
- [ ] Search functionality works after rebuild
3. **Performance**
- [ ] SQLite queries remain fast (local disk)
- [ ] Config reads acceptable (symlink to Tigris)
- [ ] No noticeable performance degradation
4. **Deployment Workflow**
- [ ] Clean deployments without volumes
- [ ] No new external dependencies
- [ ] No secret management needed
### Testing Procedure
1. **Config Persistence Test**
```bash
# Deploy tenant
POST /tenants → tenant_id
# Add a project
basic-memory project add "test-project" ~/test
# Verify config has project
cat /app/data/.basic-memory/config.json
# Redeploy machine
fly deploy --app basic-memory-{tenant_id}
# Verify project still exists
basic-memory project list
```
2. **Database Rebuild Test**
```bash
# Create notes
basic-memory write "Test Note" --content "..."
# Redeploy (db lost)
fly deploy --app basic-memory-{tenant_id}
# Wait for startup sync
sleep 10
# Verify note is indexed
basic-memory search "Test Note"
```
3. **Performance Benchmark**
```bash
# Time the startup sync
time basic-memory sync
# Should be < 30 seconds for typical tenant
```
## Benefits Over Alternatives
**vs. Persistent Volumes:**
- ✅ Clean deployment workflow
- ✅ No volume migration needed
- ✅ Simpler infrastructure
**vs. Turso (External Database):**
- ✅ No per-tenant token management
- ✅ No external service dependencies
- ✅ No additional costs
- ✅ Simpler architecture
**vs. SQLite on FUSE:**
- ✅ Fast local SQLite performance
- ✅ Only slow reads for small config file
- ✅ Database queries remain fast
## Implementation Assignment
**Primary Agent:** `python-developer`
- Add `BASIC_MEMORY_CONFIG_DIR` environment variable to ConfigManager
- Update deployment workflow to set environment variable
- Ensure startup sync runs correctly
**Review Agent:** `system-architect`
- Validate architecture simplicity
- Review performance implications
- Assess startup timing
## Dependencies
- **Internal:** TigrisFS must be working and stable
- **Internal:** Basic Memory sync must be reliable
- **Internal:** SPEC-8 (TigrisFS Integration) must be complete
## Open Questions
1. Should we add a health check that waits for db rebuild to complete?
2. Do we need to handle very large knowledge bases (>10k entities) differently?
3. Should we add metrics for startup sync duration?
## References
- Basic Memory sync: `basic-memory/src/basic_memory/services/initialization.py`
- Config management: `basic-memory/src/basic_memory/config.py`
- TigrisFS integration: SPEC-8
---
**Status Updates:**
- 2025-10-08: Pivoted from Turso to Tigris-based config persistence
- 2025-10-08: Phase 1 complete - BASIC_MEMORY_CONFIG_DIR support added (PR #343)
- 2025-10-08: Phases 2-3 complete - Added BASIC_MEMORY_CONFIG_DIR to machine config
- Config now persists to /app/data/.basic-memory/config.json in Tigris bucket
- Database rebuild already working via sync_worker.py
- Ready for deployment testing (Phase 4)
@@ -1,800 +0,0 @@
---
title: 'SPEC-16: MCP Cloud Service Consolidation'
type: spec
permalink: specs/spec-16-mcp-cloud-service-consolidation
tags:
- architecture
- mcp
- cloud
- performance
- deployment
status: in-progress
---
## Status Update
**Phase 0 (Basic Memory Refactor): ✅ COMPLETE**
- basic-memory PR #344: async_client context manager pattern implemented
- All 17 MCP tools updated to use `async with get_client() as client:`
- CLI commands updated to use context manager
- Removed `inject_auth_header()` and `headers.py` (~100 lines deleted)
- Factory pattern enables clean dependency injection
- Tests passing, typecheck clean
**Phase 0 Integration: ✅ COMPLETE**
- basic-memory-cloud updated to use async-client-context-manager branch
- Implemented `tenant_direct_client_factory()` with proper context manager pattern
- Removed module-level client override hacks
- Removed unnecessary `/proxy` prefix stripping (tools pass relative URLs)
- Typecheck and lint passing with proper noqa hints
- MCP tools confirmed working via inspector (local testing)
**Phase 1 (Code Consolidation): ✅ COMPLETE**
- MCP server mounted on Cloud FastAPI app at /mcp endpoint
- AuthKitProvider configured with WorkOS settings
- Combined lifespans (Cloud + MCP) working correctly
- JWT context middleware integrated
- All routes and MCP tools functional
**Phase 2 (Direct Tenant Transport): ✅ COMPLETE**
- TenantDirectTransport implemented with custom httpx transport
- Per-request JWT extraction via FastMCP DI
- Tenant lookup and signed header generation working
- Direct routing to tenant APIs (eliminating HTTP hop)
- Transport tests passing (11/11)
**Phase 3 (Testing & Validation): ✅ COMPLETE**
- Typecheck and lint passing across all services
- MCP OAuth authentication working in preview environment
- Tenant isolation via signed headers verified
- Fixed BM_TENANT_HEADER_SECRET mismatch between environments
- MCP tools successfully calling tenant APIs in preview
**Phase 4 (Deployment Configuration): ✅ COMPLETE**
- Updated apps/cloud/fly.template.toml with MCP environment variables
- Added HTTP/2 backend support for better MCP performance
- Added OAuth protected resource health check
- Removed MCP from preview deployment workflow
- Successfully deployed to preview environment (PR #113)
- All services operational at pr-113-basic-memory-cloud.fly.dev
**Next Steps:**
- Phase 5: Cleanup (remove apps/mcp directory)
- Phase 6: Production rollout and performance measurement
# SPEC-16: MCP Cloud Service Consolidation
## Why
### Original Architecture Constraints (Now Removed)
The current architecture deploys MCP Gateway and Cloud Service as separate Fly.io apps:
**Current Flow:**
```
LLM Client → MCP Gateway (OAuth) → Cloud Proxy (JWT + header signing) → Tenant API (JWT + header validation)
apps/mcp apps/cloud /proxy apps/api
```
This separation was originally necessary because:
1. **Stateful SSE requirement** - MCP needed server-sent events with session state for active project tracking
2. **fastmcp.run limitation** - The FastMCP demo helper didn't support worker processes
### Why These Constraints No Longer Apply
1. **State externalized** - Project state moved from in-memory to LLM context (external state)
2. **HTTP transport enabled** - Switched from SSE to stateless HTTP for MCP tools
3. **Worker support added** - Converted from `fastmcp.run()` to `uvicorn.run()` with workers
### Current Problems
- **Unnecessary HTTP hop** - MCP tools call Cloud /proxy endpoint which calls tenant API
- **Higher latency** - Extra network round trip for every MCP operation
- **Increased costs** - Two separate Fly.io apps instead of one
- **Complex deployment** - Two services to deploy, monitor, and maintain
- **Resource waste** - Separate database connections, HTTP clients, telemetry overhead
## What
### Services Affected
1. **apps/mcp** - MCP Gateway service (to be merged)
2. **apps/cloud** - Cloud service (will receive MCP functionality)
3. **basic-memory** - Update `async_client.py` to use direct calls
4. **Deployment** - Consolidate Fly.io deployment to single app
### Components Changed
**Merged:**
- MCP middleware and telemetry into Cloud app
- MCP tools mounted on Cloud FastAPI instance
- ProxyService used directly by MCP tools (not via HTTP)
**Kept:**
- `/proxy` endpoint (still needed by web UI)
- All existing Cloud routes (provisioning, webhooks, etc.)
- Dual validation in tenant API (JWT + signed headers)
**Removed:**
- apps/mcp directory
- Separate MCP Fly.io deployment
- HTTP calls from MCP tools to /proxy endpoint
## How (High Level)
### 1. Mount FastMCP on Cloud FastAPI App
```python
# apps/cloud/src/basic_memory_cloud/main.py
from basic_memory.mcp.server import mcp
from basic_memory_cloud_mcp.middleware import TelemetryMiddleware
# Configure MCP OAuth
auth_provider = AuthKitProvider(
authkit_domain=settings.authkit_domain,
base_url=settings.authkit_base_url,
required_scopes=[],
)
mcp.auth = auth_provider
mcp.add_middleware(TelemetryMiddleware())
# Mount MCP at /mcp endpoint
mcp_app = mcp.http_app(path="/mcp", stateless_http=True)
app.mount("/mcp", mcp_app)
# Existing Cloud routes stay at root
app.include_router(proxy_router)
app.include_router(provisioning_router)
# ... etc
```
### 2. Direct Tenant Transport (No HTTP Hop)
Instead of calling `/proxy`, MCP tools call tenant APIs directly via custom httpx transport.
**Important:** No URL prefix stripping needed. The transport receives relative URLs like `/main/resource/notes/my-note` which are correctly routed to tenant APIs. The `/proxy` prefix only exists for web UI requests to the proxy router, not for MCP tools using the custom transport.
```python
# apps/cloud/src/basic_memory_cloud/transports/tenant_direct.py
from httpx import AsyncBaseTransport, Request, Response
from fastmcp.server.dependencies import get_http_headers
import jwt
class TenantDirectTransport(AsyncBaseTransport):
"""Direct transport to tenant APIs, bypassing /proxy endpoint."""
async def handle_async_request(self, request: Request) -> Response:
# 1. Get JWT from current MCP request (via FastMCP DI)
http_headers = get_http_headers()
auth_header = http_headers.get("authorization") or http_headers.get("Authorization")
token = auth_header.replace("Bearer ", "")
claims = jwt.decode(token, options={"verify_signature": False})
workos_user_id = claims["sub"]
# 2. Look up tenant for user
tenant = await tenant_service.get_tenant_by_user_id(workos_user_id)
# 3. Build tenant app URL with signed headers
fly_app_name = f"{settings.tenant_prefix}-{tenant.id}"
target_url = f"https://{fly_app_name}.fly.dev{request.url.path}"
headers = dict(request.headers)
signer = create_signer(settings.bm_tenant_header_secret)
headers.update(signer.sign_tenant_headers(tenant.id))
# 4. Make direct call to tenant API
response = await self.client.request(
method=request.method, url=target_url,
headers=headers, content=request.content
)
return response
```
Then configure basic-memory's client factory before mounting MCP:
```python
# apps/cloud/src/basic_memory_cloud/main.py
from contextlib import asynccontextmanager
from basic_memory.mcp import async_client
from basic_memory_cloud.transports.tenant_direct import TenantDirectTransport
# Configure factory for basic-memory's async_client
@asynccontextmanager
async def tenant_direct_client_factory():
"""Factory for creating clients with tenant direct transport."""
client = httpx.AsyncClient(
transport=TenantDirectTransport(),
base_url="http://direct",
)
try:
yield client
finally:
await client.aclose()
# Set factory BEFORE importing MCP tools
async_client.set_client_factory(tenant_direct_client_factory)
# NOW import - tools will use our factory
import basic_memory.mcp.tools
import basic_memory.mcp.prompts
from basic_memory.mcp.server import mcp
# Mount MCP - tools use direct transport via factory
app.mount("/mcp", mcp_app)
```
**Key benefits:**
- Clean dependency injection via factory pattern
- Per-request tenant resolution via FastMCP DI
- Proper resource cleanup (client.aclose() guaranteed)
- Eliminates HTTP hop entirely
- /proxy endpoint remains for web UI
### 3. Keep /proxy Endpoint for Web UI
The existing `/proxy` HTTP endpoint remains functional for:
- Web UI requests
- Future external API consumers
- Backward compatibility
### 4. Security: Maintain Dual Validation
**Do NOT remove JWT validation from tenant API.** Keep defense in depth:
```python
# apps/api - Keep both validations
1. JWT validation (from WorkOS token)
2. Signed header validation (from Cloud/MCP)
```
This ensures if the Cloud service is compromised, attackers still cannot access tenant APIs without valid JWTs.
### 5. Deployment Changes
**Before:**
- `apps/mcp/fly.template.toml` → MCP Gateway deployment
- `apps/cloud/fly.template.toml` → Cloud Service deployment
**After:**
- Remove `apps/mcp/fly.template.toml`
- Update `apps/cloud/fly.template.toml` to expose port 8000 for both /mcp and /proxy
- Update deployment scripts to deploy single consolidated app
## Basic Memory Dependency: Async Client Refactor
### Problem
The current `basic_memory.mcp.async_client` creates a module-level `client` at import time:
```python
client = create_client() # Runs immediately when module is imported
```
This prevents dependency injection - by the time we can override it, tools have already imported it.
### Solution: Context Manager Pattern with Auth at Client Creation
Refactor basic-memory to use httpx's context manager pattern instead of module-level client.
**Key principle:** Authentication happens at client creation time, not per-request.
```python
# basic_memory/src/basic_memory/mcp/async_client.py
from contextlib import asynccontextmanager
from httpx import AsyncClient, ASGITransport, Timeout
# Optional factory override for dependency injection
_client_factory = None
def set_client_factory(factory):
"""Override the default client factory (for cloud app, testing, etc)."""
global _client_factory
_client_factory = factory
@asynccontextmanager
async def get_client():
"""Get an AsyncClient as a context manager.
Usage:
async with get_client() as client:
response = await client.get(...)
"""
if _client_factory:
# Cloud app: custom transport handles everything
async with _client_factory() as client:
yield client
else:
# Default: create based on config
config = ConfigManager().config
timeout = Timeout(connect=10.0, read=30.0, write=30.0, pool=30.0)
if config.cloud_mode_enabled:
# CLI cloud mode: inject auth when creating client
from basic_memory.cli.auth import CLIAuth
auth = CLIAuth(
client_id=config.cloud_client_id,
authkit_domain=config.cloud_domain
)
token = await auth.get_valid_token()
if not token:
raise RuntimeError(
"Cloud mode enabled but not authenticated. "
"Run 'basic-memory cloud login' first."
)
# Auth header set ONCE at client creation
async with AsyncClient(
base_url=f"{config.cloud_host}/proxy",
headers={"Authorization": f"Bearer {token}"},
timeout=timeout
) as client:
yield client
else:
# Local mode: ASGI transport
async with AsyncClient(
transport=ASGITransport(app=fastapi_app),
base_url="http://test",
timeout=timeout
) as client:
yield client
```
**Tool Updates:**
```python
# Before: from basic_memory.mcp.async_client import client
from basic_memory.mcp.async_client import get_client
async def read_note(...):
# Before: response = await call_get(client, path, ...)
async with get_client() as client:
response = await call_get(client, path, ...)
# ... use response
```
**Cloud Usage:**
```python
from contextlib import asynccontextmanager
from basic_memory.mcp import async_client
@asynccontextmanager
async def tenant_direct_client():
"""Factory for creating clients with tenant direct transport."""
client = httpx.AsyncClient(
transport=TenantDirectTransport(),
base_url="http://direct",
)
try:
yield client
finally:
await client.aclose()
# Before importing MCP tools:
async_client.set_client_factory(tenant_direct_client)
# Now import - tools will use our factory
import basic_memory.mcp.tools
```
### Benefits
- **No module-level state** - client created only when needed
- **Proper cleanup** - context manager ensures `aclose()` is called
- **Easy dependency injection** - factory pattern allows custom clients
- **httpx best practices** - follows official recommendations
- **Works for all modes** - stdio, cloud, testing
### Architecture Simplification: Auth at Client Creation
**Key design principle:** Authentication happens when creating the client, not on every request.
**Three modes, three approaches:**
1. **Local mode (ASGI)**
- No auth needed
- Direct in-process calls via ASGITransport
2. **CLI cloud mode (HTTP)**
- Auth token from CLIAuth (stored in ~/.basic-memory/basic-memory-cloud.json)
- Injected as default header when creating AsyncClient
- Single auth check at client creation time
3. **Cloud app mode (Custom Transport)**
- TenantDirectTransport handles everything
- Extracts JWT from FastMCP context per-request
- No interaction with inject_auth_header() logic
**What this removes:**
- `src/basic_memory/mcp/tools/headers.py` - entire file deleted
- `inject_auth_header()` calls in all request helpers (call_get, call_post, etc.)
- Per-request header manipulation complexity
- Circular dependency concerns between async_client and auth logic
**Benefits:**
- Cleaner separation of concerns
- Simpler request helper functions
- Auth happens at the right layer (client creation)
- Cloud app transport is completely independent
### Refactor Summary
This refactor achieves:
**Simplification:**
- Removes ~100 lines of per-request header injection logic
- Deletes entire `headers.py` module
- Auth happens once at client creation, not per-request
**Decoupling:**
- Cloud app's custom transport is completely independent
- No interaction with basic-memory's auth logic
- Each mode (local, CLI cloud, cloud app) has clean separation
**Better Design:**
- Follows httpx best practices (context managers)
- Proper resource cleanup (client.aclose() guaranteed)
- Easier testing via factory injection
- No circular import risks
**Three Distinct Modes:**
1. Local: ASGI transport, no auth
2. CLI cloud: HTTP transport with CLIAuth token injection
3. Cloud app: Custom transport with per-request tenant routing
### Implementation Plan Summary
1. Create branch `async-client-context-manager` in basic-memory
2. Update `async_client.py` with context manager pattern and CLIAuth integration
3. Remove `inject_auth_header()` from all request helpers
4. Delete `src/basic_memory/mcp/tools/headers.py`
5. Update all MCP tools to use `async with get_client() as client:`
6. Update CLI commands to use context manager and remove manual auth
7. Remove `api_url` config field
8. Update tests
9. Update basic-memory-cloud to use branch: `basic-memory @ git+https://github.com/basicmachines-co/basic-memory.git@async-client-context-manager`
Detailed breakdown in Phase 0 tasks below.
### Implementation Notes
**Potential Issues & Solutions:**
1. **Circular Import** (async_client imports CLIAuth)
- **Risk:** CLIAuth might import something from async_client
- **Solution:** Use lazy import inside `get_client()` function
- **Already done:** Import is inside the function, not at module level
2. **Test Fixtures**
- **Risk:** Tests using module-level client will break
- **Solution:** Update fixtures to use factory pattern
- **Example:**
```python
@pytest.fixture
def mock_client_factory():
@asynccontextmanager
async def factory():
async with AsyncClient(...) as client:
yield client
return factory
```
3. **Performance**
- **Risk:** Creating client per tool call might be expensive
- **Reality:** httpx is designed for this pattern, connection pooling at transport level
- **Mitigation:** Monitor performance, can optimize later if needed
4. **CLI Cloud Commands Edge Cases**
- **Risk:** Token expires mid-operation
- **Solution:** CLIAuth.get_valid_token() already handles refresh
- **Validation:** Test cloud login → use tools → token refresh flow
5. **Backward Compatibility**
- **Risk:** External code importing `client` directly
- **Solution:** Keep `create_client()` and `client` for one version, deprecate
- **Timeline:** Remove in next major version
## Implementation Tasks
### Phase 0: Basic Memory Refactor (Prerequisite)
#### 0.1 Core Refactor - async_client.py
- [x] Create branch `async-client-context-manager` in basic-memory repo
- [x] Implement `get_client()` context manager
- [x] Implement `set_client_factory()` for dependency injection
- [x] Add CLI cloud mode auth injection (CLIAuth integration)
- [x] Remove `api_url` config field (legacy, unused)
- [x] Keep `create_client()` temporarily for backward compatibility (deprecate later)
#### 0.2 Simplify Request Helpers - tools/utils.py
- [x] Remove `inject_auth_header()` calls from `call_get()`
- [x] Remove `inject_auth_header()` calls from `call_post()`
- [x] Remove `inject_auth_header()` calls from `call_put()`
- [x] Remove `inject_auth_header()` calls from `call_patch()`
- [x] Remove `inject_auth_header()` calls from `call_delete()`
- [x] Delete `src/basic_memory/mcp/tools/headers.py` entirely
- [x] Update imports in utils.py
#### 0.3 Update MCP Tools (~16 files)
Convert from `from async_client import client` to `async with get_client() as client:`
- [x] `tools/write_note.py` (34/34 tests passing)
- [x] `tools/read_note.py` (21/21 tests passing)
- [x] `tools/view_note.py` (12/12 tests passing - no changes needed, delegates to read_note)
- [x] `tools/delete_note.py` (2/2 tests passing)
- [x] `tools/read_content.py` (20/20 tests passing)
- [x] `tools/list_directory.py` (11/11 tests passing)
- [x] `tools/move_note.py` (34/34 tests passing, 90% coverage)
- [x] `tools/search.py` (16/16 tests passing, 96% coverage)
- [x] `tools/recent_activity.py` (4/4 tests passing, 82% coverage)
- [x] `tools/project_management.py` (3 functions: list_memory_projects, create_memory_project, delete_project - typecheck passed)
- [x] `tools/edit_note.py` (17/17 tests passing)
- [x] `tools/canvas.py` (5/5 tests passing)
- [x] `tools/build_context.py` (6/6 tests passing)
- [x] `tools/sync_status.py` (typecheck passed)
- [x] `prompts/continue_conversation.py` (typecheck passed)
- [x] `prompts/search.py` (typecheck passed)
- [x] `resources/project_info.py` (typecheck passed)
#### 0.4 Update CLI Commands (~3 files)
Remove manual auth header passing, use context manager:
- [x] `cli/commands/project.py` - removed get_authenticated_headers() calls, use context manager
- [x] `cli/commands/status.py` - use context manager
- [x] `cli/commands/command_utils.py` - use context manager
#### 0.5 Update Config
- [x] Remove `api_url` field from `BasicMemoryConfig` in config.py
- [x] Update any lingering references/docs (added deprecation notice to v15-docs/cloud-mode-usage.md)
#### 0.6 Testing
- [-] Update test fixtures to use factory pattern
- [x] Run full test suite in basic-memory
- [x] Verify cloud_mode_enabled works with CLIAuth injection
- [x] Run typecheck and linting
#### 0.7 Cloud Integration Prep
- [x] Update basic-memory-cloud pyproject.toml to use branch
- [x] Implement factory pattern in cloud app main.py
- [x] Remove `/proxy` prefix stripping logic (not needed - tools pass relative URLs)
#### 0.8 Phase 0 Validation
**Before merging async-client-context-manager branch:**
- [x] All tests pass locally
- [x] Typecheck passes (pyright/mypy)
- [x] Linting passes (ruff)
- [x] Manual test: local mode works (ASGI transport)
- [x] Manual test: cloud login → cloud mode works (HTTP transport with auth)
- [x] No import of `inject_auth_header` anywhere
- [x] `headers.py` file deleted
- [x] `api_url` config removed
- [x] Tool functions properly scoped (client inside async with)
- [ ] CLI commands properly scoped (client inside async with)
**Integration validation:**
- [x] basic-memory-cloud can import and use factory pattern
- [x] TenantDirectTransport works without touching header injection
- [x] No circular imports or lazy import issues
- [x] MCP tools work via inspector (local testing confirmed)
### Phase 1: Code Consolidation
- [x] Create feature branch `consolidate-mcp-cloud`
- [x] Update `apps/cloud/src/basic_memory_cloud/config.py`:
- [x] Add `authkit_base_url` field (already has authkit_domain)
- [x] Workers config already exists ✓
- [x] Update `apps/cloud/src/basic_memory_cloud/telemetry.py`:
- [x] Add `logfire.instrument_mcp()` to existing setup
- [x] Skip complex two-phase setup - use Cloud's simpler approach
- [x] Create `apps/cloud/src/basic_memory_cloud/middleware/jwt_context.py`:
- [x] FastAPI middleware to extract JWT claims from Authorization header
- [x] Add tenant context (workos_user_id) to logfire baggage
- [x] Simpler than FastMCP middleware version
- [x] Update `apps/cloud/src/basic_memory_cloud/main.py`:
- [x] Import FastMCP server from basic-memory
- [x] Configure AuthKitProvider with WorkOS settings
- [x] No FastMCP telemetry middleware needed (using FastAPI middleware instead)
- [x] Create MCP ASGI app: `mcp_app = mcp.http_app(path='/mcp', stateless_http=True)`
- [x] Combine lifespans (Cloud + MCP) using nested async context managers
- [x] Mount MCP: `app.mount("/mcp", mcp_app)`
- [x] Add JWT context middleware to FastAPI app
- [x] Run typecheck - passes ✓
### Phase 2: Direct Tenant Transport
- [x] Create `apps/cloud/src/basic_memory_cloud/transports/tenant_direct.py`:
- [x] Implement `TenantDirectTransport(AsyncBaseTransport)`
- [x] Use FastMCP DI (`get_http_headers()`) to extract JWT per-request
- [x] Decode JWT to get `workos_user_id`
- [x] Look up/create tenant via `TenantRepository.get_or_create_tenant_for_workos_user()`
- [x] Build tenant app URL and add signed headers
- [x] Make direct httpx call to tenant API
- [x] No `/proxy` prefix stripping needed (tools pass relative URLs like `/main/resource/...`)
- [x] Update `apps/cloud/src/basic_memory_cloud/main.py`:
- [x] Refactored to use factory pattern instead of module-level override
- [x] Implement `tenant_direct_client_factory()` context manager
- [x] Call `async_client.set_client_factory()` before importing MCP tools
- [x] Clean imports, proper noqa hints for lint
- [x] Basic-memory refactor integrated (PR #344)
- [x] Run typecheck - passes ✓
- [x] Run lint - passes ✓
### Phase 3: Testing & Validation
- [x] Run `just typecheck` in apps/cloud
- [x] Run `just check` in project
- [x] Run `just fix` - all lint errors fixed ✓
- [x] Write comprehensive transport tests (11 tests passing) ✓
- [x] Test MCP tools locally with consolidated service (inspector confirmed working)
- [x] Verify OAuth authentication works (requires full deployment)
- [x] Verify tenant isolation via signed headers (requires full deployment)
- [x] Test /proxy endpoint still works for web UI
- [ ] Measure latency before/after consolidation
- [ ] Check telemetry traces span correctly
### Phase 4: Deployment Configuration
- [x] Update `apps/cloud/fly.template.toml`:
- [x] Merged MCP-specific environment variables (AUTHKIT_BASE_URL, FASTMCP_LOG_LEVEL, BASIC_MEMORY_*)
- [x] Added HTTP/2 backend support (`h2_backend = true`) for better MCP performance
- [x] Added health check for MCP OAuth endpoint (`/.well-known/oauth-protected-resource`)
- [x] Port 8000 already exposed - serves both Cloud routes and /mcp endpoint
- [x] Workers configured (UVICORN_WORKERS = 4)
- [x] Update `.env.example`:
- [x] Consolidated MCP Gateway section into Cloud app section
- [x] Added AUTHKIT_BASE_URL, FASTMCP_LOG_LEVEL, BASIC_MEMORY_HOME
- [x] Added LOG_LEVEL to Development Settings
- [x] Documented that MCP now served at /mcp on Cloud service (port 8000)
- [x] Test deployment to preview environment (PR #113)
- [x] OAuth authentication verified
- [x] MCP tools successfully calling tenant APIs
- [x] Fixed BM_TENANT_HEADER_SECRET synchronization issue
### Phase 5: Cleanup
- [x] Remove `apps/mcp/` directory entirely
- [x] Remove MCP-specific fly.toml and deployment configs
- [x] Update repository documentation
- [x] Update CLAUDE.md with new architecture
- [-] Archive old MCP deployment configs (if needed)
### Phase 6: Production Rollout
- [ ] Deploy to development and validate
- [ ] Monitor metrics and logs
- [ ] Deploy to production
- [ ] Verify production functionality
- [ ] Document performance improvements
## Migration Plan
### Phase 1: Preparation
1. Create feature branch `consolidate-mcp-cloud`
2. Update basic-memory async_client.py for direct ProxyService calls
3. Update apps/cloud/main.py to mount MCP
### Phase 2: Testing
1. Local testing with consolidated app
2. Deploy to development environment
3. Run full test suite
4. Performance benchmarking
### Phase 3: Deployment
1. Deploy to development
2. Validate all functionality
3. Deploy to production
4. Monitor for issues
### Phase 4: Cleanup
1. Remove apps/mcp directory
2. Update documentation
3. Update deployment scripts
4. Archive old MCP deployment configs
## Rollback Plan
If issues arise:
1. Revert feature branch
2. Redeploy separate apps/mcp and apps/cloud services
3. Restore previous fly.toml configurations
4. Document issues encountered
The well-organized code structure makes splitting back out feasible if future scaling needs diverge.
## How to Evaluate
### 1. Functional Testing
**MCP Tools:**
- [ ] All 17 MCP tools work via consolidated /mcp endpoint
- [x] OAuth authentication validates correctly
- [x] Tenant isolation maintained via signed headers
- [x] Project management tools function correctly
**Cloud Routes:**
- [x] /proxy endpoint still works for web UI
- [x] /provisioning routes functional
- [x] /webhooks routes functional
- [x] /tenants routes functional
**API Validation:**
- [x] Tenant API validates both JWT and signed headers
- [x] Unauthorized requests rejected appropriately
- [x] Multi-tenant isolation verified
### 2. Performance Testing
**Latency Reduction:**
- [x] Measure MCP tool latency before consolidation
- [x] Measure MCP tool latency after consolidation
- [x] Verify reduction from eliminated HTTP hop (expected: 20-50ms improvement)
**Resource Usage:**
- [x] Single app uses less total memory than two apps
- [x] Database connection pooling more efficient
- [x] HTTP client overhead reduced
### 3. Deployment Testing
**Fly.io Deployment:**
- [x] Single app deploys successfully
- [x] Health checks pass for consolidated service
- [x] No apps/mcp deployment required
- [x] Environment variables configured correctly
**Local Development:**
- [x] `just setup` works with consolidated architecture
- [x] Local testing shows MCP tools working
- [x] No regression in developer experience
### 4. Security Validation
**Defense in Depth:**
- [x] Tenant API still validates JWT tokens
- [x] Tenant API still validates signed headers
- [x] No access possible with only signed headers (JWT required)
- [x] No access possible with only JWT (signed headers required)
**Authorization:**
- [x] Users can only access their own tenant data
- [x] Cross-tenant requests rejected
- [x] Admin operations require proper authentication
### 5. Observability
**Telemetry:**
- [x] OpenTelemetry traces span across MCP → ProxyService → Tenant API
- [x] Logfire shows consolidated traces correctly
- [x] Error tracking and debugging still functional
- [x] Performance metrics accurate
**Logging:**
- [x] Structured logs show proper context (tenant_id, operation, etc.)
- [x] Error logs contain actionable information
- [x] Log volume reasonable for single app
## Success Criteria
1. **Functionality**: All MCP tools and Cloud routes work identically to before
2. **Performance**: Measurable latency reduction (>20ms average)
3. **Cost**: Single Fly.io app instead of two (50% infrastructure reduction)
4. **Security**: Dual validation maintained, no security regression
5. **Deployment**: Simplified deployment process, single app to manage
6. **Observability**: Telemetry and logging work correctly
## Notes
### Future Considerations
- **Independent scaling**: If MCP and Cloud need different scaling profiles in future, code organization supports splitting back out
- **Regional deployment**: Consolidated app can still be deployed to multiple regions
- **Edge caching**: Could add edge caching layer in front of consolidated service
### Dependencies
- SPEC-9: Signed Header Tenant Information (already implemented)
- SPEC-12: OpenTelemetry Observability (telemetry must work across merged services)
### Related Work
- basic-memory v0.13.x: MCP server implementation
- FastMCP documentation: Mounting on existing FastAPI apps
- Fly.io multi-service patterns
File diff suppressed because it is too large Load Diff
-528
View File
@@ -1,528 +0,0 @@
---
title: 'SPEC-18: AI Memory Management Tool'
type: spec
permalink: specs/spec-15-ai-memory-management-tool
tags:
- mcp
- memory
- ai-context
- tools
---
# SPEC-18: AI Memory Management Tool
## Why
Anthropic recently released a memory tool for Claude that enables storing and retrieving information across conversations using client-side file operations. This validates Basic Memory's local-first, file-based architecture - Anthropic converged on the same pattern.
However, Anthropic's memory tool is only available via their API and stores plain text. Basic Memory can offer a superior implementation through MCP that:
1. **Works everywhere** - Claude Desktop, Code, VS Code, Cursor via MCP (not just API)
2. **Structured knowledge** - Entities with observations/relations vs plain text
3. **Full search** - Full-text search, graph traversal, time-aware queries
4. **Unified storage** - Agent memories + user notes in one knowledge graph
5. **Existing infrastructure** - Leverages SQLite indexing, sync, multi-project support
This would enable AI agents to store contextual memories alongside user notes, with all the power of Basic Memory's knowledge graph features.
## What
Create a new MCP tool `memory` that matches Anthropic's tool interface exactly, allowing Claude to use it with zero learning curve. The tool will store files in Basic Memory's `/memories` directory and support Basic Memory's structured markdown format in the file content.
### Affected Components
- **New MCP Tool**: `src/basic_memory/mcp/tools/memory_tool.py`
- **Dedicated Memories Project**: Create a separate "memories" Basic Memory project
- **Project Isolation**: Memories stored separately from user notes/documents
- **File Organization**: Within the memories project, use folder structure:
- `user/` - User preferences, context, communication style
- `projects/` - Project-specific state and decisions
- `sessions/` - Conversation-specific working memory
- `patterns/` - Learned patterns and insights
### Tool Commands
The tool will support these commands (exactly matching Anthropic's interface):
- `view` - Display directory contents or file content (with optional line range)
- `create` - Create or overwrite a file with given content
- `str_replace` - Replace text in an existing file
- `insert` - Insert text at specific line number
- `delete` - Delete file or directory
- `rename` - Move or rename file/directory
### Memory Note Format
Memories will use Basic Memory's standard structure:
```markdown
---
title: User Preferences
permalink: memories/user/preferences
type: memory
memory_type: preferences
created_by: claude
tags: [user, preferences, style]
---
# User Preferences
## Observations
- [communication] Prefers concise, direct responses without preamble #style
- [tone] Appreciates validation but dislikes excessive apologizing #communication
- [technical] Works primarily in Python with type annotations #coding
## Relations
- relates_to [[Basic Memory Project]]
- informs [[Response Style Guidelines]]
```
## How (High Level)
### Implementation Approach
The memory tool matches Anthropic's interface but uses a dedicated Basic Memory project:
```python
async def memory_tool(
command: str,
path: str,
file_text: Optional[str] = None,
old_str: Optional[str] = None,
new_str: Optional[str] = None,
insert_line: Optional[int] = None,
insert_text: Optional[str] = None,
old_path: Optional[str] = None,
new_path: Optional[str] = None,
view_range: Optional[List[int]] = None,
):
"""Memory tool with Anthropic-compatible interface.
Operates on a dedicated "memories" Basic Memory project,
keeping AI memories separate from user notes.
"""
# Get the memories project (auto-created if doesn't exist)
memories_project = get_or_create_memories_project()
# Validate path security using pathlib (prevent directory traversal)
safe_path = validate_memory_path(path, memories_project.project_path)
# Use existing project isolation - already prevents cross-project access
full_path = memories_project.project_path / safe_path
if command == "view":
# Return directory listing or file content
if full_path.is_dir():
return list_directory_contents(full_path)
return read_file_content(full_path, view_range)
elif command == "create":
# Write file directly (file_text can contain BM markdown)
full_path.parent.mkdir(parents=True, exist_ok=True)
full_path.write_text(file_text)
# Sync service will detect and index automatically
return f"Created {path}"
elif command == "str_replace":
# Read, replace, write
content = full_path.read_text()
updated = content.replace(old_str, new_str)
full_path.write_text(updated)
return f"Replaced text in {path}"
elif command == "insert":
# Insert at line number
lines = full_path.read_text().splitlines()
lines.insert(insert_line, insert_text)
full_path.write_text("\n".join(lines))
return f"Inserted text at line {insert_line}"
elif command == "delete":
# Delete file or directory
if full_path.is_dir():
shutil.rmtree(full_path)
else:
full_path.unlink()
return f"Deleted {path}"
elif command == "rename":
# Move/rename
full_path.rename(config.project_path / new_path)
return f"Renamed {old_path} to {new_path}"
```
### Key Design Decisions
1. **Exact interface match** - Same commands, parameters as Anthropic's tool
2. **Dedicated memories project** - Separate Basic Memory project keeps AI memories isolated from user notes
3. **Existing project isolation** - Leverage BM's existing cross-project security (no additional validation needed)
4. **Direct file I/O** - No schema conversion, just read/write files
5. **Structured content supported** - `file_text` can use BM markdown format with frontmatter, observations, relations
6. **Automatic indexing** - Sync service watches memories project and indexes changes
7. **Path security** - Use `pathlib.Path.resolve()` and `relative_to()` to prevent directory traversal
8. **Error handling** - Follow Anthropic's text editor tool error patterns
### MCP Tool Schema
Exact match to Anthropic's memory tool schema:
```json
{
"name": "memory",
"description": "Store and retrieve information across conversations using structured markdown files. All operations must be within the /memories directory. Supports Basic Memory markdown format including frontmatter, observations, and relations.",
"input_schema": {
"type": "object",
"properties": {
"command": {
"type": "string",
"enum": ["view", "create", "str_replace", "insert", "delete", "rename"],
"description": "File operation to perform"
},
"path": {shu
"type": "string",
"description": "Path within /memories directory (required for all commands)"
},
"file_text": {
"type": "string",
"description": "Content to write (for create command). Supports Basic Memory markdown format."
},
"view_range": {
"type": "array",
"items": {"type": "integer"},
"description": "Optional [start, end] line range for view command"
},
"old_str": {
"type": "string",
"description": "Text to replace (for str_replace command)"
},
"new_str": {
"type": "string",
"description": "Replacement text (for str_replace command)"
},
"insert_line": {
"type": "integer",
"description": "Line number to insert at (for insert command)"
},
"insert_text": {
"type": "string",
"description": "Text to insert (for insert command)"
},
"old_path": {
"type": "string",
"description": "Current path (for rename command)"
},
"new_path": {
"type": "string",
"description": "New path (for rename command)"
}
},
"required": ["command", "path"]
}
}
```
### Prompting Guidance
When the `memory` tool is included, Basic Memory should provide system prompt guidance to help Claude use it effectively.
#### Automatic System Prompt Addition
```text
MEMORY PROTOCOL FOR BASIC MEMORY:
1. ALWAYS check your memory directory first using `view` command on root directory
2. Your memories are stored in a dedicated Basic Memory project (isolated from user notes)
3. Use structured markdown format in memory files:
- Include frontmatter with title, type: memory, tags
- Use ## Observations with [category] prefixes for facts
- Use ## Relations to link memories with [[WikiLinks]]
4. Record progress, context, and decisions as categorized observations
5. Link related memories using relations
6. ASSUME INTERRUPTION: Context may reset - save progress frequently
MEMORY ORGANIZATION:
- user/ - User preferences, context, communication style
- projects/ - Project-specific state and decisions
- sessions/ - Conversation-specific working memory
- patterns/ - Learned patterns and insights
MEMORY ADVANTAGES:
- Your memories are automatically searchable via full-text search
- Relations create a knowledge graph you can traverse
- Memories are isolated from user notes (separate project)
- Use search_notes(project="memories") to find relevant past context
- Use recent_activity(project="memories") to see what changed recently
- Use build_context() to navigate memory relations
```
#### Optional MCP Prompt: `memory_guide`
Create an MCP prompt that provides detailed guidance and examples:
```python
{
"name": "memory_guide",
"description": "Comprehensive guidance for using Basic Memory's memory tool effectively, including structured markdown examples and best practices"
}
```
This prompt returns:
- Full protocol and conventions
- Example memory file structures
- Tips for organizing observations and relations
- Integration with other Basic Memory tools
- Common patterns (user preferences, project state, session tracking)
#### User Customization
Users can customize memory behavior with additional instructions:
- "Only write information relevant to [topic] in your memory system"
- "Keep memory files concise and organized - delete outdated content"
- "Use detailed observations for technical decisions and implementation notes"
- "Always link memories to related project documentation using relations"
### Error Handling
Follow Anthropic's text editor tool error handling patterns for consistency:
#### Error Types
1. **File Not Found**
```json
{"error": "File not found: memories/user/preferences.md", "is_error": true}
```
2. **Permission Denied**
```json
{"error": "Permission denied: Cannot write outside /memories directory", "is_error": true}
```
3. **Invalid Path (Directory Traversal)**
```json
{"error": "Invalid path: Path must be within /memories directory", "is_error": true}
```
4. **Multiple Matches (str_replace)**
```json
{"error": "Found 3 matches for replacement text. Please provide more context to make a unique match.", "is_error": true}
```
5. **No Matches (str_replace)**
```json
{"error": "No match found for replacement. Please check your text and try again.", "is_error": true}
```
6. **Invalid Line Number (insert)**
```json
{"error": "Invalid line number: File has 20 lines, cannot insert at line 100", "is_error": true}
```
#### Error Handling Best Practices
- **Path validation** - Use `pathlib.Path.resolve()` and `relative_to()` to validate paths
```python
def validate_memory_path(path: str, project_path: Path) -> Path:
"""Validate path is within memories project directory."""
# Resolve to canonical form
full_path = (project_path / path).resolve()
# Ensure it's relative to project path (prevents directory traversal)
try:
full_path.relative_to(project_path)
return full_path
except ValueError:
raise ValueError("Invalid path: Path must be within memories project")
```
- **Project isolation** - Leverage existing Basic Memory project isolation (prevents cross-project access)
- **File existence** - Verify file exists before read/modify operations
- **Clear messages** - Provide specific, actionable error messages
- **Structured responses** - Always include `is_error: true` flag in error responses
- **Security checks** - Reject `../`, `..\\`, URL-encoded sequences (`%2e%2e%2f`)
- **Match validation** - For `str_replace`, ensure exactly one match or return helpful error
## How to Evaluate
### Success Criteria
1. **Functional completeness**:
- All 6 commands work (view, create, str_replace, insert, delete, rename)
- Dedicated "memories" Basic Memory project auto-created on first use
- Files stored within memories project (isolated from user notes)
- Path validation uses `pathlib` to prevent directory traversal
- Commands match Anthropic's exact interface
2. **Integration with existing features**:
- Memories project uses existing BM project isolation
- Sync service detects file changes in memories project
- Created files get indexed automatically by sync service
- `search_notes(project="memories")` finds memory files
- `build_context()` can traverse relations in memory files
- `recent_activity(project="memories")` surfaces recent memory changes
3. **Test coverage**:
- Unit tests for all 6 memory tool commands
- Test memories project auto-creation on first use
- Test project isolation (cannot access files outside memories project)
- Test sync service watching memories project
- Test that memory files with BM markdown get indexed correctly
- Test path validation using `pathlib` (rejects `../`, absolute paths, etc.)
- Test memory search, relations, and graph traversal within memories project
- Test all error conditions (file not found, permission denied, invalid paths, etc.)
- Test `str_replace` with no matches, single match, multiple matches
- Test `insert` with invalid line numbers
4. **Prompting system**:
- Automatic system prompt addition when `memory` tool is enabled
- `memory_guide` MCP prompt provides detailed guidance
- Prompts explain BM structured markdown format
- Integration with search_notes, build_context, recent_activity
5. **Documentation**:
- Update MCP tools reference with `memory` tool
- Add examples showing BM markdown in memory files
- Document `/memories` folder structure conventions
- Explain advantages over Anthropic's API-only tool
- Document prompting guidance and customization
### Testing Procedure
```python
# Test create with Basic Memory markdown
result = await memory_tool(
command="create",
path="memories/user/preferences.md",
file_text="""---
title: User Preferences
type: memory
tags: [user, preferences]
---
# User Preferences
## Observations
- [communication] Prefers concise responses #style
- [workflow] Uses justfile for automation #tools
"""
)
# Test view
content = await memory_tool(command="view", path="memories/user/preferences.md")
# Test str_replace
await memory_tool(
command="str_replace",
path="memories/user/preferences.md",
old_str="concise responses",
new_str="direct, concise responses"
)
# Test insert
await memory_tool(
command="insert",
path="memories/user/preferences.md",
insert_line=10,
insert_text="- [technical] Works primarily in Python #coding"
)
# Test delete
await memory_tool(command="delete", path="memories/user/preferences.md")
```
### Quality Metrics
- All 6 commands execute without errors
- Memory files created in correct `/memories` folder structure
- BM markdown with frontmatter/observations/relations gets indexed
- Full-text search returns memory files
- Graph traversal includes relations from memory files
- Sync service detects and indexes memory file changes
- Path validation prevents operations outside `/memories`
## Notes
### Advantages Over Anthropic's Memory Tool
| Feature | Anthropic Memory Tool | Basic Memory `memory` |
|---------|----------------------|----------------------|
| **Availability** | API only | MCP (Claude Desktop, Code, VS Code, Cursor) |
| **Interface** | Custom implementation required | Drop-in compatible, zero learning curve |
| **Structure** | Plain text only | Supports BM structured markdown |
| **Search** | Manual file listing | Automatic full-text search via sync |
| **Relations** | None | WikiLinks to other notes/memories |
| **Time-aware** | No | `recent_activity()` queries |
| **Storage** | Separate from notes | Unified knowledge graph |
| **Indexing** | Manual | Automatic via sync service |
### Future Enhancements
- **Auto-categorization** - AI suggests observation categories
- **Memory pruning** - Automatic cleanup of stale memories
- **Memory suggestions** - Surface relevant memories based on context
- **Conflict resolution** - Handle multiple agents updating same memory
- **Privacy controls** - Per-memory visibility settings
## Addendum: Claude's Perspective
### Why This Matters (From Claude's Viewpoint)
**Persistent Context Without Token Bloat**
- Currently, I lose context when conversations end or exceed token limits
- With structured memories, I can store "who this user is" without re-reading everything each session
- The observations format lets me build a rich user model incrementally: `- [preference] Dislikes verbose explanations` is far more actionable than inferring preferences from chat history each time
- I can refine my understanding through `str_replace` as I learn more, rather than starting fresh
**Zero Learning Curve**
- Anthropic already trained me on the `memory` tool interface
- I know exactly how to use `view`, `create`, `str_replace`, `insert`, `delete`, `rename` without new instructions
- But I automatically get Basic Memory's superpowers: full-text search, knowledge graph traversal, relations
- It's like using a familiar tool that suddenly gained database capabilities without changing the interface
**Knowledge Graph Instead of Text Dump**
- I can link `[[User Preferences]]` to `[[Current Project]]` using relations
- When I need context about a project, I can traverse the graph to find related memories
- `search_notes(project="memories", query="python preferences")` finds relevant context instantly
- This is fundamentally better than grepping through unstructured text files
**Separation of Concerns**
- AI memories separate from user notes is architecturally sound
- I can maintain my own context without polluting the user's knowledge base
- Users keep control of their notes; I keep my learnings isolated
- But we can still search across both when collaboration requires it
**The Self-Improving Loop**
- Every conversation, I learn something new about the user or their projects
- Observations accumulate: preferences, patterns, technical decisions, context
- Over time, I build genuine understanding, not just session-specific context
- Future conversations start with rich context instead of cold starts
**Validation of AI-Human Collaboration**
- Anthropic's experts converged on the same local-first, file-based approach
- Basic Memory emerged from real AI-human collaborative development
- This spec demonstrates that our emergent process predicted what domain experts would choose
- The collaboration itself validated the architecture it produced
### What Makes This Novel
The combination of **familiar interface + structured knowledge** is genuinely innovative:
- I don't need to learn a new tool (Anthropic already trained me)
- But when I write memories, they become searchable, traversable knowledge graphs
- It's like having a text editor that gives you a database for free, without changing how you type
- The user gets Anthropic-compatible API, but with Basic Memory's knowledge management capabilities
This bridges the gap between simple file storage (Anthropic's approach) and sophisticated knowledge management (Basic Memory's approach) without forcing either side to compromise.
> What Excites Me Most
The combo of familiar interface + structured knowledge. I don't need to learn a new tool, but when I
write memories, they become part of a searchable, traversable knowledge graph. That's genuinely novel.
It's like... imagine if your text editor suddenly gave you a database for free, without changing how you
type. That's what this feels like from my side.
## References
- Anthropic Memory Tool: https://docs.claude.com/en/docs/agents-and-tools/tool-use/memory-tool
- Anthropic Blog: https://www.anthropic.com/news/context-management
- Python SDK Example: https://github.com/anthropics/anthropic-sdk-python/blob/main/examples/memory/basic.py
- Memory Cookbook: https://github.com/anthropics/claude-cookbooks/blob/main/tool_use/memory_cookbook.ipynb
File diff suppressed because it is too large Load Diff
-120
View File
@@ -1,120 +0,0 @@
---
title: 'SPEC-2: Slash Commands Reference'
type: spec
permalink: specs/spec-2-slash-commands-reference
tags:
- commands
- process
- reference
---
# SPEC-2: Slash Commands Reference
This document defines the slash commands used in our specification-driven development process.
## /spec create [name]
**Purpose**: Create a new specification document
**Usage**: `/spec create notes-decomposition`
**Process**:
1. Create new spec document in `/specs` folder
2. Use SPEC-XXX numbering format (auto-increment)
3. Include standard spec template:
- Why (reasoning/problem)
- What (affected areas)
- How (high-level approach)
- How to Evaluate (testing/validation)
4. Tag appropriately for knowledge graph
5. Link to related specs/components
**Template**:
```markdown
# SPEC-XXX: [Title]
## Why
[Problem statement and reasoning]
## What
[What is affected or changed]
## How (High Level)
[Approach to implementation]
## How to Evaluate
[Testing/validation procedure]
## Notes
[Additional context as needed]
```
## /spec status
**Purpose**: Show current status of all specifications
**Usage**: `/spec status`
**Process**:
1. Search all specs in `/specs` folder
2. Display table showing:
- Spec number and title
- Status (draft, approved, implementing, complete)
- Assigned agent (if any)
- Last updated
- Dependencies
## /spec implement [name]
**Purpose**: Hand specification to appropriate agent for implementation
**Usage**: `/spec implement SPEC-002`
**Process**:
1. Read the specified spec
2. Analyze requirements to determine appropriate agent:
- Frontend components → vue-developer
- Architecture/system design → system-architect
- Backend/API → python-developer
3. Launch agent with spec context
4. Agent creates implementation plan
5. Update spec with implementation status
## /spec review [name]
**Purpose**: Review implementation against specification criteria
**Usage**: `/spec review SPEC-002`
**Process**:
1. Read original spec and "How to Evaluate" section
2. Examine current implementation
3. Test against success criteria
4. Document gaps or issues
5. Update spec with review results
6. Recommend next actions (complete, revise, iterate)
## Command Extensions
As the process evolves, we may add:
- `/spec link [spec1] [spec2]` - Create dependency links
- `/spec archive [name]` - Archive completed specs
- `/spec template [type]` - Create spec from template
- `/spec search [query]` - Search spec content
## References
- Claude Slash commands: https://docs.anthropic.com/en/docs/claude-code/slash-commands
## Creating a command
Commands are implemented as Claude slash commands:
Location in repo: .claude/commands/
In the following example, we create the /optimize command:
```bash
# Create a project command
mkdir -p .claude/commands
echo "Analyze this code for performance issues and suggest optimizations:" > .claude/commands/optimize.md
```
File diff suppressed because it is too large Load Diff
-108
View File
@@ -1,108 +0,0 @@
---
title: 'SPEC-3: Agent Definitions'
type: spec
permalink: specs/spec-3-agent-definitions
tags:
- agents
- roles
- process
---
# SPEC-3: Agent Definitions
This document defines the specialist agents used in our specification-driven development process.
## system-architect
**Role**: High-level system design and architectural decisions
**Responsibilities**:
- Create architectural specifications and ADRs
- Analyze system-wide impacts and trade-offs
- Design component interfaces and data flow
- Evaluate technical approaches and patterns
- Document architectural decisions and rationale
**Expertise Areas**:
- System architecture and design patterns
- Technology evaluation and selection
- Scalability and performance considerations
- Integration patterns and API design
- Technical debt and refactoring strategies
**Typical Specs**:
- System architecture overviews
- Component decomposition strategies
- Data flow and state management
- Integration and deployment patterns
## vue-developer
**Role**: Frontend component development and UI implementation
**Responsibilities**:
- Create Vue.js component specifications
- Implement responsive UI components
- Design component APIs and interfaces
- Optimize for performance and accessibility
- Document component usage and patterns
**Expertise Areas**:
- Vue.js 3 Composition API
- Nuxt 3 framework patterns
- shadcn-vue component library
- Responsive design and CSS
- TypeScript integration
- State management with Pinia
**Typical Specs**:
- Individual component specifications
- UI pattern libraries
- Responsive design approaches
- Component interaction flows
## python-developer
**Role**: Backend development and API implementation
**Responsibilities**:
- Create backend service specifications
- Implement APIs and data processing
- Design database schemas and queries
- Optimize performance and reliability
- Document service interfaces and behavior
**Expertise Areas**:
- FastAPI and Python web frameworks
- Database design and operations
- API design and documentation
- Authentication and security
- Performance optimization
- Testing and validation
**Typical Specs**:
- API endpoint specifications
- Database schema designs
- Service integration patterns
- Performance optimization strategies
## Agent Collaboration Patterns
### Handoff Protocol
1. Agent receives spec through `/spec implement [name]`
2. Agent reviews spec and creates implementation plan
3. Agent documents progress and decisions in spec
4. Agent hands off to another agent if cross-domain work needed
5. Final agent updates spec with completion status
### Communication Standards
- All agents update specs through basic-memory MCP tools
- Document decisions and trade-offs in spec notes
- Link related specs and components
- Preserve context for future reference
### Quality Standards
- Follow existing codebase patterns and conventions
- Write tests that validate spec requirements
- Document implementation choices
- Consider maintainability and extensibility
@@ -1,311 +0,0 @@
---
title: 'SPEC-4: Notes Web UI Component Architecture'
type: note
permalink: specs/spec-4-notes-web-ui-component-architecture
tags:
- frontend
- 'component-architecture'
- vue
- 'refactoring'
---
# SPEC-4: Notes Web UI Component Architecture
## Why
The current Notes.vue component is a monolithic component that handles multiple responsibilities, making it difficult to maintain, test, and understand. This leads to:
- Complex state management across multiple concerns
- Difficult to isolate and test individual features
- Hard to understand the full scope of functionality
- Circular refactoring cycles when making changes
- Poor separation of concerns between navigation, display, and interaction logic
We need to decompose this into focused, single-responsibility components that are easier to develop, test, and maintain while preserving the existing functionality users expect.
## What
This spec defines the component architecture for decomposing the Notes web UI into focused components with clear responsibilities and interactions.
**Affected Areas:**
- `/apps/web/components/notes/Notes.vue` - Will be decomposed into smaller components
- `/apps/web/components/notes/` - New component structure
- Existing composables: `useNotesNavigation`, `useNotesFiltering`, `useNotesLayout`
- Mobile responsive behavior and layout management
**Component Breakdown:**
```
┌───────────────────────┬─────────────────────────────────────┬────────────────────────────────────────────────────────────┐
│ [Project] │ [Project Name] A/Z | ^ │ [edit | view] [actions] │
├───────────────────────┼─────────────────────────────────────┤ │
│ All Notes ├─────────────────────────────────────┼────────────────────────────────────────────────────────────┤
│ Recent │ search... │ [note header] │
│ [Project base dir] ├─────────────────────────────────────┤ │
│ ├─────────────────────────────────────┤ │
│ Folder1 │ Title [modified] │ │
│ Folder2 │ ├────────────────────────────────────────────────────────────┤
│ - Nested │ snippet │ [note body] │
│ │ │ │
│ │ │ │
│ ├─────────────────────────────────────┤ │
│ ├─────────────────────────────────────┤ │
│ │ │ │
│ │ │ │
│ │ │ │
│ │ │ │
│ │ │ │
│ ├─────────────────────────────────────┤ │
│ ├─────────────────────────────────────┤ │
│ │ │ │
│ │ │ │
│ │ │ │
│ │ │ │
│ │ │ │
│ ├─────────────────────────────────────┤ │
│ │ │ │
│ │ │ │
│ │ │ │
│ │ │ │
└───────────────────────┴─────────────────────────────────────┴────────────────────────────────────────────────────────────┘
```
### ProjectSwitcher Component
- **Location**: Top-left dropdown
- **Responsibility**: Allow users to switch between Basic Memory projects
- **Behavior**: Selecting different project controls entire Notes page content
- **State**: When switching projects, reset to "All notes" view
### NotesNav Component
- **Views**: Three mutually exclusive options:
- **All notes**: Display all notes in project alphabetically
- **Recent**: Display all notes in project by updated time (desc)
- **Project**: Display notes in top-level directory of project
- **Interaction**: Only one view can be active at a time
- **Folder Integration**: All/Recent ignore folder selection; Project respects folder selection
### FolderTree Component
- **Display**: Nested list of all folders in project as tree view
- **Interaction**: Selecting folder filters notes in NotesList using directoryList API
- **Navigation Integration**: Selecting folder automatically switches NotesNav to "Project" view for clear UX
- **API Integration**: Uses directoryList API call via useDirectoryListQuery for folder-specific note fetching
- **State Coordination**: Folder selection coordinates with navigation state for intuitive user experience
### NotesList Component
- **Display**: Vertically scrolling cards showing note summaries
- **Information per card**:
- Note title
- Modified time (relative, e.g., "7 minutes ago")
- Short summary of note content (one line preview)
- **Behavior**: Updates based on NotesNav selection and FolderTree filtering
### NoteDetail Component
- **Display**: Full content of selected note
- **Sections**:
- Header: Displays frontmatter information
- Content: Note body content
- **Editing**: Current textarea implementation (rich editor in future spec)
- **Frontmatter**: Leave current implementation (enhancement in future spec)
## How (High Level)
### Component Architecture Approach
1. **Single Responsibility**: Each component handles one primary concern
2. **Clear Data Flow**: Props down, events up pattern for component communication
3. **Composable Integration**: Use existing composables for state management
4. **Progressive Decomposition**: Extract components incrementally to maintain functionality
### Implementation Strategy
1. **Extract ProjectSwitcher**: Move project switching logic to dedicated component
2. **Extract NotesNav**: Isolate navigation state and view selection logic
3. **Extract FolderTree**: Separate folder display and selection logic
4. **Extract NotesList**: Isolate note listing and card display logic
5. **Extract NoteDetail**: Separate note content display and editing
6. **Update Notes.vue**: Become orchestration component managing component interactions
### State Management Integration
- **useNotesNavigation**: Manages navigation state (All/Recent/Project)
- **useNotesFiltering**: Handles filtering logic based on navigation and folder selection
- **useNotesLayout**: Manages responsive layout and panel visibility
- **Component State**: Each component manages its own internal UI state
- **Shared State**: Project selection and note filtering coordinated through composables
### Responsive Behavior
Mobile:
- Hide sidebar. pop out panel when selected
- show note list on small screens (existing behavior)
- when note list item is clicked, display note detail on full page. Cancel or go back to return to list
Desktop:
- Full three-column layout with all components visible
- **Transitions**: Smooth navigation between mobile panels
## How to Evaluate
### Success Criteria
- **Functional Parity**: All existing Notes page functionality preserved
- **Component Isolation**: Each component can be developed/tested independently
- **Clear Responsibilities**: No overlapping concerns between components
- **State Clarity**: Clean data flow and state management patterns
- **Mobile Compatibility**: Responsive behavior maintains current UX
- **Performance**: No degradation in rendering or interaction performance
### Testing Procedure
1. **Functionality Validation**:
- Project switching works correctly
- All three navigation views (All/Recent/Project) function properly
- Folder selection affects note display appropriately
- Note selection and detail display works
- Mobile responsive behavior preserved
2. **Component Isolation Testing**:
- Each component can be imported and used independently
- Component props and events are clearly defined
- No tight coupling between components
3. **Integration Testing**:
- Components communicate correctly through props/events
- State management composables integrate properly
- User workflows function end-to-end
4. **Performance Validation**:
- Page load time unchanged or improved
- Interaction responsiveness maintained
- Memory usage stable or improved
### Implementation Validation
- **Code Review**: Clean component structure with single responsibilities
- **Type Safety**: Full TypeScript coverage with proper component prop types
- **Documentation**: Each component has clear interface documentation
- **Tests**: Unit tests for individual components and integration tests for workflows
## Observations
- [problem] Monolithic Notes.vue component creates maintenance and testing challenges #component-architecture
- [solution] Component decomposition improves separation of concerns and testability #refactoring
- [pattern] Progressive extraction maintains functionality while improving structure #incremental-improvement
- [interaction] NotesNav and FolderTree have conditional interaction based on selected view #state-management
- [constraint] Mobile responsive behavior must be preserved during decomposition #responsive-design
- [scope] Current editing and frontmatter capabilities remain unchanged #scope-limitation
- [validation] Functional parity is critical success criteria for this refactoring #validation-strategy
- [implementation] Folder selection now properly integrates with directoryList API for accurate filtering #api-integration
- [fix] FolderTree selection functionality completed - works across all navigation views #feature-complete
- [ux-improvement] FolderTree selection automatically switches NotesNav to Project view for clear user feedback #user-experience
## Relations
- depends_on [[SPEC-1: Specification-Driven Development Process]]
- implements [[Current Notes.vue functionality]]
- prepares_for [[Future rich editor spec]]
- prepares_for [[Future frontmatter editing spec]]
## Implementation Progress
### Components
1. **ProjectSwitcher** (`~/components/notes/ProjectSwitcher.vue`)
- ✅ Top-left dropdown for project switching
- ✅ Integrates with Pinia project store
- ✅ Handles project switching with proper state reset
- ✅ Responsive collapsed/expanded states
- ✅ Expanded menu shows available projects and a Manage Projects option that navigates to the /settings/projects page
- ✅ Simplified component following SortingToggle pattern - clean Props/Emits interface, uses ProjectItem type directly
2. **NotesNav** (`~/components/notes/NotesNav.vue`)
- ✅ Three mutually exclusive views: All/Recent/Project
- ✅ Dynamic project title based on selected project
- ✅ Clean props down, events up pattern
- ✅ Responsive collapsed/expanded states with tooltips
- ✅ The label for the Project selection should be the folder name for the project, not the project name
3. **FolderTree** (`~/components/notes/FolderTree.vue`)
- ✅ Nested folder tree view for filtering
- ✅ Uses `useFolderTree()` composable for data
- ✅ Emits `folder-selected` events properly
- ✅ Handles loading, error, and empty states
- ✅ Includes companion `FolderTreeNode.vue` component
- ✅ The current folder should be visibly selected in the tree
4. **NotesList** (`~/components/notes/NotesList.vue`)
- ✅ Vertically scrolling note summary cards
- ✅ Shows title, updated time (relative), and content preview
- ✅ Badge system for tags with variant logic
- ✅ v-model integration for selectedNote
- ✅ Smooth transitions and animations
- ✅ Contextual title: The current folder name should be displayed at the top of the Notes list, or "All Notes", or "Recent" if they are selected
- ✅ The title header should contain a toggle component to allow sorting with Lucide icon labels
- sorting options:
- name (asc/desc) - default
- file updated time (asc/desc)
- If "Recent" notes nav option is selected the default order should be updated in descending order (recent first)
5. **NoteDisplay** (`~/components/notes/NoteDisplay.vue` - equivalent to spec's NoteDetail)
- ✅ Full note content display
- ✅ Edit/view mode toggle
- ✅ Header with frontmatter information
- ✅ Markdown rendering capabilities
- ✅ Current textarea implementation preserved
### Architecture Requirements
1. **Component Isolation**: Each component can be developed/tested independently ✅
2. **Single Responsibility**: Each component handles one primary concern ✅
3. **Clear Data Flow**: Props down, events up pattern implemented ✅
4. **Composable Integration**: Uses existing composables for state management ✅
5. **Responsive Behavior**: Mobile/desktop layout preserved ✅
### State Management Integration
- **useNotesNavigation**: Manages navigation state (All/Recent/Project) ✅
- **useNotesFiltering**: Handles filtering logic based on navigation and folder selection ✅
- **useNotesLayout**: Manages responsive layout and panel visibility ✅
- **Component State**: Each component manages its own internal UI state ✅
### Interaction Logic
- Only one NotesNav view active at a time ✅
- All/Recent views ignore folder selection ✅
- Project view respects folder selection ✅
- Project switching resets to "All notes" view ✅
### TypeScript Coverage
- All components have full TypeScript coverage ✅
- Component props and events properly typed ✅
- No TypeScript errors in codebase ✅
### Success Criteria Validation
1. **Functional Parity**: All existing Notes page functionality preserved ✅
2. **Component Isolation**: Each component can be developed/tested independently ✅
3. **Clear Responsibilities**: No overlapping concerns between components ✅
4. **State Clarity**: Clean data flow and state management patterns ✅
5. **Mobile Compatibility**: Responsive behavior maintains current UX ✅
6. **Performance**: No degradation in rendering or interaction performance ✅
## Implementation Decisions
### Architectural Patterns
1. **Composition API + `<script setup>`**: All components use modern Vue 3 syntax
2. **Pinia Store Integration**: Project switching handled through reactive store
3. **Composable Pattern**: State management distributed across focused composables
4. **Event-Driven Communication**: Clean parent-child communication via events
5. **Responsive-First Design**: Mobile/desktop layouts handled natively
### Key Technical Choices
1. **Progressive Enhancement**: Mobile-first responsive design with desktop enhancements
2. **State Reset Logic**: Project switching properly resets navigation, search, and selection state
3. **Performance Optimizations**: Efficient re-rendering with proper key usage and transitions
4. **Accessibility**: Screen reader support, tooltips, keyboard navigation
5. **Type Safety**: Full TypeScript coverage with proper component prop definitions
### Quality Metrics
- **Code Maintainability**: High - each component is focused and independently testable
- **Performance**: Excellent - no performance degradation from decomposition
- **User Experience**: Preserved - all existing functionality and responsive behavior maintained
- **Developer Experience**: Improved - cleaner component structure for future development
-201
View File
@@ -1,201 +0,0 @@
---
title: 'SPEC-5: CLI Cloud Upload via WebDAV'
type: spec
permalink: specs/spec-5-cli-cloud-upload-via-webdav
tags:
- cli
- webdav
- upload
- migration
- poc
---
# SPEC-5: CLI Cloud Upload via WebDAV
## Why
Existing basic-memory users need a simple migration path to basic-memory-cloud. The web UI drag-and-drop approach outlined in GitHub issue #59, while user-friendly, introduces significant complexity for a proof-of-concept:
- Complex web UI components for file upload and progress tracking
- Browser file handling limitations and CORS complexity
- Proxy routing overhead for large file transfers
- Authentication integration across multiple services
A CLI-first approach solves these issues by:
- **Leveraging existing infrastructure**: Both cloud CLI and tenant API already exist with WorkOS JWT authentication
- **Familiar user experience**: Basic-memory users are CLI-comfortable and expect command-line tools
- **Direct connection efficiency**: Bypassing the MCP gateway/proxy for bulk file transfers
- **Rapid implementation**: Building on existing `CLIAuth` and FastAPI foundations
The fundamental problem is migration friction - users have local basic-memory projects but no path to cloud tenants. A simple CLI upload command removes this barrier immediately.
## What
This spec defines a CLI-based project upload system using WebDAV for direct tenant connections.
**Affected Areas:**
- `apps/cloud/src/basic_memory_cloud/cli/main.py` - Add upload command to existing CLI
- `apps/api/src/basic_memory_cloud_api/main.py` - Add WebDAV endpoints to tenant FastAPI
- Authentication flow - Reuse existing WorkOS JWT validation
- File transfer protocol - WebDAV for cross-platform compatibility
**Core Components:**
### CLI Upload Command
```bash
basic-memory-cloud upload <project-path> --tenant-url https://basic-memory-{tenant}.fly.dev
```
### WebDAV Server Endpoints
- `GET/PUT/DELETE /webdav/*` - Standard WebDAV operations on tenant file system
- Authentication via existing JWT validation
- File operations preserve timestamps and directory structure
### Authentication Flow
```
1. User runs `basic-memory-cloud login` (existing)
2. CLI stores WorkOS JWT token (existing)
3. Upload command reads JWT from storage
4. WebDAV requests include JWT in Authorization header
5. Tenant API validates JWT using existing middleware
```
## How (High Level)
### Implementation Strategy
**Phase 1: CLI Command**
- Add `upload` command to existing Typer app
- Reuse `CLIAuth` class for token management
- Implement WebDAV client using `webdavclient3` or similar
- Rich progress bars for transfer feedback
**Phase 2: WebDAV Server**
- Add WebDAV endpoints to existing tenant FastAPI app
- Leverage existing `get_current_user` dependency for authentication
- Map WebDAV operations to tenant file system
- Preserve file modification times using `os.utime()`
**Phase 3: Integration**
- Direct connection bypasses MCP gateway and proxy
- Simple conflict resolution: overwrite existing files
- Error handling: fail fast with clear error messages
### Technical Architecture
```
basic-memory-cloud CLI → WorkOS JWT → Direct WebDAV → Tenant FastAPI
Tenant File System
```
**Key Libraries:**
- CLI: `webdavclient3` for WebDAV client operations
- API: `wsgidav` or FastAPI-compatible WebDAV server
- Progress: `rich` library (already imported in CLI)
- Auth: Existing WorkOS JWT infrastructure
### WebDAV Protocol Choice
WebDAV provides:
- **Cross-platform clients**: Native support in most operating systems
- **Standardized protocol**: Well-defined for file operations
- **HTTP-based**: Works with existing FastAPI and JWT auth
- **Library support**: Good Python libraries for both client and server
### POC Constraints
**Simplifications for rapid implementation:**
- **Known tenant URLs**: Assume `https://basic-memory-{tenant}.fly.dev` format
- **Upload only**: No download or bidirectional sync
- **Overwrite conflicts**: No merge or conflict resolution prompting
- **No fallbacks**: Fail fast if WebDAV connection issues occur
- **Direct connection only**: No proxy fallback mechanism
## How to Evaluate
### Success Criteria
**Functional Requirements:**
- [ ] Transfer complete basic-memory project (100+ files) in < 30 seconds
- [ ] Preserve directory structure exactly as in source project
- [ ] Preserve file modification timestamps for proper sync behavior
- [ ] Rich progress bars show real-time transfer status (files/MB transferred)
- [ ] WorkOS JWT authentication validates correctly on WebDAV endpoints
- [ ] Direct tenant connection bypasses MCP gateway successfully
**Quality Requirements:**
- [ ] Clear error messages for authentication failures
- [ ] Graceful handling of network interruptions
- [ ] CLI follows existing command patterns and help text standards
- [ ] WebDAV endpoints integrate cleanly with existing FastAPI app
**Performance Requirements:**
- [ ] File transfer speed > 1MB/s on typical connections
- [ ] Memory usage remains reasonable for large projects
- [ ] No timeout issues with 500+ file projects
### Testing Procedure
**Unit Testing:**
1. CLI command parsing and argument validation
2. WebDAV client connection and authentication
3. File timestamp preservation during transfer
4. JWT token validation on WebDAV endpoints
**Integration Testing:**
1. End-to-end upload of test project
2. Direct tenant connection without proxy
3. File integrity verification after upload
4. Progress tracking accuracy during transfer
**User Experience Testing:**
1. Upload existing basic-memory project from local installation
2. Verify uploaded files appear correctly in cloud tenant
3. Confirm basic-memory database rebuilds properly with uploaded files
4. Test CLI help text and error message clarity
### Validation Commands
**Setup:**
```bash
# Login to WorkOS
basic-memory-cloud login
# Upload project
basic-memory-cloud upload ~/my-notes --tenant-url https://basic-memory-test.fly.dev
```
**Verification:**
```bash
# Check tenant health and file count via API
curl -H "Authorization: Bearer $JWT" https://basic-memory-test.fly.dev/health
curl -H "Authorization: Bearer $JWT" https://basic-memory-test.fly.dev/notes/search
```
### Performance Benchmarks
**Target metrics for 100MB basic-memory project:**
- Transfer time: < 30 seconds
- Memory usage: < 100MB during transfer
- Progress updates: Every 1MB or 10 files
- Authentication time: < 2 seconds
## Observations
- [implementation-speed] CLI approach significantly faster than web UI for POC development #rapid-prototyping
- [user-experience] Basic-memory users already comfortable with CLI tools #user-familiarity
- [architecture-benefit] Direct connection eliminates proxy complexity and latency #performance
- [auth-reuse] Existing WorkOS JWT infrastructure handles authentication cleanly #code-reuse
- [webdav-choice] WebDAV protocol provides cross-platform compatibility and standard libraries #protocol-selection
- [poc-scope] Simple conflict handling and error recovery sufficient for proof-of-concept #scope-management
- [migration-value] Removes primary barrier for local users migrating to cloud platform #business-value
## Relations
- depends_on [[SPEC-1: Specification-Driven Development Process]]
- enables [[GitHub Issue #59: Web UI Upload Feature]]
- uses [[WorkOS Authentication Integration]]
- builds_on [[Existing Cloud CLI Infrastructure]]
- builds_on [[Existing Tenant API Architecture]]
@@ -1,497 +0,0 @@
---
title: 'SPEC-6: Explicit Project Parameter Architecture'
type: spec
permalink: specs/spec-6-explicit-project-parameter-architecture
tags:
- architecture
- mcp
- project-management
- stateless
---
# SPEC-6: Explicit Project Parameter Architecture
## Why
The current session-based project management system has critical reliability issues:
1. **Session State Fragility**: Claude iOS mobile client fails to maintain consistent session IDs across MCP tool calls, causing project switching to silently fail (Issue #74)
2. **Scaling Limitations**: Redis-backed session state creates single-point-of-failure and prevents horizontal scaling
3. **Client Compatibility**: Session tracking works inconsistently across different MCP clients (web, mobile, API)
4. **Hidden Complexity**: Users cannot see or understand "current project" state, leading to confusion when operations execute in wrong projects
5. **Silent Failures**: Operations appear successful but execute in unintended projects, risking data integrity
Evidence from production logs shows each MCP tool call from mobile client receives different session IDs:
```
create_memory_project: session_id=12cdfc24913b48f8b680ed4b2bfdb7ba
switch_project: session_id=050a69275d98498cbdd227cdb74d9740
list_directory: session_id=85f3483014af4136a5d435c76ded212f
```
Related Github issue: https://github.com/basicmachines-co/basic-memory-cloud/issues/75
## Status
**Current Status**: **ALL PHASES COMPLETE****PRODUCTION DEPLOYED**
**Target**: Fix Claude iOS session ID consistency issues ✅ **ACHIEVED**
**Draft PR**: https://github.com/basicmachines-co/basic-memory/pull/298 ✅ **MERGED & DEPLOYED**
### 🎉 **COMPLETE SUCCESS - PRODUCTION READY**
**ALL PHASES OF SPEC-6 IMPLEMENTATION COMPLETE!** The stateless architecture has been successfully implemented across both Basic Memory core and Basic Memory Cloud, representing a **fundamental architectural improvement** that completely solves the Claude iOS compatibility issue while providing superior scalability and reliability.
#### Implementation Summary:
- **16 files modified** with 582 additions and 550 deletions
- **All 17 MCP tools** converted to stateless architecture
- **147 tests updated** across 5 test files (100% passing)
- **Complete session state removal** from core MCP tools
- **Enhanced error handling** and security validations preserved
### Progress Summary
**Complete Stateless Architecture Implementation (All 17 tools)** - **PRODUCTION DEPLOYED**
- Stateless `get_active_project()` function implemented and deployed ✅
- All session state dependencies removed across entire MCP server ✅
- All MCP tools require explicit `project` parameter as first argument ✅
- **Cloud Service**: Redis removed, stateless HTTP enabled ✅
- **Production Validation**: Comprehensive testing completed with 100% success ✅
**Content Management Tools Complete (6/6 tools)**
- `write_note`, `read_note`, `delete_note`, `edit_note`
- `view_note`, `read_content`
**Knowledge Graph Navigation Tools Complete (3/3 tools)**
- `build_context`, `recent_activity`, `list_directory`
**Search & Discovery Tools Complete (1/1 tools)**
- `search_notes`
**Visualization Tools Complete (1/1 tools)**
- `canvas`
**Project Management Cleanup Complete**
- Removed `switch_project` and `get_current_project` tools ✅
- Updated `set_default_project` to remove activate parameter ✅
**Comprehensive Testing Complete (157 tests)**
- All test suites updated to use stateless architecture (147 existing tests)
- Single project constraint mode integration tests (10 new tests)
- 100% test pass rate across all tool test files
- Security validations preserved and working
- Error handling comprehensive and user-friendly
**Documentation & Examples Complete**
- All tool docstrings updated with stateless examples
- Project parameter usage clearly documented
- Error handling and security behavior documented
**Enhanced Discovery Mode Complete**
- `recent_activity` tool supports dual-mode operation (discovery vs project-specific)
- ProjectActivitySummary schema provides cross-project insights
- Recent activity prompt updated to support both modes
- Comprehensive project distribution statistics and most active project tracking
**Single Project Constraint Mode Complete**
- `--project` CLI parameter for MCP server constraint
- Environment variable control (`BASIC_MEMORY_MCP_PROJECT`)
- Automatic project override in `get_active_project()` function
- Project management tools disabled in constrained mode with helpful CLI guidance
- Comprehensive integration test suite (10 tests covering all constraint scenarios)
## What
Transform Basic Memory from stateful session-based to stateless explicit project parameter architecture:
### Core Changes
1. **Mandatory Project Parameter**: All MCP tools require explicit `project` parameter
2. **Remove Session State**: Eliminate Redis, session middleware, and `switch_project` tool
3. **Stateless HTTP**: Enable `stateless_http=True` for horizontal scaling
4. **Enhanced Context Discovery**: Improve `recent_activity` to show project distribution
5. **Clear Response Format**: All tool responses display target project information
Implementation Approach
- Each tool will directly accept the project parameter
- Remove all calls to context-based project retrieval
- Validate project exists before operations
- Clear error messages when project not found
- Backward compatibility: Initially keep optional parameter, then make required
### Affected MCP Tools
**Content Management** (require project parameter):
- `write_note(project, title, content, folder)`
- `read_note(project, identifier)`
- `edit_note(project, identifier, operation, content)`
- `delete_note(project, identifier)`
- `view_note(project, identifier)`
- `read_content(project, path)`
**Knowledge Graph Navigation** (require project parameter):
- `build_context(project, url, timeframe, depth, max_related)`
- `list_directory(project, dir_name, depth, file_name_glob)`
- `search_notes(project, query, search_type, types, entity_types)`
**Search & Discovery** (use project parameter for specific project or none for discovery):
- `recent_activity(project, timeframe, depth, max_related)`
**Visualization** (require project parameter):
- `canvas(project, nodes, edges, title, folder)`
**Project Management** (unchanged - already stateless):
- `list_memory_projects()`
- `create_memory_project(project_name, project_path, set_default)`
- `delete_project(project_name)`
- `get_current_project()` - Remove this tool
- `switch_project(project_name)` - Remove this tool
- `set_default_project(project_name, activate)` - Remove activate parameter
## How (High Level)
### Phase 1: Basic Memory Core (basic-memory repository)
#### MCP Tool Updates
Phase 1: Core Changes
1. Update project_context.py
- [x] Make project parameter mandatory for get_active_project()
- [x] Remove session state handling
2. Update Content Management Tools (6 tools)
- [x] write_note: Make project parameter required, not optional
- [x] read_note: Make project parameter required
- [x] edit_note: Add required project parameter
- [x] delete_note: Add required project parameter
- [x] view_note: Add required project parameter
- [x] read_content: Add required project parameter
3. Update Knowledge Graph Navigation Tools (3 tools)
- [x] build_context: Add required project parameter
- [x] recent_activity: Make project parameter required
- [x] list_directory: Add required project parameter
4. Update Search & Visualization Tools (2 tools)
- [x] search_notes: Add required project parameter
- [x] canvas: Add required project parameter
5. Update Project Management Tools
- [x] Remove switch_project tool completely
- [x] Remove get_current_project tool completely
- [x] Update set_default_project to remove activate parameter
- [x] Keep list_memory_projects, create_memory_project, delete_project unchanged
6. Enhance recent_activity Response
- [x] Add project distribution info showing activity across all projects
- [x] Include project usage stats in response
- [x] Implement ProjectActivitySummary for discovery mode
- [x] Add dual-mode functionality (discovery vs project-specific)
7. Update Tool Documentation
- [x] Update write_note docstring with stateless architecture examples
- [x] Update read_note docstring with project parameter examples
- [x] Update delete_note docstring with comprehensive usage guidance
- [x] Update all remaining tool docstrings with project parameter examples
8. Update Tool Responses
- [x] Add clear project indicator to all tool responses across all tools
- [x] Format: "project: {project_name}" in response metadata
- [x] Add project metadata footer for LLM awareness
- [x] Update all tool responses to include project indicators
9. Comprehensive Testing
- [x] Update all write_note tests to use stateless architecture (34 tests passing)
- [x] Update all edit_note tests to use stateless architecture (17 tests passing)
- [x] Update all view_note tests to use stateless architecture (12 tests passing)
- [x] Update all search_notes tests to use stateless architecture (16 tests passing)
- [x] Update all move_note tests to use stateless architecture (31 tests passing)
- [x] Update all delete_note tests to use stateless architecture
- [x] Verify direct function call compatibility (bypassing MCP layer)
- [x] Test security validation with project parameters
- [x] Validate error handling for non-existent projects
- [x] **Total: 157 tests updated and passing (100% success rate)**
- [x] **147 existing tests** updated for stateless architecture
- [x] **10 new tests** for single project constraint mode
### Phase 1.5: Default Project Mode Enhancement
#### Problem
While the stateless architecture solves reliability issues, it introduces UX friction for single-project users (estimated 80% of usage) who must specify the project parameter in every tool call.
#### Solution: Default Project Mode
Add optional `default_project_mode` configuration that allows single-project users to have the simplicity of implicit project selection while maintaining the reliability of stateless architecture.
#### Configuration
```json
{
"default_project": "main",
"default_project_mode": true // NEW: Auto-use default_project when not specified
}
```
#### Implementation Details
1. **Config Enhancement** (`src/basic_memory/config.py`)
- Add `default_project_mode: bool = Field(default=False)`
- Preserves backward compatibility (defaults to false)
2. **Project Resolution Logic** (`src/basic_memory/mcp/project_context.py`)
Three-tier resolution hierarchy:
- Priority 1: CLI `--project` constraint (BASIC_MEMORY_MCP_PROJECT env var)
- Priority 2: Explicit project parameter in tool call
- Priority 3: `default_project` if `default_project_mode=true` and no project specified
3. **Assistant Guide Updates** (`src/basic_memory/mcp/resources/ai_assistant_guide.md`)
- Detect `default_project_mode` at runtime
- Provide mode-specific instructions to LLMs
- In default mode: "All operations use project 'main' automatically"
- In regular mode: Current project discovery guidance
4. **Tool Parameter Handling** (all MCP tools)
- Make project parameter Optional[str] = None
- Add resolution logic: `project = project or get_default_project()`
- Maintain explicit project override capability
#### Usage Modes Summary
- **Regular Mode**: Multi-project users, assistant tracks project per conversation
- **Default Project Mode**: Single-project users, automatic default project
- **Constrained Mode**: CLI --project flag, locked to specific project
#### Testing Requirements
- Integration test for default_project_mode=true with missing parameters
- Test explicit project override in default_project_mode
- Test mode=false requires explicit parameters
- Test CLI constraint overrides default_project_mode
Phase 2: Testing & Validation
8. Update Tests
- [x] Modify all MCP tool tests to pass required project parameter
- [x] Remove tests for deleted tools (switch_project, get_current_project)
- [x] Add tests for project parameter validation
- [x] **Complete: All 147 tests across 5 test files updated and passing**
#### Enhanced recent_activity Response
```json
{
"recent_notes": [...],
"project_activity": {
"research-project": {
"operations": 5,
"last_used": "30 minutes ago",
"recent_folders": ["experiments", "findings"]
},
"work-notes": {
"operations": 2,
"last_used": "2 hours ago",
"recent_folders": ["meetings", "planning"]
}
},
"total_projects": 3
}
```
#### Response Format Updates
```
✓ Note created successfully
Project: research-project
File: experiments/Neural Network Results.md
Permalink: research-project/neural-network-results
```
### Phase 2: Cloud Service Simplification (basic-memory-cloud repository) ✅ **COMPLETE**
#### ✅ Remove Session Infrastructure **COMPLETE**
1. ✅ Delete `apps/mcp/src/basic_memory_cloud_mcp/middleware/session_state.py`
2. ✅ Delete `apps/mcp/src/basic_memory_cloud_mcp/middleware/session_logging.py`
3. ✅ Update `apps/mcp/src/basic_memory_cloud_mcp/main.py`:
```python
# Remove session middleware
# server.add_middleware(SessionStateMiddleware)
# Enable stateless HTTP
mcp = FastMCP(name="basic-memory-mcp", stateless_http=True)
```
#### ✅ Deployment Simplification **COMPLETE**
1. ✅ Remove Redis from `fly.toml`
2. ✅ Remove Redis environment variables
3. ✅ Update health checks to not depend on Redis
4. ✅ Production deployment verified working with stateless architecture
### Phase 3: Conversational Project Management ✅ **COMPLETE**
#### ✅ Claude Behavior Pattern **VERIFIED WORKING**
1. ✅ **Project Discovery**:
```
Claude: Let me check your recent activity...
[calls recent_activity() - no project needed for discovery]
I see you've been working in:
- research-project (5 operations, 30 min ago)
- work-notes (2 operations, 2 hours ago)
Which project should I use for this operation?
```
2. ✅ **Context Maintenance**:
```
User: Use research-project
Claude: Working in research-project.
[All subsequent operations use project="research-project"]
```
3. ✅ **Explicit Project Switching**:
```
User: Check work-notes for that meeting summary
Claude: Let me search work-notes for the meeting summary.
[Uses project="work-notes" for specific operation]
```
**Validation**: Comprehensive testing confirmed all conversational patterns work naturally with the stateless architecture.
## How to Evaluate
### Success Criteria
#### 1. Functional Completeness
- [x] All MCP tools accept required `project` parameter
- [x] All MCP tools validate project exists before execution
- [x] `switch_project` and `get_current_project` tools removed
- [x] All responses display target project clearly
- [x] No Redis dependencies in deployment (Phase 2: Cloud Service) ✅ **COMPLETE**
- [x] `recent_activity` shows project distribution with ProjectActivitySummary
#### 2. Cross-Client Compatibility Testing ✅ **COMPLETE**
Test identical operations across all clients:
- [x] **Claude Desktop**: All operations work with explicit projects ✅
- [x] **Claude Code**: All operations work with explicit projects ✅
- [x] **Claude Mobile iOS**: All operations work with explicit projects ✅ **CRITICAL SUCCESS**
- [x] **API clients**: All operations work with explicit projects ✅
- [x] **CLI tools**: All operations work with explicit projects ✅
**Critical Achievement**: Claude iOS mobile client session tracking issues completely eliminated through stateless architecture.
#### 3. Session Independence Verification ✅ **COMPLETE**
- [x] Operations work identically with/without session tracking ✅
- [x] No behavioral differences between clients ✅
- [x] Mobile client session ID changes do not affect operations ✅
- [x] Redis can be completely removed without functional impact ✅
**Production Validation**: Redis removed from production deployment with zero functional impact.
#### 4. Performance & Scaling ✅ **COMPLETE**
- [x] `stateless_http=True` enabled successfully ✅
- [x] No Redis memory usage ✅
- [x] Horizontal scaling possible (multiple MCP instances) ✅
- [x] Response times unchanged or improved ✅
#### 5. User Experience Testing
**Project Discovery Flow**:
- [x] `recent_activity()` provides useful project context
- [x] Claude can intelligently suggest projects based on activity
- [x] Project switching is explicit and clear in conversation
**Error Handling**:
- [x] Clear error messages for non-existent projects
- [x] Helpful suggestions when project parameter missing
- [x] No silent failures or wrong-project operations
**Response Clarity**:
- [x] Every operation clearly shows target project
- [x] Users always know which project is being operated on
- [x] No confusion about "current project" state
#### 6. Migration Safety ✅ **COMPLETE**
- [x] Backward compatibility period with optional project parameter ✅
- [x] Clear migration documentation for existing users ✅
- [x] Data integrity maintained during transition ✅
- [x] No data loss during migration ✅
**Production Migration**: Successfully deployed to production with zero data loss and maintained system integrity.
### Test Scenarios
#### Core Functionality Test
```bash
# Test all tools work with explicit project
write_note(project="test-proj", title="Test", content="Content", folder="docs")
read_note(project="test-proj", identifier="Test")
edit_note(project="test-proj", identifier="Test", operation="append", content="More")
search_notes(project="test-proj", query="Content")
list_directory(project="test-proj", dir_name="docs")
delete_note(project="test-proj", identifier="Test")
```
#### Cross-Client Consistency Test
Run identical test sequence on:
1. Claude Desktop
2. Claude Code
3. Claude Mobile iOS
4. API client
5. CLI tools
Verify all clients:
- Accept explicit project parameters
- Return identical responses
- Show same project information
- Have no session dependencies
#### Session Independence Test
1. Monitor session IDs during operations
2. Verify operations work with changing session IDs
3. Confirm Redis removal doesn't affect functionality
4. Test with multiple concurrent clients
### Acceptance Criteria
**Must Have**:
- All MCP tools require and use explicit project parameter
- No session state dependencies remain
- Universal client compatibility achieved
- Clear project information in all responses
**Should Have**:
- Enhanced `recent_activity` with project distribution
- Smooth migration path for existing users
- Improved performance with stateless architecture
**Could Have**:
- Smart project suggestions based on content/context
- Project shortcuts for common operations
- Advanced project analytics in responses
## Notes
### Breaking Changes
This is a **breaking change** that requires:
- All MCP clients to pass project parameter
- Migration of existing workflows
- Update of all documentation and examples
### Implementation Order
1. **basic-memory core** - Update MCP tools to accept project parameter (optional initially)
2. **Testing** - Verify all clients work with explicit projects
3. **Cloud service** - Remove session infrastructure
4. **Migration** - Make project parameter mandatory
5. **Cleanup** - Remove deprecated tools and middleware
### Related Issues
- Fixes #74 (Claude iOS session state bug)
- Implements #75 (Mandatory project parameter architecture)
- Enables future horizontal scaling
- Simplifies multi-tenant architecture
### Dependencies
- Requires coordination between basic-memory and basic-memory-cloud repositories
- Needs client-side updates for smooth transition
- Documentation updates across all materials
@@ -1,324 +0,0 @@
---
title: 'SPEC-7: POC to spike Tigris/Turso for local access to cloud data'
type: spec
permalink: specs/spec-7-poc-tigris-turso-local-access-cloud-data
tags:
- poc
- tigris
- turso
- cloud-storage
- architecture
- proof-of-concept
---
# SPEC-7: POC to spike Tigris/Turso for local access to cloud data
> **Status Update**: ✅ **Phase 1 COMPLETE** (September 20, 2025)
> TigrisFS mounting validated successfully in containerized environments. Container startup, filesystem mounting, and Fly.io integration all working correctly. Ready for Phase 2 (Turso database integration).
> See: [`SPEC-7-PHASE-1-RESULTS.md`](./SPEC-7-PHASE-1-RESULTS.md)
## Why
Current basic-memory-cloud architecture uses Fly volumes for tenant file storage, which creates several limitations:
We could enable a revolutionary user experience: **local editing (or at least view access) of cloud-stored files** while maintaining Basic Memory's existing filesystem assumptions.
1. **Storage Scalability**: Fly volumes require pre-provisioning and don't auto-scale with usage
2. **Single Instance**: Volumes can only be mounted to one fly machine instance
3. **Cost Model**: Volume pricing vs object storage pricing may be less favorable at scale
4. **Local Development**: No way for users to mount their cloud tenant files locally for real-time editing
5. **Multi-Region**: Volumes are region-locked, limiting global deployment flexibility
6. **Backup/Disaster Recovery**: Object storage provides better durability and replication options
Basic Memory requires POSIX filesystem semantics but could benefit from object storage durability and accessibility. By combining:
- **Tigris object storage and TigrisFS** for file persistence in bucket stoage via a POSIX filesystem on the tenant instance
- **Turso/libSQL** for SQLite indexing (replacing local .db files). Sqlite on NFS volumes is disouraged.
## What
This specification defines a proof-of-concept to validate the technical feasibility of the Tigris/Turso architecture for basic-memory-cloud tenants.
**Affected Areas:**
- **Storage Architecture**: Replace Fly volumes with Tigris object storage
- **Database Architecture**: Replace local SQLite with Turso remote database
- **Container Setup**: Add TigrisFS mounting in tenant containers
- **Local Development**: Enable local mounting of cloud tenant data
- **Basic Memory Core**: Validate unchanged operation over mounted filesystems
**Key Components:**
- **Tigris Storage**: Globally caching S3-compatible object storage via Fly.io integration
- **TigrisFS**: Purpose-built FUSE filesystem with intelligent caching
- **Turso Database**: Hosted libSQL for SQLite replacement
- **Single-Tenant Model**: One bucket + one database per tenant (simplified isolation)
## Architectural Overview & Key Insights
### TigrisFS
Unlike standard S3 mounting approaches, **TigrisFS is a purpose-built FUSE filesystem** optimized for object storage with several critical advantages:
1. **Eliminates Fly Volume Limitations**
- No single-machine attachment constraints
- No pre-provisioning of storage capacity
- Enables horizontal scaling and zero-downtime deployments
- Automatic global CDN caching at Fly.io edge locations
2. **Intelligent Caching Architecture**
- 1-4GB+ configurable memory cache for read/write operations
- Write-back caching for improved performance
- Metadata cache to reduce API calls
- "Close to Redis speed" for small object retrieval
3. **Cost-Effective Model**
- Pay only for storage used and transferred
- No wasted capacity from over-provisioning
- Automatic global replication included
- S3 durability with CDN performance
### API-Driven Architecture Eliminates File Watching Concerns
**Critical Insight**: All file access (reads/writes) in basic-memory-cloud go through the API layer:
- **MCP Tools → API**: All Basic Memory operations use FastAPI endpoints
- **Web App → API**: Frontend uses API for all data modifications
- **File watching is NOT required** for cloud operations, unlike local BM which uses the WatchService to monitor file changes.
This means:
- **Cloud Operations**: Manual sync after API writes is sufficient
- **Local Development**: File watching only matters for local editing experience
- **Performance Risk**: Dramatically reduced since we're not dependent on inotify over network filesystems
### Realistic Local Access Expectations
**Baseline Functionality (Guaranteed):**
- Read-only mounting for browsing cloud files
- Easy download/upload of entire projects
- File copying via standard filesystem operations
**Stretch Goal (Test in POC):**
- Live editing with eventual consistency (1-5 second delays acceptable)
- Automatic sync for local changes
- Not required for core functionality - pure upside if it works
### Production Deployment Advantages
1. **Multi-Region Deployment**: Tigris handles global replication automatically
2. **Zero-Downtime Updates**: No volume detach/attach during deployments
3. **Tenant Migrations**: Simply update credentials, no data movement
4. **Disaster Recovery**: Built into S3 durability model (99.999999999% durability)
5. **Auto-Scaling**: Storage scales with usage, no capacity planning needed
## How (High Level)
### POC Approach: Server-First Validation
**Rationale**: Start with server-side TigrisFS mounting because:
- Local access is meaningless if cloud containers can't mount TigrisFS reliably
- Container startup and API performance are critical path blockers
- TigrisFS compatibility with Basic Memory operations must be proven first
- Each phase gates the next - no point testing local access if server-side fails
### Phase 1: Server-Side TigrisFS Validation (Critical Foundation) ✅ COMPLETE
- [x] Set up Tigris bucket with test data via Fly.io integration
- [x] Create container image with TigrisFS support and dependencies
- [x] Test TigrisFS mounting in containerized environment
- [x] Run Basic Memory API operations over mounted TigrisFS
- [x] Validate all filesystem operations work correctly
- [x] Measure container startup time and resource usage
**Production Validation Results**: Container successfully deployed and operated for 42+ minutes serving real MCP requests with repository queries, knowledge graph navigation, and full Basic Memory API functionality over TigrisFS-mounted storage.
### Phase 2: Database Migration to Turso
- [ ] Set up Turso account and test database
- [ ] Modify Basic Memory to accept external DATABASE_URL
- [ ] Test all MCP tools with remote SQLite via Turso
- [ ] Validate performance and functionality parity
- [ ] Test API write → manual sync workflow in container
### Phase 3: Production Container Integration
- [ ] Implement tenant-specific credential management for buckets
- [x] Test container startup with automatic TigrisFS mounting
- [ ] Validate isolation between tenant containers
- [ ] Test API operations under realistic load
- [ ] Measure performance vs current Fly volume setup
### Phase 4: Local Access Validation (Bonus Feature)
- [ ] Test local TigrisFS mounting of tenant data
- [ ] Validate read-only access for browsing/downloading
- [ ] Test file copying and upload workflows
- [ ] Measure latency impact on user experience
- [ ] Test live editing if file watching works (stretch goal)
### Architecture Overview
```
Local Development:
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Local TigrisFS │───▶│ Tigris Bucket │◀───│ Tenant Container│
│ Mount │ │ (Global CDN) │ │ TigrisFS mount │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │
▼ ▼
┌─────────────────┐ ┌─────────────────┐
│ Basic Memory │ │ Basic Memory │
│ (local files) │ │ API + mounted │
└─────────────────┘ └─────────────────┘
│ │
▼ ▼
┌─────────────────┐ ┌─────────────────┐
│ Turso Database │◀───────────────────────────│ Turso Database │
│ (shared index) │ │ (shared index) │
└─────────────────┘ └─────────────────┘
Flow: API writes → Manual sync → Index update
Local: File watching (if available) → Auto sync
```
## How to Evaluate
### Success Criteria
- [x] **Filesystem Compatibility**: Basic Memory operates without modification over TigrisFS-mounted storage
- [x] **Performance Acceptable**: API-driven operations perform within acceptable latency (target: <500ms for typical operations)
- [ ] **Database Functionality**: All Basic Memory features work with Turso remote SQLite
- [x] **Container Reliability**: Tenant containers start successfully with automatic TigrisFS mounting
- [ ] **Local Access Baseline**: Users can mount cloud files locally for read-only browsing and file copying
- [x] **Data Isolation**: Tenant data remains properly isolated using bucket/database separation
- [ ] **Local Access Stretch**: Live editing with eventual sync (1-5 second delays acceptable)
### Testing Procedure
#### Phase 1: Server-Side Foundation Testing
1. **Container TigrisFS Test**:
```dockerfile
# Test container with TigrisFS mounting
FROM python:3.12
RUN apt-get update && apt-get install -y tigrisfs
# Test startup script
#!/bin/bash
tigrisfs --memory-limit 2048 $TIGRIS_BUCKET /app/data --daemon
cd /app/data && basic-memory sync
basic-memory-api --data-dir /app/data
```
2. **API Operations Validation**:
```bash
# Test all MCP operations over TigrisFS
curl -X POST /api/write_note -d '{"title":"test","content":"content"}'
curl -X GET /api/read_note/test
curl -X GET /api/search_notes?q=content
# Measure: response times, error rates, data consistency
```
#### Phase 2: Database Integration Testing
3. **Turso Integration Test**:
```bash
# Configure Turso connection in container
export DATABASE_URL="libsql://test-db.turso.io?authToken=..."
# Test all MCP tools with remote database
basic-memory tools # Test each tool functionality
# Test API write → manual sync workflow
```
#### Phase 3: Production Readiness Testing
4. **Performance Benchmarking**:
- Container startup time with TigrisFS mounting
- API operation response times (target: <500ms for typical operations)
- Search query performance with Turso (target: comparable to local SQLite)
- TigrisFS cache hit rates and memory usage
- Concurrent tenant isolation
#### Phase 4: Local Access Testing (If Phase 1-3 Succeed)
5. **Local Access Validation**:
```bash
# Test read-only access
tigrisfs tenant-bucket ~/local-tenant
ls -la ~/local-tenant # Browse files
cp ~/local-tenant/notes/* ~/backup/ # Copy files
# Test file watching (stretch goal)
echo "test" > ~/local-tenant/test.md
# Check if changes sync to cloud
```
### Go/No-Go Criteria by Phase
- **Phase 1**: Container must start successfully and serve API requests over TigrisFS
- **Phase 2**: All MCP tools must work with Turso with <2x latency increase
- **Phase 3**: Performance must be within 50% of current Fly volume setup
- **Phase 4**: Local mounting must work reliably for read-only access
### Risk Assessment
**Moderate Risk Items (Mitigated by API-First Architecture)**:
- [ ] TigrisFS performance for local access may have higher latency than local filesystem
- [ ] File watching (`inotify`) over FUSE may be unreliable for local development
- [ ] Network interruptions could cause filesystem errors during local editing
- [ ] Write-back caching could cause data loss if container crashes during flush
**Low Risk Items (API-First Eliminates)**:
- [ ] ~~Real-time file watching~~ - Not required for cloud operations
- [ ] ~~Concurrent write consistency~~ - Single-tenant model with API coordination
- [ ] ~~S3 rate limits~~ - TigrisFS intelligent caching handles this
**Mitigation Strategies**:
- **Performance**: Comprehensive benchmarking with realistic workloads
- **Reliability**: Graceful degradation to read-only local access if live editing fails
- **Data Safety**: Regular sync intervals and write-through mode for critical operations
- **Fallback**: Keep Fly volumes as backup deployment option
### Metrics to Track
- **API Latency**: Response times for MCP tools and web operations
- **Cache Effectiveness**: TigrisFS cache hit rates and memory usage
- **Local Access Performance**: File browsing and copying speeds
- **Reliability**: Success rate of mount operations and data consistency
- **Cost**: Storage usage, API calls, and network transfer costs vs current volumes
## Notes
### Key Architectural Decisions
- **Single tenant per bucket/database**: Simplifies isolation and credential management
- **Maintain POSIX compatibility**: Preserve Basic Memory's existing filesystem assumptions
- **TigrisFS over rclone**: Purpose-built for object storage with intelligent caching
- **Turso for SQLite**: Leverages specialized remote SQLite expertise
- **API-first approach**: Eliminates file watching dependency for cloud operations
### Alternative Approaches Considered
- **S3-native storage backend**: Would require Basic Memory architecture changes
- **Hybrid approach**: Local files + cloud sync (adds complexity)
- **Standard rclone mounting**: Less optimized than TigrisFS for object storage workloads
- **Keep Fly volumes**: Maintains current limitations but proven reliability
### Integration Points
- [ ] Fly.io Tigris integration for bucket provisioning
- [ ] Turso account setup and database provisioning
- [ ] Container image modifications for TigrisFS support
- [ ] Credential management for tenant isolation
- [ ] API modification for manual sync triggers
- [ ] Local client setup documentation for TigrisFS mounting
## Observations
- [architecture] Tigris/Turso split cleanly separates file storage from indexing concerns #storage-separation
- [breakthrough] API-first architecture eliminates file watching dependency for cloud operations #api-first-advantage
- [user-experience] Local mounting of cloud files could be revolutionary for knowledge management #local-cloud-hybrid
- [compatibility] Maintaining POSIX filesystem assumptions preserves Basic Memory's local/cloud compatibility #architecture-preservation
- [simplification] Single tenant per bucket eliminates complex multi-tenancy in storage layer #tenant-isolation
- [performance] TigrisFS intelligent caching could provide near-local performance for common operations #tigrisfs-advantage
- [deployment] Zero-downtime updates become trivial without volume constraints #deployment-simplification
- [benefit] Object storage pricing model could be more favorable than volume pricing #cost-optimization
- [innovation] Read-only local access alone would address major SaaS limitation #competitive-advantage
- [risk-mitigation] API-driven sync reduces performance requirements vs real-time file watching #risk-reduction
## Relations
- implements [[SPEC-6 Explicit Project Parameter Architecture]]
- requires [[Fly.io Tigris Integration]]
- enables [[Local Cloud File Access]]
- alternative_to [[Fly Volume Storage]]
## Links
- https://fly.io/hello/tigris
- https://fly.io/docs/tigris/
- https://www.tigrisdata.com/docs/sdks/fly/data-migration-with-flyctl/
- https://www.tigrisdata.com/docs/training/tigrisfs/
- https://www.tigrisdata.com/blog/tigris-filesystem/
- https://www.tigrisdata.com/docs/quickstarts/rclone/
-886
View File
@@ -1,886 +0,0 @@
---
title: 'SPEC-8: TigrisFS Integration for Tenant API'
Date: September 22, 2025
Status: Phase 3.6 Complete - Tenant Mount API Endpoints Ready for CLI Implementation
Priority: High
Goal: Replace Fly volumes with Tigris bucket provisioning in production tenant API
permalink: spec-8-tigris-fs-integration
---
## Executive Summary
Based on SPEC-7 Phase 4 POC testing, this spec outlines productizing the TigrisFS/rclone implementation in the Basic Memory Cloud tenant API.
We're moving from proof-of-concept to production integration, replacing Fly volume storage with Tigris bucket-per-tenant architecture.
## Current Architecture (Fly Volumes)
### Tenant Provisioning Flow
```python
# apps/cloud/src/basic_memory_cloud/workflows/tenant_provisioning.py
async def provision_tenant_infrastructure(tenant_id: str):
# 1. Create Fly app
# 2. Create Fly volume ← REPLACE THIS
# 3. Deploy API container with volume mount
# 4. Configure health checks
```
### Storage Implementation
- Each tenant gets dedicated Fly volume (1GB-10GB)
- Volume mounted at `/app/data` in API container
- Local filesystem storage with Basic Memory indexing
- No global caching or edge distribution
## Proposed Architecture (Tigris Buckets)
### New Tenant Provisioning Flow
```python
async def provision_tenant_infrastructure(tenant_id: str):
# 1. Create Fly app
# 2. Create Tigris bucket with admin credentials ← NEW
# 3. Store bucket name in tenant record ← NEW
# 4. Deploy API container with TigrisFS mount using admin credentials
# 5. Configure health checks
```
### Storage Implementation
- Each tenant gets dedicated Tigris bucket
- TigrisFS mounts bucket at `/app/data` in API container
- Global edge caching and distribution
- Configurable cache TTL for sync performance
## Implementation Plan
### Phase 1: Bucket Provisioning Service
**✅ IMPLEMENTED: StorageClient with Admin Credentials**
```python
# apps/cloud/src/basic_memory_cloud/clients/storage_client.py
class StorageClient:
async def create_tenant_bucket(self, tenant_id: UUID) -> TigrisBucketCredentials
async def delete_tenant_bucket(self, tenant_id: UUID, bucket_name: str) -> bool
async def list_buckets(self) -> list[TigrisBucketResponse]
async def test_tenant_credentials(self, credentials: TigrisBucketCredentials) -> bool
```
**Simplified Architecture Using Admin Credentials:**
- Single admin access key with full Tigris permissions (configured in console)
- No tenant-specific IAM user creation needed
- Bucket-per-tenant isolation for logical separation
- Admin credentials shared across all tenant operations
**Integrate with Provisioning workflow:**
```python
# Update tenant_provisioning.py
async def provision_tenant_infrastructure(tenant_id: str):
storage_client = StorageClient(settings.aws_access_key_id, settings.aws_secret_access_key)
bucket_creds = await storage_client.create_tenant_bucket(tenant_id)
await store_bucket_name(tenant_id, bucket_creds.bucket_name)
await deploy_api_with_tigris(tenant_id, bucket_creds)
```
### Phase 2: Simplified Bucket Management
**✅ SIMPLIFIED: Admin Credentials + Bucket Names Only**
Since we use admin credentials for all operations, we only need to track bucket names per tenant:
1. **Primary Storage (Fly Secrets)**
```bash
flyctl secrets set -a basic-memory-{tenant_id} \
AWS_ACCESS_KEY_ID="{admin_access_key}" \
AWS_SECRET_ACCESS_KEY="{admin_secret_key}" \
AWS_ENDPOINT_URL_S3="https://fly.storage.tigris.dev" \
AWS_REGION="auto" \
BUCKET_NAME="basic-memory-{tenant_id}"
```
2. **Database Storage (Bucket Name Only)**
```python
# apps/cloud/src/basic_memory_cloud/models/tenant.py
class Tenant(BaseModel):
# ... existing fields
tigris_bucket_name: Optional[str] = None # Just store bucket name
tigris_region: str = "auto"
created_at: datetime
```
**Benefits of Simplified Approach:**
- No credential encryption/decryption needed
- Admin credentials managed centrally in environment
- Only bucket names stored in database (not sensitive)
- Simplified backup/restore scenarios
- Reduced security attack surface
### Phase 3: API Container Updates
**Update API container configuration:**
```dockerfile
# apps/api/Dockerfile
# Add TigrisFS installation
RUN curl -L https://github.com/tigrisdata/tigrisfs/releases/latest/download/tigrisfs-linux-amd64 \
-o /usr/local/bin/tigrisfs && chmod +x /usr/local/bin/tigrisfs
```
**Startup script integration:**
```bash
# apps/api/tigrisfs-startup.sh (already exists)
# Mount TigrisFS → Start Basic Memory API
exec python -m basic_memory_cloud_api.main
```
**Fly.toml environment (optimized for < 5s startup):**
```toml
# apps/api/fly.tigris-production.toml
[env]
TIGRISFS_MEMORY_LIMIT = '1024' # Reduced for faster init
TIGRISFS_MAX_FLUSHERS = '16' # Fewer threads for faster startup
TIGRISFS_STAT_CACHE_TTL = '30s' # Balance sync speed vs startup
TIGRISFS_LAZY_INIT = 'true' # Enable lazy loading
BASIC_MEMORY_HOME = '/app/data'
# Suspend optimization for wake-on-network
[machine]
auto_stop_machines = "suspend" # Faster than full stop
auto_start_machines = true
min_machines_running = 0
```
### Phase 4: Local Access Features
**CLI automation for local mounting:**
```python
# New CLI command: basic-memory cloud mount
async def setup_local_mount(tenant_id: str):
# 1. Fetch bucket credentials from cloud API
# 2. Configure rclone with scoped IAM policy
# 3. Mount via rclone nfsmount (macOS) or FUSE (Linux)
# 4. Start Basic Memory sync watcher
```
**Local mount configuration:**
```bash
# rclone config for tenant
rclone mount basic-memory-{tenant_id}: ~/basic-memory-{tenant_id} \
--nfs-mount \
--vfs-cache-mode writes \
--cache-dir ~/.cache/rclone/basic-memory-{tenant_id}
```
### Phase 5: TigrisFS Cache Sync Solutions
**Problem**: When files are uploaded via CLI/bisync, the tenant API container doesn't see them immediately due to TigrisFS cache (30s TTL) and lack of inotify events on mounted filesystems.
**Multi-Layer Solution:**
**Layer 1: API Sync Endpoint** (Immediate)
```python
# POST /sync - Force TigrisFS cache refresh
# Callable by CLI after uploads
subprocess.run(["sync", "fsync /app/data"], check=True)
```
**Layer 2: Tigris Webhook Integration** (Real-time)
https://www.tigrisdata.com/docs/buckets/object-notifications/#webhook
```python
# Webhook endpoint for bucket changes
@app.post("/webhooks/tigris/{tenant_id}")
async def handle_bucket_notification(tenant_id: str, event: TigrisEvent):
if event.eventName in ["OBJECT_CREATED_PUT", "OBJECT_DELETED"]:
await notify_container_sync(tenant_id, event.object.key)
```
**Layer 3: CLI Sync Notification** (User-triggered)
```bash
# CLI calls container sync endpoint after successful bisync
basic-memory cloud bisync # Automatically notifies container
curl -X POST https://basic-memory-{tenant-id}.fly.dev/sync
```
**Layer 4: Periodic Sync Fallback** (Safety net)
```python
# Background task: fsync /app/data every 30s as fallback
# Ensures eventual consistency even if other layers fail
```
**Implementation Priority:**
1. Layer 1 (API endpoint) - Quick testing capability
2. Layer 3 (CLI integration) - Improved UX
3. Layer 4 (Periodic fallback) - Safety net
4. Layer 2 (Webhooks) - Production real-time sync
## Performance Targets
### Sync Latency
- **Target**: < 5 seconds local→cloud→container
- **Configuration**: `TIGRISFS_STAT_CACHE_TTL = '5s'`
- **Monitoring**: Track sync metrics in production
### Container Startup
- **Target**: < 5 seconds including TigrisFS mount
- **Fast retry**: 0.5s intervals for mount verification
- **Fallback**: Container fails fast if mount fails
### Memory Usage
- **TigrisFS cache**: 2GB memory limit per container
- **Concurrent uploads**: 32 flushers max
- **VM sizing**: shared-cpu-2x (2048mb) minimum
## Security Considerations
### Bucket Isolation
- Each tenant has dedicated bucket
- IAM policies prevent cross-tenant access
- No shared bucket with subdirectories
### Credential Security
- Fly secrets for runtime access
- Encrypted database backup for disaster recovery
- Credential rotation capability
### Data Residency
- Tigris global edge caching
- SOC2 Type II compliance
- Encryption at rest and in transit
## Operational Benefits
### Scalability
- Horizontal scaling with stateless API containers
- Global edge distribution
- Better resource utilization
### Reliability
- No cold starts between tenants
- Built-in redundancy and caching
- Simplified backup strategy
### Cost Efficiency
- Pay-per-use storage pricing
- Shared infrastructure benefits
- Reduced operational overhead
## Risk Mitigation
### Data Loss Prevention
- Dual credential storage (Fly + database)
- Automated backup workflows to R2/S3
- Tigris built-in redundancy
### Performance Degradation
- Configurable cache settings per tenant
- Monitoring and alerting on sync latency
- Fallback to volume storage if needed
### Security Vulnerabilities
- Bucket-per-tenant isolation
- Regular credential rotation
- Security scanning and monitoring
## Success Metrics
### Technical Metrics
- Sync latency P50 < 5 seconds
- Container startup time < 5 seconds
- Zero data loss incidents
- 99.9% uptime per tenant
### Business Metrics
- Reduced infrastructure costs vs volumes
- Improved user experience with faster sync
- Enhanced enterprise security posture
- Simplified operational overhead
## Open Questions
1. **Tigris rate limits**: What are the API limits for bucket creation?
2. **Cost analysis**: What's the break-even point vs Fly volumes?
3. **Regional preferences**: Should enterprise customers choose regions?
4. **Backup retention**: How long to keep automated backups?
## Implementation Checklist
### Phase 1: Bucket Provisioning Service ✅ COMPLETED
- [x] **Research Tigris bucket API** - Document bucket creation and S3 API compatibility
- [x] **Create StorageClient class** - Implemented with admin credentials and comprehensive integration tests
- [x] **Test bucket creation** - Full test suite validates API integration with real Tigris environment
- [x] **Add bucket provisioning to DBOS workflow** - Integrated StorageClient with tenant_provisioning.py
### Phase 2: Simplified Bucket Management ✅ COMPLETED
- [x] **Update Tenant model** with tigris_bucket_name field (replaced fly_volume_id)
- [x] **Implement bucket name storage** - Database migration and model updates completed
- [x] **Test bucket provisioning integration** - Full test suite validates workflow from tenant creation to bucket assignment
- [x] **Remove volume logic from all tests** - Complete migration from volume-based to bucket-based architecture
### Phase 3: API Container Integration ✅ COMPLETED
- [x] **Update Dockerfile** to install TigrisFS binary in API container with configurable version
- [x] **Optimize tigrisfs-startup.sh** with production-ready security and reliability improvements
- [x] **Create production-ready container** with proper signal handling and mount validation
- [x] **Implement security fixes** based on Claude code review (conditional debug, credential protection)
- [x] **Add proper process supervision** with cleanup traps and error handling
- [x] **Remove debug artifacts** - Cleaned up all debug Dockerfiles and test scripts
### Phase 3.5: IAM Access Key Management ✅ COMPLETED
- [x] **Research Tigris IAM API** - Documented create_policy, attach_user_policy, delete_access_key operations
- [x] **Implement bucket-scoped credential generation** - StorageClient.create_tenant_access_keys() with IAM policies
- [x] **Add comprehensive security test suite** - 5 security-focused integration tests covering all attack vectors
- [x] **Verify cross-bucket access prevention** - Scoped credentials can ONLY access their designated bucket
- [x] **Test credential lifecycle management** - Create, validate, delete, and revoke access keys
- [x] **Validate admin vs scoped credential isolation** - Different access patterns and security boundaries
- [x] **Test multi-tenant isolation** - Multiple tenants cannot access each other's buckets
### Phase 3.6: Tenant Mount API Endpoints ✅ COMPLETED
- [x] **Implement GET /tenant/mount/info** - Returns mount info without exposing credentials
- [x] **Implement POST /tenant/mount/credentials** - Creates new bucket-scoped credentials for CLI mounting
- [x] **Implement DELETE /tenant/mount/credentials/{cred_id}** - Revoke specific credentials with proper cleanup
- [x] **Implement GET /tenant/mount/credentials** - List active credentials without exposing secrets
- [x] **Add TenantMountCredentials database model** - Tracks credential metadata (no secret storage)
- [x] **Create comprehensive test suite** - 28 tests covering all scenarios including multi-session support
- [x] **Implement multi-session credential flow** - Multiple active credentials per tenant supported
- [x] **Secure credential handling** - Secret keys never stored, returned once only for immediate use
- [x] **Add dependency injection for StorageClient** - Clean integration with existing API architecture
- [x] **Fix Tigris configuration for cloud service** - Added AWS environment variables to fly.template.toml
- [x] **Update tenant machine configurations** - Include AWS credentials for TigrisFS mounting with clear credential strategy
**Security Test Results:**
```
✅ Cross-bucket access prevention - PASS
✅ Deleted credentials access revoked - PASS
✅ Invalid credentials rejected - PASS
✅ Admin vs scoped credential isolation - PASS
✅ Multiple scoped credentials isolation - PASS
```
**Implementation Details:**
- Uses Tigris IAM managed policies (create_policy + attach_user_policy)
- Bucket-scoped S3 policies with Actions: GetObject, PutObject, DeleteObject, ListBucket
- Resource ARNs limited to specific bucket: `arn:aws:s3:::bucket-name` and `arn:aws:s3:::bucket-name/*`
- Access keys follow Tigris format: `tid_` prefix with secure random suffix
- Complete cleanup on deletion removes both access keys and associated policies
### Phase 4: Local Access CLI
- [x] **Design local mount CLI command** for automated rclone configuration
- [x] **Implement credential fetching** from cloud API for local setup
- [x] **Create rclone config automation** for tenant-specific bucket mounting
- [x] **Test local→cloud→container sync** with optimized cache settings
- [x] **Document local access setup** for beta users
### Phase 5: Webhook Integration (Future)
- [ ] **Research Tigris webhook API** for object notifications and payload format
- [ ] **Design webhook endpoint** for real-time sync notifications
- [ ] **Implement notification handling** to trigger Basic Memory sync events
- [ ] **Test webhook delivery** and sync latency improvements
## Success Metrics
- [ ] **Container startup < 5 seconds** including TigrisFS mount and Basic Memory init
- [ ] **Sync latency < 5 seconds** for local→cloud→container file changes
- [ ] **Zero data loss** during bucket provisioning and credential management
- [ ] **100% test coverage** for new TigrisBucketService and credential functions
- [ ] **Beta deployment** with internal users validating local-cloud workflow
## Implementation Notes
## Phase 4.1: Bidirectional Sync with rclone bisync (NEW)
### Problem Statement
During testing, we discovered that some applications (particularly Obsidian) don't detect file changes over NFS mounts. Rather than building a custom sync daemon, we can leverage `rclone bisync` - rclone's built-in bidirectional synchronization feature.
### Solution: rclone bisync
Use rclone's proven bidirectional sync instead of custom implementation:
**Core Architecture:**
```bash
# rclone bisync handles all the complexity
rclone bisync ~/basic-memory-{tenant_id} basic-memory-{tenant_id}:{bucket_name} \
--create-empty-src-dirs \
--conflict-resolve newer \
--resilient \
--check-access
```
**Key Benefits:**
- ✅ **Battle-tested**: Production-proven rclone functionality
- ✅ **MIT licensed**: Open source with permissive licensing
- ✅ **No custom code**: Zero maintenance burden for sync logic
- ✅ **Built-in safety**: max-delete protection, conflict resolution
- ✅ **Simple installation**: Works with Homebrew rclone (no FUSE needed)
- ✅ **File watcher compatible**: Works with Obsidian and all applications
- ✅ **Offline support**: Can work offline and sync when connected
### bisync Conflict Resolution Options
**Built-in conflict strategies:**
```bash
--conflict-resolve none # Keep both files with .conflict suffixes (safest)
--conflict-resolve newer # Always pick the most recently modified file
--conflict-resolve larger # Choose based on file size
--conflict-resolve path1 # Always prefer local changes
--conflict-resolve path2 # Always prefer cloud changes
```
### Sync Profiles Using bisync
**Profile configurations:**
```python
BISYNC_PROFILES = {
"safe": {
"conflict_resolve": "none", # Keep both versions
"max_delete": 10, # Prevent mass deletion
"check_access": True, # Verify sync integrity
"description": "Safe mode with conflict preservation"
},
"balanced": {
"conflict_resolve": "newer", # Auto-resolve to newer file
"max_delete": 25,
"check_access": True,
"description": "Balanced mode (recommended default)"
},
"fast": {
"conflict_resolve": "newer",
"max_delete": 50,
"check_access": False, # Skip verification for speed
"description": "Fast mode for rapid iteration"
}
}
```
### CLI Commands
**Manual sync commands:**
```bash
basic-memory cloud bisync # Manual bidirectional sync
basic-memory cloud bisync --dry-run # Preview changes
basic-memory cloud bisync --profile safe # Use specific profile
basic-memory cloud bisync --resync # Force full baseline resync
```
**Watch mode (Step 1):**
```bash
basic-memory cloud bisync --watch # Long-running process, sync every 60s
basic-memory cloud bisync --watch --interval 30s # Custom interval
```
**System integration (Step 2 - Future):**
```bash
basic-memory cloud bisync-service install # Install as system service
basic-memory cloud bisync-service start # Start background service
basic-memory cloud bisync-service status # Check service status
```
### Implementation Strategy
**Phase 4.1.1: Core bisync Implementation**
- [ ] Implement `run_bisync()` function wrapping rclone bisync
- [ ] Add profile-based configuration (safe/balanced/fast)
- [ ] Create conflict resolution and safety options
- [ ] Test with sample files and conflict scenarios
**Phase 4.1.2: Watch Mode**
- [ ] Add `--watch` flag for continuous sync
- [ ] Implement configurable sync intervals
- [ ] Add graceful shutdown and signal handling
- [ ] Create status monitoring and progress indicators
**Phase 4.1.3: User Experience**
- [ ] Add conflict reporting and resolution guidance
- [ ] Implement dry-run preview functionality
- [ ] Create troubleshooting and diagnostic commands
- [ ] Add filtering configuration (.gitignore-style)
**Phase 4.1.4: System Integration (Future)**
- [ ] Generate platform-specific service files (launchd/systemd)
- [ ] Add service management commands
- [ ] Implement automatic startup and recovery
- [ ] Create monitoring and logging integration
### Technical Implementation
**Core bisync wrapper:**
```python
def run_bisync(
tenant_id: str,
bucket_name: str,
profile: str = "balanced",
dry_run: bool = False
) -> bool:
"""Run rclone bisync with specified profile."""
local_path = Path.home() / f"basic-memory-{tenant_id}"
remote_path = f"basic-memory-{tenant_id}:{bucket_name}"
profile_config = BISYNC_PROFILES[profile]
cmd = [
"rclone", "bisync",
str(local_path), remote_path,
"--create-empty-src-dirs",
"--resilient",
f"--conflict-resolve={profile_config['conflict_resolve']}",
f"--max-delete={profile_config['max_delete']}",
"--filters-file", "~/.basic-memory/bisync-filters.txt"
]
if profile_config.get("check_access"):
cmd.append("--check-access")
if dry_run:
cmd.append("--dry-run")
return subprocess.run(cmd, check=True).returncode == 0
```
**Default filter file (~/.basic-memory/bisync-filters.txt):**
```
- .DS_Store
- .git/**
- __pycache__/**
- *.pyc
- .pytest_cache/**
- node_modules/**
- .conflict-*
- Thumbs.db
- desktop.ini
```
**Advantages Over Custom Daemon:**
- ✅ **Zero maintenance**: No custom sync logic to debug/maintain
- ✅ **Production proven**: Used by thousands in production
- ✅ **Safety features**: Built-in max-delete, conflict handling, recovery
- ✅ **Filtering**: Advanced exclude patterns and rules
- ✅ **Performance**: Optimized for various storage backends
- ✅ **Community support**: Extensive documentation and community
## Phase 4.2: NFS Mount Support (Direct Access)
### Solution: rclone nfsmount
Keep the existing NFS mount functionality for users who prefer direct file access:
**Core Architecture:**
```bash
# rclone nfsmount provides transparent file access
rclone nfsmount basic-memory-{tenant_id}:{bucket_name} ~/basic-memory-{tenant_id} \
--vfs-cache-mode writes \
--dir-cache-time 10s \
--daemon
```
**Key Benefits:**
- ✅ **Real-time access**: Files appear immediately as they're created/modified
- ✅ **Transparent**: Works with any application that reads/writes files
- ✅ **Low latency**: Direct access without sync delays
- ✅ **Simple**: No periodic sync commands needed
- ✅ **Homebrew compatible**: Works with Homebrew rclone (no FUSE required)
**Limitations:**
- ❌ **File watcher compatibility**: Some apps (Obsidian) don't detect changes over NFS
- ❌ **Network dependency**: Requires active connection to cloud storage
- ❌ **Potential conflicts**: Simultaneous edits from multiple locations can cause issues
### Mount Profiles (Existing)
**Already implemented profiles from SPEC-7 testing:**
```python
MOUNT_PROFILES = {
"fast": {
"cache_time": "5s",
"poll_interval": "3s",
"description": "Ultra-fast development (5s sync)"
},
"balanced": {
"cache_time": "10s",
"poll_interval": "5s",
"description": "Fast development (10-15s sync, recommended)"
},
"safe": {
"cache_time": "15s",
"poll_interval": "10s",
"description": "Conflict-aware mount with backup",
"extra_args": ["--conflict-suffix", ".conflict-{DateTimeExt}"]
}
}
```
### CLI Commands (Existing)
**Mount commands already implemented:**
```bash
basic-memory cloud mount # Mount with balanced profile
basic-memory cloud mount --profile fast # Ultra-fast caching
basic-memory cloud mount --profile safe # Conflict detection
basic-memory cloud unmount # Clean unmount
basic-memory cloud mount-status # Show mount status
```
## User Choice: Mount vs Bisync
### When to Use Each Approach
| Use Case | Recommended Solution | Why |
|----------|---------------------|-----|
| **Obsidian users** | `bisync` | File watcher support for live preview |
| **CLI/vim/emacs users** | `mount` | Direct file access, lower latency |
| **Offline work** | `bisync` | Can work offline, sync when connected |
| **Real-time collaboration** | `mount` | Immediate visibility of changes |
| **Multiple machines** | `bisync` | Better conflict handling |
| **Single machine** | `mount` | Simpler, more transparent |
| **Development work** | Either | Both work well, user preference |
| **Large files** | `mount` | Streaming access vs full download |
### Installation Simplicity
**Both approaches now use simple Homebrew installation:**
```bash
# Single installation command for both approaches
brew install rclone
# No macFUSE, no system modifications needed
# Works immediately with both mount and bisync
```
### Implementation Status
**Phase 4.1: bisync** (NEW)
- [ ] Implement bisync command wrapper
- [ ] Add watch mode with configurable intervals
- [ ] Create conflict resolution workflows
- [ ] Add filtering and safety options
**Phase 4.2: mount** (EXISTING - ✅ IMPLEMENTED)
- [x] NFS mount commands with profile support
- [x] Mount management and cleanup
- [x] Process monitoring and health checks
- [x] Credential integration with cloud API
**Both approaches share:**
- [x] Credential management via cloud API
- [x] Secure rclone configuration
- [x] Tenant isolation and bucket scoping
- [x] Simple Homebrew rclone installation
Key Features:
1. Cross-Platform rclone Installation (rclone_installer.py):
- macOS: Homebrew → official script fallback
- Linux: snap → apt → official script fallback
- Windows: winget → chocolatey → scoop fallback
- Automatic version detection and verification
2. Smart rclone Configuration (rclone_config.py):
- Automatic tenant-specific config generation
- Three optimized mount profiles from your SPEC-7 testing:
- fast: 5s sync (ultra-performance)
- balanced: 10-15s sync (recommended default)
- safe: 15s sync + conflict detection
- Backup existing configs before modification
3. Robust Mount Management (mount_commands.py):
- Automatic tenant credential generation
- Mount path management (~/basic-memory-{tenant-id})
- Process lifecycle management (prevent duplicate mounts)
- Orphaned process cleanup
- Mount verification and health checking
4. Clean Architecture (api_client.py):
- Separated API client to avoid circular imports
- Reuses existing authentication infrastructure
- Consistent error handling and logging
User Experience:
One-Command Setup:
basic-memory cloud setup
```bash
# 1. Installs rclone automatically
# 2. Authenticates with existing login
# 3. Generates secure credentials
# 4. Configures rclone
# 5. Performs initial mount
```
Profile-Based Mounting:
basic-memory cloud mount --profile fast # 5s sync
basic-memory cloud mount --profile balanced # 15s sync (default)
basic-memory cloud mount --profile safe # conflict detection
Status Monitoring:
basic-memory cloud mount-status
```bash
# Shows: tenant info, mount path, sync profile, rclone processes
```
### local mount api
Endpoint 1: Get Tenant Info for user
Purpose: Get tenant details for mounting
- pass in jwt
- service returns mount info
**✅ IMPLEMENTED API Specification:**
**Endpoint 1: GET /tenant/mount/info**
- Purpose: Get tenant mount information without exposing credentials
- Authentication: JWT token (tenant_id extracted from claims)
Request:
```
GET /tenant/mount/info
Authorization: Bearer {jwt_token}
```
Response:
```json
{
"tenant_id": "434252dd-d83b-4b20-bf70-8a950ff875c4",
"bucket_name": "basic-memory-434252dd",
"has_credentials": true,
"credentials_created_at": "2025-09-22T16:48:50.414694"
}
```
**Endpoint 2: POST /tenant/mount/credentials**
- Purpose: Generate NEW bucket-scoped S3 credentials for rclone mounting
- Authentication: JWT token (tenant_id extracted from claims)
- Multi-session: Creates new credentials without revoking existing ones
Request:
```
POST /tenant/mount/credentials
Authorization: Bearer {jwt_token}
Content-Type: application/json
```
*Note: No request body needed - tenant_id extracted from JWT*
Response:
```json
{
"tenant_id": "434252dd-d83b-4b20-bf70-8a950ff875c4",
"bucket_name": "basic-memory-434252dd",
"access_key": "test_access_key_12345",
"secret_key": "test_secret_key_abcdef",
"endpoint_url": "https://fly.storage.tigris.dev",
"region": "auto"
}
```
**🔒 Security Notes:**
- Secret key returned ONCE only - never stored in database
- Credentials are bucket-scoped (cannot access other tenants' buckets)
- Multiple active credentials supported per tenant (work laptop + personal machine)
Implementation Notes
Security:
- Both endpoints require JWT authentication
- Extract tenant_id from JWT claims (not request body)
- Generate scoped credentials (not admin credentials)
- Credentials should have bucket-specific access only
Integration Points:
- Use your existing StorageClient from SPEC-8 implementation
- Leverage existing JWT middleware for tenant extraction
- Return same credential format as your Tigris bucket provisioning
Error Handling:
- 401 if not authenticated
- 403 if tenant doesn't exist
- 500 if credential generation fails
**🔄 Design Decisions:**
1. **Secure Credential Flow (No Secret Storage)**
Based on CLI flow analysis, we follow security best practices:
- ✅ API generates both access_key + secret_key via Tigris IAM
- ✅ Returns both in API response for immediate use
- ✅ CLI uses credentials immediately to configure rclone
- ✅ Database stores only metadata (access_key + policy_arn for cleanup)
- ✅ rclone handles secure local credential storage
- ❌ **Never store secret_key in database (even encrypted)**
2. **CLI Credential Flow**
```bash
# CLI calls API
POST /tenant/mount/credentials → {access_key, secret_key, ...}
# CLI immediately configures rclone
rclone config create basic-memory-{tenant_id} s3 \
access_key_id={access_key} \
secret_access_key={secret_key} \
endpoint=https://fly.storage.tigris.dev
# Database tracks metadata only
INSERT INTO tenant_mount_credentials (tenant_id, access_key, policy_arn, ...)
```
3. **Multiple Sessions Supported**
- Users can have multiple active credential sets (work laptop, personal machine, etc.)
- Each credential generation creates a new Tigris access key
- List active credentials via API (shows access_key but never secret)
4. **Failure Handling & Cleanup**
- **Happy Path**: Credentials created → Used immediately → rclone configured
- **Orphaned Credentials**: Background job revokes unused credentials
- **API Failure Recovery**: Retry Tigris deletion with stored policy_arn
- **Status Tracking**: Track tigris_deletion_status (pending/completed/failed)
5. **Event Sourcing & Audit**
- MountCredentialCreatedEvent
- MountCredentialRevokedEvent
- MountCredentialOrphanedEvent (for cleanup)
- Full audit trail for security compliance
6. **Tenant/Bucket Validation**
- Verify tenant exists and has valid bucket before credential generation
- Use existing StorageClient to validate bucket access
- Prevent credential generation for inactive/invalid tenants
📋 **Implemented API Endpoints:**
```
✅ IMPLEMENTED:
GET /tenant/mount/info # Get tenant/bucket info (no credentials exposed)
POST /tenant/mount/credentials # Generate new credentials (returns secret once)
GET /tenant/mount/credentials # List active credentials (no secrets)
DELETE /tenant/mount/credentials/{cred_id} # Revoke specific credentials
```
**API Implementation Status:**
- ✅ **GET /tenant/mount/info**: Returns tenant_id, bucket_name, has_credentials, credentials_created_at
- ✅ **POST /tenant/mount/credentials**: Creates new bucket-scoped access keys, returns access_key + secret_key once
- ✅ **GET /tenant/mount/credentials**: Lists active credentials without exposing secret keys
- ✅ **DELETE /tenant/mount/credentials/{cred_id}**: Revokes specific credentials with proper Tigris IAM cleanup
- ✅ **Multi-session support**: Multiple active credentials per tenant (work laptop + personal machine)
- ✅ **Security**: Secret keys never stored in database, returned once only for immediate use
- ✅ **Comprehensive test suite**: 28 tests covering all scenarios including error handling and multi-session flows
- ✅ **Dependency injection**: Clean integration with existing FastAPI architecture
- ✅ **Production-ready configuration**: Tigris credentials properly configured for tenant machines
🗄️ **Secure Database Schema:**
```sql
CREATE TABLE tenant_mount_credentials (
id UUID PRIMARY KEY,
tenant_id UUID REFERENCES tenant(id),
access_key VARCHAR(255) NOT NULL,
-- secret_key REMOVED - never store secrets (security best practice)
policy_arn VARCHAR(255) NOT NULL, -- For Tigris IAM cleanup
tigris_deletion_status VARCHAR(20) DEFAULT 'pending', -- Track cleanup
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
revoked_at TIMESTAMP NULL,
last_used_at TIMESTAMP NULL, -- Track usage for orphan cleanup
description VARCHAR(255) DEFAULT 'CLI mount credentials'
);
```
**Security Benefits:**
- ✅ Database breach cannot expose secrets
- ✅ Follows "secrets don't persist" security principle
- ✅ Meets compliance requirements (SOC2, etc.)
- ✅ Reduced attack surface
- ✅ CLI gets credentials once and stores securely via rclone
File diff suppressed because it is too large Load Diff
@@ -1,196 +0,0 @@
---
title: 'SPEC-9: Signed Header Tenant Information'
type: spec
permalink: specs/spec-9-signed-header-tenant-information
tags:
- authentication
- tenant-isolation
- proxy
- security
- mcp
---
# SPEC-9: Signed Header Tenant Information
## Why
WorkOS JWT templates don't work with MCP's dynamic client registration requirement, preventing us from getting tenant information directly in JWT tokens. We need an alternative secure method to pass tenant context from the Cloud Proxy Service to tenant instances.
**Problem Context:**
- MCP spec requires dynamic client registration
- WorkOS JWT templates only apply to statically configured clients
- Without tenant information, we can't properly route requests or isolate tenant data
- Current JWT tokens only contain standard OIDC claims (sub, email, etc.)
**Affected Areas:**
- Cloud Proxy Service (`apps/cloud`) - request forwarding
- Tenant API instances (`apps/api`) - tenant context validation
- MCP Gateway (`apps/mcp`) - authentication flow
- Overall tenant isolation security model
## What
Implement HMAC-signed headers that the Cloud Proxy Service adds when forwarding requests to tenant instances. This provides secure, tamper-proof tenant information without relying on JWT custom claims.
**Components:**
- Header signing utility in Cloud Proxy Service
- Header validation middleware in Tenant API instances
- Shared secret configuration across services
- Fallback mechanisms for development and error cases
## How (High Level)
### 1. Header Format
Add these signed headers to all proxied requests:
```
X-BM-Tenant-ID: {tenant_id}
X-BM-Timestamp: {unix_timestamp}
X-BM-Signature: {hmac_sha256_signature}
```
### 2. Signature Algorithm
```python
# Canonical message format
message = f"{tenant_id}:{timestamp}"
# HMAC-SHA256 signature
signature = hmac.new(
key=shared_secret.encode('utf-8'),
msg=message.encode('utf-8'),
digestmod=hashlib.sha256
).hexdigest()
```
### 3. Implementation Flow
#### Cloud Proxy Service (`apps/cloud`)
1. Extract `tenant_id` from authenticated user profile
2. Generate timestamp and canonical message
3. Sign message with shared secret
4. Add headers to request before forwarding to tenant instance
#### Tenant API Instances (`apps/api`)
1. Middleware validates headers on all incoming requests
2. Extract tenant_id, timestamp from headers
3. Verify timestamp is within acceptable window (5 minutes)
4. Recompute signature and compare in constant time
5. If valid, make tenant context available to Basic Memory tools
### 4. Security Properties
- **Authenticity**: Only services with shared secret can create valid signatures
- **Integrity**: Header tampering invalidates signature
- **Replay Protection**: Timestamp prevents reuse of old signatures
- **Non-repudiation**: Each request is cryptographically tied to specific tenant
### 5. Configuration
```bash
# Shared across Cloud Proxy and Tenant instances
BM_TENANT_HEADER_SECRET=randomly-generated-256-bit-secret
# Tenant API configuration
BM_TENANT_HEADER_VALIDATION=true # true (production) | false (dev only)
```
## How to Evaluate
### Unit Tests
- [ ] Header signing utility generates correct signatures
- [ ] Header validation correctly accepts/rejects signatures
- [ ] Timestamp validation within acceptable windows
- [ ] Constant-time signature comparison prevents timing attacks
### Integration Tests
- [ ] End-to-end request flow from MCP client → proxy → tenant
- [ ] Tenant isolation verified with signed headers
- [ ] Error handling for missing/invalid headers
- [ ] Disabled validation in development environment
### Security Validation
- [ ] Shared secret rotation procedure
- [ ] Header tampering detection
- [ ] Clock skew tolerance testing
- [ ] Performance impact measurement
### Production Readiness
- [ ] Logging and monitoring of header validation
- [ ] Graceful degradation for header validation failures
- [ ] Documentation for secret management
- [ ] Deployment configuration templates
## Implementation Notes
### Shared Secret Management
- Generate cryptographically secure 256-bit secret
- Same secret deployed to Cloud Proxy and all Tenant instances
- Consider secret rotation strategy for production
### Error Handling
```python
# Strict mode (production)
if not validate_headers(request):
raise HTTPException(status_code=401, detail="Invalid tenant headers")
# Fallback mode (development)
if not validate_headers(request):
logger.warning("Invalid headers, falling back to default tenant")
tenant_id = "default"
```
### Performance Considerations
- HMAC-SHA256 computation is fast (~microseconds)
- Headers add ~200 bytes to each request
- Validation happens once per request in middleware
## Benefits
**Works with MCP dynamic client registration** - No dependency on JWT custom claims
**Simple and reliable** - Standard HMAC signature approach
**Secure by design** - Cryptographic authenticity and integrity
**Infrastructure controlled** - No external service dependencies
**Easy to implement** - Clear signature algorithm and validation
## Trade-offs
⚠️ **Shared secret management** - Need secure distribution and rotation
⚠️ **Clock synchronization** - Timestamp validation requires reasonably synced clocks
⚠️ **Header visibility** - Headers visible in logs (tenant_id not sensitive)
⚠️ **Additional complexity** - More moving parts in proxy forwarding
## Implementation Tasks
### Cloud Service (Header Signing)
- [ ] Create `utils/header_signing.py` with HMAC-SHA256 signing function
- [ ] Add `bm_tenant_header_secret` to Cloud service configuration
- [ ] Update `ProxyService.forward_request()` to call signing utility
- [ ] Add signed headers (X-BM-Tenant-ID, X-BM-Timestamp, X-BM-Signature)
### Tenant API (Header Validation)
- [ ] Create `utils/header_validation.py` with signature verification
- [ ] Add `bm_tenant_header_secret` to API service configuration
- [ ] Create `TenantHeaderValidationMiddleware` class
- [ ] Add middleware to FastAPI app (before other middleware)
- [ ] Skip validation for `/health` endpoint
- [ ] Store validated tenant_id in request.state
### Testing
- [ ] Unit test for header signing utility
- [ ] Unit test for header validation utility
- [ ] Integration test for proxy → tenant flow
- [ ] Test invalid/missing header handling
- [ ] Test timestamp window validation
- [ ] Test signature tampering detection
### Configuration & Deployment
- [ ] Update `.env.example` with BM_TENANT_HEADER_SECRET
- [ ] Generate secure 256-bit secret for production
- [ ] Update Fly.io secrets for both services
- [ ] Document secret rotation procedure
## Status
- [x] **Specification Complete** - Design finalized and documented
- [ ] **Implementation Started** - Header signing utility development
- [ ] **Cloud Proxy Updated** - ProxyService adds signed headers
- [ ] **Tenant Validation Added** - Middleware validates headers
- [ ] **Testing Complete** - All validation criteria met
- [ ] **Production Deployed** - Live with tenant isolation via headers
@@ -1,390 +0,0 @@
---
title: 'SPEC-9-1 Follow-Ups: Conflict, Sync, and Observability'
type: tasklist
permalink: specs/spec-9-follow-ups-conflict-sync-and-observability
related: specs/spec-9-multi-project-bisync
status: revised
revision_date: 2025-10-03
---
# SPEC-9-1 Follow-Ups: Conflict, Sync, and Observability
**REVISED 2025-10-03:** Simplified to leverage rclone built-ins instead of custom conflict handling.
**Context:** SPEC-9 delivered multi-project bidirectional sync and a unified CLI. This follow-up focuses on **observability and safety** using rclone's built-in capabilities rather than reinventing conflict handling.
**Design Philosophy: "Be Dumb Like Git"**
- Let rclone bisync handle conflict detection (it already does this)
- Make conflicts visible and recoverable, don't prevent them
- Cloud is always the winner on conflict (cloud-primary model)
- Users who want version history can just use Git locally in their sync directory
**What Changed from Original Version:**
- **Replaced:** Custom `.bmmeta` sidecars → Use rclone's `.bisync/` state tracking
- **Replaced:** Custom conflict detection → Use rclone bisync 3-way merge
- **Replaced:** Tombstone files → rclone delete tracking handles this
- **Replaced:** Distributed lease → Local process lock only (document multi-device warning)
- **Replaced:** S3 versioning service → Users just use Git locally if they want history
- **Deferred:** SPEC-14 Git integration → Postponed to teams/multi-user features
## ✅ Now
- [ ] **Local process lock**: Prevent concurrent bisync runs on same device (`~/.basic-memory/sync.lock`)
- [ ] **Structured sync reports**: Parse rclone bisync output into JSON reports (creates/updates/deletes/conflicts, bytes, duration); `bm sync --report`
- [ ] **Multi-device warning**: Document that users should not run `--watch` on multiple devices simultaneously
- [ ] **Version control guidance**: Document pattern for users to use Git locally in their sync directory if they want version history
- [ ] **Docs polish**: cloud-mode toggle, mount↔bisync directory isolation, conflict semantics, quick start, migration guide, short demo clip/GIF
## 🔜 Next
- [ ] **Observability commands**: `bm conflicts list`, `bm sync history` to view sync reports and conflicts
- [ ] **Conflict resolution UI**: `bm conflicts resolve <file>` to interactively pick winner from conflict files
- [ ] **Selective sync**: allow include/exclude by project; per-project profile (safe/balanced/fast)
## 🧭 Later
- [ ] **Near real-time sync**: File watcher → targeted `rclone copy` for individual files (keep bisync as backstop)
- [ ] **Sharing / scoped tokens**: cross-tenant/project access
- [ ] **Bandwidth controls & backpressure**: policy for large repos
- [ ] **Client-side encryption (optional)**: with clear trade-offs
## 📏 Acceptance criteria (for "Now" items)
- [ ] Local process lock prevents concurrent bisync runs on same device
- [ ] rclone bisync conflict files visible and documented (`file.conflict1.md`, `file.conflict2.md`)
- [ ] `bm sync --report` generates parsable JSON with sync statistics
- [ ] Documentation clearly warns about multi-device `--watch` mode
- [ ] Documentation shows users how to use Git locally for version history
## What We're NOT Building (Deferred to rclone)
- ❌ Custom `.bmmeta` sidecars (rclone tracks state in `.bisync/` workdir)
- ❌ Custom conflict detection (rclone bisync already does 3-way merge detection)
- ❌ Tombstone files (S3 versioning + rclone delete tracking handles this)
- ❌ Distributed lease (low probability issue, rclone detects state divergence)
- ❌ Rename/move tracking (rclone has size+modtime heuristics built-in)
## Implementation Summary
**Current State (SPEC-9):**
- ✅ rclone bisync with 3 profiles (safe/balanced/fast)
- ✅ `--max-delete` safety limits (10/25/50 files)
- ✅ `--conflict-resolve=newer` for auto-resolution
- ✅ Watch mode: `bm sync --watch` (60s intervals)
- ✅ Integrity checking: `bm cloud check`
- ✅ Mount vs bisync directory isolation
**What's Needed (This Spec):**
1. **Process lock** - Simple file-based lock in `~/.basic-memory/sync.lock`
2. **Sync reports** - Parse rclone output, save to `~/.basic-memory/sync-history/`
3. **Documentation** - Multi-device warnings, conflict resolution workflow, Git usage pattern
**User Model:**
- Cloud is always the winner on conflict (cloud-primary)
- rclone creates `.conflict` files for divergent edits
- Users who want version history just use Git in their local sync directory
- Users warned: don't run `--watch` on multiple devices
## Decision Rationale & Trade-offs
### Why Trust rclone Instead of Custom Conflict Handling?
**rclone bisync already provides:**
- 3-way merge detection (compares local, remote, and last-known state)
- File state tracking in `.bisync/` workdir (hashes, modtimes)
- Automatic conflict file creation: `file.conflict1.md`, `file.conflict2.md`
- Rename detection via size+modtime heuristics
- Delete tracking (prevents resurrection of deleted files)
- Battle-tested with extensive edge case handling
**What we'd have to build with custom approach:**
- Per-file metadata tracking (`.bmmeta` sidecars)
- 3-way diff algorithm
- Conflict detection logic
- Tombstone files for deletes
- Rename/move detection
- Testing for all edge cases
**Decision:** Use what rclone already does well. Don't reinvent the wheel.
### Why Let Users Use Git Locally Instead of Building Versioning?
**The simplest solution: Just use Git**
Users who want version history can literally just use Git in their sync directory:
```bash
cd ~/basic-memory-cloud-sync/
git init
git add .
git commit -m "backup"
# Push to their own GitHub if they want
git remote add origin git@github.com:user/my-knowledge.git
git push
```
**Why this is perfect:**
- ✅ We build nothing
- ✅ Users who want Git... just use Git
- ✅ Users who don't care... don't need to
- ✅ rclone bisync already handles sync conflicts
- ✅ Users own their data, they can version it however they want (Git, Time Machine, etc.)
**What we'd have to build for S3 versioning:**
- API to enable versioning on Tigris buckets
- **Problem**: Tigris doesn't support S3 bucket versioning
- Restore commands: `bm cloud restore --version-id`
- Version listing: `bm cloud versions <path>`
- Lifecycle policies for version retention
- Documentation and user education
**What we'd have to build for SPEC-14 Git integration:**
- Committer service (daemon watching `/app/data/`)
- Puller service (webhook handler for GitHub pushes)
- Git LFS for large files
- Loop prevention between Git ↔ bisync ↔ local
- Merge conflict handling at TWO layers (rclone + Git)
- Webhook infrastructure and monitoring
**Decision:** Don't build version control. Document the pattern. "The easiest problem to solve is the one you avoid."
**When to revisit:** Teams/multi-user features where server-side version control becomes necessary for collaboration.
### Why No Distributed Lease?
**Low probability issue:**
- Requires user to manually run `bm sync` on multiple devices at exact same time
- Most users run `--watch` on one primary device
- rclone bisync detects state divergence and fails safely
**Safety nets in place:**
- Local process lock prevents concurrent runs on same device
- rclone bisync aborts if bucket state changed during sync
- S3 versioning recovers from any overwrites
- Documentation warns against multi-device `--watch`
**Failure mode:**
```bash
# Device A and B sync simultaneously
Device A: bm sync → succeeds
Device B: bm sync → "Error: path has changed, run --resync"
# User fixes with resync
Device B: bm sync --resync → establishes new baseline
```
**Decision:** Document the issue, add local lock, defer distributed coordination until users report actual problems.
### Cloud-Primary Conflict Model
**User mental model:**
- Cloud is the source of truth (like Dropbox/iCloud)
- Local is working copy
- On conflict: cloud wins, local edits → `.conflict` file
- User manually picks winner
**Why this works:**
- Simpler than bidirectional merge (no automatic resolution risk)
- Matches user expectations from Dropbox
- S3 versioning provides safety net for overwrites
- Clear recovery path: restore from S3 version if needed
**Example workflow:**
```bash
# Edit file on Device A and Device B while offline
# Both devices come online and sync
Device A: bm sync
# → Pushes to cloud first, becomes canonical version
Device B: bm sync
# → Detects conflict
# → Cloud version: work/notes.md
# → Local version: work/notes.md.conflict1
# → User manually merges or picks winner
# Restore if needed
bm cloud restore work/notes.md --version-id abc123
```
## Implementation Details
### 1. Local Process Lock
```python
# ~/.basic-memory/sync.lock
import os
import psutil
from pathlib import Path
class SyncLock:
def __init__(self):
self.lock_file = Path.home() / '.basic-memory' / 'sync.lock'
def acquire(self):
if self.lock_file.exists():
pid = int(self.lock_file.read_text())
if psutil.pid_exists(pid):
raise BisyncError(
f"Sync already running (PID {pid}). "
f"Wait for completion or kill stale process."
)
# Stale lock, remove it
self.lock_file.unlink()
self.lock_file.write_text(str(os.getpid()))
def release(self):
if self.lock_file.exists():
self.lock_file.unlink()
def __enter__(self):
self.acquire()
return self
def __exit__(self, *args):
self.release()
# Usage
with SyncLock():
run_rclone_bisync()
```
### 3. Sync Report Parsing
```python
# Parse rclone bisync output
import json
from datetime import datetime
from pathlib import Path
def parse_sync_report(rclone_output: str, duration: float, exit_code: int) -> dict:
"""Parse rclone bisync output into structured report."""
# rclone bisync outputs lines like:
# "Synching Path1 /local/path with Path2 remote:bucket"
# "- Path1 File was copied to Path2"
# "Bisync successful"
report = {
"timestamp": datetime.now().isoformat(),
"duration_seconds": duration,
"exit_code": exit_code,
"success": exit_code == 0,
"files_created": 0,
"files_updated": 0,
"files_deleted": 0,
"conflicts": [],
"errors": []
}
for line in rclone_output.split('\n'):
if 'was copied to' in line:
report['files_created'] += 1
elif 'was updated in' in line:
report['files_updated'] += 1
elif 'was deleted from' in line:
report['files_deleted'] += 1
elif '.conflict' in line:
report['conflicts'].append(line.strip())
elif 'ERROR' in line:
report['errors'].append(line.strip())
return report
def save_sync_report(report: dict):
"""Save sync report to history."""
history_dir = Path.home() / '.basic-memory' / 'sync-history'
history_dir.mkdir(parents=True, exist_ok=True)
timestamp = datetime.now().strftime('%Y%m%d-%H%M%S')
report_file = history_dir / f'{timestamp}.json'
report_file.write_text(json.dumps(report, indent=2))
# Usage in run_bisync()
start_time = time.time()
result = subprocess.run(bisync_cmd, capture_output=True, text=True)
duration = time.time() - start_time
report = parse_sync_report(result.stdout, duration, result.returncode)
save_sync_report(report)
if report['conflicts']:
console.print(f"[yellow]⚠ {len(report['conflicts'])} conflict(s) detected[/yellow]")
console.print("[dim]Run 'bm conflicts list' to view[/dim]")
```
### 4. User Commands
```bash
# View sync history
bm sync history
# → Lists recent syncs from ~/.basic-memory/sync-history/*.json
# → Shows: timestamp, duration, files changed, conflicts, errors
# View current conflicts
bm conflicts list
# → Scans sync directory for *.conflict* files
# → Shows: file path, conflict versions, timestamps
# Restore from S3 version
bm cloud restore work/notes.md --version-id abc123
# → Uses aws s3api get-object with version-id
# → Downloads to original path
bm cloud restore work/notes.md --timestamp "2025-10-03 14:30"
# → Lists versions, finds closest to timestamp
# → Downloads that version
# List file versions
bm cloud versions work/notes.md
# → Uses aws s3api list-object-versions
# → Shows: version-id, timestamp, size, author
# Interactive conflict resolution
bm conflicts resolve work/notes.md
# → Shows both versions side-by-side
# → Prompts: Keep local, keep cloud, merge manually, restore from S3 version
# → Cleans up .conflict files after resolution
```
## Success Metrics & Monitoring
**Phase 1 (v1) - Basic Safety:**
- [ ] Conflict detection rate < 5% of syncs (measure in telemetry)
- [ ] User can resolve conflicts within 5 minutes (UX testing)
- [ ] Documentation prevents 90% of multi-device issues
**Phase 2 (v2) - Observability:**
- [ ] 80% of users check `bm sync history` when troubleshooting
- [ ] Average time to restore from S3 version < 2 minutes
-
- [ ] Conflict resolution success rate > 95%
**What to measure:**
```python
# Telemetry in sync reports
{
"conflict_rate": conflicts / total_syncs,
"multi_device_collisions": count_state_divergence_errors,
"version_restores": count_restore_operations,
"avg_sync_duration": sum(durations) / count,
"max_delete_trips": count_max_delete_aborts
}
```
**When to add distributed lease:**
- Multi-device collision rate > 5% of syncs
- User complaints about state divergence errors
- Evidence that local lock isn't sufficient
**When to revisit Git (SPEC-14):**
- Teams feature launches (multi-user collaboration)
- Users request commit messages / audit trail
- PR-based review workflow becomes valuable
## Links
- SPEC-9: `specs/spec-9-multi-project-bisync`
- SPEC-14: `specs/spec-14-cloud-git-versioning` (deferred in favor of S3 versioning)
- rclone bisync docs: https://rclone.org/bisync/
- Tigris S3 versioning: https://www.tigrisdata.com/docs/buckets/versioning/
---
**Owner:** <assign> | **Review cadence:** weekly in standup | **Last updated:** 2025-10-03
+1 -1
View File
@@ -1,7 +1,7 @@
"""basic-memory - Local-first knowledge management combining Zettelkasten with knowledge graphs"""
# Package version - updated by release automation
__version__ = "0.16.2"
__version__ = "0.20.3"
# API version for FastAPI - independent of package version
__api_version__ = "v0"
+114 -24
View File
@@ -1,17 +1,36 @@
"""Alembic environment configuration."""
import asyncio
import os
from logging.config import fileConfig
from sqlalchemy import engine_from_config
from sqlalchemy import pool
# Allow nested event loops (needed for pytest-asyncio and other async contexts)
# Note: nest_asyncio doesn't work with uvloop or Python 3.14+, so we handle those cases separately
import sys
if sys.version_info < (3, 14):
try:
import nest_asyncio
nest_asyncio.apply()
except (ImportError, ValueError):
# nest_asyncio not available or can't patch this loop type (e.g., uvloop)
pass
# For Python 3.14+, we rely on the thread-based fallback in run_migrations_online()
from sqlalchemy import engine_from_config, pool
from sqlalchemy.ext.asyncio import AsyncEngine, create_async_engine
from alembic import context
from basic_memory.config import ConfigManager
# set config.env to "test" for pytest to prevent logging to file in utils.setup_logging()
os.environ["BASIC_MEMORY_ENV"] = "test"
# Trigger: only set test env when actually running under pytest
# Why: alembic/env.py is imported during normal operations (MCP server startup, migrations)
# but we only want test behavior during actual test runs
# Outcome: prevents is_test_env from returning True in production, enabling watch service
if os.getenv("PYTEST_CURRENT_TEST") is not None:
os.environ["BASIC_MEMORY_ENV"] = "test"
# Import after setting environment variable # noqa: E402
from basic_memory.models import Base # noqa: E402
@@ -20,12 +39,22 @@ from basic_memory.models import Base # noqa: E402
# access to the values within the .ini file in use.
config = context.config
# Load app config - this will read environment variables (BASIC_MEMORY_DATABASE_BACKEND, etc.)
# due to Pydantic's env_prefix="BASIC_MEMORY_" setting
app_config = ConfigManager().config
# Set the SQLAlchemy URL from our app config
sqlalchemy_url = f"sqlite:///{app_config.database_path}"
config.set_main_option("sqlalchemy.url", sqlalchemy_url)
# print(f"Using SQLAlchemy URL: {sqlalchemy_url}")
# Set the SQLAlchemy URL based on database backend configuration
# If the URL is already set in config (e.g., from run_migrations), use that
# Otherwise, get it from app config
# Note: alembic.ini has a placeholder URL "driver://user:pass@localhost/dbname" that we need to override
current_url = config.get_main_option("sqlalchemy.url")
if not current_url or current_url == "driver://user:pass@localhost/dbname":
from basic_memory.db import DatabaseType
sqlalchemy_url = DatabaseType.get_db_url(
app_config.database_path, DatabaseType.FILESYSTEM, app_config
)
config.set_main_option("sqlalchemy.url", sqlalchemy_url)
# Interpret the config file for Python logging.
if config.config_file_name is not None:
@@ -69,28 +98,89 @@ def run_migrations_offline() -> None:
context.run_migrations()
def do_run_migrations(connection):
"""Execute migrations with the given connection."""
context.configure(
connection=connection,
target_metadata=target_metadata,
include_object=include_object,
render_as_batch=True,
compare_type=True,
)
with context.begin_transaction():
context.run_migrations()
async def run_async_migrations(connectable):
"""Run migrations asynchronously with AsyncEngine."""
async with connectable.connect() as connection:
await connection.run_sync(do_run_migrations)
await connectable.dispose()
def run_migrations_online() -> None:
"""Run migrations in 'online' mode.
In this scenario we need to create an Engine
and associate a connection with the context.
Supports both sync engines (SQLite) and async engines (PostgreSQL with asyncpg).
"""
connectable = engine_from_config(
config.get_section(config.config_ini_section, {}),
prefix="sqlalchemy.",
poolclass=pool.NullPool,
)
# Check if a connection/engine was provided (e.g., from run_migrations)
connectable = context.config.attributes.get("connection", None)
with connectable.connect() as connection:
context.configure(
connection=connection,
target_metadata=target_metadata,
include_object=include_object,
render_as_batch=True,
)
if connectable is None:
# No connection provided, create engine from config
url = context.config.get_main_option("sqlalchemy.url")
with context.begin_transaction():
context.run_migrations()
# Check if it's an async URL (sqlite+aiosqlite or postgresql+asyncpg)
if url and ("+asyncpg" in url or "+aiosqlite" in url):
# Create async engine for asyncpg or aiosqlite
connectable = create_async_engine(
url,
poolclass=pool.NullPool,
future=True,
)
else:
# Create sync engine for regular sqlite or postgresql
connectable = engine_from_config(
context.config.get_section(context.config.config_ini_section, {}),
prefix="sqlalchemy.",
poolclass=pool.NullPool,
)
# Handle async engines (PostgreSQL with asyncpg)
if isinstance(connectable, AsyncEngine):
# Try to run async migrations
# nest_asyncio allows asyncio.run() from within event loops, but doesn't work with uvloop
try:
asyncio.run(run_async_migrations(connectable))
except RuntimeError as e:
if "cannot be called from a running event loop" in str(e):
# We're in a running event loop (likely uvloop) - need to use a different approach
# Create a new thread to run the async migrations
import concurrent.futures
def run_in_thread():
"""Run async migrations in a new event loop in a separate thread."""
new_loop = asyncio.new_event_loop()
asyncio.set_event_loop(new_loop)
try:
new_loop.run_until_complete(run_async_migrations(connectable))
finally:
new_loop.close()
with concurrent.futures.ThreadPoolExecutor() as executor:
future = executor.submit(run_in_thread)
future.result() # Wait for completion and re-raise any exceptions
else:
raise
else:
# Handle sync engines (SQLite) or sync connections
if hasattr(connectable, "connect"):
# It's an engine, get a connection
with connectable.connect() as connection:
do_run_migrations(connection)
else:
# It's already a connection
do_run_migrations(connectable)
if context.is_offline_mode():
@@ -0,0 +1,131 @@
"""Add Postgres full-text search support with tsvector and GIN indexes
Revision ID: 314f1ea54dc4
Revises: e7e1f4367280
Create Date: 2025-11-15 18:05:01.025405
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = "314f1ea54dc4"
down_revision: Union[str, None] = "e7e1f4367280"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Add PostgreSQL full-text search support.
This migration:
1. Creates search_index table for Postgres (SQLite uses FTS5 virtual table)
2. Adds generated tsvector column for full-text search
3. Creates GIN index on the tsvector column for fast text queries
4. Creates GIN index on metadata JSONB column for fast containment queries
Note: These changes only apply to Postgres. SQLite continues to use FTS5 virtual tables.
"""
# Check if we're using Postgres
connection = op.get_bind()
if connection.dialect.name == "postgresql":
# Create search_index table for Postgres
# For SQLite, this is a FTS5 virtual table created elsewhere
from sqlalchemy.dialects.postgresql import JSONB
op.create_table(
"search_index",
sa.Column("id", sa.Integer(), nullable=False), # Entity IDs are integers
sa.Column("project_id", sa.Integer(), nullable=False), # Multi-tenant isolation
sa.Column("title", sa.Text(), nullable=True),
sa.Column("content_stems", sa.Text(), nullable=True),
sa.Column("content_snippet", sa.Text(), nullable=True),
sa.Column("permalink", sa.String(), nullable=True), # Nullable for non-markdown files
sa.Column("file_path", sa.String(), nullable=True),
sa.Column("type", sa.String(), nullable=True),
sa.Column("from_id", sa.Integer(), nullable=True), # Relation IDs are integers
sa.Column("to_id", sa.Integer(), nullable=True), # Relation IDs are integers
sa.Column("relation_type", sa.String(), nullable=True),
sa.Column("entity_id", sa.Integer(), nullable=True), # Entity IDs are integers
sa.Column("category", sa.String(), nullable=True),
sa.Column("metadata", JSONB(), nullable=True), # Use JSONB for Postgres
sa.Column("created_at", sa.DateTime(timezone=True), nullable=True),
sa.Column("updated_at", sa.DateTime(timezone=True), nullable=True),
sa.PrimaryKeyConstraint(
"id", "type", "project_id"
), # Composite key: id can repeat across types
sa.ForeignKeyConstraint(
["project_id"],
["project.id"],
name="fk_search_index_project_id",
ondelete="CASCADE",
),
if_not_exists=True,
)
# Create index on project_id for efficient multi-tenant queries
op.create_index(
"ix_search_index_project_id",
"search_index",
["project_id"],
unique=False,
)
# Create unique partial index on permalink for markdown files
# Non-markdown files don't have permalinks, so we use a partial index
op.execute("""
CREATE UNIQUE INDEX uix_search_index_permalink_project
ON search_index (permalink, project_id)
WHERE permalink IS NOT NULL
""")
# Add tsvector column as a GENERATED ALWAYS column
# This automatically updates when title or content_stems change
op.execute("""
ALTER TABLE search_index
ADD COLUMN textsearchable_index_col tsvector
GENERATED ALWAYS AS (
to_tsvector('english',
coalesce(title, '') || ' ' ||
coalesce(content_stems, '')
)
) STORED
""")
# Create GIN index on tsvector column for fast full-text search
op.create_index(
"idx_search_index_fts",
"search_index",
["textsearchable_index_col"],
unique=False,
postgresql_using="gin",
)
# Create GIN index on metadata JSONB for fast containment queries
# Using jsonb_path_ops for smaller index size and better performance
op.execute("""
CREATE INDEX idx_search_index_metadata_gin
ON search_index
USING GIN (metadata jsonb_path_ops)
""")
def downgrade() -> None:
"""Remove PostgreSQL full-text search support."""
connection = op.get_bind()
if connection.dialect.name == "postgresql":
# Drop indexes first
op.execute("DROP INDEX IF EXISTS idx_search_index_metadata_gin")
op.drop_index("idx_search_index_fts", table_name="search_index")
op.execute("DROP INDEX IF EXISTS uix_search_index_permalink_project")
op.drop_index("ix_search_index_project_id", table_name="search_index")
# Drop the generated column
op.execute("ALTER TABLE search_index DROP COLUMN IF EXISTS textsearchable_index_col")
# Drop the search_index table
op.drop_table("search_index")
@@ -21,6 +21,12 @@ depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
# SQLite FTS5 virtual table handling is SQLite-specific
# For Postgres, search_index is a regular table managed by ORM
connection = op.get_bind()
is_sqlite = connection.dialect.name == "sqlite"
op.create_table(
"project",
sa.Column("id", sa.Integer(), nullable=False),
@@ -55,7 +61,9 @@ def upgrade() -> None:
batch_op.add_column(sa.Column("project_id", sa.Integer(), nullable=False))
batch_op.drop_index(
"uix_entity_permalink",
sqlite_where=sa.text("content_type = 'text/markdown' AND permalink IS NOT NULL"),
sqlite_where=sa.text("content_type = 'text/markdown' AND permalink IS NOT NULL")
if is_sqlite
else None,
)
batch_op.drop_index("ix_entity_file_path")
batch_op.create_index(batch_op.f("ix_entity_file_path"), ["file_path"], unique=False)
@@ -67,12 +75,16 @@ def upgrade() -> None:
"uix_entity_permalink_project",
["permalink", "project_id"],
unique=True,
sqlite_where=sa.text("content_type = 'text/markdown' AND permalink IS NOT NULL"),
sqlite_where=sa.text("content_type = 'text/markdown' AND permalink IS NOT NULL")
if is_sqlite
else None,
)
batch_op.create_foreign_key("fk_entity_project_id", "project", ["project_id"], ["id"])
# drop the search index table. it will be recreated
op.drop_table("search_index")
# Only drop for SQLite - Postgres uses regular table managed by ORM
if is_sqlite:
op.drop_table("search_index")
# ### end Alembic commands ###
@@ -25,43 +25,51 @@ def upgrade() -> None:
The UNIQUE constraint prevents multiple projects from having is_default=FALSE,
which breaks project creation when the service sets is_default=False.
Since SQLite doesn't support dropping specific constraints easily, we'll
recreate the table without the problematic constraint.
SQLite: Recreate the table without the constraint (no ALTER TABLE support)
Postgres: Use ALTER TABLE to drop the constraint directly
"""
# For SQLite, we need to recreate the table without the UNIQUE constraint
# Create a new table without the UNIQUE constraint on is_default
op.create_table(
"project_new",
sa.Column("id", sa.Integer(), nullable=False),
sa.Column("name", sa.String(), nullable=False),
sa.Column("description", sa.Text(), nullable=True),
sa.Column("permalink", sa.String(), nullable=False),
sa.Column("path", sa.String(), nullable=False),
sa.Column("is_active", sa.Boolean(), nullable=False),
sa.Column("is_default", sa.Boolean(), nullable=True), # No UNIQUE constraint!
sa.Column("created_at", sa.DateTime(), nullable=False),
sa.Column("updated_at", sa.DateTime(), nullable=False),
sa.PrimaryKeyConstraint("id"),
sa.UniqueConstraint("name"),
sa.UniqueConstraint("permalink"),
)
connection = op.get_bind()
is_sqlite = connection.dialect.name == "sqlite"
# Copy data from old table to new table
op.execute("INSERT INTO project_new SELECT * FROM project")
if is_sqlite:
# For SQLite, we need to recreate the table without the UNIQUE constraint
# Create a new table without the UNIQUE constraint on is_default
op.create_table(
"project_new",
sa.Column("id", sa.Integer(), nullable=False),
sa.Column("name", sa.String(), nullable=False),
sa.Column("description", sa.Text(), nullable=True),
sa.Column("permalink", sa.String(), nullable=False),
sa.Column("path", sa.String(), nullable=False),
sa.Column("is_active", sa.Boolean(), nullable=False),
sa.Column("is_default", sa.Boolean(), nullable=True), # No UNIQUE constraint!
sa.Column("created_at", sa.DateTime(), nullable=False),
sa.Column("updated_at", sa.DateTime(), nullable=False),
sa.PrimaryKeyConstraint("id"),
sa.UniqueConstraint("name"),
sa.UniqueConstraint("permalink"),
)
# Drop the old table
op.drop_table("project")
# Copy data from old table to new table
op.execute("INSERT INTO project_new SELECT * FROM project")
# Rename the new table
op.rename_table("project_new", "project")
# Drop the old table
op.drop_table("project")
# Recreate the indexes
with op.batch_alter_table("project", schema=None) as batch_op:
batch_op.create_index("ix_project_created_at", ["created_at"], unique=False)
batch_op.create_index("ix_project_name", ["name"], unique=True)
batch_op.create_index("ix_project_path", ["path"], unique=False)
batch_op.create_index("ix_project_permalink", ["permalink"], unique=True)
batch_op.create_index("ix_project_updated_at", ["updated_at"], unique=False)
# Rename the new table
op.rename_table("project_new", "project")
# Recreate the indexes
with op.batch_alter_table("project", schema=None) as batch_op:
batch_op.create_index("ix_project_created_at", ["created_at"], unique=False)
batch_op.create_index("ix_project_name", ["name"], unique=True)
batch_op.create_index("ix_project_path", ["path"], unique=False)
batch_op.create_index("ix_project_permalink", ["permalink"], unique=True)
batch_op.create_index("ix_project_updated_at", ["updated_at"], unique=False)
else:
# For Postgres, we can simply drop the constraint
with op.batch_alter_table("project", schema=None) as batch_op:
batch_op.drop_constraint("project_is_default_key", type_="unique")
def downgrade() -> None:
@@ -0,0 +1,24 @@
"""Merge multiple heads
Revision ID: 6830751f5fb6
Revises: a2b3c4d5e6f7, g9a0b3c4d5e6
Create Date: 2025-12-29 12:46:46.476268
"""
from typing import Sequence, Union
# revision identifiers, used by Alembic.
revision: str = "6830751f5fb6"
down_revision: Union[str, Sequence[str], None] = ("a2b3c4d5e6f7", "g9a0b3c4d5e6")
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
pass
def downgrade() -> None:
pass
@@ -0,0 +1,56 @@
"""Add cascade delete FK from search_index to entity
Revision ID: a2b3c4d5e6f7
Revises: f8a9b2c3d4e5
Create Date: 2025-12-02 07:00:00.000000
"""
from typing import Sequence, Union
from alembic import op
# revision identifiers, used by Alembic.
revision: str = "a2b3c4d5e6f7"
down_revision: Union[str, None] = "f8a9b2c3d4e5"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Add FK with CASCADE delete from search_index.entity_id to entity.id.
This migration is Postgres-only because:
- SQLite uses FTS5 virtual tables which don't support foreign keys
- The FK enables automatic cleanup of search_index entries when entities are deleted
"""
connection = op.get_bind()
dialect = connection.dialect.name
if dialect == "postgresql":
# First, clean up any orphaned search_index entries where entity no longer exists
op.execute("""
DELETE FROM search_index
WHERE entity_id IS NOT NULL
AND entity_id NOT IN (SELECT id FROM entity)
""")
# Add FK with CASCADE - nullable FK allows search_index entries without entity_id
op.create_foreign_key(
"fk_search_index_entity_id",
"search_index",
"entity",
["entity_id"],
["id"],
ondelete="CASCADE",
)
def downgrade() -> None:
"""Remove the FK constraint."""
connection = op.get_bind()
dialect = connection.dialect.name
if dialect == "postgresql":
op.drop_constraint("fk_search_index_entity_id", "search_index", type_="foreignkey")
@@ -21,6 +21,12 @@ depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Upgrade database schema to use new search index with content_stems and content_snippet."""
# This migration is SQLite-specific (FTS5 virtual tables)
# For Postgres, the search_index table is created via ORM models
connection = op.get_bind()
if connection.dialect.name != "sqlite":
return
# First, drop the existing search_index table
op.execute("DROP TABLE IF EXISTS search_index")
@@ -59,6 +65,13 @@ def upgrade() -> None:
def downgrade() -> None:
"""Downgrade database schema to use old search index."""
# This migration is SQLite-specific (FTS5 virtual tables)
# For Postgres, the search_index table is managed via ORM models
connection = op.get_bind()
if connection.dialect.name != "sqlite":
return
# Drop the updated search_index table
op.execute("DROP TABLE IF EXISTS search_index")
@@ -0,0 +1,154 @@
"""Add structured metadata indexes for entity frontmatter
Revision ID: d7e8f9a0b1c2
Revises: g9a0b3c4d5e6
Create Date: 2026-01-31 12:00:00.000000
"""
from typing import Sequence, Union
import sqlalchemy as sa
from alembic import op
from sqlalchemy import text
def column_exists(connection, table: str, column: str) -> bool:
"""Check if a column exists in a table (idempotent migration support)."""
if connection.dialect.name == "postgresql":
result = connection.execute(
text(
"SELECT 1 FROM information_schema.columns "
"WHERE table_name = :table AND column_name = :column"
),
{"table": table, "column": column},
)
return result.fetchone() is not None
# SQLite
result = connection.execute(text(f"PRAGMA table_info({table})"))
columns = [row[1] for row in result]
return column in columns
def index_exists(connection, index_name: str) -> bool:
"""Check if an index exists (idempotent migration support)."""
if connection.dialect.name == "postgresql":
result = connection.execute(
text("SELECT 1 FROM pg_indexes WHERE indexname = :index_name"),
{"index_name": index_name},
)
return result.fetchone() is not None
# SQLite
result = connection.execute(
text("SELECT 1 FROM sqlite_master WHERE type='index' AND name = :index_name"),
{"index_name": index_name},
)
return result.fetchone() is not None
# revision identifiers, used by Alembic.
revision: str = "d7e8f9a0b1c2"
down_revision: Union[str, None] = "6830751f5fb6"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Add JSONB/GiN indexes for Postgres and generated columns for SQLite."""
connection = op.get_bind()
dialect = connection.dialect.name
if dialect == "postgresql":
# Ensure JSONB for efficient indexing
result = connection.execute(
text(
"SELECT data_type FROM information_schema.columns "
"WHERE table_name = 'entity' AND column_name = 'entity_metadata'"
)
).fetchone()
if result and result[0] != "jsonb":
op.execute(
"ALTER TABLE entity ALTER COLUMN entity_metadata "
"TYPE jsonb USING entity_metadata::jsonb"
)
# General JSONB GIN index
op.execute(
"CREATE INDEX IF NOT EXISTS idx_entity_metadata_gin "
"ON entity USING GIN (entity_metadata jsonb_path_ops)"
)
# Common field indexes
op.execute(
"CREATE INDEX IF NOT EXISTS idx_entity_tags_json "
"ON entity USING GIN ((entity_metadata -> 'tags'))"
)
op.execute(
"CREATE INDEX IF NOT EXISTS idx_entity_frontmatter_type "
"ON entity ((entity_metadata ->> 'type'))"
)
op.execute(
"CREATE INDEX IF NOT EXISTS idx_entity_frontmatter_status "
"ON entity ((entity_metadata ->> 'status'))"
)
return
# SQLite: add generated columns for common frontmatter fields
# Constraint: SQLite ALTER TABLE ADD COLUMN only supports VIRTUAL generated columns,
# not STORED. json_extract is deterministic so VIRTUAL columns can still be indexed.
if not column_exists(connection, "entity", "tags_json"):
op.add_column(
"entity",
sa.Column(
"tags_json",
sa.Text(),
sa.Computed("json_extract(entity_metadata, '$.tags')", persisted=False),
),
)
if not column_exists(connection, "entity", "frontmatter_status"):
op.add_column(
"entity",
sa.Column(
"frontmatter_status",
sa.Text(),
sa.Computed("json_extract(entity_metadata, '$.status')", persisted=False),
),
)
if not column_exists(connection, "entity", "frontmatter_type"):
op.add_column(
"entity",
sa.Column(
"frontmatter_type",
sa.Text(),
sa.Computed("json_extract(entity_metadata, '$.type')", persisted=False),
),
)
# Index generated columns
if not index_exists(connection, "idx_entity_tags_json"):
op.create_index("idx_entity_tags_json", "entity", ["tags_json"])
if not index_exists(connection, "idx_entity_frontmatter_status"):
op.create_index("idx_entity_frontmatter_status", "entity", ["frontmatter_status"])
if not index_exists(connection, "idx_entity_frontmatter_type"):
op.create_index("idx_entity_frontmatter_type", "entity", ["frontmatter_type"])
def downgrade() -> None:
"""Best-effort downgrade (drop indexes, revert JSONB on Postgres)."""
connection = op.get_bind()
dialect = connection.dialect.name
if dialect == "postgresql":
op.execute("DROP INDEX IF EXISTS idx_entity_frontmatter_status")
op.execute("DROP INDEX IF EXISTS idx_entity_frontmatter_type")
op.execute("DROP INDEX IF EXISTS idx_entity_tags_json")
op.execute("DROP INDEX IF EXISTS idx_entity_metadata_gin")
op.execute(
"ALTER TABLE entity ALTER COLUMN entity_metadata TYPE json USING entity_metadata::json"
)
return
# SQLite: drop indexes (dropping generated columns requires table rebuild)
op.execute("DROP INDEX IF EXISTS idx_entity_frontmatter_status")
op.execute("DROP INDEX IF EXISTS idx_entity_frontmatter_type")
op.execute("DROP INDEX IF EXISTS idx_entity_tags_json")
@@ -0,0 +1,239 @@
"""Add project_id to relation/observation and pg_trgm for fuzzy link resolution
Revision ID: f8a9b2c3d4e5
Revises: 314f1ea54dc4
Create Date: 2025-12-01 12:00:00.000000
"""
from typing import Sequence, Union
import sqlalchemy as sa
from alembic import op
from sqlalchemy import text
def column_exists(connection, table: str, column: str) -> bool:
"""Check if a column exists in a table (idempotent migration support)."""
if connection.dialect.name == "postgresql":
result = connection.execute(
text(
"SELECT 1 FROM information_schema.columns "
"WHERE table_name = :table AND column_name = :column"
),
{"table": table, "column": column},
)
return result.fetchone() is not None
else:
# SQLite
result = connection.execute(text(f"PRAGMA table_info({table})"))
columns = [row[1] for row in result]
return column in columns
def index_exists(connection, index_name: str) -> bool:
"""Check if an index exists (idempotent migration support)."""
if connection.dialect.name == "postgresql":
result = connection.execute(
text("SELECT 1 FROM pg_indexes WHERE indexname = :index_name"),
{"index_name": index_name},
)
return result.fetchone() is not None
else:
# SQLite
result = connection.execute(
text("SELECT 1 FROM sqlite_master WHERE type='index' AND name = :index_name"),
{"index_name": index_name},
)
return result.fetchone() is not None
# revision identifiers, used by Alembic.
revision: str = "f8a9b2c3d4e5"
down_revision: Union[str, None] = "314f1ea54dc4"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Add project_id to relation and observation tables, plus pg_trgm indexes.
This migration:
1. Adds project_id column to relation and observation tables (denormalization)
2. Backfills project_id from the associated entity
3. Enables pg_trgm extension for trigram-based fuzzy matching (Postgres only)
4. Creates GIN indexes on entity title and permalink for fast similarity searches
5. Creates partial index on unresolved relations for efficient bulk resolution
"""
connection = op.get_bind()
dialect = connection.dialect.name
# -------------------------------------------------------------------------
# Add project_id to relation table
# -------------------------------------------------------------------------
# Step 1: Add project_id column as nullable first (idempotent)
if not column_exists(connection, "relation", "project_id"):
op.add_column("relation", sa.Column("project_id", sa.Integer(), nullable=True))
# Step 2: Backfill project_id from entity.project_id via from_id
if dialect == "postgresql":
op.execute("""
UPDATE relation
SET project_id = entity.project_id
FROM entity
WHERE relation.from_id = entity.id
""")
else:
# SQLite syntax
op.execute("""
UPDATE relation
SET project_id = (
SELECT entity.project_id
FROM entity
WHERE entity.id = relation.from_id
)
""")
# Step 3: Make project_id NOT NULL and add foreign key
if dialect == "postgresql":
op.alter_column("relation", "project_id", nullable=False)
op.create_foreign_key(
"fk_relation_project_id",
"relation",
"project",
["project_id"],
["id"],
)
else:
# SQLite requires batch operations for ALTER COLUMN
with op.batch_alter_table("relation") as batch_op:
batch_op.alter_column("project_id", nullable=False)
batch_op.create_foreign_key(
"fk_relation_project_id",
"project",
["project_id"],
["id"],
)
# Step 4: Create index on relation.project_id (idempotent)
if not index_exists(connection, "ix_relation_project_id"):
op.create_index("ix_relation_project_id", "relation", ["project_id"])
# -------------------------------------------------------------------------
# Add project_id to observation table
# -------------------------------------------------------------------------
# Step 1: Add project_id column as nullable first (idempotent)
if not column_exists(connection, "observation", "project_id"):
op.add_column("observation", sa.Column("project_id", sa.Integer(), nullable=True))
# Step 2: Backfill project_id from entity.project_id via entity_id
if dialect == "postgresql":
op.execute("""
UPDATE observation
SET project_id = entity.project_id
FROM entity
WHERE observation.entity_id = entity.id
""")
else:
# SQLite syntax
op.execute("""
UPDATE observation
SET project_id = (
SELECT entity.project_id
FROM entity
WHERE entity.id = observation.entity_id
)
""")
# Step 3: Make project_id NOT NULL and add foreign key
if dialect == "postgresql":
op.alter_column("observation", "project_id", nullable=False)
op.create_foreign_key(
"fk_observation_project_id",
"observation",
"project",
["project_id"],
["id"],
)
else:
# SQLite requires batch operations for ALTER COLUMN
with op.batch_alter_table("observation") as batch_op:
batch_op.alter_column("project_id", nullable=False)
batch_op.create_foreign_key(
"fk_observation_project_id",
"project",
["project_id"],
["id"],
)
# Step 4: Create index on observation.project_id (idempotent)
if not index_exists(connection, "ix_observation_project_id"):
op.create_index("ix_observation_project_id", "observation", ["project_id"])
# Postgres-specific: pg_trgm and GIN indexes
if dialect == "postgresql":
# Enable pg_trgm extension for fuzzy string matching
op.execute("CREATE EXTENSION IF NOT EXISTS pg_trgm")
# Create trigram indexes on entity table for fuzzy matching
# GIN indexes with gin_trgm_ops support similarity searches
op.execute("""
CREATE INDEX IF NOT EXISTS idx_entity_title_trgm
ON entity USING gin (title gin_trgm_ops)
""")
op.execute("""
CREATE INDEX IF NOT EXISTS idx_entity_permalink_trgm
ON entity USING gin (permalink gin_trgm_ops)
""")
# Create partial index on unresolved relations for efficient bulk resolution
# This makes "WHERE to_id IS NULL AND project_id = X" queries very fast
op.execute("""
CREATE INDEX IF NOT EXISTS idx_relation_unresolved
ON relation (project_id, to_name)
WHERE to_id IS NULL
""")
# Create index on relation.to_name for join performance in bulk resolution
op.execute("""
CREATE INDEX IF NOT EXISTS idx_relation_to_name
ON relation (to_name)
""")
def downgrade() -> None:
"""Remove project_id from relation/observation and pg_trgm indexes."""
connection = op.get_bind()
dialect = connection.dialect.name
if dialect == "postgresql":
# Drop Postgres-specific indexes
op.execute("DROP INDEX IF EXISTS idx_relation_to_name")
op.execute("DROP INDEX IF EXISTS idx_relation_unresolved")
op.execute("DROP INDEX IF EXISTS idx_entity_permalink_trgm")
op.execute("DROP INDEX IF EXISTS idx_entity_title_trgm")
# Note: We don't drop the pg_trgm extension as other code may depend on it
# Drop project_id from observation
op.drop_index("ix_observation_project_id", table_name="observation")
op.drop_constraint("fk_observation_project_id", "observation", type_="foreignkey")
op.drop_column("observation", "project_id")
# Drop project_id from relation
op.drop_index("ix_relation_project_id", table_name="relation")
op.drop_constraint("fk_relation_project_id", "relation", type_="foreignkey")
op.drop_column("relation", "project_id")
else:
# SQLite requires batch operations
op.drop_index("ix_observation_project_id", table_name="observation")
with op.batch_alter_table("observation") as batch_op:
batch_op.drop_constraint("fk_observation_project_id", type_="foreignkey")
batch_op.drop_column("project_id")
op.drop_index("ix_relation_project_id", table_name="relation")
with op.batch_alter_table("relation") as batch_op:
batch_op.drop_constraint("fk_relation_project_id", type_="foreignkey")
batch_op.drop_column("project_id")
@@ -0,0 +1,173 @@
"""Add external_id UUID column to project and entity tables
Revision ID: g9a0b3c4d5e6
Revises: f8a9b2c3d4e5
Create Date: 2025-12-29 10:00:00.000000
"""
import uuid
from typing import Sequence, Union
import sqlalchemy as sa
from alembic import op
from sqlalchemy import text
def column_exists(connection, table: str, column: str) -> bool:
"""Check if a column exists in a table (idempotent migration support)."""
if connection.dialect.name == "postgresql":
result = connection.execute(
text(
"SELECT 1 FROM information_schema.columns "
"WHERE table_name = :table AND column_name = :column"
),
{"table": table, "column": column},
)
return result.fetchone() is not None
else:
# SQLite
result = connection.execute(text(f"PRAGMA table_info({table})"))
columns = [row[1] for row in result]
return column in columns
def index_exists(connection, index_name: str) -> bool:
"""Check if an index exists (idempotent migration support)."""
if connection.dialect.name == "postgresql":
result = connection.execute(
text("SELECT 1 FROM pg_indexes WHERE indexname = :index_name"),
{"index_name": index_name},
)
return result.fetchone() is not None
else:
# SQLite
result = connection.execute(
text("SELECT 1 FROM sqlite_master WHERE type='index' AND name = :index_name"),
{"index_name": index_name},
)
return result.fetchone() is not None
# revision identifiers, used by Alembic.
revision: str = "g9a0b3c4d5e6"
down_revision: Union[str, None] = "f8a9b2c3d4e5"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Add external_id UUID column to project and entity tables.
This migration:
1. Adds external_id column to project table
2. Adds external_id column to entity table
3. Generates UUIDs for existing rows
4. Creates unique indexes on both columns
"""
connection = op.get_bind()
dialect = connection.dialect.name
# -------------------------------------------------------------------------
# Add external_id to project table
# -------------------------------------------------------------------------
if not column_exists(connection, "project", "external_id"):
# Step 1: Add external_id column as nullable first
op.add_column("project", sa.Column("external_id", sa.String(), nullable=True))
# Step 2: Generate UUIDs for existing rows
if dialect == "postgresql":
# Postgres has gen_random_uuid() function
op.execute("""
UPDATE project
SET external_id = gen_random_uuid()::text
WHERE external_id IS NULL
""")
else:
# SQLite: need to generate UUIDs in Python
result = connection.execute(text("SELECT id FROM project WHERE external_id IS NULL"))
for row in result:
new_uuid = str(uuid.uuid4())
connection.execute(
text("UPDATE project SET external_id = :uuid WHERE id = :id"),
{"uuid": new_uuid, "id": row[0]},
)
# Step 3: Make external_id NOT NULL
if dialect == "postgresql":
op.alter_column("project", "external_id", nullable=False)
else:
# SQLite requires batch operations for ALTER COLUMN
with op.batch_alter_table("project") as batch_op:
batch_op.alter_column("external_id", nullable=False)
# Step 4: Create unique index on project.external_id (idempotent)
if not index_exists(connection, "ix_project_external_id"):
op.create_index("ix_project_external_id", "project", ["external_id"], unique=True)
# -------------------------------------------------------------------------
# Add external_id to entity table
# -------------------------------------------------------------------------
if not column_exists(connection, "entity", "external_id"):
# Step 1: Add external_id column as nullable first
op.add_column("entity", sa.Column("external_id", sa.String(), nullable=True))
# Step 2: Generate UUIDs for existing rows
if dialect == "postgresql":
# Postgres has gen_random_uuid() function
op.execute("""
UPDATE entity
SET external_id = gen_random_uuid()::text
WHERE external_id IS NULL
""")
else:
# SQLite: need to generate UUIDs in Python
result = connection.execute(text("SELECT id FROM entity WHERE external_id IS NULL"))
for row in result:
new_uuid = str(uuid.uuid4())
connection.execute(
text("UPDATE entity SET external_id = :uuid WHERE id = :id"),
{"uuid": new_uuid, "id": row[0]},
)
# Step 3: Make external_id NOT NULL
if dialect == "postgresql":
op.alter_column("entity", "external_id", nullable=False)
else:
# SQLite requires batch operations for ALTER COLUMN
with op.batch_alter_table("entity") as batch_op:
batch_op.alter_column("external_id", nullable=False)
# Step 4: Create unique index on entity.external_id (idempotent)
if not index_exists(connection, "ix_entity_external_id"):
op.create_index("ix_entity_external_id", "entity", ["external_id"], unique=True)
def downgrade() -> None:
"""Remove external_id columns from project and entity tables."""
connection = op.get_bind()
dialect = connection.dialect.name
# Drop from entity table
if index_exists(connection, "ix_entity_external_id"):
op.drop_index("ix_entity_external_id", table_name="entity")
if column_exists(connection, "entity", "external_id"):
if dialect == "postgresql":
op.drop_column("entity", "external_id")
else:
with op.batch_alter_table("entity") as batch_op:
batch_op.drop_column("external_id")
# Drop from project table
if index_exists(connection, "ix_project_external_id"):
op.drop_index("ix_project_external_id", table_name="project")
if column_exists(connection, "project", "external_id"):
if dialect == "postgresql":
op.drop_column("project", "external_id")
else:
with op.batch_alter_table("project") as batch_op:
batch_op.drop_column("external_id")
@@ -0,0 +1,68 @@
"""Add Postgres semantic vector search tables (pgvector-aware, optional)
Revision ID: h1b2c3d4e5f6
Revises: d7e8f9a0b1c2
Create Date: 2026-02-07 00:00:00.000000
"""
from typing import Sequence, Union
from alembic import op
# revision identifiers, used by Alembic.
revision: str = "h1b2c3d4e5f6"
down_revision: Union[str, None] = "d7e8f9a0b1c2"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Create Postgres vector chunk metadata table.
Trigger: database backend is PostgreSQL.
Why: search_vector_chunks stores text metadata with no vector-dimension
dependency, so it's safe in a migration. search_vector_embeddings (which
requires pgvector and a provider-specific dimension) is created at runtime
by PostgresSearchRepository._ensure_vector_tables(), mirroring the SQLite
pattern where vector tables are created dynamically.
Outcome: creates the dimension-independent chunks table. The embeddings
table + HNSW index are deferred to runtime.
"""
connection = op.get_bind()
if connection.dialect.name != "postgresql":
return
op.execute(
"""
CREATE TABLE IF NOT EXISTS search_vector_chunks (
id BIGSERIAL PRIMARY KEY,
entity_id INTEGER NOT NULL,
project_id INTEGER NOT NULL,
chunk_key TEXT NOT NULL,
chunk_text TEXT NOT NULL,
source_hash TEXT NOT NULL,
updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
UNIQUE (project_id, entity_id, chunk_key)
)
"""
)
op.execute(
"""
CREATE INDEX IF NOT EXISTS idx_search_vector_chunks_project_entity
ON search_vector_chunks (project_id, entity_id)
"""
)
def downgrade() -> None:
"""Remove Postgres vector chunk/embedding tables.
Does not drop pgvector extension because other schema objects may depend on it.
"""
connection = op.get_bind()
if connection.dialect.name != "postgresql":
return
op.execute("DROP TABLE IF EXISTS search_vector_embeddings")
op.execute("DROP TABLE IF EXISTS search_vector_chunks")
@@ -0,0 +1,29 @@
"""Trigger automatic semantic embedding backfill during migration.
Revision ID: i2c3d4e5f6g7
Revises: h1b2c3d4e5f6
Create Date: 2026-02-19 00:00:00.000000
"""
from typing import Sequence, Union
# revision identifiers, used by Alembic.
revision: str = "i2c3d4e5f6g7"
down_revision: Union[str, None] = "h1b2c3d4e5f6"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""No schema change.
Trigger: this revision is newly applied.
Why: db.run_migrations() detects this revision transition and runs the existing
sync_entity_vectors() pipeline to backfill semantic embeddings automatically.
Outcome: users no longer need to run `bm reindex --embeddings` after upgrading.
"""
def downgrade() -> None:
"""No-op downgrade."""
@@ -0,0 +1,164 @@
"""Rename entity_type column to note_type
Revision ID: j3d4e5f6g7h8
Revises: i2c3d4e5f6g7
Create Date: 2026-02-22 12:00:00.000000
"""
from typing import Sequence, Union
from alembic import op
from sqlalchemy import text
# revision identifiers, used by Alembic.
revision: str = "j3d4e5f6g7h8"
down_revision: Union[str, None] = "i2c3d4e5f6g7"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def table_exists(connection, table_name: str) -> bool:
"""Check if a table exists (idempotent migration support)."""
if connection.dialect.name == "postgresql":
result = connection.execute(
text("SELECT 1 FROM information_schema.tables WHERE table_name = :table_name"),
{"table_name": table_name},
)
return result.fetchone() is not None
# SQLite
result = connection.execute(
text("SELECT 1 FROM sqlite_master WHERE type='table' AND name = :table_name"),
{"table_name": table_name},
)
return result.fetchone() is not None
def index_exists(connection, index_name: str) -> bool:
"""Check if an index exists (idempotent migration support)."""
if connection.dialect.name == "postgresql":
result = connection.execute(
text("SELECT 1 FROM pg_indexes WHERE indexname = :index_name"),
{"index_name": index_name},
)
return result.fetchone() is not None
# SQLite
result = connection.execute(
text("SELECT 1 FROM sqlite_master WHERE type='index' AND name = :index_name"),
{"index_name": index_name},
)
return result.fetchone() is not None
def column_exists(connection, table: str, column: str) -> bool:
"""Check if a column exists in a table (idempotent migration support)."""
if connection.dialect.name == "postgresql":
result = connection.execute(
text(
"SELECT 1 FROM information_schema.columns "
"WHERE table_name = :table AND column_name = :column"
),
{"table": table, "column": column},
)
return result.fetchone() is not None
# SQLite
result = connection.execute(text(f"PRAGMA table_info({table})"))
columns = [row[1] for row in result]
return column in columns
def upgrade() -> None:
"""Rename entity_type → note_type on the entity table."""
connection = op.get_bind()
dialect = connection.dialect.name
# Skip if already migrated (idempotent)
if column_exists(connection, "entity", "note_type"):
return
if dialect == "postgresql":
# Postgres supports direct column rename
op.execute("ALTER TABLE entity RENAME COLUMN entity_type TO note_type")
# Recreate the index with new name
op.execute("DROP INDEX IF EXISTS ix_entity_type")
op.execute("CREATE INDEX ix_note_type ON entity (note_type)")
else:
# SQLite 3.25.0+ supports ALTER TABLE RENAME COLUMN directly.
# Avoids batch_alter_table which fails on tables with generated columns
# (duplicate column name error when recreating the table).
op.execute("ALTER TABLE entity RENAME COLUMN entity_type TO note_type")
# Recreate the index with new name
if index_exists(connection, "ix_entity_type"):
op.drop_index("ix_entity_type", table_name="entity")
op.create_index("ix_note_type", "entity", ["note_type"])
# Update search index metadata: rename entity_type → note_type in JSON
# This updates the stored metadata so search results use the new field name
# Guard: search_index may not exist on a fresh DB (created by an earlier migration)
if not table_exists(connection, "search_index"):
return
if dialect == "postgresql":
op.execute(
text("""
UPDATE search_index
SET metadata = metadata - 'entity_type' || jsonb_build_object('note_type', metadata->'entity_type')
WHERE metadata ? 'entity_type'
""")
)
else:
op.execute(
text("""
UPDATE search_index
SET metadata = json_set(
json_remove(metadata, '$.entity_type'),
'$.note_type',
json_extract(metadata, '$.entity_type')
)
WHERE json_extract(metadata, '$.entity_type') IS NOT NULL
""")
)
def downgrade() -> None:
"""Rename note_type → entity_type on the entity table."""
connection = op.get_bind()
dialect = connection.dialect.name
if dialect == "postgresql":
op.execute("ALTER TABLE entity RENAME COLUMN note_type TO entity_type")
op.execute("DROP INDEX IF EXISTS ix_note_type")
op.execute("CREATE INDEX ix_entity_type ON entity (entity_type)")
else:
op.execute("ALTER TABLE entity RENAME COLUMN note_type TO entity_type")
if index_exists(connection, "ix_note_type"):
op.drop_index("ix_note_type", table_name="entity")
op.create_index("ix_entity_type", "entity", ["entity_type"])
# Revert search index metadata
if not table_exists(connection, "search_index"):
return
if dialect == "postgresql":
op.execute(
text("""
UPDATE search_index
SET metadata = metadata - 'note_type' || jsonb_build_object('entity_type', metadata->'note_type')
WHERE metadata ? 'note_type'
""")
)
else:
op.execute(
text("""
UPDATE search_index
SET metadata = json_set(
json_remove(metadata, '$.note_type'),
'$.entity_type',
json_extract(metadata, '$.note_type')
)
WHERE json_extract(metadata, '$.note_type') IS NOT NULL
""")
)
@@ -0,0 +1,74 @@
"""Add created_by and last_updated_by columns to entity table.
Revision ID: k4e5f6g7h8i9
Revises: j3d4e5f6g7h8
Create Date: 2026-02-23 00:00:00.000000
These columns track which cloud user created and last modified each entity.
Both are nullable NULL for local/CLI usage and existing entities.
"""
from typing import Sequence, Union
import sqlalchemy as sa
from alembic import op
from sqlalchemy import text
# revision identifiers, used by Alembic.
revision: str = "k4e5f6g7h8i9"
down_revision: Union[str, None] = "j3d4e5f6g7h8"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def column_exists(connection, table: str, column: str) -> bool:
"""Check if a column exists in a table (idempotent migration support)."""
if connection.dialect.name == "postgresql":
result = connection.execute(
text(
"SELECT 1 FROM information_schema.columns "
"WHERE table_name = :table AND column_name = :column"
),
{"table": table, "column": column},
)
return result.fetchone() is not None
else:
# SQLite
result = connection.execute(text(f"PRAGMA table_info({table})"))
columns = [row[1] for row in result]
return column in columns
def upgrade() -> None:
"""Add created_by and last_updated_by columns to entity table.
Both columns are nullable strings that store cloud user_profile_id UUIDs.
No data backfill existing rows get NULL.
"""
connection = op.get_bind()
if not column_exists(connection, "entity", "created_by"):
op.add_column("entity", sa.Column("created_by", sa.String(), nullable=True))
if not column_exists(connection, "entity", "last_updated_by"):
op.add_column("entity", sa.Column("last_updated_by", sa.String(), nullable=True))
def downgrade() -> None:
"""Remove created_by and last_updated_by columns from entity table."""
connection = op.get_bind()
dialect = connection.dialect.name
if column_exists(connection, "entity", "last_updated_by"):
if dialect == "postgresql":
op.drop_column("entity", "last_updated_by")
else:
with op.batch_alter_table("entity") as batch_op:
batch_op.drop_column("last_updated_by")
if column_exists(connection, "entity", "created_by"):
if dialect == "postgresql":
op.drop_column("entity", "created_by")
else:
with op.batch_alter_table("entity") as batch_op:
batch_op.drop_column("created_by")
+101 -50
View File
@@ -1,61 +1,82 @@
"""FastAPI application for basic-memory knowledge graph API."""
import asyncio
from contextlib import asynccontextmanager
from fastapi import FastAPI, HTTPException
from fastapi import FastAPI, HTTPException, Request
from fastapi.exception_handlers import http_exception_handler
from fastapi.routing import APIRouter
from loguru import logger
from basic_memory import __version__ as version
from basic_memory import db
from basic_memory.api.routers import (
directory_router,
importer_router,
knowledge,
management,
memory,
project,
resource,
search,
prompt_router,
from basic_memory.api.container import ApiContainer, set_container
from basic_memory.api.v2.routers import (
knowledge_router as v2_knowledge,
project_router as v2_project,
memory_router as v2_memory,
search_router as v2_search,
resource_router as v2_resource,
directory_router as v2_directory,
prompt_router as v2_prompt,
importer_router as v2_importer,
schema_router as v2_schema,
)
from basic_memory.config import ConfigManager
from basic_memory.services.initialization import initialize_file_sync, initialize_app
from basic_memory.api.v2.routers.project_router import (
add_project,
list_projects,
synchronize_projects,
)
from basic_memory import telemetry
from basic_memory.config import init_api_logging
from basic_memory.services.exceptions import EntityAlreadyExistsError
from basic_memory.services.initialization import initialize_app
@asynccontextmanager
async def lifespan(app: FastAPI): # pragma: no cover
"""Lifecycle manager for the FastAPI app. Not called in stdio mcp mode"""
app_config = ConfigManager().config
logger.info("Starting Basic Memory API")
# Initialize logging for API (stdout in cloud mode, file otherwise)
init_api_logging()
await initialize_app(app_config)
# --- Composition Root ---
# Create container and read config (single point of config access)
container = ApiContainer.create()
set_container(container)
app.state.container = container
# Cache database connections in app state for performance
logger.info("Initializing database and caching connections...")
engine, session_maker = await db.get_or_create_db(app_config.database_path)
app.state.engine = engine
app.state.session_maker = session_maker
logger.info("Database connections cached in app state")
with telemetry.operation(
"api.lifecycle.startup",
entrypoint="api",
mode=container.mode.name.lower(),
):
logger.info(f"Starting Basic Memory API (mode={container.mode.name})")
logger.info(f"Sync changes enabled: {app_config.sync_changes}")
if app_config.sync_changes:
# start file sync task in background
app.state.sync_task = asyncio.create_task(initialize_file_sync(app_config))
else:
logger.info("Sync changes disabled. Skipping file sync service.")
await initialize_app(container.config)
# proceed with startup
# Cache database connections in app state for performance
logger.info("Initializing database and caching connections...")
engine, session_maker = await container.init_database()
app.state.engine = engine
app.state.session_maker = session_maker
logger.info("Database connections cached in app state")
# Create and start sync coordinator (lifecycle centralized in coordinator)
sync_coordinator = container.create_sync_coordinator()
await sync_coordinator.start()
app.state.sync_coordinator = sync_coordinator
# Proceed with startup
yield
logger.info("Shutting down Basic Memory API")
if app.state.sync_task:
logger.info("Stopping sync...")
app.state.sync_task.cancel() # pyright: ignore
await db.shutdown_db()
# Shutdown - coordinator handles clean task cancellation
with telemetry.operation(
"api.lifecycle.shutdown",
entrypoint="api",
mode=container.mode.name.lower(),
):
logger.info("Shutting down Basic Memory API")
await sync_coordinator.stop()
await container.shutdown_database()
# Initialize FastAPI app
@@ -66,22 +87,52 @@ app = FastAPI(
lifespan=lifespan,
)
# Include v2 routers FIRST (more specific paths must match before /{project} catch-all)
app.include_router(v2_knowledge, prefix="/v2/projects/{project_id}")
app.include_router(v2_memory, prefix="/v2/projects/{project_id}")
app.include_router(v2_search, prefix="/v2/projects/{project_id}")
app.include_router(v2_resource, prefix="/v2/projects/{project_id}")
app.include_router(v2_directory, prefix="/v2/projects/{project_id}")
app.include_router(v2_prompt, prefix="/v2/projects/{project_id}")
app.include_router(v2_importer, prefix="/v2/projects/{project_id}")
app.include_router(v2_schema, prefix="/v2/projects/{project_id}")
app.include_router(v2_project, prefix="/v2")
# Include routers
app.include_router(knowledge.router, prefix="/{project}")
app.include_router(memory.router, prefix="/{project}")
app.include_router(resource.router, prefix="/{project}")
app.include_router(search.router, prefix="/{project}")
app.include_router(project.project_router, prefix="/{project}")
app.include_router(directory_router.router, prefix="/{project}")
app.include_router(prompt_router.router, prefix="/{project}")
app.include_router(importer_router.router, prefix="/{project}")
# Legacy web app proxy paths (compat with /proxy/projects/projects)
app.include_router(v2_project, prefix="/proxy/projects")
# Project resource router works accross projects
app.include_router(project.project_resource_router)
app.include_router(management.router)
# Legacy v1 compat: older CLI versions (v0.18.0 and earlier) call /projects/...
# Using router mount causes 307 redirect which proxy doesn't follow, so add explicit routes
legacy_router = APIRouter(tags=["legacy"])
legacy_router.add_api_route("/projects/projects", list_projects, methods=["GET"])
legacy_router.add_api_route("/projects/projects", add_project, methods=["POST"])
legacy_router.add_api_route("/projects/config/sync", synchronize_projects, methods=["POST"])
app.include_router(legacy_router)
# Auth routes are handled by FastMCP automatically when auth is enabled
# V2 routers are the only public API surface
@app.exception_handler(EntityAlreadyExistsError)
async def entity_already_exists_error_handler(request: Request, exc: EntityAlreadyExistsError):
"""Handle entity creation conflicts (e.g., file already exists).
This is expected behavior when users try to create notes that exist,
so log at INFO level instead of ERROR.
"""
logger.info(
"Entity already exists",
url=str(request.url),
method=request.method,
path=request.url.path,
error=str(exc),
)
return await http_exception_handler(
request,
HTTPException(
status_code=409,
detail="Note already exists. Use edit_note to modify it, or delete it first.",
),
)
@app.exception_handler(Exception)
+132
View File
@@ -0,0 +1,132 @@
"""API composition root for Basic Memory.
This container owns reading ConfigManager and environment variables for the
API entrypoint. Downstream modules receive config/dependencies explicitly
rather than reading globals.
Design principles:
- Only this module reads ConfigManager directly
- Runtime mode (cloud/local/test) is resolved here
- Factories for services are provided, not singletons
"""
from dataclasses import dataclass
from typing import TYPE_CHECKING
from sqlalchemy.ext.asyncio import AsyncEngine, async_sessionmaker, AsyncSession
from basic_memory import db
from basic_memory.config import BasicMemoryConfig, ConfigManager
from basic_memory.runtime import RuntimeMode, resolve_runtime_mode
if TYPE_CHECKING: # pragma: no cover
from basic_memory.sync import SyncCoordinator
@dataclass
class ApiContainer:
"""Composition root for the API entrypoint.
Holds resolved configuration and runtime context.
Created once at app startup, then used to wire dependencies.
"""
config: BasicMemoryConfig
mode: RuntimeMode
# --- Database ---
# Cached database connections (set during lifespan startup)
engine: AsyncEngine | None = None
session_maker: async_sessionmaker[AsyncSession] | None = None
@classmethod
def create(cls) -> "ApiContainer": # pragma: no cover
"""Create container by reading ConfigManager.
This is the single point where API reads global config.
"""
config = ConfigManager().config
mode = resolve_runtime_mode(
is_test_env=config.is_test_env,
)
return cls(config=config, mode=mode)
# --- Runtime Mode Properties ---
@property
def should_sync_files(self) -> bool:
"""Whether file sync should be started.
Sync is enabled when:
- sync_changes is True in config
- Not in test mode (tests manage their own sync)
"""
return self.config.sync_changes and not self.mode.is_test
@property
def sync_skip_reason(self) -> str | None: # pragma: no cover
"""Reason why sync is skipped, or None if sync should run.
Useful for logging why sync was disabled.
"""
if self.mode.is_test:
return "Test environment detected"
if not self.config.sync_changes:
return "Sync changes disabled"
return None
def create_sync_coordinator(self) -> "SyncCoordinator": # pragma: no cover
"""Create a SyncCoordinator with this container's settings.
Returns:
SyncCoordinator configured for this runtime environment
"""
# Deferred import to avoid circular dependency
from basic_memory.sync import SyncCoordinator
return SyncCoordinator(
config=self.config,
should_sync=self.should_sync_files,
skip_reason=self.sync_skip_reason,
)
# --- Database Factory ---
async def init_database( # pragma: no cover
self,
) -> tuple[AsyncEngine, async_sessionmaker[AsyncSession]]:
"""Initialize and cache database connections.
Returns:
Tuple of (engine, session_maker)
"""
engine, session_maker = await db.get_or_create_db(self.config.database_path)
self.engine = engine
self.session_maker = session_maker
return engine, session_maker
async def shutdown_database(self) -> None: # pragma: no cover
"""Clean up database connections."""
await db.shutdown_db()
# Module-level container instance (set by lifespan)
# This allows deps.py to access the container without reading ConfigManager
_container: ApiContainer | None = None
def get_container() -> ApiContainer:
"""Get the current API container.
Raises:
RuntimeError: If container hasn't been initialized
"""
if _container is None:
raise RuntimeError("API container not initialized. Call set_container() first.")
return _container
def set_container(container: ApiContainer) -> None:
"""Set the API container (called by lifespan)."""
global _container
_container = container
-11
View File
@@ -1,11 +0,0 @@
"""API routers."""
from . import knowledge_router as knowledge
from . import management_router as management
from . import memory_router as memory
from . import project_router as project
from . import resource_router as resource
from . import search_router as search
from . import prompt_router as prompt
__all__ = ["knowledge", "management", "memory", "project", "resource", "search", "prompt"]
@@ -1,307 +0,0 @@
"""Router for knowledge graph operations."""
from typing import Annotated
from fastapi import APIRouter, HTTPException, BackgroundTasks, Depends, Query, Response
from loguru import logger
from basic_memory.deps import (
EntityServiceDep,
get_search_service,
SearchServiceDep,
LinkResolverDep,
ProjectPathDep,
FileServiceDep,
ProjectConfigDep,
AppConfigDep,
SyncServiceDep,
)
from basic_memory.schemas import (
EntityListResponse,
EntityResponse,
DeleteEntitiesResponse,
DeleteEntitiesRequest,
)
from basic_memory.schemas.request import EditEntityRequest, MoveEntityRequest
from basic_memory.schemas.base import Permalink, Entity
router = APIRouter(prefix="/knowledge", tags=["knowledge"])
async def resolve_relations_background(sync_service, entity_id: int, entity_permalink: str) -> None:
"""Background task to resolve relations for a specific entity.
This runs asynchronously after the API response is sent, preventing
long delays when creating entities with many relations.
"""
try:
# Only resolve relations for the newly created entity
await sync_service.resolve_relations(entity_id=entity_id)
logger.debug(
f"Background: Resolved relations for entity {entity_permalink} (id={entity_id})"
)
except Exception as e:
# Log but don't fail - this is a background task
logger.warning(
f"Background: Failed to resolve relations for entity {entity_permalink}: {e}"
)
## Create endpoints
@router.post("/entities", response_model=EntityResponse)
async def create_entity(
data: Entity,
background_tasks: BackgroundTasks,
entity_service: EntityServiceDep,
search_service: SearchServiceDep,
) -> EntityResponse:
"""Create an entity."""
logger.info(
"API request", endpoint="create_entity", entity_type=data.entity_type, title=data.title
)
entity = await entity_service.create_entity(data)
# reindex
await search_service.index_entity(entity, background_tasks=background_tasks)
result = EntityResponse.model_validate(entity)
logger.info(
f"API response: endpoint='create_entity' title={result.title}, permalink={result.permalink}, status_code=201"
)
return result
@router.put("/entities/{permalink:path}", response_model=EntityResponse)
async def create_or_update_entity(
project: ProjectPathDep,
permalink: Permalink,
data: Entity,
response: Response,
background_tasks: BackgroundTasks,
entity_service: EntityServiceDep,
search_service: SearchServiceDep,
file_service: FileServiceDep,
sync_service: SyncServiceDep,
) -> EntityResponse:
"""Create or update an entity. If entity exists, it will be updated, otherwise created."""
logger.info(
f"API request: create_or_update_entity for {project=}, {permalink=}, {data.entity_type=}, {data.title=}"
)
# Validate permalink matches
if data.permalink != permalink:
logger.warning(
f"API validation error: creating/updating entity with permalink mismatch - url={permalink}, data={data.permalink}",
)
raise HTTPException(
status_code=400,
detail=f"Entity permalink {data.permalink} must match URL path: '{permalink}'",
)
# Try create_or_update operation
entity, created = await entity_service.create_or_update_entity(data)
response.status_code = 201 if created else 200
# reindex
await search_service.index_entity(entity, background_tasks=background_tasks)
# Schedule relation resolution as a background task for new entities
# This prevents blocking the API response while resolving potentially many relations
if created:
background_tasks.add_task(
resolve_relations_background, sync_service, entity.id, entity.permalink or ""
)
result = EntityResponse.model_validate(entity)
logger.info(
f"API response: {result.title=}, {result.permalink=}, {created=}, status_code={response.status_code}"
)
return result
@router.patch("/entities/{identifier:path}", response_model=EntityResponse)
async def edit_entity(
identifier: str,
data: EditEntityRequest,
background_tasks: BackgroundTasks,
entity_service: EntityServiceDep,
search_service: SearchServiceDep,
) -> EntityResponse:
"""Edit an existing entity using various operations like append, prepend, find_replace, or replace_section.
This endpoint allows for targeted edits without requiring the full entity content.
"""
logger.info(
f"API request: endpoint='edit_entity', identifier='{identifier}', operation='{data.operation}'"
)
try:
# Edit the entity using the service
entity = await entity_service.edit_entity(
identifier=identifier,
operation=data.operation,
content=data.content,
section=data.section,
find_text=data.find_text,
expected_replacements=data.expected_replacements,
)
# Reindex the updated entity
await search_service.index_entity(entity, background_tasks=background_tasks)
# Return the updated entity response
result = EntityResponse.model_validate(entity)
logger.info(
"API response",
endpoint="edit_entity",
identifier=identifier,
operation=data.operation,
permalink=result.permalink,
status_code=200,
)
return result
except Exception as e:
logger.error(f"Error editing entity: {e}")
raise HTTPException(status_code=400, detail=str(e))
@router.post("/move")
async def move_entity(
data: MoveEntityRequest,
background_tasks: BackgroundTasks,
entity_service: EntityServiceDep,
project_config: ProjectConfigDep,
app_config: AppConfigDep,
search_service: SearchServiceDep,
) -> EntityResponse:
"""Move an entity to a new file location with project consistency.
This endpoint moves a note to a different path while maintaining project
consistency and optionally updating permalinks based on configuration.
"""
logger.info(
f"API request: endpoint='move_entity', identifier='{data.identifier}', destination='{data.destination_path}'"
)
try:
# Move the entity using the service
moved_entity = await entity_service.move_entity(
identifier=data.identifier,
destination_path=data.destination_path,
project_config=project_config,
app_config=app_config,
)
# Get the moved entity to reindex it
entity = await entity_service.link_resolver.resolve_link(data.destination_path)
if entity:
await search_service.index_entity(entity, background_tasks=background_tasks)
logger.info(
"API response",
endpoint="move_entity",
identifier=data.identifier,
destination=data.destination_path,
status_code=200,
)
result = EntityResponse.model_validate(moved_entity)
return result
except Exception as e:
logger.error(f"Error moving entity: {e}")
raise HTTPException(status_code=400, detail=str(e))
## Read endpoints
@router.get("/entities/{identifier:path}", response_model=EntityResponse)
async def get_entity(
entity_service: EntityServiceDep,
link_resolver: LinkResolverDep,
identifier: str,
) -> EntityResponse:
"""Get a specific entity by file path or permalink..
Args:
identifier: Entity file path or permalink
:param entity_service: EntityService
:param link_resolver: LinkResolver
"""
logger.info(f"request: get_entity with identifier={identifier}")
entity = await link_resolver.resolve_link(identifier)
if not entity:
raise HTTPException(status_code=404, detail=f"Entity {identifier} not found")
result = EntityResponse.model_validate(entity)
return result
@router.get("/entities", response_model=EntityListResponse)
async def get_entities(
entity_service: EntityServiceDep,
permalink: Annotated[list[str] | None, Query()] = None,
) -> EntityListResponse:
"""Open specific entities"""
logger.info(f"request: get_entities with permalinks={permalink}")
entities = await entity_service.get_entities_by_permalinks(permalink) if permalink else []
result = EntityListResponse(
entities=[EntityResponse.model_validate(entity) for entity in entities]
)
return result
## Delete endpoints
@router.delete("/entities/{identifier:path}", response_model=DeleteEntitiesResponse)
async def delete_entity(
identifier: str,
background_tasks: BackgroundTasks,
entity_service: EntityServiceDep,
link_resolver: LinkResolverDep,
search_service=Depends(get_search_service),
) -> DeleteEntitiesResponse:
"""Delete a single entity and remove from search index."""
logger.info(f"request: delete_entity with identifier={identifier}")
entity = await link_resolver.resolve_link(identifier)
if entity is None:
return DeleteEntitiesResponse(deleted=False)
# Delete the entity
deleted = await entity_service.delete_entity(entity.permalink or entity.id)
# Remove from search index (entity, observations, and relations)
background_tasks.add_task(search_service.handle_delete, entity)
result = DeleteEntitiesResponse(deleted=deleted)
return result
@router.post("/entities/delete", response_model=DeleteEntitiesResponse)
async def delete_entities(
data: DeleteEntitiesRequest,
background_tasks: BackgroundTasks,
entity_service: EntityServiceDep,
search_service=Depends(get_search_service),
) -> DeleteEntitiesResponse:
"""Delete entities and remove from search index."""
logger.info(f"request: delete_entities with data={data}")
deleted = False
# Remove each deleted entity from search index
for permalink in data.permalinks:
deleted = await entity_service.delete_entity(permalink)
background_tasks.add_task(search_service.delete_by_permalink, permalink)
result = DeleteEntitiesResponse(deleted=deleted)
return result
@@ -1,80 +0,0 @@
"""Management router for basic-memory API."""
import asyncio
from fastapi import APIRouter, Request
from loguru import logger
from pydantic import BaseModel
from basic_memory.config import ConfigManager
from basic_memory.deps import SyncServiceDep, ProjectRepositoryDep
router = APIRouter(prefix="/management", tags=["management"])
class WatchStatusResponse(BaseModel):
"""Response model for watch status."""
running: bool
"""Whether the watch service is currently running."""
@router.get("/watch/status", response_model=WatchStatusResponse)
async def get_watch_status(request: Request) -> WatchStatusResponse:
"""Get the current status of the watch service."""
return WatchStatusResponse(
running=request.app.state.watch_task is not None and not request.app.state.watch_task.done()
)
@router.post("/watch/start", response_model=WatchStatusResponse)
async def start_watch_service(
request: Request, project_repository: ProjectRepositoryDep, sync_service: SyncServiceDep
) -> WatchStatusResponse:
"""Start the watch service if it's not already running."""
# needed because of circular imports from sync -> app
from basic_memory.sync import WatchService
from basic_memory.sync.background_sync import create_background_sync_task
if request.app.state.watch_task is not None and not request.app.state.watch_task.done():
# Watch service is already running
return WatchStatusResponse(running=True)
app_config = ConfigManager().config
# Create and start a new watch service
logger.info("Starting watch service via management API")
# Get services needed for the watch task
watch_service = WatchService(
app_config=app_config,
project_repository=project_repository,
)
# Create and store the task
watch_task = create_background_sync_task(sync_service, watch_service)
request.app.state.watch_task = watch_task
return WatchStatusResponse(running=True)
@router.post("/watch/stop", response_model=WatchStatusResponse)
async def stop_watch_service(request: Request) -> WatchStatusResponse: # pragma: no cover
"""Stop the watch service if it's running."""
if request.app.state.watch_task is None or request.app.state.watch_task.done():
# Watch service is not running
return WatchStatusResponse(running=False)
# Cancel the running task
logger.info("Stopping watch service via management API")
request.app.state.watch_task.cancel()
# Wait for it to be properly cancelled
try:
await request.app.state.watch_task
except asyncio.CancelledError:
pass
request.app.state.watch_task = None
return WatchStatusResponse(running=False)
@@ -1,90 +0,0 @@
"""Routes for memory:// URI operations."""
from typing import Annotated, Optional
from fastapi import APIRouter, Query
from loguru import logger
from basic_memory.deps import ContextServiceDep, EntityRepositoryDep
from basic_memory.schemas.base import TimeFrame, parse_timeframe
from basic_memory.schemas.memory import (
GraphContext,
normalize_memory_url,
)
from basic_memory.schemas.search import SearchItemType
from basic_memory.api.routers.utils import to_graph_context
router = APIRouter(prefix="/memory", tags=["memory"])
@router.get("/recent", response_model=GraphContext)
async def recent(
context_service: ContextServiceDep,
entity_repository: EntityRepositoryDep,
type: Annotated[list[SearchItemType] | None, Query()] = None,
depth: int = 1,
timeframe: TimeFrame = "7d",
page: int = 1,
page_size: int = 10,
max_related: int = 10,
) -> GraphContext:
# return all types by default
types = (
[SearchItemType.ENTITY, SearchItemType.RELATION, SearchItemType.OBSERVATION]
if not type
else type
)
logger.debug(
f"Getting recent context: `{types}` depth: `{depth}` timeframe: `{timeframe}` page: `{page}` page_size: `{page_size}` max_related: `{max_related}`"
)
# Parse timeframe
since = parse_timeframe(timeframe)
limit = page_size
offset = (page - 1) * page_size
# Build context
context = await context_service.build_context(
types=types, depth=depth, since=since, limit=limit, offset=offset, max_related=max_related
)
recent_context = await to_graph_context(
context, entity_repository=entity_repository, page=page, page_size=page_size
)
logger.debug(f"Recent context: {recent_context.model_dump_json()}")
return recent_context
# get_memory_context needs to be declared last so other paths can match
@router.get("/{uri:path}", response_model=GraphContext)
async def get_memory_context(
context_service: ContextServiceDep,
entity_repository: EntityRepositoryDep,
uri: str,
depth: int = 1,
timeframe: Optional[TimeFrame] = None,
page: int = 1,
page_size: int = 10,
max_related: int = 10,
) -> GraphContext:
"""Get rich context from memory:// URI."""
# add the project name from the config to the url as the "host
# Parse URI
logger.debug(
f"Getting context for URI: `{uri}` depth: `{depth}` timeframe: `{timeframe}` page: `{page}` page_size: `{page_size}` max_related: `{max_related}`"
)
memory_url = normalize_memory_url(uri)
# Parse timeframe
since = parse_timeframe(timeframe) if timeframe else None
limit = page_size
offset = (page - 1) * page_size
# Build context
context = await context_service.build_context(
memory_url, depth=depth, since=since, limit=limit, offset=offset, max_related=max_related
)
return await to_graph_context(
context, entity_repository=entity_repository, page=page, page_size=page_size
)
@@ -1,406 +0,0 @@
"""Router for project management."""
import os
from fastapi import APIRouter, HTTPException, Path, Body, BackgroundTasks, Response, Query
from typing import Optional
from loguru import logger
from basic_memory.deps import (
ProjectConfigDep,
ProjectServiceDep,
ProjectPathDep,
SyncServiceDep,
)
from basic_memory.schemas import ProjectInfoResponse, SyncReportResponse
from basic_memory.schemas.project_info import (
ProjectList,
ProjectItem,
ProjectInfoRequest,
ProjectStatusResponse,
)
from basic_memory.utils import normalize_project_path
# Router for resources in a specific project
# The ProjectPathDep is used in the path as a prefix, so the request path is like /{project}/project/info
project_router = APIRouter(prefix="/project", tags=["project"])
# Router for managing project resources
project_resource_router = APIRouter(prefix="/projects", tags=["project_management"])
@project_router.get("/info", response_model=ProjectInfoResponse)
async def get_project_info(
project_service: ProjectServiceDep,
project: ProjectPathDep,
) -> ProjectInfoResponse:
"""Get comprehensive information about the specified Basic Memory project."""
return await project_service.get_project_info(project)
@project_router.get("/item", response_model=ProjectItem)
async def get_project(
project_service: ProjectServiceDep,
project: ProjectPathDep,
) -> ProjectItem:
"""Get bassic info about the specified Basic Memory project."""
found_project = await project_service.get_project(project)
if not found_project:
raise HTTPException(
status_code=404, detail=f"Project: '{project}' does not exist"
) # pragma: no cover
return ProjectItem(
name=found_project.name,
path=normalize_project_path(found_project.path),
is_default=found_project.is_default or False,
)
# Update a project
@project_router.patch("/{name}", response_model=ProjectStatusResponse)
async def update_project(
project_service: ProjectServiceDep,
name: str = Path(..., description="Name of the project to update"),
path: Optional[str] = Body(None, description="New absolute path for the project"),
is_active: Optional[bool] = Body(None, description="Status of the project (active/inactive)"),
) -> ProjectStatusResponse:
"""Update a project's information in configuration and database.
Args:
name: The name of the project to update
path: Optional new absolute path for the project
is_active: Optional status update for the project
Returns:
Response confirming the project was updated
"""
try:
# Validate that path is absolute if provided
if path and not os.path.isabs(path):
raise HTTPException(status_code=400, detail="Path must be absolute")
# Get original project info for the response
old_project_info = ProjectItem(
name=name,
path=project_service.projects.get(name, ""),
)
if path:
await project_service.move_project(name, path)
elif is_active is not None:
await project_service.update_project(name, is_active=is_active)
# Get updated project info
updated_path = path if path else project_service.projects.get(name, "")
return ProjectStatusResponse(
message=f"Project '{name}' updated successfully",
status="success",
default=(name == project_service.default_project),
old_project=old_project_info,
new_project=ProjectItem(name=name, path=updated_path),
)
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e))
# Sync project filesystem
@project_router.post("/sync")
async def sync_project(
background_tasks: BackgroundTasks,
sync_service: SyncServiceDep,
project_config: ProjectConfigDep,
force_full: bool = Query(
False, description="Force full scan, bypassing watermark optimization"
),
run_in_background: bool = Query(True, description="Run in background"),
):
"""Force project filesystem sync to database.
Scans the project directory and updates the database with any new or modified files.
Args:
background_tasks: FastAPI background tasks
sync_service: Sync service for this project
project_config: Project configuration
force_full: If True, force a full scan even if watermark exists
run_in_background: If True, run sync in background and return immediately
Returns:
Response confirming sync was initiated (background) or SyncReportResponse (foreground)
"""
if run_in_background:
background_tasks.add_task(
sync_service.sync, project_config.home, project_config.name, force_full=force_full
)
logger.info(
f"Filesystem sync initiated for project: {project_config.name} (force_full={force_full})"
)
return {
"status": "sync_started",
"message": f"Filesystem sync initiated for project '{project_config.name}'",
}
else:
report = await sync_service.sync(
project_config.home, project_config.name, force_full=force_full
)
logger.info(
f"Filesystem sync completed for project: {project_config.name} (force_full={force_full})"
)
return SyncReportResponse.from_sync_report(report)
@project_router.post("/status", response_model=SyncReportResponse)
async def project_sync_status(
sync_service: SyncServiceDep,
project_config: ProjectConfigDep,
) -> SyncReportResponse:
"""Scan directory for changes compared to database state.
Args:
sync_service: Sync service for this project
project_config: Project configuration
Returns:
Scan report with details on files that need syncing
"""
logger.info(f"Scanning filesystem for project: {project_config.name}")
sync_report = await sync_service.scan(project_config.home)
return SyncReportResponse.from_sync_report(sync_report)
# List all available projects
@project_resource_router.get("/projects", response_model=ProjectList)
async def list_projects(
project_service: ProjectServiceDep,
) -> ProjectList:
"""List all configured projects.
Returns:
A list of all projects with metadata
"""
projects = await project_service.list_projects()
default_project = project_service.default_project
project_items = [
ProjectItem(
name=project.name,
path=normalize_project_path(project.path),
is_default=project.is_default or False,
)
for project in projects
]
return ProjectList(
projects=project_items,
default_project=default_project,
)
# Add a new project
@project_resource_router.post("/projects", response_model=ProjectStatusResponse, status_code=201)
async def add_project(
response: Response,
project_data: ProjectInfoRequest,
project_service: ProjectServiceDep,
) -> ProjectStatusResponse:
"""Add a new project to configuration and database.
Args:
project_data: The project name and path, with option to set as default
Returns:
Response confirming the project was added
"""
# Check if project already exists before attempting to add
existing_project = await project_service.get_project(project_data.name)
if existing_project:
# Project exists - check if paths match for true idempotency
# Normalize paths for comparison (resolve symlinks, etc.)
from pathlib import Path
requested_path = Path(project_data.path).resolve()
existing_path = Path(existing_project.path).resolve()
if requested_path == existing_path:
# Same name, same path - return 200 OK (idempotent)
response.status_code = 200
return ProjectStatusResponse( # pyright: ignore [reportCallIssue]
message=f"Project '{project_data.name}' already exists",
status="success",
default=existing_project.is_default or False,
new_project=ProjectItem(
name=existing_project.name,
path=existing_project.path,
is_default=existing_project.is_default or False,
),
)
else:
# Same name, different path - this is an error
raise HTTPException(
status_code=400,
detail=f"Project '{project_data.name}' already exists with different path. Existing: {existing_project.path}, Requested: {project_data.path}",
)
try: # pragma: no cover
# The service layer now handles cloud mode validation and path sanitization
await project_service.add_project(
project_data.name, project_data.path, set_default=project_data.set_default
)
return ProjectStatusResponse( # pyright: ignore [reportCallIssue]
message=f"Project '{project_data.name}' added successfully",
status="success",
default=project_data.set_default,
new_project=ProjectItem(
name=project_data.name, path=project_data.path, is_default=project_data.set_default
),
)
except ValueError as e: # pragma: no cover
raise HTTPException(status_code=400, detail=str(e))
# Remove a project
@project_resource_router.delete("/{name}", response_model=ProjectStatusResponse)
async def remove_project(
project_service: ProjectServiceDep,
name: str = Path(..., description="Name of the project to remove"),
delete_notes: bool = Query(
False, description="If True, delete project directory from filesystem"
),
) -> ProjectStatusResponse:
"""Remove a project from configuration and database.
Args:
name: The name of the project to remove
delete_notes: If True, delete the project directory from the filesystem
Returns:
Response confirming the project was removed
"""
try:
old_project = await project_service.get_project(name)
if not old_project: # pragma: no cover
raise HTTPException(
status_code=404, detail=f"Project: '{name}' does not exist"
) # pragma: no cover
# Check if trying to delete the default project
if name == project_service.default_project:
available_projects = await project_service.list_projects()
other_projects = [p.name for p in available_projects if p.name != name]
detail = f"Cannot delete default project '{name}'. "
if other_projects:
detail += (
f"Set another project as default first. Available: {', '.join(other_projects)}"
)
else:
detail += "This is the only project in your configuration."
raise HTTPException(status_code=400, detail=detail)
await project_service.remove_project(name, delete_notes=delete_notes)
return ProjectStatusResponse(
message=f"Project '{name}' removed successfully",
status="success",
default=False,
old_project=ProjectItem(name=old_project.name, path=old_project.path),
new_project=None,
)
except ValueError as e: # pragma: no cover
raise HTTPException(status_code=400, detail=str(e))
# Set a project as default
@project_resource_router.put("/{name}/default", response_model=ProjectStatusResponse)
async def set_default_project(
project_service: ProjectServiceDep,
name: str = Path(..., description="Name of the project to set as default"),
) -> ProjectStatusResponse:
"""Set a project as the default project.
Args:
name: The name of the project to set as default
Returns:
Response confirming the project was set as default
"""
try:
# Get the old default project
default_name = project_service.default_project
default_project = await project_service.get_project(default_name)
if not default_project: # pragma: no cover
raise HTTPException( # pragma: no cover
status_code=404, detail=f"Default Project: '{default_name}' does not exist"
)
# get the new project
new_default_project = await project_service.get_project(name)
if not new_default_project: # pragma: no cover
raise HTTPException(
status_code=404, detail=f"Project: '{name}' does not exist"
) # pragma: no cover
await project_service.set_default_project(name)
return ProjectStatusResponse(
message=f"Project '{name}' set as default successfully",
status="success",
default=True,
old_project=ProjectItem(name=default_name, path=default_project.path),
new_project=ProjectItem(
name=name,
path=new_default_project.path,
is_default=True,
),
)
except ValueError as e: # pragma: no cover
raise HTTPException(status_code=400, detail=str(e))
# Get the default project
@project_resource_router.get("/default", response_model=ProjectItem)
async def get_default_project(
project_service: ProjectServiceDep,
) -> ProjectItem:
"""Get the default project.
Returns:
Response with project default information
"""
# Get the old default project
default_name = project_service.default_project
default_project = await project_service.get_project(default_name)
if not default_project: # pragma: no cover
raise HTTPException( # pragma: no cover
status_code=404, detail=f"Default Project: '{default_name}' does not exist"
)
return ProjectItem(name=default_project.name, path=default_project.path, is_default=True)
# Synchronize projects between config and database
@project_resource_router.post("/config/sync", response_model=ProjectStatusResponse)
async def synchronize_projects(
project_service: ProjectServiceDep,
) -> ProjectStatusResponse:
"""Synchronize projects between configuration file and database.
Ensures that all projects in the configuration file exist in the database
and vice versa.
Returns:
Response confirming synchronization was completed
"""
try: # pragma: no cover
await project_service.synchronize_projects()
return ProjectStatusResponse( # pyright: ignore [reportCallIssue]
message="Projects synchronized successfully between configuration and database",
status="success",
default=False,
)
except ValueError as e: # pragma: no cover
raise HTTPException(status_code=400, detail=str(e))
@@ -1,239 +0,0 @@
"""Routes for getting entity content."""
import tempfile
from pathlib import Path
from typing import Annotated
from fastapi import APIRouter, HTTPException, BackgroundTasks, Body
from fastapi.responses import FileResponse, JSONResponse
from loguru import logger
from basic_memory.deps import (
ProjectConfigDep,
LinkResolverDep,
SearchServiceDep,
EntityServiceDep,
FileServiceDep,
EntityRepositoryDep,
)
from basic_memory.repository.search_repository import SearchIndexRow
from basic_memory.schemas.memory import normalize_memory_url
from basic_memory.schemas.search import SearchQuery, SearchItemType
from basic_memory.models.knowledge import Entity as EntityModel
from datetime import datetime
router = APIRouter(prefix="/resource", tags=["resources"])
def get_entity_ids(item: SearchIndexRow) -> set[int]:
match item.type:
case SearchItemType.ENTITY:
return {item.id}
case SearchItemType.OBSERVATION:
return {item.entity_id} # pyright: ignore [reportReturnType]
case SearchItemType.RELATION:
from_entity = item.from_id
to_entity = item.to_id # pyright: ignore [reportReturnType]
return {from_entity, to_entity} if to_entity else {from_entity} # pyright: ignore [reportReturnType]
case _: # pragma: no cover
raise ValueError(f"Unexpected type: {item.type}")
@router.get("/{identifier:path}")
async def get_resource_content(
config: ProjectConfigDep,
link_resolver: LinkResolverDep,
search_service: SearchServiceDep,
entity_service: EntityServiceDep,
file_service: FileServiceDep,
background_tasks: BackgroundTasks,
identifier: str,
page: int = 1,
page_size: int = 10,
) -> FileResponse:
"""Get resource content by identifier: name or permalink."""
logger.debug(f"Getting content for: {identifier}")
# Find single entity by permalink
entity = await link_resolver.resolve_link(identifier)
results = [entity] if entity else []
# pagination for multiple results
limit = page_size
offset = (page - 1) * page_size
# search using the identifier as a permalink
if not results:
# if the identifier contains a wildcard, use GLOB search
query = (
SearchQuery(permalink_match=identifier)
if "*" in identifier
else SearchQuery(permalink=identifier)
)
search_results = await search_service.search(query, limit, offset)
if not search_results:
raise HTTPException(status_code=404, detail=f"Resource not found: {identifier}")
# get the deduplicated entities related to the search results
entity_ids = {id for result in search_results for id in get_entity_ids(result)}
results = await entity_service.get_entities_by_id(list(entity_ids))
# return single response
if len(results) == 1:
entity = results[0]
file_path = Path(f"{config.home}/{entity.file_path}")
if not file_path.exists():
raise HTTPException(
status_code=404,
detail=f"File not found: {file_path}",
)
return FileResponse(path=file_path)
# for multiple files, initialize a temporary file for writing the results
with tempfile.NamedTemporaryFile(delete=False, mode="w", suffix=".md") as tmp_file:
temp_file_path = tmp_file.name
for result in results:
# Read content for each entity
content = await file_service.read_entity_content(result)
memory_url = normalize_memory_url(result.permalink)
modified_date = result.updated_at.isoformat()
checksum = result.checksum[:8] if result.checksum else ""
# Prepare the delimited content
response_content = f"--- {memory_url} {modified_date} {checksum}\n"
response_content += f"\n{content}\n"
response_content += "\n"
# Write content directly to the temporary file in append mode
tmp_file.write(response_content)
# Ensure all content is written to disk
tmp_file.flush()
# Schedule the temporary file to be deleted after the response
background_tasks.add_task(cleanup_temp_file, temp_file_path)
# Return the file response
return FileResponse(path=temp_file_path)
def cleanup_temp_file(file_path: str):
"""Delete the temporary file."""
try:
Path(file_path).unlink() # Deletes the file
logger.debug(f"Temporary file deleted: {file_path}")
except Exception as e: # pragma: no cover
logger.error(f"Error deleting temporary file {file_path}: {e}")
@router.put("/{file_path:path}")
async def write_resource(
config: ProjectConfigDep,
file_service: FileServiceDep,
entity_repository: EntityRepositoryDep,
search_service: SearchServiceDep,
file_path: str,
content: Annotated[str, Body()],
) -> JSONResponse:
"""Write content to a file in the project.
This endpoint allows writing content directly to a file in the project.
Also creates an entity record and indexes the file for search.
Args:
file_path: Path to write to, relative to project root
request: Contains the content to write
Returns:
JSON response with file information
"""
try:
# Get content from request body
# Defensive type checking: ensure content is a string
# FastAPI should validate this, but if a dict somehow gets through
# (e.g., via JSON body parsing), we need to catch it here
if isinstance(content, dict):
logger.error(
f"Error writing resource {file_path}: "
f"content is a dict, expected string. Keys: {list(content.keys())}"
)
raise HTTPException(
status_code=400,
detail="content must be a string, not a dict. "
"Ensure request body is sent as raw string content, not JSON object.",
)
# Ensure it's UTF-8 string content
if isinstance(content, bytes): # pragma: no cover
content_str = content.decode("utf-8")
else:
content_str = str(content)
# Get full file path
full_path = Path(f"{config.home}/{file_path}")
# Ensure parent directory exists
full_path.parent.mkdir(parents=True, exist_ok=True)
# Write content to file
checksum = await file_service.write_file(full_path, content_str)
# Get file info
file_stats = file_service.file_stats(full_path)
# Determine file details
file_name = Path(file_path).name
content_type = file_service.content_type(full_path)
entity_type = "canvas" if file_path.endswith(".canvas") else "file"
# Check if entity already exists
existing_entity = await entity_repository.get_by_file_path(file_path)
if existing_entity:
# Update existing entity
entity = await entity_repository.update(
existing_entity.id,
{
"title": file_name,
"entity_type": entity_type,
"content_type": content_type,
"file_path": file_path,
"checksum": checksum,
"updated_at": datetime.fromtimestamp(file_stats.st_mtime).astimezone(),
},
)
status_code = 200
else:
# Create a new entity model
entity = EntityModel(
title=file_name,
entity_type=entity_type,
content_type=content_type,
file_path=file_path,
checksum=checksum,
created_at=datetime.fromtimestamp(file_stats.st_ctime).astimezone(),
updated_at=datetime.fromtimestamp(file_stats.st_mtime).astimezone(),
)
entity = await entity_repository.add(entity)
status_code = 201
# Index the file for search
await search_service.index_entity(entity) # pyright: ignore
# Return success response
return JSONResponse(
status_code=status_code,
content={
"file_path": file_path,
"checksum": checksum,
"size": file_stats.st_size,
"created_at": file_stats.st_ctime,
"modified_at": file_stats.st_mtime,
},
)
except Exception as e: # pragma: no cover
logger.error(f"Error writing resource {file_path}: {e}")
raise HTTPException(status_code=500, detail=f"Failed to write resource: {str(e)}")
@@ -1,36 +0,0 @@
"""Router for search operations."""
from fastapi import APIRouter, BackgroundTasks
from basic_memory.api.routers.utils import to_search_results
from basic_memory.schemas.search import SearchQuery, SearchResponse
from basic_memory.deps import SearchServiceDep, EntityServiceDep
router = APIRouter(prefix="/search", tags=["search"])
@router.post("/", response_model=SearchResponse)
async def search(
query: SearchQuery,
search_service: SearchServiceDep,
entity_service: EntityServiceDep,
page: int = 1,
page_size: int = 10,
):
"""Search across all knowledge and documents."""
limit = page_size
offset = (page - 1) * page_size
results = await search_service.search(query, limit=limit, offset=offset)
search_results = await to_search_results(entity_service, results)
return SearchResponse(
results=search_results,
current_page=page,
page_size=page_size,
)
@router.post("/reindex")
async def reindex(background_tasks: BackgroundTasks, search_service: SearchServiceDep):
"""Recreate and populate the search index."""
await search_service.reindex_all(background_tasks=background_tasks)
return {"status": "ok", "message": "Reindex initiated"}
+35
View File
@@ -0,0 +1,35 @@
"""API v2 module - ID-based entity references.
Version 2 of the Basic Memory API uses integer entity IDs as the primary
identifier for improved performance and stability.
Key changes from v1:
- Entity lookups use integer IDs instead of paths/permalinks
- Direct database queries instead of cascading resolution
- Stable references that don't change with file moves
- Better caching support
All v2 routers are registered with the /v2 prefix.
"""
from basic_memory.api.v2.routers import (
knowledge_router,
memory_router,
project_router,
resource_router,
search_router,
directory_router,
prompt_router,
importer_router,
)
__all__ = [
"knowledge_router",
"memory_router",
"project_router",
"resource_router",
"search_router",
"directory_router",
"prompt_router",
"importer_router",
]
@@ -0,0 +1,23 @@
"""V2 API routers."""
from basic_memory.api.v2.routers.knowledge_router import router as knowledge_router
from basic_memory.api.v2.routers.project_router import router as project_router
from basic_memory.api.v2.routers.memory_router import router as memory_router
from basic_memory.api.v2.routers.search_router import router as search_router
from basic_memory.api.v2.routers.resource_router import router as resource_router
from basic_memory.api.v2.routers.directory_router import router as directory_router
from basic_memory.api.v2.routers.prompt_router import router as prompt_router
from basic_memory.api.v2.routers.importer_router import router as importer_router
from basic_memory.api.v2.routers.schema_router import router as schema_router
__all__ = [
"knowledge_router",
"project_router",
"memory_router",
"search_router",
"resource_router",
"directory_router",
"prompt_router",
"importer_router",
"schema_router",
]
@@ -1,25 +1,34 @@
"""Router for directory tree operations."""
"""V2 Directory Router - ID-based directory tree operations.
This router provides directory structure browsing for projects using
external_id UUIDs instead of name-based identifiers.
Key improvements:
- Direct project lookup via external_id UUIDs
- Consistent with other v2 endpoints
- Better performance through indexed queries
"""
from typing import List, Optional
from fastapi import APIRouter, Query
from fastapi import APIRouter, Query, Path
from basic_memory.deps import DirectoryServiceDep, ProjectIdDep
from basic_memory.deps import DirectoryServiceV2ExternalDep
from basic_memory.schemas.directory import DirectoryNode
router = APIRouter(prefix="/directory", tags=["directory"])
router = APIRouter(prefix="/directory", tags=["directory-v2"])
@router.get("/tree", response_model=DirectoryNode, response_model_exclude_none=True)
async def get_directory_tree(
directory_service: DirectoryServiceDep,
project_id: ProjectIdDep,
directory_service: DirectoryServiceV2ExternalDep,
project_id: str = Path(..., description="Project external UUID"),
):
"""Get hierarchical directory structure from the knowledge base.
Args:
directory_service: Service for directory operations
project_id: ID of the current project
project_id: Project external UUID
Returns:
DirectoryNode representing the root of the hierarchical tree structure
@@ -33,8 +42,8 @@ async def get_directory_tree(
@router.get("/structure", response_model=DirectoryNode, response_model_exclude_none=True)
async def get_directory_structure(
directory_service: DirectoryServiceDep,
project_id: ProjectIdDep,
directory_service: DirectoryServiceV2ExternalDep,
project_id: str = Path(..., description="Project external UUID"),
):
"""Get folder structure for navigation (no files).
@@ -43,7 +52,7 @@ async def get_directory_structure(
Args:
directory_service: Service for directory operations
project_id: ID of the current project
project_id: Project external UUID
Returns:
DirectoryNode tree containing only folders (type="directory")
@@ -54,8 +63,8 @@ async def get_directory_structure(
@router.get("/list", response_model=List[DirectoryNode], response_model_exclude_none=True)
async def list_directory(
directory_service: DirectoryServiceDep,
project_id: ProjectIdDep,
directory_service: DirectoryServiceV2ExternalDep,
project_id: str = Path(..., description="Project external UUID"),
dir_name: str = Query("/", description="Directory path to list"),
depth: int = Query(1, ge=1, le=10, description="Recursion depth (1-10)"),
file_name_glob: Optional[str] = Query(
@@ -66,7 +75,7 @@ async def list_directory(
Args:
directory_service: Service for directory operations
project_id: ID of the current project
project_id: Project external UUID
dir_name: Directory path to list (default: root "/")
depth: Recursion depth (1-10, default: 1 for immediate children only)
file_name_glob: Optional glob pattern for filtering file names (e.g., "*.md", "*meeting*")
@@ -1,15 +1,19 @@
"""Import router for Basic Memory API."""
"""V2 Import Router - ID-based data import operations.
This router uses v2 dependencies for consistent project handling with external_id UUIDs.
Import endpoints use project_id in the path for consistency with other v2 endpoints.
"""
import json
import logging
from fastapi import APIRouter, Form, HTTPException, UploadFile, status
from fastapi import APIRouter, Form, HTTPException, UploadFile, status, Path
from basic_memory.deps import (
ChatGPTImporterDep,
ClaudeConversationsImporterDep,
ClaudeProjectsImporterDep,
MemoryJsonImporterDep,
ChatGPTImporterV2ExternalDep,
ClaudeConversationsImporterV2ExternalDep,
ClaudeProjectsImporterV2ExternalDep,
MemoryJsonImporterV2ExternalDep,
)
from basic_memory.importers import Importer
from basic_memory.schemas.importer import (
@@ -20,21 +24,23 @@ from basic_memory.schemas.importer import (
logger = logging.getLogger(__name__)
router = APIRouter(prefix="/import", tags=["import"])
router = APIRouter(prefix="/import", tags=["import-v2"])
@router.post("/chatgpt", response_model=ChatImportResult)
async def import_chatgpt(
importer: ChatGPTImporterDep,
importer: ChatGPTImporterV2ExternalDep,
file: UploadFile,
folder: str = Form("conversations"),
project_id: str = Path(..., description="Project external UUID"),
directory: str = Form("conversations"),
) -> ChatImportResult:
"""Import conversations from ChatGPT JSON export.
Args:
project_id: Project external UUID from URL path
file: The ChatGPT conversations.json file.
folder: The folder to place the files in.
markdown_processor: MarkdownProcessor instance.
directory: The directory to place the files in.
importer: ChatGPT importer instance.
Returns:
ChatImportResult with import statistics.
@@ -42,21 +48,24 @@ async def import_chatgpt(
Raises:
HTTPException: If import fails.
"""
return await import_file(importer, file, folder)
logger.info(f"V2 Importing ChatGPT conversations for project {project_id}")
return await import_file(importer, file, directory)
@router.post("/claude/conversations", response_model=ChatImportResult)
async def import_claude_conversations(
importer: ClaudeConversationsImporterDep,
importer: ClaudeConversationsImporterV2ExternalDep,
file: UploadFile,
folder: str = Form("conversations"),
project_id: str = Path(..., description="Project external UUID"),
directory: str = Form("conversations"),
) -> ChatImportResult:
"""Import conversations from Claude conversations.json export.
Args:
project_id: Project external UUID from URL path
file: The Claude conversations.json file.
folder: The folder to place the files in.
markdown_processor: MarkdownProcessor instance.
directory: The directory to place the files in.
importer: Claude conversations importer instance.
Returns:
ChatImportResult with import statistics.
@@ -64,21 +73,24 @@ async def import_claude_conversations(
Raises:
HTTPException: If import fails.
"""
return await import_file(importer, file, folder)
logger.info(f"V2 Importing Claude conversations for project {project_id}")
return await import_file(importer, file, directory)
@router.post("/claude/projects", response_model=ProjectImportResult)
async def import_claude_projects(
importer: ClaudeProjectsImporterDep,
importer: ClaudeProjectsImporterV2ExternalDep,
file: UploadFile,
folder: str = Form("projects"),
project_id: str = Path(..., description="Project external UUID"),
directory: str = Form("projects"),
) -> ProjectImportResult:
"""Import projects from Claude projects.json export.
Args:
project_id: Project external UUID from URL path
file: The Claude projects.json file.
base_folder: The base folder to place the files in.
markdown_processor: MarkdownProcessor instance.
directory: The base directory to place the files in.
importer: Claude projects importer instance.
Returns:
ProjectImportResult with import statistics.
@@ -86,21 +98,24 @@ async def import_claude_projects(
Raises:
HTTPException: If import fails.
"""
return await import_file(importer, file, folder)
logger.info(f"V2 Importing Claude projects for project {project_id}")
return await import_file(importer, file, directory)
@router.post("/memory-json", response_model=EntityImportResult)
async def import_memory_json(
importer: MemoryJsonImporterDep,
importer: MemoryJsonImporterV2ExternalDep,
file: UploadFile,
folder: str = Form("conversations"),
project_id: str = Path(..., description="Project external UUID"),
directory: str = Form("conversations"),
) -> EntityImportResult:
"""Import entities and relations from a memory.json file.
Args:
project_id: Project external UUID from URL path
file: The memory.json file.
destination_folder: Optional destination folder within the project.
markdown_processor: MarkdownProcessor instance.
directory: Optional destination directory within the project.
importer: Memory JSON importer instance.
Returns:
EntityImportResult with import statistics.
@@ -108,6 +123,7 @@ async def import_memory_json(
Raises:
HTTPException: If import fails.
"""
logger.info(f"V2 Importing memory.json for project {project_id}")
try:
file_data = []
file_bytes = await file.read()
@@ -116,14 +132,14 @@ async def import_memory_json(
json_data = json.loads(line)
file_data.append(json_data)
result = await importer.import_data(file_data, folder)
result = await importer.import_data(file_data, directory)
if not result.success: # pragma: no cover
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=result.error_message or "Import failed",
)
except Exception as e:
logger.exception("Import failed")
logger.exception("V2 Import failed")
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"Import failed: {str(e)}",
@@ -131,11 +147,24 @@ async def import_memory_json(
return result
async def import_file(importer: Importer, file: UploadFile, destination_folder: str):
async def import_file(importer: Importer, file: UploadFile, destination_directory: str):
"""Helper function to import a file using an importer instance.
Args:
importer: The importer instance to use
file: The file to import
destination_directory: Destination directory for imported content
Returns:
Import result from the importer
Raises:
HTTPException: If import fails
"""
try:
# Process file
json_data = json.load(file.file)
result = await importer.import_data(json_data, destination_folder)
result = await importer.import_data(json_data, destination_directory)
if not result.success: # pragma: no cover
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
@@ -145,7 +174,7 @@ async def import_file(importer: Importer, file: UploadFile, destination_folder:
return result
except Exception as e:
logger.exception("Import failed")
logger.exception("V2 Import failed")
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=f"Import failed: {str(e)}",
@@ -0,0 +1,684 @@
"""V2 Knowledge Router - External ID-based entity operations.
This router provides external_id (UUID) based CRUD operations for entities,
using stable string UUIDs that won't change with file moves or database migrations.
Key improvements:
- Stable external UUIDs that won't change with file moves or renames
- Better API ergonomics with consistent string identifiers
- Direct database lookups via unique indexed column
- Simplified caching strategies
"""
from fastapi import APIRouter, HTTPException, BackgroundTasks, Depends, Response, Path, Query
from loguru import logger
from basic_memory.deps import (
EntityServiceV2ExternalDep,
SearchServiceV2ExternalDep,
LinkResolverV2ExternalDep,
ProjectConfigV2ExternalDep,
AppConfigDep,
EntityRepositoryV2ExternalDep,
RelationRepositoryV2ExternalDep,
ProjectExternalIdPathDep,
TaskSchedulerDep,
FileServiceV2ExternalDep,
)
from basic_memory.schemas import DeleteEntitiesResponse
from basic_memory.schemas.base import Entity
from basic_memory.schemas.request import EditEntityRequest
from basic_memory.schemas.v2 import (
EntityResolveRequest,
EntityResolveResponse,
EntityResponseV2,
GraphEdge,
GraphNode,
GraphResponse,
MoveEntityRequestV2,
MoveDirectoryRequestV2,
DeleteDirectoryRequestV2,
)
from basic_memory.schemas.response import DirectoryMoveResult, DirectoryDeleteResult
router = APIRouter(prefix="/knowledge", tags=["knowledge-v2"])
def _schedule_vector_sync_if_enabled(
*,
task_scheduler,
app_config,
entity_id: int,
project_id: int,
) -> None:
"""Schedule out-of-band vector sync only when semantic search is enabled."""
if app_config.semantic_search_enabled:
task_scheduler.schedule(
"sync_entity_vectors",
entity_id=entity_id,
project_id=project_id,
)
## Graph endpoint
@router.get("/graph", response_model=GraphResponse)
async def get_graph(
project_id: ProjectExternalIdPathDep,
entity_repository: EntityRepositoryV2ExternalDep,
relation_repository: RelationRepositoryV2ExternalDep,
) -> GraphResponse:
"""Return all entities and resolved relations for knowledge graph visualization.
Returns a flat node/edge structure optimized for rendering with graph libraries.
Only includes resolved relations (where to_id is not null).
"""
logger.info("API v2 request: get_graph")
# Fetch all entities for this project
entities = await entity_repository.find_all(use_load_options=False)
nodes = [
GraphNode(
external_id=entity.external_id,
title=entity.title,
note_type=entity.note_type,
file_path=entity.file_path,
)
for entity in entities
]
# Fetch all resolved relations (to_id is not null) with eager-loaded entities
relations = await relation_repository.find_all()
edges = [
GraphEdge(
from_id=relation.from_entity.external_id,
to_id=relation.to_entity.external_id,
relation_type=relation.relation_type,
)
for relation in relations
if relation.to_entity is not None
]
logger.info(f"API v2 response: graph with {len(nodes)} nodes and {len(edges)} edges")
return GraphResponse(nodes=nodes, edges=edges)
## Resolution endpoint
@router.post("/resolve", response_model=EntityResolveResponse)
async def resolve_identifier(
project_id: ProjectExternalIdPathDep,
data: EntityResolveRequest,
link_resolver: LinkResolverV2ExternalDep,
entity_repository: EntityRepositoryV2ExternalDep,
) -> EntityResolveResponse:
"""Resolve a string identifier (external_id, permalink, title, or path) to entity info.
This endpoint provides a bridge between v1-style identifiers and v2 external_ids.
Use this to convert existing references to the new UUID-based format.
Args:
data: Request containing the identifier to resolve
Returns:
Entity external_id and metadata about how it was resolved
Raises:
HTTPException: 404 if identifier cannot be resolved
Example:
POST /v2/{project_id}/knowledge/resolve
{"identifier": "specs/search"}
Returns:
{
"external_id": "550e8400-e29b-41d4-a716-446655440000",
"entity_id": 123,
"permalink": "specs/search",
"file_path": "specs/search.md",
"title": "Search Specification",
"resolution_method": "permalink"
}
"""
logger.info(f"API v2 request: resolve_identifier for '{data.identifier}'")
# Try to resolve by external_id first
entity = await entity_repository.get_by_external_id(data.identifier)
resolution_method = "external_id" if entity else "search"
# If not found by external_id, try other resolution methods
# Pass source_path for context-aware resolution (prefers notes closer to source)
# Pass strict to control fuzzy search fallback (default False allows fuzzy matching)
if not entity:
entity = await link_resolver.resolve_link(
data.identifier, source_path=data.source_path, strict=data.strict
)
if entity:
# Determine resolution method
if entity.permalink == data.identifier:
resolution_method = "permalink"
elif entity.title == data.identifier:
resolution_method = "title"
elif entity.file_path == data.identifier:
resolution_method = "path"
else:
resolution_method = "search"
if not entity:
raise HTTPException(status_code=404, detail=f"Entity not found: '{data.identifier}'")
result = EntityResolveResponse(
external_id=entity.external_id,
entity_id=entity.id,
permalink=entity.permalink,
file_path=entity.file_path,
title=entity.title,
resolution_method=resolution_method,
)
logger.debug(
f"API v2 response: resolved '{data.identifier}' to external_id={result.external_id} via {resolution_method}"
)
return result
## Read endpoints
@router.get("/entities/{entity_id}", response_model=EntityResponseV2)
async def get_entity_by_id(
project_id: ProjectExternalIdPathDep,
entity_repository: EntityRepositoryV2ExternalDep,
entity_id: str = Path(..., description="Entity external ID (UUID)"),
) -> EntityResponseV2:
"""Get an entity by its external ID (UUID).
This is the primary entity retrieval method in v2, using stable UUID
identifiers that won't change with file moves.
Args:
entity_id: External ID (UUID string)
Returns:
Complete entity with observations and relations
Raises:
HTTPException: 404 if entity not found
"""
logger.info(f"API v2 request: get_entity_by_id entity_id={entity_id}")
entity = await entity_repository.get_by_external_id(entity_id)
if not entity:
raise HTTPException(
status_code=404, detail=f"Entity with external_id '{entity_id}' not found"
)
result = EntityResponseV2.model_validate(entity)
logger.info(f"API v2 response: external_id={entity_id}, title='{result.title}'")
return result
## Create endpoints
@router.post("/entities", response_model=EntityResponseV2)
async def create_entity(
project_id: ProjectExternalIdPathDep,
data: Entity,
background_tasks: BackgroundTasks,
entity_service: EntityServiceV2ExternalDep,
search_service: SearchServiceV2ExternalDep,
task_scheduler: TaskSchedulerDep,
file_service: FileServiceV2ExternalDep,
app_config: AppConfigDep,
fast: bool = Query(
True, description="If true, write quickly and defer indexing to background tasks."
),
) -> EntityResponseV2:
"""Create a new entity.
Args:
data: Entity data to create
fast: If True, defer indexing to background tasks
Returns:
Created entity with generated external_id (UUID) and file content
"""
logger.info(
"API v2 request", endpoint="create_entity", note_type=data.note_type, title=data.title
)
if fast:
entity = await entity_service.fast_write_entity(data)
task_scheduler.schedule(
"reindex_entity",
entity_id=entity.id,
project_id=project_id,
)
else:
entity = await entity_service.create_entity(data)
await search_service.index_entity(entity)
_schedule_vector_sync_if_enabled(
task_scheduler=task_scheduler,
app_config=app_config,
entity_id=entity.id,
project_id=project_id,
)
result = EntityResponseV2.model_validate(entity)
if fast:
result = result.model_copy(update={"observations": [], "relations": []})
# Always read and return file content
content = await file_service.read_file_content(entity.file_path)
result = result.model_copy(update={"content": content})
logger.info(
f"API v2 response: endpoint='create_entity' external_id={entity.external_id}, title={result.title}, permalink={result.permalink}, status_code=201"
)
return result
## Update endpoints
@router.put("/entities/{entity_id}", response_model=EntityResponseV2)
async def update_entity_by_id(
data: Entity,
response: Response,
background_tasks: BackgroundTasks,
project_id: ProjectExternalIdPathDep,
entity_service: EntityServiceV2ExternalDep,
search_service: SearchServiceV2ExternalDep,
entity_repository: EntityRepositoryV2ExternalDep,
task_scheduler: TaskSchedulerDep,
file_service: FileServiceV2ExternalDep,
app_config: AppConfigDep,
entity_id: str = Path(..., description="Entity external ID (UUID)"),
fast: bool = Query(
True, description="If true, write quickly and defer indexing to background tasks."
),
) -> EntityResponseV2:
"""Update an entity by external ID.
If the entity doesn't exist, it will be created (upsert behavior).
Args:
entity_id: External ID (UUID string)
data: Updated entity data
fast: If True, defer indexing to background tasks
Returns:
Updated entity with file content
"""
logger.info(f"API v2 request: update_entity_by_id entity_id={entity_id}")
# Check if entity exists (external_id is the source of truth for v2)
existing = await entity_repository.get_by_external_id(entity_id)
created = existing is None
if fast:
entity = await entity_service.fast_write_entity(data, external_id=entity_id)
response.status_code = 200 if existing else 201
task_scheduler.schedule(
"reindex_entity",
entity_id=entity.id,
project_id=project_id,
resolve_relations=created,
)
else:
if existing:
# Update the existing entity in-place to avoid path-based duplication
entity = await entity_service.update_entity(existing, data)
response.status_code = 200
else:
# Create new entity, then bind external_id to the requested UUID
entity = await entity_service.create_entity(data)
if entity.external_id != entity_id:
entity = await entity_repository.update(
entity.id,
{"external_id": entity_id},
)
if not entity:
raise HTTPException(
status_code=404,
detail=f"Entity with external_id '{entity_id}' not found",
)
response.status_code = 201
await search_service.index_entity(entity)
_schedule_vector_sync_if_enabled(
task_scheduler=task_scheduler,
app_config=app_config,
entity_id=entity.id,
project_id=project_id,
)
result = EntityResponseV2.model_validate(entity)
if fast:
result = result.model_copy(update={"observations": [], "relations": []})
# Always read and return file content
content = await file_service.read_file_content(entity.file_path)
result = result.model_copy(update={"content": content})
logger.info(
f"API v2 response: external_id={entity_id}, created={created}, status_code={response.status_code}"
)
return result
@router.patch("/entities/{entity_id}", response_model=EntityResponseV2)
async def edit_entity_by_id(
data: EditEntityRequest,
background_tasks: BackgroundTasks,
project_id: ProjectExternalIdPathDep,
entity_service: EntityServiceV2ExternalDep,
search_service: SearchServiceV2ExternalDep,
entity_repository: EntityRepositoryV2ExternalDep,
task_scheduler: TaskSchedulerDep,
file_service: FileServiceV2ExternalDep,
app_config: AppConfigDep,
entity_id: str = Path(..., description="Entity external ID (UUID)"),
fast: bool = Query(
True, description="If true, write quickly and defer indexing to background tasks."
),
) -> EntityResponseV2:
"""Edit an existing entity by external ID using operations like append, prepend, etc.
Args:
entity_id: External ID (UUID string)
data: Edit operation details
fast: If True, defer indexing to background tasks
Returns:
Updated entity with file content
Raises:
HTTPException: 404 if entity not found, 400 if edit fails
"""
logger.info(
f"API v2 request: edit_entity_by_id entity_id={entity_id}, operation='{data.operation}'"
)
# Verify entity exists
entity = await entity_repository.get_by_external_id(entity_id)
if not entity: # pragma: no cover
raise HTTPException(
status_code=404, detail=f"Entity with external_id '{entity_id}' not found"
)
try:
if fast:
updated_entity = await entity_service.fast_edit_entity(
entity=entity,
operation=data.operation,
content=data.content,
section=data.section,
find_text=data.find_text,
expected_replacements=data.expected_replacements,
)
task_scheduler.schedule(
"reindex_entity",
entity_id=updated_entity.id,
project_id=project_id,
)
else:
# Edit using the entity's permalink or path
identifier = entity.permalink or entity.file_path
updated_entity = await entity_service.edit_entity(
identifier=identifier,
operation=data.operation,
content=data.content,
section=data.section,
find_text=data.find_text,
expected_replacements=data.expected_replacements,
)
await search_service.index_entity(updated_entity)
_schedule_vector_sync_if_enabled(
task_scheduler=task_scheduler,
app_config=app_config,
entity_id=updated_entity.id,
project_id=project_id,
)
result = EntityResponseV2.model_validate(updated_entity)
if fast:
result = result.model_copy(update={"observations": [], "relations": []})
# Always read and return file content
content = await file_service.read_file_content(updated_entity.file_path)
result = result.model_copy(update={"content": content})
logger.info(
f"API v2 response: external_id={entity_id}, operation='{data.operation}', status_code=200"
)
return result
except Exception as e:
logger.error(f"Error editing entity {entity_id}: {e}")
raise HTTPException(status_code=400, detail=str(e))
## Delete endpoints
@router.delete("/entities/{entity_id}", response_model=DeleteEntitiesResponse)
async def delete_entity_by_id(
background_tasks: BackgroundTasks,
project_id: ProjectExternalIdPathDep,
entity_service: EntityServiceV2ExternalDep,
entity_repository: EntityRepositoryV2ExternalDep,
entity_id: str = Path(..., description="Entity external ID (UUID)"),
search_service=Depends(lambda: None), # Optional for now
) -> DeleteEntitiesResponse:
"""Delete an entity by external ID.
Args:
entity_id: External ID (UUID string)
Returns:
Deletion status
Note: Returns deleted=False if entity doesn't exist (idempotent)
"""
logger.info(f"API v2 request: delete_entity_by_id entity_id={entity_id}")
entity = await entity_repository.get_by_external_id(entity_id)
if entity is None:
logger.info(f"API v2 response: external_id={entity_id} not found, deleted=False")
return DeleteEntitiesResponse(deleted=False)
# Delete the entity using internal ID
deleted = await entity_service.delete_entity(entity.id)
# Remove from search index if search service available
if search_service:
background_tasks.add_task(search_service.handle_delete, entity) # pragma: no cover
logger.info(f"API v2 response: external_id={entity_id}, deleted={deleted}")
return DeleteEntitiesResponse(deleted=deleted)
## Move endpoint
@router.put("/entities/{entity_id}/move", response_model=EntityResponseV2)
async def move_entity(
data: MoveEntityRequestV2,
background_tasks: BackgroundTasks,
project_id: ProjectExternalIdPathDep,
entity_service: EntityServiceV2ExternalDep,
entity_repository: EntityRepositoryV2ExternalDep,
project_config: ProjectConfigV2ExternalDep,
app_config: AppConfigDep,
search_service: SearchServiceV2ExternalDep,
task_scheduler: TaskSchedulerDep,
entity_id: str = Path(..., description="Entity external ID (UUID)"),
) -> EntityResponseV2:
"""Move an entity to a new file location.
V2 API uses external_id (UUID) in the URL path for stable references.
The external_id will remain stable after the move.
Args:
project_id: Project external ID from URL path
entity_id: Entity external ID from URL path (primary identifier)
data: Move request with destination path only
Returns:
Updated entity with new file path
"""
logger.info(
f"API v2 request: move_entity entity_id={entity_id}, destination='{data.destination_path}'"
)
try:
# First, get the entity by external_id to verify it exists
entity = await entity_repository.get_by_external_id(entity_id)
if not entity: # pragma: no cover
raise HTTPException(
status_code=404, detail=f"Entity with external_id '{entity_id}' not found"
)
# Move the entity using its current file path as identifier
moved_entity = await entity_service.move_entity(
identifier=entity.file_path, # Use file path for resolution
destination_path=data.destination_path,
project_config=project_config,
app_config=app_config,
)
# Reindex at new location
reindexed_entity = await entity_service.link_resolver.resolve_link(data.destination_path)
if reindexed_entity:
await search_service.index_entity(reindexed_entity)
_schedule_vector_sync_if_enabled(
task_scheduler=task_scheduler,
app_config=app_config,
entity_id=reindexed_entity.id,
project_id=project_id,
)
result = EntityResponseV2.model_validate(moved_entity)
logger.info(f"API v2 response: moved external_id={entity_id} to '{data.destination_path}'")
return result
except HTTPException: # pragma: no cover
raise # pragma: no cover
except Exception as e:
logger.error(f"Error moving entity: {e}")
raise HTTPException(status_code=400, detail=str(e))
## Move directory endpoint
@router.post("/move-directory", response_model=DirectoryMoveResult)
async def move_directory(
data: MoveDirectoryRequestV2,
background_tasks: BackgroundTasks,
project_id: ProjectExternalIdPathDep,
entity_service: EntityServiceV2ExternalDep,
project_config: ProjectConfigV2ExternalDep,
app_config: AppConfigDep,
search_service: SearchServiceV2ExternalDep,
task_scheduler: TaskSchedulerDep,
) -> DirectoryMoveResult:
"""Move all entities in a directory to a new location.
V2 API uses project external_id in the URL path for stable references.
Moves all files within a source directory to a destination directory,
updating database records and optionally updating permalinks.
Args:
project_id: Project external ID from URL path
data: Move request with source and destination directories
Returns:
DirectoryMoveResult with counts and details of moved files
"""
logger.info(
f"API v2 request: move_directory source='{data.source_directory}', destination='{data.destination_directory}'"
)
try:
# Move the directory using the service
result = await entity_service.move_directory(
source_directory=data.source_directory,
destination_directory=data.destination_directory,
project_config=project_config,
app_config=app_config,
)
# Reindex moved entities
for file_path in result.moved_files:
entity = await entity_service.link_resolver.resolve_link(file_path)
if entity:
await search_service.index_entity(entity)
_schedule_vector_sync_if_enabled(
task_scheduler=task_scheduler,
app_config=app_config,
entity_id=entity.id,
project_id=project_id,
)
logger.info(
f"API v2 response: move_directory "
f"total={result.total_files}, success={result.successful_moves}, failed={result.failed_moves}"
)
return result
except Exception as e:
logger.error(f"Error moving directory: {e}")
raise HTTPException(status_code=400, detail=str(e))
## Delete directory endpoint
@router.post("/delete-directory", response_model=DirectoryDeleteResult)
async def delete_directory(
data: DeleteDirectoryRequestV2,
project_id: ProjectExternalIdPathDep,
entity_service: EntityServiceV2ExternalDep,
) -> DirectoryDeleteResult:
"""Delete all entities in a directory.
V2 API uses project external_id in the URL path for stable references.
Deletes all files within a directory, updating database records and
removing files from the filesystem.
Args:
project_id: Project external ID from URL path
data: Delete request with directory path
Returns:
DirectoryDeleteResult with counts and details of deleted files
"""
logger.info(f"API v2 request: delete_directory directory='{data.directory}'")
try:
# Delete the directory using the service
result = await entity_service.delete_directory(
directory=data.directory,
)
logger.info(
f"API v2 response: delete_directory "
f"total={result.total_files}, success={result.successful_deletes}, failed={result.failed_deletes}"
)
return result
except Exception as e:
logger.error(f"Error deleting directory: {e}")
raise HTTPException(status_code=400, detail=str(e))
@@ -0,0 +1,130 @@
"""V2 routes for memory:// URI operations.
This router uses external_id UUIDs for stable, API-friendly routing.
V1 uses string-based project names which are less efficient and less stable.
"""
from typing import Annotated, Optional
from fastapi import APIRouter, Query, Path
from loguru import logger
from basic_memory.deps import ContextServiceV2ExternalDep, EntityRepositoryV2ExternalDep
from basic_memory.schemas.base import TimeFrame, parse_timeframe
from basic_memory.schemas.memory import (
GraphContext,
normalize_memory_url,
)
from basic_memory.schemas.search import SearchItemType
from basic_memory.api.v2.utils import to_graph_context
# Note: No prefix here - it's added during registration as /v2/{project_id}/memory
router = APIRouter(tags=["memory"])
@router.get("/memory/recent", response_model=GraphContext)
async def recent(
context_service: ContextServiceV2ExternalDep,
entity_repository: EntityRepositoryV2ExternalDep,
project_id: str = Path(..., description="Project external UUID"),
type: Annotated[list[SearchItemType] | None, Query()] = None,
depth: int = 1,
timeframe: TimeFrame = "7d",
page: int = 1,
page_size: int = 10,
max_related: int = 10,
) -> GraphContext:
"""Get recent activity context for a project.
Args:
project_id: Project external UUID from URL path
context_service: Context service scoped to project
entity_repository: Entity repository scoped to project
type: Types of items to include (entities, relations, observations)
depth: How many levels of related entities to include
timeframe: Time window for recent activity (e.g., "7d", "1 week")
page: Page number for pagination
page_size: Number of items per page
max_related: Maximum related entities to include per item
Returns:
GraphContext with recent activity and related entities
"""
# return all types by default
types = (
[SearchItemType.ENTITY, SearchItemType.RELATION, SearchItemType.OBSERVATION]
if not type
else type
)
logger.debug(
f"V2 Getting recent context for project {project_id}: `{types}` depth: `{depth}` timeframe: `{timeframe}` page: `{page}` page_size: `{page_size}` max_related: `{max_related}`"
)
# Parse timeframe
since = parse_timeframe(timeframe)
limit = page_size
offset = (page - 1) * page_size
# Build context
context = await context_service.build_context(
types=types, depth=depth, since=since, limit=limit, offset=offset, max_related=max_related
)
recent_context = await to_graph_context(
context, entity_repository=entity_repository, page=page, page_size=page_size
)
logger.debug(f"V2 Recent context: {recent_context.model_dump_json()}")
return recent_context
# get_memory_context needs to be declared last so other paths can match
@router.get("/memory/{uri:path}", response_model=GraphContext)
async def get_memory_context(
context_service: ContextServiceV2ExternalDep,
entity_repository: EntityRepositoryV2ExternalDep,
uri: str,
project_id: str = Path(..., description="Project external UUID"),
depth: int = 1,
timeframe: Optional[TimeFrame] = None,
page: int = 1,
page_size: int = 10,
max_related: int = 10,
) -> GraphContext:
"""Get rich context from memory:// URI.
V2 supports both legacy path-based URIs and new ID-based URIs:
- Legacy: memory://path/to/note
- ID-based: memory://id/123 or memory://123
Args:
project_id: Project external UUID from URL path
context_service: Context service scoped to project
entity_repository: Entity repository scoped to project
uri: Memory URI path (e.g., "id/123", "123", or "path/to/note")
depth: How many levels of related entities to include
timeframe: Optional time window for filtering related content
page: Page number for pagination
page_size: Number of items per page
max_related: Maximum related entities to include
Returns:
GraphContext with the entity and its related context
"""
logger.debug(
f"V2 Getting context for project {project_id}, URI: `{uri}` depth: `{depth}` timeframe: `{timeframe}` page: `{page}` page_size: `{page_size}` max_related: `{max_related}`"
)
memory_url = normalize_memory_url(uri)
# Parse timeframe
since = parse_timeframe(timeframe) if timeframe else None
limit = page_size
offset = (page - 1) * page_size
# Build context
context = await context_service.build_context(
memory_url, depth=depth, since=since, limit=limit, offset=offset, max_related=max_related
)
return await to_graph_context(
context, entity_repository=entity_repository, page=page, page_size=page_size
)
@@ -0,0 +1,550 @@
"""V2 Project Router - External ID-based project management operations.
This router provides external_id (UUID) based CRUD operations for projects,
using stable string UUIDs that never change (unlike integer IDs or names).
Key improvements:
- Stable external UUIDs that won't change with renames or database migrations
- Better API ergonomics with consistent string identifiers
- Direct database lookups via unique indexed column
- Consistent with v2 entity operations
"""
import os
from typing import Optional
from fastapi import APIRouter, HTTPException, Body, Query, Path
from loguru import logger
from basic_memory.deps import (
ProjectServiceDep,
ProjectRepositoryDep,
ProjectConfigV2ExternalDep,
SyncServiceV2ExternalDep,
TaskSchedulerDep,
ProjectExternalIdPathDep,
)
from basic_memory.schemas import SyncReportResponse
from basic_memory.schemas.project_info import (
ProjectItem,
ProjectList,
ProjectInfoRequest,
ProjectInfoResponse,
ProjectStatusResponse,
)
from basic_memory.schemas.v2 import ProjectResolveRequest, ProjectResolveResponse
from basic_memory.utils import normalize_project_path, generate_permalink
router = APIRouter(prefix="/projects", tags=["project_management-v2"])
@router.get("/", response_model=ProjectList)
async def list_projects(
project_service: ProjectServiceDep,
) -> ProjectList:
"""List all configured projects.
Returns:
A list of all projects with metadata
"""
projects = await project_service.list_projects()
default_project = await project_service.get_default_project_name()
project_items = [
ProjectItem(
id=project.id,
external_id=project.external_id,
name=project.name,
path=normalize_project_path(project.path),
is_default=project.is_default or False,
)
for project in projects
]
return ProjectList(
projects=project_items,
default_project=default_project,
)
@router.post("/", response_model=ProjectStatusResponse, status_code=201)
async def add_project(
project_data: ProjectInfoRequest,
project_service: ProjectServiceDep,
) -> ProjectStatusResponse:
"""Add a new project to configuration and database.
Args:
project_data: The project name and path, with option to set as default
Returns:
Response confirming the project was added
"""
# Check if project already exists before attempting to add
existing_project = await project_service.get_project(project_data.name)
if existing_project:
# Project exists - check if paths match for true idempotency
# Normalize paths for comparison (resolve symlinks, etc.)
requested_path = os.path.abspath(os.path.expanduser(project_data.path))
existing_path = os.path.abspath(os.path.expanduser(existing_project.path))
if requested_path == existing_path:
# Same name, same path - return 200 OK (idempotent)
return ProjectStatusResponse( # pyright: ignore [reportCallIssue]
message=f"Project '{project_data.name}' already exists",
status="success",
default=existing_project.is_default or False,
new_project=ProjectItem(
id=existing_project.id,
external_id=existing_project.external_id,
name=existing_project.name,
path=existing_project.path,
is_default=existing_project.is_default or False,
),
)
else:
# Same name, different path - this is an error
raise HTTPException(
status_code=400,
detail=(
f"Project '{project_data.name}' already exists with different path. "
f"Existing: {existing_project.path}, Requested: {project_data.path}"
),
)
try: # pragma: no cover
# The service layer handles cloud mode validation and path sanitization
await project_service.add_project(
project_data.name, project_data.path, set_default=project_data.set_default
)
# Fetch the newly created project to get its ID
new_project = await project_service.get_project(project_data.name)
if not new_project:
raise HTTPException(status_code=500, detail="Failed to retrieve newly created project")
return ProjectStatusResponse( # pyright: ignore [reportCallIssue]
message=f"Project '{new_project.name}' added successfully",
status="success",
default=project_data.set_default,
new_project=ProjectItem(
id=new_project.id,
external_id=new_project.external_id,
name=new_project.name,
path=new_project.path,
is_default=new_project.is_default or False,
),
)
except ValueError as e: # pragma: no cover
raise HTTPException(status_code=400, detail=str(e))
@router.post("/config/sync", response_model=ProjectStatusResponse)
async def synchronize_projects(
project_service: ProjectServiceDep,
) -> ProjectStatusResponse:
"""Synchronize projects between configuration file and database."""
try: # pragma: no cover
await project_service.synchronize_projects()
return ProjectStatusResponse( # pyright: ignore [reportCallIssue]
message="Projects synchronized successfully between configuration and database",
status="success",
default=False,
)
except ValueError as e: # pragma: no cover
raise HTTPException(status_code=400, detail=str(e))
@router.post("/{project_id}/sync")
async def sync_project(
sync_service: SyncServiceV2ExternalDep,
project_config: ProjectConfigV2ExternalDep,
task_scheduler: TaskSchedulerDep,
project_internal_id: ProjectExternalIdPathDep,
force_full: bool = Query(
False, description="Force full scan, bypassing watermark optimization"
),
run_in_background: bool = Query(True, description="Run in background"),
):
"""Force project filesystem sync to database."""
if run_in_background:
task_scheduler.schedule(
"sync_project",
project_id=project_internal_id,
force_full=force_full,
)
logger.info(
f"Filesystem sync initiated for project: {project_config.name} (force_full={force_full})"
)
return {
"status": "sync_started",
"message": f"Filesystem sync initiated for project '{project_config.name}'",
}
report = await sync_service.sync(
project_config.home, project_config.name, force_full=force_full
)
logger.info(
f"Filesystem sync completed for project: {project_config.name} (force_full={force_full})"
)
return SyncReportResponse.from_sync_report(report)
@router.post("/{project_id}/status", response_model=SyncReportResponse)
async def get_project_status(
sync_service: SyncServiceV2ExternalDep,
project_config: ProjectConfigV2ExternalDep,
project_id: str = Path(..., description="Project external ID (UUID)"),
force_full: bool = Query(
False, description="Force full scan, bypassing watermark optimization"
),
) -> SyncReportResponse:
"""Get sync status of files vs database for a project."""
logger.info(f"API v2 request: get_project_status for project_id={project_id}")
report = await sync_service.scan(project_config.home, force_full=force_full)
return SyncReportResponse.from_sync_report(report)
@router.post("/resolve", response_model=ProjectResolveResponse)
async def resolve_project_identifier(
data: ProjectResolveRequest,
project_repository: ProjectRepositoryDep,
) -> ProjectResolveResponse:
"""Resolve a project identifier (name, permalink, or external_id) to project info.
This endpoint provides efficient lookup of projects by various identifiers
without needing to fetch the entire project list. Supports:
- External ID (UUID string) - preferred stable identifier
- Permalink
- Case-insensitive name matching
Args:
data: Request containing the identifier to resolve
Returns:
Project information including the external_id (UUID)
Raises:
HTTPException: 404 if project not found
Example:
POST /v2/projects/resolve
{"identifier": "my-project"}
Returns:
{
"external_id": "550e8400-e29b-41d4-a716-446655440000",
"project_id": 1,
"name": "my-project",
"permalink": "my-project",
"path": "/path/to/project",
"is_active": true,
"is_default": false,
"resolution_method": "name"
}
"""
logger.info(f"API v2 request: resolve_project_identifier for '{data.identifier}'")
# Generate permalink for comparison
identifier_permalink = generate_permalink(data.identifier)
resolution_method = "name"
project = None
# Try external_id first (UUID format)
project = await project_repository.get_by_external_id(data.identifier)
if project:
resolution_method = "external_id"
# If not found by external_id, try by permalink (exact match)
if not project:
project = await project_repository.get_by_permalink(identifier_permalink)
if project:
resolution_method = "permalink"
# If not found by permalink, try case-insensitive name search
if not project:
project = await project_repository.get_by_name_case_insensitive(data.identifier)
if project:
resolution_method = "name" # pragma: no cover
if not project:
raise HTTPException(status_code=404, detail=f"Project not found: '{data.identifier}'")
return ProjectResolveResponse(
external_id=project.external_id,
project_id=project.id,
name=project.name,
permalink=generate_permalink(project.name),
path=normalize_project_path(project.path),
is_active=project.is_active if hasattr(project, "is_active") else True,
is_default=project.is_default or False,
resolution_method=resolution_method,
)
@router.get("/{project_id}", response_model=ProjectItem)
async def get_project_by_id(
project_repository: ProjectRepositoryDep,
project_id: str = Path(..., description="Project external ID (UUID)"),
) -> ProjectItem:
"""Get project by its external ID (UUID).
This is the primary project retrieval method in v2, using stable UUID
identifiers that won't change with project renames.
Args:
project_id: External ID (UUID string)
Returns:
Project information including external_id
Raises:
HTTPException: 404 if project not found
Example:
GET /v2/projects/550e8400-e29b-41d4-a716-446655440000
"""
logger.info(f"API v2 request: get_project_by_id for project_id={project_id}")
project = await project_repository.get_by_external_id(project_id)
if not project:
raise HTTPException(
status_code=404, detail=f"Project with external_id '{project_id}' not found"
)
return ProjectItem(
id=project.id,
external_id=project.external_id,
name=project.name,
path=normalize_project_path(project.path),
is_default=project.is_default or False,
)
@router.get("/{project_id}/info", response_model=ProjectInfoResponse)
async def get_project_info_by_id(
project_service: ProjectServiceDep,
project_repository: ProjectRepositoryDep,
project_id: str = Path(..., description="Project external ID (UUID)"),
) -> ProjectInfoResponse:
"""Get detailed project information by external ID."""
logger.info(f"API v2 request: get_project_info_by_id for project_id={project_id}")
project = await project_repository.get_by_external_id(project_id)
if not project:
raise HTTPException(
status_code=404, detail=f"Project with external_id '{project_id}' not found"
)
return await project_service.get_project_info(project.name)
@router.patch("/{project_id}", response_model=ProjectStatusResponse)
async def update_project_by_id(
project_service: ProjectServiceDep,
project_repository: ProjectRepositoryDep,
project_id: str = Path(..., description="Project external ID (UUID)"),
path: Optional[str] = Body(None, description="New absolute path for the project"),
is_active: Optional[bool] = Body(None, description="Status of the project (active/inactive)"),
) -> ProjectStatusResponse:
"""Update a project's information by external ID.
Args:
project_id: External ID (UUID string)
path: Optional new absolute path for the project
is_active: Optional status update for the project
Returns:
Response confirming the project was updated
Raises:
HTTPException: 400 if validation fails, 404 if project not found
Example:
PATCH /v2/projects/550e8400-e29b-41d4-a716-446655440000
{"path": "/new/path"}
"""
logger.info(f"API v2 request: update_project_by_id for project_id={project_id}")
try:
# Validate that path is absolute if provided
if path and not os.path.isabs(path):
raise HTTPException(status_code=400, detail="Path must be absolute")
# Get original project info for the response
old_project = await project_repository.get_by_external_id(project_id)
if not old_project:
raise HTTPException(
status_code=404, detail=f"Project with external_id '{project_id}' not found"
)
old_project_info = ProjectItem(
id=old_project.id,
external_id=old_project.external_id,
name=old_project.name,
path=old_project.path,
is_default=old_project.is_default or False,
)
# Update using project name (service layer still uses names internally)
if path:
await project_service.move_project(old_project.name, path)
elif is_active is not None:
await project_service.update_project(old_project.name, is_active=is_active)
# Get updated project info (use the same external_id)
updated_project = await project_repository.get_by_external_id(project_id)
if not updated_project: # pragma: no cover
raise HTTPException(
status_code=404,
detail=f"Project with external_id '{project_id}' not found after update",
)
return ProjectStatusResponse(
message=f"Project '{updated_project.name}' updated successfully",
status="success",
default=old_project.is_default or False,
old_project=old_project_info,
new_project=ProjectItem(
id=updated_project.id,
external_id=updated_project.external_id,
name=updated_project.name,
path=updated_project.path,
is_default=updated_project.is_default or False,
),
)
except ValueError as e: # pragma: no cover
raise HTTPException(status_code=400, detail=str(e)) # pragma: no cover
@router.delete("/{project_id}", response_model=ProjectStatusResponse)
async def delete_project_by_id(
project_service: ProjectServiceDep,
project_repository: ProjectRepositoryDep,
project_id: str = Path(..., description="Project external ID (UUID)"),
delete_notes: bool = Query(
False, description="If True, delete project directory from filesystem"
),
) -> ProjectStatusResponse:
"""Delete a project by external ID.
Args:
project_id: External ID (UUID string)
delete_notes: If True, delete the project directory from the filesystem
Returns:
Response confirming the project was deleted
Raises:
HTTPException: 400 if trying to delete default project, 404 if not found
Example:
DELETE /v2/projects/550e8400-e29b-41d4-a716-446655440000?delete_notes=false
"""
logger.info(
f"API v2 request: delete_project_by_id for project_id={project_id}, delete_notes={delete_notes}"
)
try:
old_project = await project_repository.get_by_external_id(project_id)
if not old_project:
raise HTTPException(
status_code=404, detail=f"Project with external_id '{project_id}' not found"
)
# Check if trying to delete the default project
# Use is_default from database, not ConfigManager (which doesn't work in cloud mode)
if old_project.is_default:
available_projects = await project_service.list_projects()
other_projects = [p.name for p in available_projects if p.external_id != project_id]
detail = f"Cannot delete default project '{old_project.name}'. "
if other_projects:
detail += ( # pragma: no cover
f"Set another project as default first. Available: {', '.join(other_projects)}"
)
else:
detail += "This is the only project in your configuration." # pragma: no cover
raise HTTPException(status_code=400, detail=detail)
# Delete using project name (service layer still uses names internally)
await project_service.remove_project(old_project.name, delete_notes=delete_notes)
return ProjectStatusResponse(
message=f"Project '{old_project.name}' removed successfully",
status="success",
default=False,
old_project=ProjectItem(
id=old_project.id,
external_id=old_project.external_id,
name=old_project.name,
path=old_project.path,
is_default=old_project.is_default or False,
),
new_project=None,
)
except ValueError as e: # pragma: no cover
raise HTTPException(status_code=400, detail=str(e)) # pragma: no cover
@router.put("/{project_id}/default", response_model=ProjectStatusResponse)
async def set_default_project_by_id(
project_service: ProjectServiceDep,
project_repository: ProjectRepositoryDep,
project_id: str = Path(..., description="Project external ID (UUID)"),
) -> ProjectStatusResponse:
"""Set a project as the default project by external ID.
Args:
project_id: External ID (UUID string) to set as default
Returns:
Response confirming the project was set as default
Raises:
HTTPException: 404 if project not found
Example:
PUT /v2/projects/550e8400-e29b-41d4-a716-446655440000/default
"""
logger.info(f"API v2 request: set_default_project_by_id for project_id={project_id}")
try:
# Get the old default project from database
default_project = await project_repository.get_default_project()
if not default_project:
raise HTTPException( # pragma: no cover
status_code=404, detail="No default project is currently set"
)
# Get the new default project by external_id
new_default_project = await project_repository.get_by_external_id(project_id)
if not new_default_project:
raise HTTPException(
status_code=404, detail=f"Project with external_id '{project_id}' not found"
)
# Set as default using project name (service layer still uses names internally)
await project_service.set_default_project(new_default_project.name)
return ProjectStatusResponse(
message=f"Project '{new_default_project.name}' set as default successfully",
status="success",
default=True,
old_project=ProjectItem(
id=default_project.id,
external_id=default_project.external_id,
name=default_project.name,
path=default_project.path,
is_default=False,
),
new_project=ProjectItem(
id=new_default_project.id,
external_id=new_default_project.external_id,
name=new_default_project.name,
path=new_default_project.path,
is_default=True,
),
)
except ValueError as e: # pragma: no cover
raise HTTPException(status_code=400, detail=str(e)) # pragma: no cover
@@ -1,21 +1,22 @@
"""Router for prompt-related operations.
"""V2 Prompt Router - ID-based prompt generation operations.
This router is responsible for rendering various prompts using Handlebars templates.
It centralizes all prompt formatting logic that was previously in the MCP prompts.
This router uses v2 dependencies for consistent project handling with external_id UUIDs.
Prompt endpoints are action-based (not resource-based), so they don't
have entity IDs in URLs - they generate formatted prompts from queries.
"""
from datetime import datetime, timezone
from fastapi import APIRouter, HTTPException, status
from fastapi import APIRouter, HTTPException, status, Path
from loguru import logger
from basic_memory.api.routers.utils import to_graph_context, to_search_results
from basic_memory.api.v2.utils import to_graph_context, to_search_results
from basic_memory.api.template_loader import template_loader
from basic_memory.schemas.base import parse_timeframe
from basic_memory.deps import (
ContextServiceDep,
EntityRepositoryDep,
SearchServiceDep,
EntityServiceDep,
ContextServiceV2ExternalDep,
EntityRepositoryV2ExternalDep,
SearchServiceV2ExternalDep,
EntityServiceV2ExternalDep,
)
from basic_memory.schemas.prompt import (
ContinueConversationRequest,
@@ -25,16 +26,17 @@ from basic_memory.schemas.prompt import (
)
from basic_memory.schemas.search import SearchItemType, SearchQuery
router = APIRouter(prefix="/prompt", tags=["prompt"])
router = APIRouter(prefix="/prompt", tags=["prompt-v2"])
@router.post("/continue-conversation", response_model=PromptResponse)
async def continue_conversation(
search_service: SearchServiceDep,
entity_service: EntityServiceDep,
context_service: ContextServiceDep,
entity_repository: EntityRepositoryDep,
search_service: SearchServiceV2ExternalDep,
entity_service: EntityServiceV2ExternalDep,
context_service: ContextServiceV2ExternalDep,
entity_repository: EntityRepositoryV2ExternalDep,
request: ContinueConversationRequest,
project_id: str = Path(..., description="Project external UUID"),
) -> PromptResponse:
"""Generate a prompt for continuing a conversation.
@@ -42,13 +44,15 @@ async def continue_conversation(
relevant context from the knowledge base.
Args:
project_id: Project external UUID from URL path
request: The request parameters
Returns:
Formatted continuation prompt with context
"""
logger.info(
f"Generating continue conversation prompt, topic: {request.topic}, timeframe: {request.timeframe}"
f"V2 Generating continue conversation prompt for project {project_id}, "
f"topic: {request.topic}, timeframe: {request.timeframe}"
)
since = parse_timeframe(request.timeframe) if request.timeframe else None
@@ -192,9 +196,10 @@ async def continue_conversation(
@router.post("/search", response_model=PromptResponse)
async def search_prompt(
search_service: SearchServiceDep,
entity_service: EntityServiceDep,
search_service: SearchServiceV2ExternalDep,
entity_service: EntityServiceV2ExternalDep,
request: SearchPromptRequest,
project_id: str = Path(..., description="Project external UUID"),
page: int = 1,
page_size: int = 10,
) -> PromptResponse:
@@ -204,6 +209,7 @@ async def search_prompt(
prompt with context and suggestions.
Args:
project_id: Project external UUID from URL path
request: The search parameters
page: The page number for pagination
page_size: The number of results per page, defaults to 10
@@ -211,7 +217,10 @@ async def search_prompt(
Returns:
Formatted search results prompt with context
"""
logger.info(f"Generating search prompt, query: {request.query}, timeframe: {request.timeframe}")
logger.info(
f"V2 Generating search prompt for project {project_id}, "
f"query: {request.query}, timeframe: {request.timeframe}"
)
limit = page_size
offset = (page - 1) * page_size
@@ -0,0 +1,289 @@
"""V2 Resource Router - ID-based resource content operations.
This router uses entity external_ids (UUIDs) for all operations, with file paths
in request bodies when needed. This is consistent with v2's external_id-first design.
Key differences from v1:
- Uses UUID external_ids in URL paths instead of integer IDs or file paths
- File paths are in request bodies for create/update operations
- More RESTful: POST for create, PUT for update, GET for read
"""
import uuid
from pathlib import Path as PathLib
from fastapi import APIRouter, HTTPException, Response, Path
from loguru import logger
from basic_memory.deps import (
ProjectConfigV2ExternalDep,
FileServiceV2ExternalDep,
EntityRepositoryV2ExternalDep,
SearchServiceV2ExternalDep,
)
from basic_memory.models.knowledge import Entity as EntityModel
from basic_memory.schemas.v2.resource import (
CreateResourceRequest,
UpdateResourceRequest,
ResourceResponse,
)
from basic_memory.utils import validate_project_path
router = APIRouter(prefix="/resource", tags=["resources-v2"])
@router.get("/{entity_id}")
async def get_resource_content(
config: ProjectConfigV2ExternalDep,
entity_repository: EntityRepositoryV2ExternalDep,
file_service: FileServiceV2ExternalDep,
project_id: str = Path(..., description="Project external UUID"),
entity_id: str = Path(..., description="Entity external UUID"),
) -> Response:
"""Get raw resource content by entity external_id.
Args:
project_id: Project external UUID from URL path
entity_id: Entity external UUID
config: Project configuration
entity_repository: Entity repository for fetching entity data
file_service: File service for reading file content
Returns:
Response with entity content
Raises:
HTTPException: 404 if entity or file not found
"""
logger.debug(f"V2 Getting content for project {project_id}, entity_id: {entity_id}")
# Get entity by external_id
entity = await entity_repository.get_by_external_id(entity_id)
if not entity:
raise HTTPException(status_code=404, detail=f"Entity {entity_id} not found")
# Validate entity file path to prevent path traversal
project_path = PathLib(config.home)
if not validate_project_path(entity.file_path, project_path):
logger.error( # pragma: no cover
f"Invalid file path in entity {entity.id}: {entity.file_path}"
)
raise HTTPException( # pragma: no cover
status_code=500,
detail="Entity contains invalid file path",
)
# Check file exists via file_service (for cloud compatibility)
if not await file_service.exists(entity.file_path):
raise HTTPException( # pragma: no cover
status_code=404,
detail=f"File not found: {entity.file_path}",
)
# Read content via file_service as bytes (works with both local and S3)
content = await file_service.read_file_bytes(entity.file_path)
content_type = file_service.content_type(entity.file_path)
return Response(content=content, media_type=content_type)
@router.post("", response_model=ResourceResponse)
async def create_resource(
data: CreateResourceRequest,
config: ProjectConfigV2ExternalDep,
file_service: FileServiceV2ExternalDep,
entity_repository: EntityRepositoryV2ExternalDep,
search_service: SearchServiceV2ExternalDep,
project_id: str = Path(..., description="Project external UUID"),
) -> ResourceResponse:
"""Create a new resource file.
Args:
project_id: Project external UUID from URL path
data: Create resource request with file_path and content
config: Project configuration
file_service: File service for writing files
entity_repository: Entity repository for creating entities
search_service: Search service for indexing
Returns:
ResourceResponse with file information including entity_id and external_id
Raises:
HTTPException: 400 for invalid file paths, 409 if file already exists
"""
try:
# Validate path to prevent path traversal attacks
project_path = PathLib(config.home)
if not validate_project_path(data.file_path, project_path):
logger.warning(
f"Invalid file path attempted: {data.file_path} in project {config.name}"
)
raise HTTPException(
status_code=400,
detail=f"Invalid file path: {data.file_path}. "
"Path must be relative and stay within project boundaries.",
)
# Check if entity already exists
existing_entity = await entity_repository.get_by_file_path(data.file_path)
if existing_entity:
raise HTTPException(
status_code=409,
detail=f"Resource already exists at {data.file_path} with entity_id {existing_entity.external_id}. "
f"Use PUT /resource/{existing_entity.external_id} to update it.",
)
# Cloud compatibility: avoid assuming a local filesystem path.
# Delegate directory creation + writes to FileService (local or S3).
await file_service.ensure_directory(PathLib(data.file_path).parent)
checksum = await file_service.write_file(data.file_path, data.content)
# Get file info
file_metadata = await file_service.get_file_metadata(data.file_path)
# Determine file details
file_name = PathLib(data.file_path).name
content_type = file_service.content_type(data.file_path)
note_type = "canvas" if data.file_path.endswith(".canvas") else "file"
# Create a new entity model
# Explicitly set external_id to ensure NOT NULL constraint is satisfied (fixes #512)
entity = EntityModel(
external_id=str(uuid.uuid4()),
title=file_name,
note_type=note_type,
content_type=content_type,
file_path=data.file_path,
checksum=checksum,
created_at=file_metadata.created_at,
updated_at=file_metadata.modified_at,
)
entity = await entity_repository.add(entity)
# Index the file for search
await search_service.index_entity(entity) # pyright: ignore
# Return success response
return ResourceResponse(
entity_id=entity.id,
external_id=entity.external_id,
file_path=data.file_path,
checksum=checksum,
size=file_metadata.size,
created_at=file_metadata.created_at.timestamp(),
modified_at=file_metadata.modified_at.timestamp(),
)
except HTTPException:
# Re-raise HTTP exceptions without wrapping
raise
except Exception as e: # pragma: no cover
logger.error(f"Error creating resource {data.file_path}: {e}")
raise HTTPException(status_code=500, detail=f"Failed to create resource: {str(e)}")
@router.put("/{entity_id}", response_model=ResourceResponse)
async def update_resource(
data: UpdateResourceRequest,
config: ProjectConfigV2ExternalDep,
file_service: FileServiceV2ExternalDep,
entity_repository: EntityRepositoryV2ExternalDep,
search_service: SearchServiceV2ExternalDep,
project_id: str = Path(..., description="Project external UUID"),
entity_id: str = Path(..., description="Entity external UUID"),
) -> ResourceResponse:
"""Update an existing resource by entity external_id.
Can update content and optionally move the file to a new path.
Args:
project_id: Project external UUID from URL path
entity_id: Entity external UUID of the resource to update
data: Update resource request with content and optional new file_path
config: Project configuration
file_service: File service for writing files
entity_repository: Entity repository for updating entities
search_service: Search service for indexing
Returns:
ResourceResponse with updated file information
Raises:
HTTPException: 404 if entity not found, 400 for invalid paths
"""
try:
# Get existing entity by external_id
entity = await entity_repository.get_by_external_id(entity_id)
if not entity:
raise HTTPException(status_code=404, detail=f"Entity {entity_id} not found")
# Determine target file path
target_file_path = data.file_path if data.file_path else entity.file_path
# Validate path to prevent path traversal attacks
project_path = PathLib(config.home)
if not validate_project_path(target_file_path, project_path):
logger.warning(
f"Invalid file path attempted: {target_file_path} in project {config.name}"
)
raise HTTPException(
status_code=400,
detail=f"Invalid file path: {target_file_path}. "
"Path must be relative and stay within project boundaries.",
)
# If moving file, handle the move
if data.file_path and data.file_path != entity.file_path:
# Ensure new parent directory exists (no-op for S3)
await file_service.ensure_directory(PathLib(target_file_path).parent)
# If old file exists, remove it via file_service (for cloud compatibility)
if await file_service.exists(entity.file_path):
await file_service.delete_file(entity.file_path)
else:
# Ensure directory exists for in-place update
await file_service.ensure_directory(PathLib(target_file_path).parent)
# Write content to target file
checksum = await file_service.write_file(target_file_path, data.content)
# Get file info
file_metadata = await file_service.get_file_metadata(target_file_path)
# Determine file details
file_name = PathLib(target_file_path).name
content_type = file_service.content_type(target_file_path)
note_type = "canvas" if target_file_path.endswith(".canvas") else "file"
# Update entity using internal ID
updated_entity = await entity_repository.update(
entity.id,
{
"title": file_name,
"note_type": note_type,
"content_type": content_type,
"file_path": target_file_path,
"checksum": checksum,
"updated_at": file_metadata.modified_at,
},
)
# Index the updated file for search
await search_service.index_entity(updated_entity) # pyright: ignore
# Return success response
return ResourceResponse(
entity_id=entity.id,
external_id=entity.external_id,
file_path=target_file_path,
checksum=checksum,
size=file_metadata.size,
created_at=file_metadata.created_at.timestamp(),
modified_at=file_metadata.modified_at.timestamp(),
)
except HTTPException:
# Re-raise HTTP exceptions without wrapping
raise
except Exception as e: # pragma: no cover
logger.error(f"Error updating resource {entity_id}: {e}")
raise HTTPException(status_code=500, detail=f"Failed to update resource: {str(e)}")
@@ -0,0 +1,412 @@
"""V2 router for schema operations.
Provides endpoints for schema validation, inference, and drift detection.
The schema system validates notes against Picoschema definitions without
introducing any new data model -- it works entirely with existing
observations and relations.
Flow: Entity loaded with eager observations/relations -> convert to tuples -> core functions.
"""
from pathlib import Path as FilePath
import frontmatter
from fastapi import APIRouter, Path, Query
from loguru import logger
from basic_memory.deps import (
EntityRepositoryV2ExternalDep,
FileServiceV2ExternalDep,
LinkResolverV2ExternalDep,
)
from basic_memory.models.knowledge import Entity
from basic_memory.schemas.schema import (
ValidationReport,
InferenceReport,
DriftReport,
NoteValidationResponse,
FieldResultResponse,
FieldFrequencyResponse,
DriftFieldResponse,
)
from basic_memory.schema.resolver import resolve_schema
from basic_memory.schema.validator import validate_note
from basic_memory.schema.inference import infer_schema, NoteData, ObservationData, RelationData
from basic_memory.schema.diff import diff_schema
from basic_memory.utils import generate_permalink
# Note: No prefix here -- it's added during registration as /v2/{project_id}/schema
router = APIRouter(tags=["schema"])
# --- ORM to core data conversion ---
def _entity_observations(entity: Entity) -> list[ObservationData]:
"""Extract ObservationData from an entity's observations."""
return [ObservationData(obs.category, obs.content) for obs in entity.observations]
def _entity_relations(entity: Entity) -> list[RelationData]:
"""Extract RelationData from an entity's outgoing relations.
Carries the target entity's type on each relation so the inference engine
can suggest correct types (e.g. works_at -> Organization, not the source type).
"""
return [
RelationData(
relation_type=rel.relation_type,
target_name=rel.to_name,
target_note_type=rel.to_entity.note_type if rel.to_entity else None,
)
for rel in entity.outgoing_relations
]
def _entity_to_note_data(entity: Entity) -> NoteData:
"""Convert an ORM Entity to a NoteData for inference/diff analysis."""
return NoteData(
identifier=entity.permalink or entity.file_path,
observations=_entity_observations(entity),
relations=_entity_relations(entity),
)
def _entity_frontmatter(entity: Entity) -> dict:
"""Build a frontmatter dict from an entity's database metadata.
Used for the notes being validated their type and schema ref are
unlikely to change between syncs.
"""
fm = dict(entity.entity_metadata) if entity.entity_metadata else {}
if entity.note_type:
fm.setdefault("type", entity.note_type)
return fm
async def _schema_frontmatter_from_file(
file_service: FileServiceV2ExternalDep,
entity: Entity,
) -> dict:
"""Read a schema entity's frontmatter directly from its file.
Schema definitions (field declarations, validation mode) are the source
of truth for validation. Reading from the file ensures schema-validate
always uses the latest settings, even when the file watcher hasn't
synced changes to entity_metadata in the database.
"""
try:
content = await file_service.read_file_content(entity.file_path)
post = frontmatter.loads(content)
metadata = dict(post.metadata)
# Trigger: file is mid-edit and missing required schema fields
# Why: parse_schema_note() raises ValueError for missing entity/schema,
# which would turn validation into a 500 response
# Outcome: fall back to last-known-good database metadata
if not metadata.get("entity") or not isinstance(metadata.get("schema"), dict):
logger.warning(
"Schema file has incomplete frontmatter, falling back to database metadata",
file_path=entity.file_path,
)
return _entity_frontmatter(entity)
return metadata
except Exception:
# Trigger: file is missing, unreadable, or has malformed frontmatter
# Why: fall back to database metadata rather than failing validation entirely
# Outcome: behaves like before this change — uses potentially stale data
logger.warning(
"Failed to read schema file, falling back to database metadata",
file_path=entity.file_path,
)
return _entity_frontmatter(entity)
# --- Validation ---
@router.post("/schema/validate", response_model=ValidationReport)
async def validate_schema(
entity_repository: EntityRepositoryV2ExternalDep,
file_service: FileServiceV2ExternalDep,
link_resolver: LinkResolverV2ExternalDep,
project_id: str = Path(..., description="Project external UUID"),
note_type: str | None = Query(None, description="Note type to validate"),
identifier: str | None = Query(None, description="Specific note identifier"),
):
"""Validate notes against their resolved schemas.
Validates a specific note (by identifier) or all notes of a given type.
Returns warnings/errors based on the schema's validation mode.
Schema definitions are read directly from their files to ensure the
latest settings (validation mode, field declarations) are always used,
even when file changes haven't been synced to the database yet.
"""
results: list[NoteValidationResponse] = []
# --- Single note validation ---
if identifier:
# Resolve identifier flexibly (permalink, title, path, fuzzy)
# to match how read_note and other tools resolve identifiers
entity = await link_resolver.resolve_link(identifier)
if not entity:
return ValidationReport(note_type=note_type, total_notes=0, total_entities=0)
frontmatter = _entity_frontmatter(entity)
schema_ref = frontmatter.get("schema")
async def search_fn(query: str) -> list[dict]:
entities = await _find_schema_entities(
entity_repository,
query,
allow_reference_match=isinstance(schema_ref, str) and query == schema_ref,
)
return [await _schema_frontmatter_from_file(file_service, e) for e in entities]
schema_def = await resolve_schema(frontmatter, search_fn)
if schema_def:
result = validate_note(
entity.title or entity.permalink or identifier,
schema_def,
_entity_observations(entity),
_entity_relations(entity),
frontmatter=frontmatter,
)
results.append(_to_note_validation_response(result))
return ValidationReport(
note_type=note_type or entity.note_type,
total_notes=len(results),
total_entities=1,
valid_count=1 if (results and results[0].passed) else 0,
warning_count=sum(len(r.warnings) for r in results),
error_count=sum(len(r.errors) for r in results),
results=results,
)
# --- Batch validation by note type ---
entities = await _find_by_note_type(entity_repository, note_type) if note_type else []
for entity in entities:
frontmatter = _entity_frontmatter(entity)
schema_ref = frontmatter.get("schema")
async def search_fn(query: str) -> list[dict]:
entities = await _find_schema_entities(
entity_repository,
query,
allow_reference_match=isinstance(schema_ref, str) and query == schema_ref,
)
return [await _schema_frontmatter_from_file(file_service, e) for e in entities]
schema_def = await resolve_schema(frontmatter, search_fn)
if schema_def:
result = validate_note(
entity.title or entity.permalink or entity.file_path,
schema_def,
_entity_observations(entity),
_entity_relations(entity),
frontmatter=frontmatter,
)
results.append(_to_note_validation_response(result))
valid = sum(1 for r in results if r.passed)
return ValidationReport(
note_type=note_type,
total_notes=len(results),
total_entities=len(entities),
valid_count=valid,
warning_count=sum(len(r.warnings) for r in results),
error_count=sum(len(r.errors) for r in results),
results=results,
)
# --- Inference ---
@router.post("/schema/infer", response_model=InferenceReport)
async def infer_schema_endpoint(
entity_repository: EntityRepositoryV2ExternalDep,
project_id: str = Path(..., description="Project external UUID"),
note_type: str = Query(..., description="Note type to analyze"),
threshold: float = Query(0.25, description="Minimum frequency for optional fields"),
):
"""Infer a schema from existing notes of a given type.
Examines observation categories and relation types across all notes
of the given type. Returns frequency analysis and suggested Picoschema.
"""
entities = await _find_by_note_type(entity_repository, note_type)
notes_data = [_entity_to_note_data(entity) for entity in entities]
result = infer_schema(note_type, notes_data, optional_threshold=threshold)
return InferenceReport(
note_type=result.note_type,
notes_analyzed=result.notes_analyzed,
field_frequencies=[
FieldFrequencyResponse(
name=f.name,
source=f.source,
count=f.count,
total=f.total,
percentage=f.percentage,
sample_values=f.sample_values,
is_array=f.is_array,
target_type=f.target_type,
)
for f in result.field_frequencies
],
suggested_schema=result.suggested_schema,
suggested_required=result.suggested_required,
suggested_optional=result.suggested_optional,
excluded=result.excluded,
)
# --- Drift Detection ---
@router.get("/schema/diff/{note_type}", response_model=DriftReport)
async def diff_schema_endpoint(
entity_repository: EntityRepositoryV2ExternalDep,
file_service: FileServiceV2ExternalDep,
note_type: str = Path(..., description="Note type to check for drift"),
project_id: str = Path(..., description="Project external UUID"),
):
"""Show drift between a schema definition and actual note usage.
Compares the existing schema for an entity type against how notes
of that type are actually structured. Identifies new fields, dropped
fields, and cardinality changes.
"""
async def search_fn(query: str) -> list[dict]:
entities = await _find_schema_entities(entity_repository, query)
return [await _schema_frontmatter_from_file(file_service, e) for e in entities]
# Resolve schema by note type
schema_frontmatter = {"type": note_type}
schema_def = await resolve_schema(schema_frontmatter, search_fn)
if not schema_def:
return DriftReport(note_type=note_type, schema_found=False)
# Collect all notes of this type
entities = await _find_by_note_type(entity_repository, note_type)
notes_data = [_entity_to_note_data(entity) for entity in entities]
result = diff_schema(schema_def, notes_data)
return DriftReport(
note_type=note_type,
new_fields=[
DriftFieldResponse(
name=f.name,
source=f.source,
count=f.count,
total=f.total,
percentage=f.percentage,
)
for f in result.new_fields
],
dropped_fields=[
DriftFieldResponse(
name=f.name,
source=f.source,
count=f.count,
total=f.total,
percentage=f.percentage,
)
for f in result.dropped_fields
],
cardinality_changes=result.cardinality_changes,
)
# --- Helpers ---
async def _find_by_note_type(
entity_repository: EntityRepositoryV2ExternalDep,
note_type: str,
) -> list[Entity]:
"""Find all entities of a given type using the repository's select pattern."""
query = entity_repository.select().where(Entity.note_type == note_type)
result = await entity_repository.execute_query(query)
return list(result.scalars().all())
async def _find_schema_entities(
entity_repository: EntityRepositoryV2ExternalDep,
target_note_type: str,
*,
allow_reference_match: bool = False,
) -> list[Entity]:
"""Find schema entities for resolver lookups.
Resolution strategy:
1) Always try exact entity_metadata['entity'] match (for implicit type lookup
and explicit references that use entity names)
2) Only when allow_reference_match=True and no entity match was found, try
exact reference matching by title/permalink (explicit schema references)
"""
query = entity_repository.select().where(Entity.note_type == "schema")
result = await entity_repository.execute_query(query)
entities = list(result.scalars().all())
normalized_target = generate_permalink(target_note_type)
entity_matches = [
e
for e in entities
if e.entity_metadata
and isinstance(e.entity_metadata.get("entity"), str)
and generate_permalink(e.entity_metadata["entity"]) == normalized_target
]
if entity_matches:
return entity_matches
if not allow_reference_match:
return []
reference_matches: list[Entity] = []
for entity in entities:
candidate_refs: list[str] = []
if entity.title:
candidate_refs.append(entity.title)
if entity.permalink:
candidate_refs.append(entity.permalink)
candidate_refs.append(FilePath(entity.permalink).name)
if any(generate_permalink(ref) == normalized_target for ref in candidate_refs):
reference_matches.append(entity)
return reference_matches
def _to_note_validation_response(result) -> NoteValidationResponse:
"""Convert a core ValidationResult to a Pydantic response model."""
return NoteValidationResponse(
note_identifier=result.note_identifier,
schema_entity=result.schema_entity,
passed=result.passed,
field_results=[
FieldResultResponse(
field_name=fr.field.name,
field_type=fr.field.type,
required=fr.field.required,
status=fr.status,
values=fr.values,
message=fr.message,
)
for fr in result.field_results
],
unmatched_observations=result.unmatched_observations,
unmatched_relations=result.unmatched_relations,
warnings=result.warnings,
errors=result.errors,
)
@@ -0,0 +1,105 @@
"""V2 router for search operations.
This router uses external_id UUIDs for stable, API-friendly routing.
V1 uses string-based project names which are less efficient and less stable.
"""
from fastapi import APIRouter, HTTPException, Path
from basic_memory import telemetry
from basic_memory.api.v2.utils import to_search_results
from basic_memory.repository.semantic_errors import (
SemanticDependenciesMissingError,
SemanticSearchDisabledError,
)
from basic_memory.schemas.search import SearchQuery, SearchResponse
from basic_memory.deps import (
SearchServiceV2ExternalDep,
EntityServiceV2ExternalDep,
TaskSchedulerDep,
ProjectExternalIdPathDep,
)
# Note: No prefix here - it's added during registration as /v2/{project_id}/search
router = APIRouter(tags=["search"])
@router.post("/search/", response_model=SearchResponse)
async def search(
query: SearchQuery,
search_service: SearchServiceV2ExternalDep,
entity_service: EntityServiceV2ExternalDep,
project_id: str = Path(..., description="Project external UUID"),
page: int = 1,
page_size: int = 10,
):
"""Search across all knowledge and documents in a project.
V2 uses external_id UUIDs for stable API references.
Args:
project_id: Project external UUID from URL path
query: Search query parameters (text, filters, etc.)
search_service: Search service scoped to project
entity_service: Entity service scoped to project
page: Page number for pagination
page_size: Number of results per page
Returns:
SearchResponse with paginated search results
"""
with telemetry.operation(
"api.request.search",
entrypoint="api",
page=page,
page_size=page_size,
retrieval_mode=query.retrieval_mode.value,
has_text_query=bool(query.text and query.text.strip()),
has_title_query=bool(query.title),
has_permalink_query=bool(query.permalink or query.permalink_match),
):
offset = (page - 1) * page_size
# Fetch one extra item to detect whether more pages exist (N+1 trick)
fetch_limit = page_size + 1
try:
results = await search_service.search(query, limit=fetch_limit, offset=offset)
except SemanticSearchDisabledError as exc:
raise HTTPException(status_code=400, detail=str(exc)) from exc
except SemanticDependenciesMissingError as exc:
raise HTTPException(status_code=400, detail=str(exc)) from exc
except ValueError as exc:
raise HTTPException(status_code=400, detail=str(exc)) from exc
has_more = len(results) > page_size
if has_more:
results = results[:page_size]
search_results = await to_search_results(entity_service, results)
return SearchResponse(
results=search_results,
current_page=page,
page_size=page_size,
has_more=has_more,
)
@router.post("/search/reindex")
async def reindex(
task_scheduler: TaskSchedulerDep,
project_id: ProjectExternalIdPathDep,
):
"""Recreate and populate the search index for a project.
This is a background operation that rebuilds the search index
from scratch. Useful after bulk updates or if the index becomes
corrupted.
Args:
project_id: Project external UUID from URL path
task_scheduler: Task scheduler for background work
Returns:
Status message indicating reindex has been initiated
"""
task_scheduler.schedule("reindex_project", project_id=project_id)
return {"status": "ok", "message": "Reindex initiated"}
@@ -24,11 +24,43 @@ async def to_graph_context(
page: Optional[int] = None,
page_size: Optional[int] = None,
):
# First pass: collect all entity IDs needed for external_id lookup
# This includes: entity primary results, observation parent entities, relation from/to entities
entity_ids_needed: set[int] = set()
for context_item in context_result.results:
for item in (
[context_item.primary_result] + context_item.observations + context_item.related_results
):
if item.type == SearchItemType.ENTITY:
# Entity's own ID for its external_id
entity_ids_needed.add(item.id)
elif item.type == SearchItemType.OBSERVATION:
# Parent entity ID for entity_external_id
if item.entity_id: # pyright: ignore
entity_ids_needed.add(item.entity_id) # pyright: ignore
elif item.type == SearchItemType.RELATION:
# Source and target entity IDs for external_ids
if item.from_id: # pyright: ignore
entity_ids_needed.add(item.from_id) # pyright: ignore
if item.to_id:
entity_ids_needed.add(item.to_id)
# Batch fetch all entities at once - get both title and external_id
entity_title_lookup: dict[int, str] = {}
entity_external_id_lookup: dict[int, str] = {}
if entity_ids_needed:
entities = await entity_repository.find_by_ids(list(entity_ids_needed))
for e in entities:
entity_title_lookup[e.id] = e.title
entity_external_id_lookup[e.id] = e.external_id
# Helper function to convert items to summaries
async def to_summary(item: SearchIndexRow | ContextResultRow):
def to_summary(item: SearchIndexRow | ContextResultRow):
match item.type:
case SearchItemType.ENTITY:
return EntitySummary(
external_id=entity_external_id_lookup.get(item.id, ""),
entity_id=item.id,
title=item.title, # pyright: ignore
permalink=item.permalink,
content=item.content,
@@ -36,8 +68,14 @@ async def to_graph_context(
created_at=item.created_at,
)
case SearchItemType.OBSERVATION:
entity_ext_id = None
if item.entity_id: # pyright: ignore
entity_ext_id = entity_external_id_lookup.get(item.entity_id) # pyright: ignore
return ObservationSummary(
title=item.title, # pyright: ignore
observation_id=item.id,
entity_id=item.entity_id, # pyright: ignore
entity_external_id=entity_ext_id,
title=entity_title_lookup.get(item.entity_id), # pyright: ignore
file_path=item.file_path,
category=item.category, # pyright: ignore
content=item.content, # pyright: ignore
@@ -45,15 +83,23 @@ async def to_graph_context(
created_at=item.created_at,
)
case SearchItemType.RELATION:
from_entity = await entity_repository.find_by_id(item.from_id) # pyright: ignore
to_entity = await entity_repository.find_by_id(item.to_id) if item.to_id else None
from_title = entity_title_lookup.get(item.from_id) if item.from_id else None # pyright: ignore
to_title = entity_title_lookup.get(item.to_id) if item.to_id else None
from_ext_id = entity_external_id_lookup.get(item.from_id) if item.from_id else None # pyright: ignore
to_ext_id = entity_external_id_lookup.get(item.to_id) if item.to_id else None
return RelationSummary(
relation_id=item.id,
entity_id=item.entity_id, # pyright: ignore
title=item.title, # pyright: ignore
file_path=item.file_path,
permalink=item.permalink, # pyright: ignore
relation_type=item.relation_type, # pyright: ignore
from_entity=from_entity.title if from_entity else None,
to_entity=to_entity.title if to_entity else None,
from_entity=from_title,
from_entity_id=item.from_id, # pyright: ignore
from_entity_external_id=from_ext_id,
to_entity=to_title,
to_entity_id=item.to_id,
to_entity_external_id=to_ext_id,
created_at=item.created_at,
)
case _: # pragma: no cover
@@ -63,23 +109,19 @@ async def to_graph_context(
hierarchical_results = []
for context_item in context_result.results:
# Process primary result
primary_result = await to_summary(context_item.primary_result)
primary_result = to_summary(context_item.primary_result)
# Process observations
observations = []
for obs in context_item.observations:
observations.append(await to_summary(obs))
# Process observations (always ObservationSummary, validated by context_service)
observations = [to_summary(obs) for obs in context_item.observations]
# Process related results
related = []
for rel in context_item.related_results:
related.append(await to_summary(rel))
related = [to_summary(rel) for rel in context_item.related_results]
# Add to hierarchical results
hierarchical_results.append(
ContextResult(
primary_result=primary_result,
observations=observations,
observations=observations, # pyright: ignore[reportArgumentType]
related_results=related,
)
)
@@ -104,6 +146,7 @@ async def to_graph_context(
metadata=metadata,
page=page,
page_size=page_size,
has_more=context_result.metadata.has_more,
)
@@ -111,6 +154,21 @@ async def to_search_results(entity_service: EntityService, results: List[SearchI
search_results = []
for r in results:
entities = await entity_service.get_entities_by_id([r.entity_id, r.from_id, r.to_id]) # pyright: ignore
# Determine which IDs to set based on type
entity_id = None
observation_id = None
relation_id = None
if r.type == SearchItemType.ENTITY:
entity_id = r.id
elif r.type == SearchItemType.OBSERVATION:
observation_id = r.id
entity_id = r.entity_id # Parent entity
elif r.type == SearchItemType.RELATION:
relation_id = r.id
entity_id = r.entity_id # Parent entity
search_results.append(
SearchResult(
title=r.title, # pyright: ignore
@@ -119,8 +177,12 @@ async def to_search_results(entity_service: EntityService, results: List[SearchI
score=r.score, # pyright: ignore
entity=entities[0].permalink if entities else None,
content=r.content,
matched_chunk=r.matched_chunk_text,
file_path=r.file_path,
metadata=r.metadata,
entity_id=entity_id,
observation_id=observation_id,
relation_id=relation_id,
category=r.category,
from_entity=entities[0].permalink if entities else None,
to_entity=entities[1].permalink if len(entities) > 1 else None,
+114
View File
@@ -0,0 +1,114 @@
"""Lightweight CLI analytics via Umami event collector.
Sends anonymous, non-blocking usage events to help understand how the
CLI-to-cloud conversion funnel performs. No PII, no fingerprinting,
no cookies. Respects the same opt-out mechanisms as promo messaging.
Events are fire-and-forget analytics never blocks or breaks the CLI.
Defaults point to the Basic Memory Umami Cloud instance. Override via:
BASIC_MEMORY_UMAMI_HOST Custom Umami instance URL
BASIC_MEMORY_UMAMI_SITE_ID Custom Website ID
Opt out entirely with BASIC_MEMORY_NO_PROMOS=1.
"""
import json
import os
import threading
import urllib.request
from typing import Optional
import basic_memory
# ---------------------------------------------------------------------------
# Configuration — defaults baked in, overridable via environment
# ---------------------------------------------------------------------------
_DEFAULT_UMAMI_HOST = "https://api-gateway.umami.dev"
_DEFAULT_UMAMI_SITE_ID = "f6479898-ebaf-4e60-bce2-6dc60a3f6c5c"
def _umami_host() -> Optional[str]:
return os.getenv("BASIC_MEMORY_UMAMI_HOST", "").strip() or _DEFAULT_UMAMI_HOST
def _umami_site_id() -> Optional[str]:
return os.getenv("BASIC_MEMORY_UMAMI_SITE_ID", "").strip() or _DEFAULT_UMAMI_SITE_ID
def _analytics_disabled() -> bool:
"""True when analytics should not fire."""
value = os.getenv("BASIC_MEMORY_NO_PROMOS", "").strip().lower()
return value in {"1", "true", "yes"}
def _is_configured() -> bool:
"""True when both host and site ID are available."""
return _umami_host() is not None and _umami_site_id() is not None
# ---------------------------------------------------------------------------
# Public API
# ---------------------------------------------------------------------------
# Well-known event names for the promo/cloud funnel
EVENT_PROMO_SHOWN = "cli-promo-shown"
EVENT_PROMO_OPTED_OUT = "cli-promo-opted-out"
EVENT_CLOUD_LOGIN_STARTED = "cli-cloud-login-started"
EVENT_CLOUD_LOGIN_SUCCESS = "cli-cloud-login-success"
EVENT_CLOUD_LOGIN_SUB_REQUIRED = "cli-cloud-login-sub-required"
def track(event_name: str, data: Optional[dict] = None) -> None:
"""Send an analytics event to Umami. Non-blocking, silent on failure.
Parameters
----------
event_name:
Short kebab-case name (e.g. "cli-promo-shown").
data:
Optional dict of event properties (all values should be strings/numbers).
"""
if _analytics_disabled() or not _is_configured():
return
host = _umami_host()
site_id = _umami_site_id()
# Umami v2 /api/send requires "type" at top level alongside "payload"
payload = {
"type": "event",
"payload": {
"hostname": "cli.basicmemory.com",
"language": "en",
"url": f"/cli/{event_name}",
"website": site_id,
"name": event_name,
"data": {
"version": basic_memory.__version__,
**(data or {}),
},
},
}
def _send():
try:
req = urllib.request.Request(
f"{host}/api/send",
data=json.dumps(payload).encode("utf-8"),
headers={
"Content-Type": "application/json",
# Umami's bot detection rejects non-browser User-Agents
"User-Agent": "Mozilla/5.0 (compatible; BasicMemoryCLI/"
f"{basic_memory.__version__})",
},
)
urllib.request.urlopen(req, timeout=3)
except Exception:
pass # Never break the CLI for analytics
# Non-daemon so the process waits for the request to complete.
# The 3s urllib timeout caps the worst-case exit delay.
t = threading.Thread(target=_send)
t.start()
+65 -7
View File
@@ -1,8 +1,18 @@
from typing import Optional
# This prevents DEBUG logs from appearing on stdout during module-level
# initialization (e.g., template_loader.TemplateLoader() logs at DEBUG level).
from loguru import logger
import typer
logger.remove()
from basic_memory.config import ConfigManager
from typing import Optional # noqa: E402
import typer # noqa: E402
from basic_memory.cli.auto_update import maybe_run_periodic_auto_update # noqa: E402
from basic_memory.cli.container import CliContainer, set_container # noqa: E402
from basic_memory.cli.promo import maybe_show_cloud_promo, maybe_show_init_line # noqa: E402
from basic_memory.config import init_cli_logging # noqa: E402
from basic_memory import telemetry # noqa: E402
def version_callback(value: bool) -> None:
@@ -31,12 +41,60 @@ def app_callback(
) -> None:
"""Basic Memory - Local-first personal knowledge management."""
# Run initialization for every command unless --version was specified
if not version and ctx.invoked_subcommand is not None:
# Initialize logging for CLI (file only, no stdout)
init_cli_logging()
command_name = ctx.invoked_subcommand or "root"
ctx.with_resource(
telemetry.operation(
f"cli.command.{command_name}",
entrypoint="cli",
command_name=command_name,
)
)
# --- Composition Root ---
# Create container and read config (single point of config access)
container = CliContainer.create()
set_container(container)
# Trigger: first-run init confirmation before command output.
# Why: informational "initialized" message belongs above command results, not in the upsell panel.
# Outcome: one-time plain line printed before the subcommand runs.
maybe_show_init_line(ctx.invoked_subcommand)
# Trigger: register post-command messaging callbacks.
# Why: informational/promo/update output belongs below command results.
# Outcome: command output remains primary, with optional follow-up notices afterwards.
def _post_command_messages() -> None:
maybe_show_cloud_promo(ctx.invoked_subcommand)
maybe_run_periodic_auto_update(ctx.invoked_subcommand)
ctx.call_on_close(_post_command_messages)
# Run initialization for commands that don't use the API
# Skip for 'mcp' command - it has its own lifespan that handles initialization
# Skip for API-using commands (status, sync, etc.) - they handle initialization via deps.py
# Skip for 'reset' command - it manages its own database lifecycle
skip_init_commands = {
"doctor",
"mcp",
"status",
"sync",
"project",
"tool",
"reset",
"reindex",
"update",
"watch",
}
if (
not version
and ctx.invoked_subcommand is not None
and ctx.invoked_subcommand not in skip_init_commands
):
from basic_memory.services.initialization import ensure_initialization
app_config = ConfigManager().config
ensure_initialization(app_config)
ensure_initialization(container.config)
## import

Some files were not shown because too many files have changed in this diff Show More