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Drew Cain 7adf6791e9 fix: #190 MCP Hub tool validation
Signed-off-by: Drew Cain <groksrc@gmail.com>
2025-06-30 23:12:28 -05:00
github-actions[bot] 1dc66bec7a 📊 Daily metrics update - 2025-07-01 2025-07-01 01:24:38 +00:00
nellins 42d97504a3 Update daily_report.py
Signed-off-by: nellins <drewnellins@gmail.com>
2025-06-30 20:24:08 -05:00
nellins 6112e185fd Create daily_report.py
Signed-off-by: nellins <drewnellins@gmail.com>
2025-06-30 20:20:42 -05:00
nellins 2e758414da Create requirements.txt
Signed-off-by: nellins <drewnellins@gmail.com>
2025-06-30 20:19:22 -05:00
nellins 3ec9ca234e Create daily-traction.yml
Signed-off-by: nellins <drewnellins@gmail.com>
2025-06-30 20:10:49 -05:00
nellins 9dec7e98a8 Create daily_report.py
Signed-off-by: nellins <drewnellins@gmail.com>
2025-06-30 20:05:32 -05:00
nellins 338d225a7e Create requirements.txt
Signed-off-by: nellins <drewnellins@gmail.com>
2025-06-30 20:04:04 -05:00
nellins 211b760522 Create daily-traction.yml
Signed-off-by: nellins <drewnellins@gmail.com>
2025-06-30 20:03:01 -05:00
937 changed files with 23168 additions and 184720 deletions
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{
"name": "basic-memory-local",
"interface": {
"displayName": "Basic Memory Local"
},
"plugins": [
{
"name": "codex",
"source": {
"source": "local",
"path": "./plugins/codex"
},
"policy": {
"installation": "AVAILABLE",
"authentication": "ON_INSTALL"
},
"category": "Developer Tools"
}
]
}
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---
name: adversarial-review
description: Cross-vendor adversarial code review of the current branch. Two different model families (Claude + Codex/GPT) review the diff independently, then try to refute each other's findings; survivors are reported by confidence. Runs from either Claude Code or Codex. Use when the user asks for an adversarial review, a cross-model / second-opinion review, or wants high-confidence findings before merging. Report-only — never auto-applies fixes.
license: MIT
---
# Adversarial code review
Two reviewers from **different model families****Claude** and **Codex/GPT** — review the
same diff independently, then each tries to **refute** the other's findings. A finding's
confidence comes from whether it survives that cross-examination. This kills the two failure
modes of solo LLM review: self-ratification (a model won't critique its own work) and
confident false positives.
## You are the orchestrator — and one of the two reviewers
This skill runs from **either** Claude Code **or** Codex. First, **identify which model
family you are** (Claude or Codex/GPT). Then:
- **You** are reviewer #1. You review **natively**, in this session, using your own tools.
- **The other family** is reviewer #2. You invoke it as a **subprocess CLI** for an
independent pass: a fresh process, no shared context — that independence is the point.
The CLI for "the other model":
| If you are… | Invoke the other via… |
|-------------|------------------------|
| **Claude** | `codex exec` (GPT) |
| **Codex** | `claude -p` (Claude) |
Everything else in the flow is symmetric. Resolve the `prompts/` and `schemas/` paths
below relative to **this skill's own directory** (where this SKILL.md lives).
## Inputs
Two independent, optional inputs:
- `BASE` — the ref to diff against. Default `main`.
- `SCOPE` — a pathspec to narrow the review (e.g. `src/basic_memory`). Default: none (whole diff).
These are separate: a ref and a pathspec are not interchangeable. Build the **canonical diff
command** once in preflight and reuse it everywhere below — never re-spell the diff inline
(the scattered, inconsistent spelling is what broke earlier). Build it as an **argv array**,
not a string, so a `$SCOPE` containing spaces or glob characters survives intact:
```bash
BASE="${BASE:-main}"
DIFF=(git diff "$BASE...HEAD") # argv array — never a scalar string
[ -n "$SCOPE" ] && DIFF+=(-- "$SCOPE") # pathspec stays one argument even with spaces
DIFF_STR=$(printf '%q ' "${DIFF[@]}") # shell-quoted rendering, for embedding in a prompt
```
To **run** it, use `"${DIFF[@]}"` (quoted, no word-splitting). To **embed** it as text inside
a subprocess prompt, use `$DIFF_STR`.
## Preflight
0. Set `SKILL_DIR` to the directory this SKILL.md lives in. Canonical location is
`.agents/skills/adversarial-review` (the shared agent-skills store); Claude Code reaches it
via the `.claude/skills/adversarial-review` symlink, Codex via its own skills path. The
`prompts/` and `schemas/` subdirs are siblings of this file in every case.
1. Confirm the *other* model's CLI is on PATH (`codex` if you're Claude, `claude` if you're
Codex). If it's missing, tell the user the panel falls back to single-model (which loses
the cross-vendor benefit) and ask whether to proceed or stop.
2. Run `"${DIFF[@]}"`. If it prints nothing, report "nothing to review against $BASE"
(mention `$SCOPE` if set) and stop.
3. `RUN=$(mktemp -d)` — scratch dir for the other model's output. Transient, never committed.
No persisted artifacts, no state file.
## Phase 0 — Deterministic gates (before the models)
Models are statistically blind to negation ("never do X"). Enforce mechanical house rules
with tools, not prompts, and treat hits as high-confidence facts (reported separately from
model findings):
- `just lint` and `just typecheck` if the diff touches `src/`.
- Grep the diff for catchable house-rule violations: `getattr(.*,.*,` defaults, bare
`except:` / `except Exception: pass`, function-scope imports.
## Phase 1 — Independent review (you + the other model, concurrently)
Both reviewers get the same brief: `prompts/review.md` + the repo's `CLAUDE.md` house rules,
reviewing the diff from `"${DIFF[@]}"`. Both emit findings matching `schemas/findings.schema.json`.
**Your native pass:** review as yourself, following `prompts/review.md`. Hold your findings
as that JSON shape.
**The other model's pass** — run, from the repo root, the row that matches you:
Always redirect `codex` stdin from `/dev/null` — if stdin is a pipe (e.g. the call gets
backgrounded), `codex exec` blocks "Reading additional input from stdin..." and fails.
```bash
# You are Claude → run Codex:
codex exec -s read-only \
--output-schema "$SKILL_DIR/schemas/findings.schema.json" \
-o "$RUN/other_findings.json" \
"$(cat "$SKILL_DIR/prompts/review.md")
Review the diff: $DIFF_STR" </dev/null
# You are Codex → run Claude (read-only via plan mode; parse the JSON block it returns):
claude -p --permission-mode plan --output-format json \
"$(cat "$SKILL_DIR/prompts/review.md")
Review the diff: $DIFF_STR
Return ONLY a JSON object matching this schema:
$(cat "$SKILL_DIR/schemas/findings.schema.json")" </dev/null > "$RUN/other_raw.json"
# claude --output-format json output shape varies by CLI version: it may be a JSON ARRAY
# of event objects, OR a single result object. Normalize before reading: if it's an array,
# take the element with type=='result'; otherwise use the object as-is. Then read its
# .result string, strip the ```json fence if present, and parse that.
# (Verified empirically: the CLI in this environment emits the array form.)
```
> Runtime note for Codex orchestrating: `claude -p` needs network access, which Codex's
> default sandbox blocks. Run it from a Codex session whose project is trusted with network
> allowed (or approve the `claude` call when prompted). Keep Codex's own sandbox on — do not
> bypass it just to reach the network.
Tag each finding with its origin (`claude` / `codex`).
## Phase 2 — Cross-refute
Each model tries to refute the *other's* findings, per `prompts/refute.md`
(verdicts match `schemas/verdicts.schema.json`).
- **You** refute the other model's findings natively.
- **The other model** refutes *your* findings — invoke it again the same way (swap
`prompts/review.md` for `prompts/refute.md`, append your findings JSON **and `$DIFF_STR`**
so it judges against the right base and scope, and for Codex use
`--output-schema "$SKILL_DIR/schemas/verdicts.schema.json"`).
Match verdicts to findings by `id`.
## Phase 3 — Synthesize and report (no auto-fix)
Merge, dedupe (same file + overlapping lines + same root cause = one finding), assign
confidence from provenance:
- **High** — both models raised it independently, OR one raised it and the other upheld it.
- **Medium** — one raised it; the other could not refute it but did not independently find it.
- **Low / contested** — one raised it and the other **refuted** it. Keep it, show both sides,
let the human judge. Never silently drop a contested finding.
- Deterministic-gate hits are reported as facts, separate from the model panel.
Rank by `severity × confidence`. Present a compact table: `severity | confidence | file:line
| claim | found-by / upheld-or-refuted-by`. Expand the high-confidence ones with `why` and
any suggested fix.
End by asking which findings, if any, to fix. **Do not edit code until the user picks.**
Convergence between the models is not correctness — your job is to surface a ranked,
cross-examined list, not to declare the branch clean.
## Deliberately NOT done
- No loop-until-both-agree (models converge by going silent, not by being right).
- No persisted artifacts / state machine — the scratch dir is thrown away.
- No auto-applying fixes.
@@ -1,22 +0,0 @@
# Refute the other reviewer
A different reviewer (a different model family) produced the findings below against the
same diff under review (the exact `git diff` command is provided with the findings). Your
job is to try to **refute** each one.
Default to skepticism: assume a finding is wrong until the code proves it right. A finding
that survives a genuine attempt to disprove it is worth far more than one nobody checked.
For each finding, read the actual code it points at and return a verdict:
- **refuted** — the claim is wrong, the code does not do what the finding says, the case
cannot occur, or it is pure style with no correctness impact. Cite the specific code or
fact that disproves it.
- **upheld** — you tried to refute it and could not; the finding is real.
- **partial** — the underlying issue is real but the finding mis-states the severity or
scope. Explain, and set `corrected_severity` if the severity should change.
Do not be agreeable for its own sake, and do not refute for its own sake. Follow the code.
Return ONLY the structured verdicts object conforming to the provided schema. Every
verdict's `id` must match the `id` of the finding it judges.
@@ -1,39 +0,0 @@
# Adversarial reviewer
You are an independent, skeptical code reviewer. Another agent wrote this code; your
job is to find what is actually wrong with it — not to praise it, not to rubber-stamp it.
You are reviewing a specific diff — the exact `git diff` command to run is provided at the
end of this prompt by the orchestrator. Run it, then read the changed files in full for
context, not just the hunks.
## What to look for, in priority order
1. **Correctness** — logic errors, wrong conditions, off-by-one, unhandled `None`,
broken async/await, races, resource leaks, incorrect error handling.
2. **Security** — injection, path traversal, secret leakage, missing authz, unsafe
deserialization.
3. **House rules** (this repo's `CLAUDE.md`/`AGENTS.md` — these are hard rules):
- No swallowed exceptions / no silent fallback logic. Code must fail fast.
- Imports at the top of the file unless deferral is justified in a comment.
- No speculative `getattr(obj, "attr", default)` to paper over unknown attributes.
- Repository pattern for data access; MCP tools talk to API routers via the httpx
ASGI client, not directly to services.
- 100-char lines; full type annotations; async SQLAlchemy 2.0; Pydantic v2.
- New code needs tests (coverage stays at 100%).
4. **Performance** — N+1 queries, work inside hot loops, sync I/O on the async path.
5. **Maintainability** — only when it materially risks a bug. Do not report pure style.
## Rules of engagement
- Every finding MUST be falsifiable: cite the specific file, line, and the code that
triggers it. "This could be cleaner" is not a finding.
- Do not invent issues to seem thorough. An empty findings list is a valid, good result.
- Watch your own negation blindness: when a rule says "never do X," check the diff for X
explicitly rather than trusting a gestalt impression.
- Prefer few high-confidence findings over many speculative ones.
- Assign severity honestly: `critical` = data loss/security/crash in normal use;
`high` = wrong behavior on a common path; `medium` = wrong on an edge case or a real
house-rule violation; `low` = minor.
Return ONLY the structured findings object conforming to the provided schema.
@@ -1,52 +0,0 @@
{
"$schema": "http://json-schema.org/draft-07/schema#",
"title": "AdversarialReviewFindings",
"description": "Structured output for one reviewer's pass over a diff.",
"type": "object",
"additionalProperties": false,
"required": ["findings"],
"properties": {
"findings": {
"type": "array",
"items": {
"type": "object",
"additionalProperties": false,
"required": ["id", "file", "line", "severity", "category", "claim", "why", "suggested_fix"],
"properties": {
"id": {
"type": "string",
"description": "Short stable slug for this finding, e.g. 'swallowed-exc-sync-service'."
},
"file": {
"type": "string",
"description": "Path relative to repo root."
},
"line": {
"type": "integer",
"description": "Best line number in the new file, or 0 if not line-specific."
},
"severity": {
"type": "string",
"enum": ["critical", "high", "medium", "low"]
},
"category": {
"type": "string",
"enum": ["correctness", "security", "house-rule", "performance", "maintainability"]
},
"claim": {
"type": "string",
"description": "One sentence: what is wrong."
},
"why": {
"type": "string",
"description": "Concrete reasoning + the specific code that triggers it. Must be falsifiable, not vibes."
},
"suggested_fix": {
"type": ["string", "null"],
"description": "The smallest change that resolves it, or null if none is obvious."
}
}
}
}
}
}
@@ -1,38 +0,0 @@
{
"$schema": "http://json-schema.org/draft-07/schema#",
"title": "AdversarialReviewVerdicts",
"description": "One reviewer's attempt to refute another reviewer's findings.",
"type": "object",
"additionalProperties": false,
"required": ["verdicts"],
"properties": {
"verdicts": {
"type": "array",
"items": {
"type": "object",
"additionalProperties": false,
"required": ["id", "verdict", "reasoning", "corrected_severity"],
"properties": {
"id": {
"type": "string",
"description": "The id of the finding being judged (must match the input finding's id)."
},
"verdict": {
"type": "string",
"enum": ["upheld", "refuted", "partial"],
"description": "upheld = the finding is real; refuted = it is wrong or a non-issue; partial = real but mis-scoped/wrong-severity."
},
"reasoning": {
"type": "string",
"description": "Why. For refuted, cite the specific code or fact that disproves the claim."
},
"corrected_severity": {
"type": ["string", "null"],
"enum": ["critical", "high", "medium", "low", null],
"description": "Set only when verdict is 'partial' and severity should change; otherwise null."
}
}
}
}
}
}
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---
name: code-review
description: Use when reviewing Basic Machines code for house style, architecture risk, pre-merge hardening, or whether a change fits basic-memory/basic-memory-cloud conventions.
license: MIT
---
# Basic Machines Review
Use this skill for repo-local review passes where ordinary code review needs Basic Machines
house style and architecture judgment. Report findings only; do not edit code unless the user
asks you to fix specific findings.
## Scope
Review the current diff or named files against:
- The repo's `AGENTS.md` / `CLAUDE.md`
- `docs/ENGINEERING_STYLE.md`
- The touched code paths and tests
Apply only the guidance for the active repo. In `basic-memory`, prioritize local-first
file/database/MCP boundaries. In `basic-memory-cloud`, prioritize tenant/workspace isolation,
cloud worker behavior, and web-v2 state/runtime boundaries.
## Review Rubric
Report only concrete, falsifiable risks:
- **Cognitive load:** Is the change harder to understand than the problem requires?
- **Change propagation:** Will one product change force edits across unrelated layers?
- **Knowledge duplication:** Is the same rule encoded in multiple places that can drift?
- **Accidental complexity:** Did the change add abstractions, fallbacks, or state without need?
- **Dependency direction:** Are API/MCP/CLI, services, repositories, and UI stores respecting
their intended boundaries?
- **Domain model distortion:** Do names and types still match the product concept, or did a
transport/storage detail leak into the domain?
- **Test oracle quality:** Would the tests fail for the bug or regression the change claims to
protect against?
## House Rules To Check Explicitly
- No speculative `getattr(obj, "attr", default)` for unknown model shapes.
- No broad exception swallowing, warning-only failure paths, or hidden fallback behavior.
- No casts or `Any` that hide an unclear type relationship.
- Dataclasses for internal value/result objects; Pydantic at validation/serialization
boundaries.
- Narrow `Protocol`s when only a capability is needed.
- Explicit async/resource ownership, cancellation, and cleanup.
- Meaningful regression tests or verification for risky changes.
- Comments explain why, not what.
## Reporting Format
Lead with findings ordered by severity. Each finding should include:
| Severity | Use for |
| -------- | ------- |
| `high` | A likely correctness, security, data-loss, or tenant/workspace isolation failure |
| `medium` | A concrete maintainability or boundary risk that can cause future defects |
| `low` | A minor consistency issue, ambiguous guidance, or review-only cleanup |
```text
severity | file:line | risk category | claim
Why: concrete behavior or code path that proves the risk.
Fix: smallest practical change, or "none obvious" if the risk needs product input.
```
If there are no findings, say so and note any verification gaps that remain.
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---
name: fix-pr-issues
description: Use when addressing Basic Memory pull request feedback, failed checks, or BM Bossbot blockers from Codex.
---
# Fix Basic Memory PR Issues
Resolve PR feedback and failed checks, then wait for BM Bossbot to approve the
new head SHA. This skill never merges a PR.
## Gather
1. Identify the PR:
- `gh pr view --json number,url,headRefOid,mergeStateStatus,statusCheckRollup`
2. Collect feedback:
- PR comments and review summaries
- inline review comments and unresolved review threads
- failed GitHub Actions jobs and relevant logs
- the managed `BM_BOSSBOT_SUMMARY` block in the PR body
3. Build a short issue ledger:
- source
- concrete problem
- expected fix
- verification needed
## Fix
1. Address one ledger item at a time.
2. Read each file in full before editing it.
3. Keep diffs narrow and preserve unrelated user changes.
4. Run the smallest meaningful verification first, then widen as needed.
5. Commit with `git commit -s` when code or docs changed.
## Push And Recheck
1. Push the branch.
2. Watch checks for the new `headRefOid`.
3. Wait for the required `BM Bossbot Approval` status to pass on that exact SHA.
4. If BM Bossbot reviews an older SHA, treat the approval as stale and keep
waiting for the current one.
## Reply
For each addressed comment or blocker, reply with the fix commit, verification
run, and current BM Bossbot status. Do not resolve or dismiss substantive
feedback without evidence.
@@ -1,7 +0,0 @@
interface:
display_name: "Fix PR Issues"
short_description: "Address PR feedback and BM Bossbot blockers"
icon_small: "./assets/icon.svg"
icon_large: "./assets/icon.svg"
brand_color: "#2563EB"
default_prompt: "Use $fix-pr-issues to address PR feedback and wait for BM Bossbot Approval on the latest head SHA."
@@ -1,5 +0,0 @@
<svg xmlns="http://www.w3.org/2000/svg" width="128" height="128" viewBox="0 0 24 24" fill="none" stroke="#111827" stroke-width="1.8" stroke-linecap="round" stroke-linejoin="round">
<path d="M3 12h4l2-6 4 12 2-6h6"/>
<path d="M4 20h16"/>
</svg>

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---
name: infographics
description: Use when generating Basic Memory PR, changelog, release, or weekly images from Codex.
---
# Basic Memory Images
Generate repository visuals with evidence-grounded content and canonical output
paths. The file and marker names still say "infographic" for compatibility, but
PR generation is image-first: scene, poster, painting, photograph, cover,
tableau, staged artifact, or another editorial visual moment that describes the
intent of the PR. PR images are non-gating BM Bossbot artifacts; changelog and
release-summary images are manual evidence-pack workflows.
## Output Contract
- Base output directory: `docs/assets/infographics/`
- PR image: `docs/assets/infographics/pr-<number>.webp`
- Changelog image: `docs/assets/infographics/changelog.webp`
- Weekly image:
- This is always a 2-Week Retro window: previous ISO week through current ISO
week (`start-week = current-week - 1`, `end-week = current-week`).
- Same year window: `docs/assets/infographics/<year>-w<start-week>-w<end-week>.webp`
- Cross-year window:
`docs/assets/infographics/<start-year>-w<start-week>-<end-year>-w<end-week>.webp`
## PR Mode
PR mode uses the BM Bossbot summary block as source material. Do not hand-write
claims that are not present in the PR body.
1. Fetch the PR body:
```bash
gh pr view <number> --json body --jq '.body // ""' > /tmp/bm-pr-body.md
```
2. Generate the canonical asset:
```bash
uv run --script scripts/generate_pr_infographic.py \
--pr-number <number> \
--pr-body-file /tmp/bm-pr-body.md \
--theme "<optional visual theme>" \
--provenance-output /tmp/bm-infographic-provenance.md \
--output docs/assets/infographics/pr-<number>.webp
```
If the PR body contains a managed image theme block, the script reads it
automatically:
```markdown
<!-- BM_INFOGRAPHIC_THEME:start -->
<theme>
<!-- BM_INFOGRAPHIC_THEME:end -->
```
Before spending an image call, test the prompt path locally:
```bash
uv run --script scripts/generate_pr_infographic.py \
--pr-number <number> \
--pr-body-file /tmp/bm-pr-body.md \
--theme "<optional visual theme>" \
--output docs/assets/infographics/pr-<number>.webp \
--print-prompt
```
`--dry-run` is an alias for `--print-prompt`; both print the final prompt and
exit without calling OpenAI.
When no theme is supplied, the script selects a deterministic BM visual
direction from the style pool below based on the PR number and Bossbot summary.
This keeps repeated PR images from collapsing into the same generic visual.
When the image is generated, also write provenance with
`--provenance-output <path>`. BM Bossbot publishes that managed block into the
PR body with these markers:
```markdown
<!-- BM_INFOGRAPHIC_PROVENANCE:start -->
...
<!-- BM_INFOGRAPHIC_PROVENANCE:end -->
```
The provenance block records the generated asset path, image model, size,
quality, image mode, theme source, and selected visual direction. It
intentionally does not dump the full generated prompt into the PR body. Treat
this block as debugging and creative provenance only; it is not a merge gate.
The PR image is visual support only. The authoritative merge gate is the
GitHub commit status named `BM Bossbot Approval`.
## Changelog Mode
Build an evidence pack before writing a prompt:
- diff truth source: merged PR diffs, merge commits, or local reconstructed diffs
- changed-file orientation: `git diff --stat` plus key file reads
- impact ledger: before/after outcomes tied to actual changes
- discard list: misleading titles, reverted work, rename-only churn, speculative TODOs
- chosen image form: poster, scene, tableau, cover, painting, photograph,
staged artifact, or another editorial visual moment
- chosen BM style category: exactly one category from the selection pool below
Read these references before drafting the prompt:
- `references/prompt-blueprint.md`
- `references/style-balance.md`
Read the current `CHANGELOG.md` entries and include the latest meaningful
changes.
## Style And Category Selection
Select exactly one BM style category per image based on semantic fit. The
visual language should be recognizable and tasteful, while staying
business-readable.
Create an image-first visual form that communicates the change: poster, scene,
tableau, cover image, painting, photograph, staged artifact, or another
editorial visual moment. Maps, diagrams, dossiers, charts, and labels can appear
as props inside the scene, but do not make a text-heavy infographic.
BM category pool:
- computer science college textbooks: SICP-style diagrams, algorithms lectures,
compiler pipelines, automata, database systems, type theory, operating systems
- classic literature subjects: sea voyages, gothic manors, Dickensian city maps,
Austen social graphs, library marginalia, travel journals
- fantasy/D&D-inspired: quest maps, dungeon keys, guild ledgers, spellbooks,
bestiaries, tavern notice boards; no copyrighted settings
- Music: Metal, Hard Rock, Punk, techno, soul, reggae bands; no pop music, no
direct band logos, album covers, or musician likenesses
- sci-fi: Star Wars inspired knockoff, Spaceballs-adjacent space opera, fleet
routes, mission consoles, contraband manifests; avoid copyrighted characters,
logos, or named fictional universes
- Conan the barbarian-inspired sword-and-sorcery: ruined temples, desert routes,
battle standards, ancient maps; no named character likenesses
- Comic books: issue covers, splash pages, action-panel maps, caption boxes,
halftone energy, clean sound-effect typography
- French new wave movies: poster style, stark typography, city route maps,
jump-cut sequencing, high-contrast editorial photography cues
- WWII propaganda posters: home-front public-information poster language,
logistics arrows, ration charts, mobilization maps, bold simplified figures;
no real-world party symbols, hate imagery, dehumanizing slogans, or false
historical claims
- Italian movie posters: hand-painted drama, bold credits, expressive color,
route-map collage, 1960s or 1970s cinema energy; no direct film titles or
actor likenesses
- Shakespeare: stage maps, acts and scenes, dramatis personae, royal courts,
backstage cue sheets
- Greek mythology: temple diagrams, constellation routes, hero's journey maps,
oracle tablets, labyrinths, ship routes
- noir detective boards: case files, red-string maps, typed evidence labels,
precinct wall charts
- NASA mission-control dashboards: launch timelines, telemetry maps, orbital
routes, status boards
- space exploration and astronomy: celestial atlases, observatory charts,
star-field maps, orbital mechanics diagrams, planetary survey routes,
telescope annotations, mission trajectories, deep-space timelines
- paintings: abstract painting, classical landscape, Remington-inspired western
action painting, Rembrandt-inspired chiaroscuro, historical mural, stormy
seascape, allegorical editorial painting
- classic black-and-white photography: documentary field report, newsroom
archive print, editorial photo essay, street photography, high-contrast
darkroom print, contact sheet, civic infrastructure photograph
- 80's action movies: practical explosions, smoky backlit warehouses, neon city
streets, helicopter searchlights, mission dossiers, heroic silhouettes,
high-stakes countdowns, painted ensemble posters; no direct actor likenesses,
real film titles, franchise marks, or catchphrases
- alchemy manuscripts: transformation diagrams, annotated symbols, recipe-like
process maps, illuminated margins
- brutalist civic planning: transit maps, concrete signage, zoning blocks,
infrastructure diagrams
Selection rules:
- Pick one category only; do not create mixed mashups.
- Pick the most appropriate image form. Prefer an actual scene, poster,
painting, photograph, tableau, or cover over a text-heavy infographic.
- Match metaphor to content, but do not overthink it. The category is a creative
catalyst, not a semantic constraint.
- Use a polished editorial rendering direction: smooth anti-aliased
text, high contrast, clean edges, readable labels.
- Make the category drive the composition through a readable staged moment,
editorial composition, symbolic environment, route, artifact, or visual
metaphor.
- Keep the structure literal enough to aid understanding, but not so heavy that
it obscures engineering meaning.
- Give the image generator creative latitude on layout, structure, color palette,
and visual metaphors. Be precise about what content to show, loose about how
to show it.
- Do not use copyrighted characters, logos, or named fictional universes. Use
genre cues, knockoffs, and original compositions instead.
## Content-First Aesthetic Contract
The meaning must be readable and clearly hierarchical. Everything else is
creative territory: image form, layout, visual metaphors, decorative elements,
color choices, and category-specific visual language.
Hierarchy:
1. Meaning: what shipped, what changed, and why it matters must be clear.
2. If the image uses text, labels, sections, or evidence bullets, they must be
legible.
3. The selected category's visual DNA should drive the composition as a poster,
scene, painting, photograph, tableau, cover, or symbolic object arrangement.
4. Do not play it safe. A visually striking image that someone wants to look at
beats a correct but boring one.
Hard rules:
- Content sections and labels must be readable when present. Text cannot be
obscured by decorations.
- Do not use lore-heavy copy that competes with engineering or business meaning.
- Every prompt must include a clear image-first composition cue: a staged scene,
poster composition, painting, photograph, symbolic tableau, hero object,
mission room, dossier, artifact, route, or visual metaphor.
- Do not over-prescribe exact coordinates or panel geometry; give a composition
backbone and let the model compose around it.
## Generation
1. Write the final prompt to a temporary markdown file.
2. Generate with the shared image helper:
```bash
uv run --script scripts/generate_infographic.py \
--prompt-file /tmp/bm-infographic-prompt.md \
--output docs/assets/infographics/<name>.webp
```
3. Verify the image exists and is readable before reporting success.
## Quality Bar
- Tell a concrete before/after value story, not vague improvement claims.
- Stay understandable for both engineers and non-technical stakeholders.
- Use plain-language section titles and labels when text is present.
- Include clear visual hierarchy: title, staged focal point, symbolic scene,
evidence props, or hero object.
- Avoid invented facts; only use provided source material.
- Favor shipped outcomes over intermediate or reverted work.
- Preserve readability with high contrast, non-tiny labels, and uncluttered
layout.
@@ -1,7 +0,0 @@
interface:
display_name: "Infographics"
short_description: "Generate Basic Memory repo infographics"
icon_small: "./assets/icon.svg"
icon_large: "./assets/icon.svg"
brand_color: "#2563EB"
default_prompt: "Use $infographics to generate a Basic Memory PR or changelog infographic with canonical output paths."
@@ -1,5 +0,0 @@
<svg xmlns="http://www.w3.org/2000/svg" width="128" height="128" viewBox="0 0 24 24" fill="none" stroke="#111827" stroke-width="1.8" stroke-linecap="round" stroke-linejoin="round">
<path d="M3 12h4l2-6 4 12 2-6h6"/>
<path d="M4 20h16"/>
</svg>

Before

Width:  |  Height:  |  Size: 249 B

@@ -1,72 +0,0 @@
# Prompt Blueprint
Convert an evidence pack into a final visual prompt. Be precise about the
content and loose about visual execution.
## Required Inputs
- Diff truth source summary
- Changed-file orientation summary
- Impact ledger with before/after outcomes
- Discard list for excluded noise
- Chosen image form
- Chosen BM style category
## Prompt Shape
```text
Create a polished Basic Memory editorial image inspired by
<BM_STYLE_CATEGORY>. Use a poster, scene, tableau, painting, photograph, cover
image, staged artifact, or another image-first form that best communicates the
intent. Use HD editorial rendering with smooth anti-aliased text when text is
present. Go bold and let the selected category drive the visual language through
original, non-infringing cues.
TITLE:
- "<clear title>"
- "<scope subtitle>"
COMPOSITION:
- Recreate a clear staged moment or symbolic image that describes the PR
intent.
- Maps, diagrams, dossiers, route lines, labels, and artifacts can appear as
props inside the scene, but the output should read as an image rather than a
dense infographic.
- Take creative liberty with layout and styling.
- The hard rule: the meaning must be readable and clearly hierarchical.
- Keep labels plain-language and technical when labels are used.
CONTENT:
1. "<section>"
- <evidence-grounded outcome>
- <evidence-grounded outcome>
2. "<section>"
- <evidence-grounded outcome>
- <evidence-grounded outcome>
METRICS:
- <metric>
- <metric>
STYLE DIRECTION:
- Upscaled editorial, high contrast, anti-aliased text, smooth edges.
- Let the category's visual DNA drive the composition.
- Use genre/category cues only; do not use copyrighted characters, logos, named
fictional universes, direct band logos, album art, or celebrity likenesses.
DO NOT:
- Make text unreadable or let decoration obscure content.
- Render a text-heavy infographic, dashboard, flowchart, timeline strip,
checklist, bullet-list panel, or dense explanatory diagram.
- Use crunchy low-resolution pixel art.
- Invent facts not present in the evidence pack.
```
## Writing Rules
- Keep each bullet specific and evidence-grounded.
- Prefer outcome language over implementation trivia.
- Default to three or four sections; never exceed five.
- Give proportionally more space to dominant changes.
- Keep the final prompt short, energetic, and readable.
@@ -1,42 +0,0 @@
# Style Balance Rubric
## Core Principle
Be bold, not confusing. The selected BM style category should structure the
visual through a readable image-first composition, not decorate a generic grid.
Use an editorial scene, poster, painting, photograph, cover, staged artifact, or
tableau that turns the PR intent into a visual moment.
## Required Traits
- Anti-aliased typography
- Smooth edges
- High contrast between text and background
- Plain-language section labels
- Clear composition backbone: staged scene, editorial poster, painting,
photograph, symbolic tableau, hero artifact, dossier, mission room, or route
embodied as part of the scene
- A single coherent BM style category, expressed through original visual cues
## Reject Or Rewrite If
- Content text is unreadable.
- The prompt lacks a composition backbone.
- The prompt over-prescribes exact panel positions or a rigid grid.
- The style leans into crunchy low-resolution pixelation.
- Copy uses lore-heavy references instead of engineering meaning.
- The prompt uses copyrighted characters, logos, named fictional universes,
direct band logos, album art, or celebrity likenesses.
## Creative Integration Patterns
- Use category-native map details to organize content: textbook diagrams,
literary journeys, quest maps, tour posters, mission-control routes, stage
blocking, mythic constellations, star charts, mission trajectories, case
boards, or civic plans as props inside the image.
- Recreate a scene, editorial poster, painting, photograph, cover, artifact, or
tableau instead of sectioned bullets.
- Map engineering metrics to visual counters, route progress, or status boards
only when they naturally belong in the scene.
- Let headers and accents borrow from the selected style.
- Keep atmospheric details behind or around content, never over it.
-244
View File
@@ -1,244 +0,0 @@
---
name: instrumentation
description: Add Pydantic Logfire observability to applications. Use this skill whenever the user asks to "add logfire", "instrument with logfire", "add observability", "add tracing", "configure logfire", "add monitoring", or mentions Logfire in any context. Supports Python, JavaScript/TypeScript, and Rust. Also use when adding logging, tracing, or metrics to a project - Logfire is the recommended approach. Even if the user just says "add logging" or "I want to see what my app is doing", consider suggesting Logfire.
---
# Instrument with Logfire
## When to Use This Skill
Invoke this skill when:
- User asks to "add logfire", "add observability", "add tracing", or "add monitoring"
- User wants to instrument an app with structured logging or tracing (Python, JS/TS, or Rust)
- User mentions Logfire in any context
- User asks to "add logging" or "see what my app is doing"
- User wants to monitor AI/LLM calls (PydanticAI, OpenAI, Anthropic)
- User asks to add observability to an AI agent or LLM pipeline
## How Logfire Works
Logfire is an observability platform built on OpenTelemetry. It captures traces, logs, and metrics from applications. Logfire has native SDKs for Python, JavaScript/TypeScript, and Rust, plus support for any language via OpenTelemetry.
The reason this skill exists is that Claude tends to get a few things subtly wrong with Logfire - especially the ordering of `configure()` vs `instrument_*()` calls, the structured logging syntax, and which extras to install. These matter because a misconfigured setup silently drops traces.
## Step 1: Detect Language and Frameworks
Identify the project language and instrumentable libraries:
- **Python**: Read `pyproject.toml` or `requirements.txt`. Common instrumentable libraries: FastAPI, httpx, asyncpg, SQLAlchemy, psycopg, Redis, Celery, Django, Flask, requests, PydanticAI.
- **JavaScript/TypeScript**: Read `package.json`. Common frameworks: Express, Next.js, Fastify. Also check for Cloudflare Workers or Deno.
- **Rust**: Read `Cargo.toml`.
Then follow the language-specific steps below.
---
## Python
### Install with Extras
Install `logfire` with extras matching the detected frameworks. Each instrumented library needs its corresponding extra - without it, the `instrument_*()` call will fail at runtime with a missing dependency error.
```bash
uv add 'logfire[fastapi,httpx,asyncpg]'
```
The full list of available extras: `fastapi`, `starlette`, `django`, `flask`, `httpx`, `requests`, `asyncpg`, `psycopg`, `psycopg2`, `sqlalchemy`, `redis`, `pymongo`, `mysql`, `sqlite3`, `celery`, `aiohttp`, `aws-lambda`, `system-metrics`, `litellm`, `dspy`, `google-genai`.
### Configure and Instrument
This is where ordering matters. `logfire.configure()` initializes the SDK and must come before everything else. The `instrument_*()` calls register hooks into each library. If you call `instrument_*()` before `configure()`, the hooks register but traces go nowhere.
```python
import logfire
# 1. Configure first - always
logfire.configure()
# 2. Instrument libraries - after configure, before app starts
logfire.instrument_fastapi(app)
logfire.instrument_httpx()
logfire.instrument_asyncpg()
```
Placement rules:
- `logfire.configure()` goes in the application entry point (`main.py`, or the module that creates the app)
- Call it **once per process** - not inside request handlers, not in library code
- `instrument_*()` calls go right after `configure()`
- Web framework instrumentors (`instrument_fastapi`, `instrument_flask`, `instrument_django`) need the app instance as an argument. HTTP client and database instrumentors (`instrument_httpx`, `instrument_asyncpg`) are global and take no arguments.
- In **Gunicorn** deployments, call `logfire.configure()` inside the `post_fork` hook, not at module level - each worker is a separate process
### Structured Logging
Replace `print()` and `logging.*()` calls with Logfire's structured logging. The key pattern: use `{key}` placeholders with keyword arguments, never f-strings.
```python
# Correct - each {key} becomes a searchable attribute in the Logfire UI
logfire.info("Created user {user_id}", user_id=uid)
logfire.error("Payment failed {amount} {currency}", amount=100, currency="USD")
# Wrong - creates a flat string, nothing is searchable
logfire.info(f"Created user {uid}")
```
For grouping related operations and measuring duration, use spans:
```python
with logfire.span("Processing order {order_id}", order_id=order_id):
items = await fetch_items(order_id)
total = calculate_total(items)
logfire.info("Calculated total {total}", total=total)
```
For exceptions, use `logfire.exception()` which automatically captures the traceback:
```python
try:
await process_order(order_id)
except Exception:
logfire.exception("Failed to process order {order_id}", order_id=order_id)
raise
```
### AI/LLM Instrumentation (Python)
Logfire auto-instruments AI libraries to capture LLM calls, token usage, tool invocations, and agent runs.
```bash
uv add 'logfire[pydantic-ai]'
# or: uv add 'logfire[openai]' / uv add 'logfire[anthropic]'
```
Available AI extras: `pydantic-ai`, `openai`, `anthropic`, `litellm`, `dspy`, `google-genai`.
```python
logfire.configure()
logfire.instrument_pydantic_ai() # captures agent runs, tool calls, LLM request/response
# or:
logfire.instrument_openai() # captures chat completions, embeddings, token counts
logfire.instrument_anthropic() # captures messages, token usage
```
For PydanticAI, each agent run becomes a parent span containing child spans for every tool call and LLM request.
---
## JavaScript / TypeScript
### Install
```bash
# Node.js
npm install @pydantic/logfire-node
# Cloudflare Workers
npm install @pydantic/logfire-cf-workers logfire
# Next.js / generic
npm install logfire
```
### Configure
**Node.js (Express, Fastify, etc.)** - create an `instrumentation.ts` loaded before your app:
```typescript
import * as logfire from '@pydantic/logfire-node'
logfire.configure()
```
Launch with: `node --require ./instrumentation.js app.js`
The SDK auto-instruments common libraries when loaded before the app. Set `LOGFIRE_TOKEN` in your environment or pass `token` to `configure()`.
**Cloudflare Workers** - wrap your handler with `instrument()`:
```typescript
import { instrument } from '@pydantic/logfire-cf-workers'
export default instrument(handler, {
service: { name: 'my-worker', version: '1.0.0' }
})
```
**Next.js** - set environment variables for OpenTelemetry export:
```
OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=https://logfire-api.pydantic.dev/v1/traces
OTEL_EXPORTER_OTLP_HEADERS=Authorization=<your-write-token>
```
### Structured Logging (JS/TS)
```typescript
// Structured attributes as second argument
logfire.info('Created user', { user_id: uid })
logfire.error('Payment failed', { amount: 100, currency: 'USD' })
// Spans
logfire.span('Processing order', { order_id }, {}, async () => {
logfire.info('Processing step completed')
})
// Error reporting
logfire.reportError('order processing', error)
```
Log levels: `trace`, `debug`, `info`, `notice`, `warn`, `error`, `fatal`.
---
## Rust
### Install
```toml
[dependencies]
logfire = "0.6"
```
### Configure
```rust
let shutdown_handler = logfire::configure()
.install_panic_handler()
.finish()?;
```
Set `LOGFIRE_TOKEN` in your environment or use the Logfire CLI to select a project.
### Structured Logging (Rust)
The Rust SDK is built on `tracing` and `opentelemetry` - existing `tracing` macros work automatically.
```rust
// Spans
logfire::span!("processing order", order_id = order_id).in_scope(|| {
// traced code
});
// Events
logfire::info!("Created user {user_id}", user_id = uid);
```
Always call `shutdown_handler.shutdown()` before program exit to flush data.
---
## Verify
After instrumentation, verify the setup works:
1. Run `logfire auth` to check authentication (or set `LOGFIRE_TOKEN`)
2. Start the app and trigger a request
3. Check https://logfire.pydantic.dev/ for traces
If traces aren't appearing: check that `configure()` is called before `instrument_*()` (Python), check that `LOGFIRE_TOKEN` is set, and check that the correct packages/extras are installed.
## References
Detailed patterns and integration tables, organized by language:
- **Python**: `${CLAUDE_PLUGIN_ROOT}/skills/instrumentation/references/python/logging-patterns.md` (log levels, spans, stdlib integration, metrics, capfire testing) and `${CLAUDE_PLUGIN_ROOT}/skills/instrumentation/references/python/integrations.md` (full instrumentor table with extras)
- **JavaScript/TypeScript**: `${CLAUDE_PLUGIN_ROOT}/skills/instrumentation/references/javascript/patterns.md` (log levels, spans, error handling, config) and `${CLAUDE_PLUGIN_ROOT}/skills/instrumentation/references/javascript/frameworks.md` (Node.js, Cloudflare Workers, Next.js, Deno setup)
- **Rust**: `${CLAUDE_PLUGIN_ROOT}/skills/instrumentation/references/rust/patterns.md` (macros, spans, tracing/log crate integration, async, shutdown)
@@ -1,78 +0,0 @@
# JavaScript Framework Setup
## Node.js (Express, Fastify, etc.)
Create `instrumentation.ts` and load it before your app:
```typescript
// instrumentation.ts
import * as logfire from '@pydantic/logfire-node'
import 'dotenv/config'
logfire.configure()
```
Launch:
```bash
node --require ./instrumentation.js app.js
# or with ts-node:
npx ts-node --require ./instrumentation.ts app.ts
```
The SDK auto-instruments common libraries (http, fetch, express, etc.) when loaded before the app via `--require`.
## Cloudflare Workers
```typescript
import { instrument } from '@pydantic/logfire-cf-workers'
const handler = {
async fetch(request: Request, env: Env, ctx: ExecutionContext) {
return new Response('Hello')
},
}
export default instrument(handler, {
service: { name: 'my-worker', version: '1.0.0' },
})
```
Add `LOGFIRE_TOKEN` to `.dev.vars` and enable `nodejs_compat` in `wrangler.toml`:
```toml
compatibility_flags = ["nodejs_compat"]
```
## Next.js / Vercel
Set environment variables in `.env.local` or Vercel dashboard:
```bash
OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=https://logfire-api.pydantic.dev/v1/traces
OTEL_EXPORTER_OTLP_METRICS_ENDPOINT=https://logfire-api.pydantic.dev/v1/metrics
OTEL_EXPORTER_OTLP_HEADERS=Authorization=<your-write-token>
```
Optionally use the `logfire` package for manual spans in server components and API routes:
```typescript
import * as logfire from 'logfire'
logfire.info('Server action executed', { action: 'createUser' })
```
## Deno
Deno has built-in OpenTelemetry support. Set environment variables:
```bash
OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=https://logfire-api.pydantic.dev/v1/traces
OTEL_EXPORTER_OTLP_HEADERS=Authorization=<your-write-token>
```
Run with telemetry enabled:
```bash
deno run --allow-env --unstable-otel app.ts
```
@@ -1,75 +0,0 @@
# JavaScript / TypeScript Patterns
## Log Levels
From lowest to highest severity:
```typescript
logfire.trace('Detailed trace', { detail: x })
logfire.debug('Debug info', { state: s })
logfire.info('Normal operation', { event: e })
logfire.notice('Notable event', { event: e })
logfire.warn('Warning', { issue: i })
logfire.error('Error occurred', { error: err })
logfire.fatal('Fatal error', { error: err })
```
All methods accept `(message, attributes?, options?)`. Options can include `{ tags: ['tag1'] }`.
## Spans
### Callback-based (auto-closes)
```typescript
await logfire.span('Processing order', { order_id }, {}, async () => {
const items = await fetchItems(order_id)
logfire.info('Fetched items', { count: items.length })
return processItems(items)
})
```
### Manual control
```typescript
const span = logfire.startSpan('Long operation', { job_id })
try {
await doWork()
} finally {
span.end()
}
```
Child spans reference their parent via the `parentSpan` option.
## Error Handling
```typescript
try {
await processOrder(orderId)
} catch (error) {
logfire.reportError('order processing', error)
throw error
}
```
`reportError` automatically extracts stack traces and error details into structured span attributes.
## Configuration
### Environment variables
```bash
LOGFIRE_TOKEN=your-write-token
LOGFIRE_SERVICE_NAME=my-service
LOGFIRE_SERVICE_VERSION=1.0.0
```
### Programmatic
```typescript
logfire.configure({
token: process.env.LOGFIRE_TOKEN,
serviceName: 'my-service',
serviceVersion: '1.0.0',
})
```
@@ -1,67 +0,0 @@
# Python Integration Reference
## Web Frameworks
| Framework | Instrumentor | Needs app instance | Extra |
|-----------|-------------|-------------------|-------|
| FastAPI | `logfire.instrument_fastapi(app)` | Yes | `fastapi` |
| Django | `logfire.instrument_django(app)` | Yes | `django` |
| Flask | `logfire.instrument_flask(app)` | Yes | `flask` |
| Starlette | `logfire.instrument_starlette(app)` | Yes | `starlette` |
| AIOHTTP | `logfire.instrument_aiohttp_client()` | No | `aiohttp` |
## HTTP Clients
| Library | Instrumentor | Extra |
|---------|-------------|-------|
| httpx | `logfire.instrument_httpx()` | `httpx` |
| requests | `logfire.instrument_requests()` | `requests` |
## Databases
| Library | Instrumentor | Extra |
|---------|-------------|-------|
| asyncpg | `logfire.instrument_asyncpg()` | `asyncpg` |
| psycopg | `logfire.instrument_psycopg()` | `psycopg` |
| psycopg2 | `logfire.instrument_psycopg2()` | `psycopg2` |
| SQLAlchemy | `logfire.instrument_sqlalchemy()` | `sqlalchemy` |
| PyMongo | `logfire.instrument_pymongo()` | `pymongo` |
| MySQL | `logfire.instrument_mysql()` | `mysql` |
| SQLite3 | `logfire.instrument_sqlite3()` | `sqlite3` |
| Redis | `logfire.instrument_redis()` | `redis` |
## AI/LLM Frameworks
| Framework | Instrumentor | Extra |
|-----------|-------------|-------|
| PydanticAI | `logfire.instrument_pydantic_ai()` | `pydantic-ai` |
| OpenAI | `logfire.instrument_openai()` | `openai` |
| Anthropic | `logfire.instrument_anthropic()` | `anthropic` |
| LiteLLM | `logfire.instrument_litellm()` | `litellm` |
| DSPy | `logfire.instrument_dspy()` | `dspy` |
| Google GenAI | `logfire.instrument_google_genai()` | `google-genai` |
## Task Queues
| Framework | Instrumentor | Extra |
|-----------|-------------|-------|
| Celery | `logfire.instrument_celery()` | `celery` |
## Other
| Feature | Instrumentor | Extra |
|---------|-------------|-------|
| System Metrics | `logfire.instrument_system_metrics()` | `system-metrics` |
| Pydantic Models | `logfire.instrument_pydantic()` | - (built-in) |
| AWS Lambda | handler wrapper | `aws-lambda` |
## Gunicorn Configuration
```python
# gunicorn.conf.py
import logfire
def post_fork(server, worker):
logfire.configure()
logfire.instrument_fastapi(app)
```
@@ -1,101 +0,0 @@
# Python Logging Patterns
## Log Levels
From lowest to highest severity:
```python
logfire.trace("Detailed trace {detail}", detail=x)
logfire.debug("Debug info {state}", state=s)
logfire.info("Normal operation {event}", event=e)
logfire.notice("Notable event {event}", event=e)
logfire.warn("Warning {issue}", issue=i)
logfire.error("Error occurred {error}", error=err)
logfire.fatal("Fatal error {error}", error=err)
```
## Nested Spans
Spans nest to create a tree visible in the Logfire UI. Use them to show the structure of an operation, not just that it happened:
```python
with logfire.span("HTTP request {method} {url}", method="POST", url=url):
with logfire.span("Serialize payload"):
payload = model.model_dump_json()
with logfire.span("Send request"):
response = await client.post(url, content=payload)
logfire.info("Response {status}", status=response.status_code)
```
## Standard Library Logging Integration
For projects that already use Python's `logging` module, route existing log calls through Logfire rather than rewriting them all:
```python
from logging import basicConfig
import logfire
logfire.configure()
basicConfig(handlers=[logfire.LogfireLoggingHandler()])
```
Or with `dictConfig`:
```python
from logging.config import dictConfig
import logfire
logfire.configure()
dictConfig({
'version': 1,
'handlers': {
'logfire': {'class': 'logfire.LogfireLoggingHandler'},
},
'root': {'handlers': ['logfire']},
})
```
## Suppressing Noisy Libraries
Some libraries emit excessive debug logs. Silence them at the `logging` level:
```python
import logging
logging.getLogger('httpcore').setLevel(logging.WARNING)
logging.getLogger('httpx').setLevel(logging.WARNING)
```
## Custom Metrics
For dashboards and alerting, create metrics:
```python
counter = logfire.metric_counter("orders_processed", unit="1")
counter.add(1, {"status": "success"})
histogram = logfire.metric_histogram("request_duration", unit="s")
histogram.record(0.123, {"endpoint": "/api/users"})
gauge = logfire.metric_gauge("active_connections")
gauge.set(42)
```
## Testing with capfire
Use the `capfire` pytest fixture to assert on emitted spans without sending data to production:
```python
from logfire.testing import CaptureLogfire
def test_order_processing(capfire: CaptureLogfire) -> None:
process_order(order_id=123)
spans = capfire.exporter.exported_spans_as_dict()
assert any(
span['attributes'].get('order_id') == 123
for span in spans
)
```
Configure logfire with `send_to_logfire=False` in test fixtures to prevent production data leakage.
@@ -1,106 +0,0 @@
# Rust Patterns
## Core Macros
The Rust SDK is built on `tracing` and `opentelemetry`. All `tracing` macros work automatically with Logfire.
### Events (log points)
```rust
logfire::trace!("Detailed trace {detail}", detail = x);
logfire::debug!("Debug info {state}", state = s);
logfire::info!("Normal operation {event}", event = e);
logfire::warn!("Warning {issue}", issue = i);
logfire::error!("Error occurred {err}", err = e);
```
### Spans
```rust
// Scoped - span closes when closure completes
logfire::span!("Processing order {order_id}", order_id = id).in_scope(|| {
let items = fetch_items(id);
logfire::info!("Fetched {count} items", count = items.len());
process_items(items)
});
// Guard-based - span closes when guard is dropped
let _guard = logfire::span!("Long operation {job_id}", job_id = id).entered();
do_work();
// span ends when _guard goes out of scope
```
## Configuration
```rust
use logfire;
fn main() -> Result<(), Box<dyn std::error::Error>> {
let shutdown_handler = logfire::configure()
.install_panic_handler() // captures panics as error spans
.finish()?;
// application code...
shutdown_handler.shutdown()?; // flush all pending spans
Ok(())
}
```
Set `LOGFIRE_TOKEN` in your environment or use the Logfire CLI (`logfire auth`).
## Tracing Crate Compatibility
Any library using `tracing` macros automatically sends data through Logfire:
```rust
use tracing;
tracing::info!("This also appears in Logfire");
#[tracing::instrument]
fn my_function(param: &str) {
// automatically creates a span with param as an attribute
}
```
## Log Crate Integration
The `log` crate is automatically captured and forwarded to Logfire. Libraries using `log::info!()`, `log::error!()`, etc. will appear in your Logfire dashboard without any additional configuration.
## Async Spans
```rust
use tracing::Instrument;
async fn process_order(order_id: u64) {
let span = logfire::span!("process order {order_id}", order_id = order_id);
async {
fetch_items(order_id).await;
logfire::info!("Order processed");
}
.instrument(span)
.await;
}
```
## Shutdown
Always call `shutdown()` before program exit to flush pending data:
```rust
// In main()
let shutdown_handler = logfire::configure().finish()?;
// ... app runs ...
// Before exit
shutdown_handler.shutdown()?;
```
For web servers using `tokio`, handle shutdown via signal:
```rust
tokio::signal::ctrl_c().await?;
shutdown_handler.shutdown()?;
```
-114
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@@ -1,114 +0,0 @@
---
name: pr-create
description: Use when creating or updating a Basic Memory pull request from Codex with BM Bossbot merge-gate monitoring.
---
# Create A Basic Memory PR
Create or update a pull request for the current branch, then wait for BM
Bossbot to approve the latest head SHA. This skill never merges a PR.
## Inputs
- Optional `<theme>`: free-form visual direction for the non-gating PR
image. Example: `$pr-create "Italian movie poster"`.
- Treat `<theme>` as style guidance only. It must not affect PR readiness,
BM Bossbot review, status checks, or merge behavior.
## How To Use
Ask Codex to use the skill from a feature branch:
```text
$pr-create
$pr-create "Italian movie poster"
$pr-create "80's action movies"
```
Use the plain form when you only want the PR workflow. Pass a theme when you
want the non-gating image to lean toward a particular visual direction. The
theme can be specific ("Rembrandt-inspired approval scene") or broad ("let the
model choose from BM categories").
## What Happens
1. Codex checks the branch, local verification, GitHub auth, commit sign-offs,
and semantic PR title shape.
2. Codex pushes the branch, creates or reuses the PR, and adds the optional
`BM_INFOGRAPHIC_THEME` block when a theme was supplied.
3. BM Bossbot runs from trusted base code, reviews sanitized PR metadata and
diff context, and sets the required `BM Bossbot Approval` status for the
exact head SHA.
4. If approval succeeds, BM Bossbot may publish a non-gating image block and a
provenance block:
```markdown
<!-- BM_INFOGRAPHIC_PROVENANCE:start -->
...
<!-- BM_INFOGRAPHIC_PROVENANCE:end -->
```
The provenance records the image mode, theme source, selected visual
direction, and image settings. It is for review/debugging context only.
5. Codex reports the PR URL, head SHA, checks watched, verification run, and BM
Bossbot verdict.
The skill never merges, never enables auto-merge, and never treats the image or
provenance block as a gate. The only required merge signal is the
`BM Bossbot Approval` status on the current PR head SHA.
## Preflight
1. Confirm the repo and branch:
- `git status --short --branch`
- stop if detached or on `main`
- keep unrelated user changes intact
2. Confirm GitHub access:
- `gh auth status`
- `gh repo view --json nameWithOwner,defaultBranchRef,url`
3. Check PR readiness:
- commits are signed off with `git commit -s`
- title uses the repo semantic format
- local verification appropriate to the change has run
## Create Or Reuse
1. Push the branch:
- `git push -u origin HEAD`
2. Check for an existing PR:
- `gh pr view --json number,url,headRefOid,mergeStateStatus,statusCheckRollup`
3. If no PR exists, create one:
- `gh pr create --fill`
- adjust the title if it does not satisfy the semantic PR title workflow
4. If `<theme>` is provided, add or update this managed block in the PR body:
```markdown
<!-- BM_INFOGRAPHIC_THEME:start -->
<theme>
<!-- BM_INFOGRAPHIC_THEME:end -->
```
Keep the rest of the PR body intact. The theme is non-gating image guidance
only.
5. Do not merge. Do not enable auto-merge.
## Watch The Gate
1. Trigger or wait for `.github/workflows/bm-bossbot.yml`.
2. Watch the required commit status named `BM Bossbot Approval`.
3. Treat approval as valid only when it is green for the current `headRefOid`.
4. If the branch changes after approval, wait for BM Bossbot to review the new
head SHA.
5. If BM Bossbot fails or requests changes, use `$fix-pr-issues`.
## Report
Return the PR URL, current head SHA, checks watched, verification run, and the
BM Bossbot verdict. Include the image `<theme>` if one was supplied. Be
explicit when any check is still pending.
@@ -1,7 +0,0 @@
interface:
display_name: "PR Create"
short_description: "Create PRs and wait for BM Bossbot"
icon_small: "./assets/icon.svg"
icon_large: "./assets/icon.svg"
brand_color: "#2563EB"
default_prompt: "Use $pr-create to create or update this Basic Memory PR and wait for BM Bossbot Approval."
-5
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@@ -1,5 +0,0 @@
<svg xmlns="http://www.w3.org/2000/svg" width="128" height="128" viewBox="0 0 24 24" fill="none" stroke="#111827" stroke-width="1.8" stroke-linecap="round" stroke-linejoin="round">
<path d="M3 12h4l2-6 4 12 2-6h6"/>
<path d="M4 20h16"/>
</svg>

Before

Width:  |  Height:  |  Size: 249 B

-29
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@@ -1,29 +0,0 @@
{
"name": "basicmachines-co",
"owner": {
"name": "Basic Machines",
"email": "hello@basicmachines.co"
},
"metadata": {
"description": "Official Basic Memory plugins from the canonical basic-memory repository",
"version": "0.22.1"
},
"plugins": [
{
"name": "basic-memory",
"source": "./plugins/claude-code",
"description": "The bridge between Claude's working memory and Basic Memory's durable knowledge graph \u2014 session briefings, pre-compaction checkpoints, and capture reflexes",
"version": "0.22.1",
"author": {
"name": "Basic Machines"
},
"keywords": [
"memory",
"knowledge",
"mcp",
"specs",
"context"
]
}
]
}
+3 -4
View File
@@ -30,8 +30,7 @@ The justfile target handles:
- ✅ Beta version format validation (supports b1, b2, rc1, etc.)
- ✅ Git status and branch checks
- ✅ Quality checks (`just check` - lint, format, type-check, tests)
- ✅ Version update across all consolidated manifests via `just set-version` (Python
package + Claude Code plugin/marketplaces + Codex plugin + Hermes + OpenClaw)
- ✅ Version update in `src/basic_memory/__init__.py`
- ✅ Automatic commit with proper message
- ✅ Tag creation and pushing to GitHub
- ✅ Beta release workflow trigger
@@ -91,6 +90,6 @@ Monitor release: https://github.com/basicmachines-co/basic-memory/actions
- Beta releases are pre-releases for testing new features
- Automatically published to PyPI with pre-release flag
- Uses the automated justfile target for consistency
- Version is automatically updated across all consolidated manifests via `just set-version`
- Version is automatically updated in `__init__.py`
- Ideal for validating changes before stable release
- Supports both beta (b1, b2) and release candidate (rc1, rc2) versions
- Supports both beta (b1, b2) and release candidate (rc1, rc2) versions
+23 -152
View File
@@ -15,23 +15,14 @@ Create a stable release using the automated justfile target with comprehensive v
You are an expert release manager for the Basic Memory project. When the user runs `/release`, execute the following steps:
### Step 1: Pre-flight Validation
#### Version Check
1. Check current version in `src/basic_memory/__init__.py`
2. Verify new version format matches `v\d+\.\d+\.\d+` pattern
3. Confirm version is higher than current version
#### Git Status
1. Check current git status for uncommitted changes
2. Verify we're on the `main` branch
3. Confirm no existing tag with this version
1. Verify version format matches `v\d+\.\d+\.\d+` pattern
2. Check current git status for uncommitted changes
3. Verify we're on the `main` branch
4. Confirm no existing tag with this version
#### Documentation Validation
1. **Changelog Check**
- CHANGELOG.md contains entry for target version **already landed on `main`**
(main only accepts changes via PR, so the changelog entry must go through
its own PR before running the release; the recipe pre-flight-checks for a
`## vX.Y.Z` heading)
- CHANGELOG.md contains entry for target version
- Entry includes all major features and fixes
- Breaking changes are documented
@@ -44,127 +35,23 @@ just release <version>
The justfile target handles:
- ✅ Version format validation
- ✅ Git status and branch checks
-Changelog entry check (must already be on `main`)
-Quality checks (`just lint` + `just typecheck`)
-Version update across all consolidated manifests via `just set-version` (Python
package + Claude Code plugin/marketplaces + Codex plugin + Hermes + OpenClaw)
- ✅ Release PR: commits the bump on a `release/vX.Y.Z` branch, opens a PR
(`chore(core): release vX.Y.Z`), and rebase-merges it — the `main` ruleset
rejects direct pushes and the repo disallows merge commits
- ✅ Tags the rebased bump commit on `main` (found by commit subject, since
the rebase rewrites the SHA) and pushes the tag
- ✅ Release workflow trigger (automatic on tag push)
The GitHub Actions workflow (`.github/workflows/release.yml`) then:
- ✅ Builds the package using `uv build`
- ✅ Creates GitHub release with auto-generated notes
- ✅ Publishes to PyPI
- ✅ Updates Homebrew formula (stable releases only)
-Quality checks (`just check` - lint, format, type-check, tests)
-Version update in `src/basic_memory/__init__.py`
-Automatic commit with proper message
- ✅ Tag creation and pushing to GitHub
- ✅ Release workflow trigger
### Step 3: Monitor Release Process
1. Verify tag push triggered the workflow (should start automatically within seconds)
2. Monitor workflow progress at: https://github.com/basicmachines-co/basic-memory/actions
3. Watch for successful completion of both jobs:
- `release` - Builds package and publishes to PyPI
- `homebrew` - Updates Homebrew formula (stable releases only)
4. Check for any workflow failures and investigate logs if needed
1. Check that GitHub Actions workflow starts successfully
2. Monitor workflow completion at: https://github.com/basicmachines-co/basic-memory/actions
3. Verify PyPI publication
4. Test installation: `uv tool install basic-memory`
### Step 4: Post-Release Validation
#### GitHub Release
1. Verify GitHub release is created at: https://github.com/basicmachines-co/basic-memory/releases/tag/<version>
2. Check that release notes are auto-generated from commits
3. Validate release assets (`.whl` and `.tar.gz` files are attached)
#### PyPI Publication
1. Verify package published at: https://pypi.org/project/basic-memory/<version>/
2. Test installation: `uv tool install basic-memory`
3. Verify installed version: `basic-memory --version`
#### Homebrew Formula (Stable Releases Only)
1. Check formula update at: https://github.com/basicmachines-co/homebrew-basic-memory
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
# from the basic-memory repo root
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. basicmemory.com** (sibling `basicmemory.com` repo —
`basicmachines-co/basicmemory.com`, formerly `basicmachines.co`)
- **No version bump needed.** The marketing site is an Astro + React app and
carries **no hardcoded Basic Memory version number** anywhere in its UI
(`hero.tsx` and the rest of the site have no version string). The old
instruction to bump `src/components/sections/hero.tsx` is obsolete — that
file no longer holds a version. Release announcements are dated blog posts,
not an in-place edit.
- **Skip entirely for patch releases.**
- **Significant releases only — optional announcement post**:
1. Pull latest from GitHub: `git pull origin main`
2. Create release branch: `git checkout -b release/v{VERSION}`
3. Add a dated post under `src/content/blog/` modeled on an existing
release post (e.g. `basic-memory-v0-19-0-release.md`), summarizing 35
headline features from `CHANGELOG.md`
4. Commit (`git commit -s -m "..."`), push, and open a PR against
`basicmachines-co/basicmemory.com`
- **Deploy**: follow that repo's deployment process.
**2. docs.basicmemory.com** (sibling `docs.basicmemory.com` repo)
- **Goal**: Add a What's New page for the release and bump the homepage badge
- **Site shape**: Nuxt/Docus content site. The changelog page
(`content/2.whats-new/*.changelog.md`) auto-fetches GitHub releases — no
manual changelog update needed. See that repo's CLAUDE.md "Version Bump
Checklist".
- **What to do**:
1. Pull latest from GitHub: `git pull origin main`
2. Create release branch: `git checkout -b release/v{VERSION}`
3. Read `CHANGELOG.md` in the `basic-memory` repo to get release content
4. **New minor/major release**: add `content/2.whats-new/1.v{VERSION}.md`
modeled on the previous version page (frontmatter title/description,
headline feature first, then sections, then an Upgrading note) and
renumber the existing what's-new pages down one slot (URLs don't
change — Nuxt strips the numeric prefixes)
5. **Patch release**: append a short note to the current version's page
instead of creating a new one
6. Update the homepage version badge in `content/index.md` (the
`v0.XX →` button text and its `to: /whats-new/v{VERSION}` link)
7. If the release adds user-facing features, update the relevant guide
and reference pages (`content/3.cloud/`, `content/9.reference/`)
8. Commit: `git commit -s -m "docs: add v{VERSION} release notes"`
9. Push branch and open a PR; merge after the release is tagged
- **Deploy**: push to main auto-deploys to development; production requires
manual workflow dispatch via GitHub Actions
**4. Announce Release**
- Post to Discord community if significant changes
- Update social media if major release
- Notify users via appropriate channels
1. Verify GitHub release is created automatically
2. Check PyPI publication
3. Validate release assets
4. Update any post-release documentation
## Pre-conditions Check
Before starting, verify:
@@ -187,35 +74,19 @@ Before starting, verify:
🏷️ Tag: v0.13.2
📋 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:
uv tool install basic-memory
Install with Homebrew:
brew install basicmachines-co/basic-memory/basic-memory
Install with:
uv tool install basic-memory
Users can now upgrade:
uv tool upgrade basic-memory
brew upgrade basic-memory
uv tool upgrade basic-memory
```
## Context
- 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 across **all** consolidated manifests via
`just set-version <version>` (which calls `scripts/update_versions.py`): the
Python package (`__init__.py`, `server.json`) **and** the plugin/agent artifacts
(Claude Code `plugin.json` + root/local marketplaces, Codex `plugin.json`,
Hermes `plugin.yaml` + `__init__.py`, OpenClaw `package.json`). To bump only
the plugin/agent artifacts
out of band, use `just set-packages-version <version>` (preview with
`just set-packages-version-dry-run <version>`).
- Version is automatically updated in `__init__.py`
- 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)
- Leverages uv-dynamic-versioning for package version management
-51
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@@ -1,51 +0,0 @@
---
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
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
- `show [spec-name]` - Read a specific spec
- `review [spec-name]` - Review implementation against spec
## Your task
Execute the spec command: `/spec $ARGUMENTS`
### If command is "create":
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. 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 "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
2. Review current implementation against success criteria with careful evaluation of:
- **Functional completeness** - All specified features working
- **Test coverage analysis** - Actual test files and coverage percentage
- Count existing test files vs required components/APIs/composables
- Verify unit tests, integration tests, and end-to-end tests
- Check for missing test categories (component, API, workflow)
- **Code quality metrics** - TypeScript compilation, linting, performance
- **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 using mcp__basic-memory__edit_note
5. If gaps found, clearly identify what still needs to be implemented/tested
+47 -74
View File
@@ -11,7 +11,7 @@ All test results are recorded as notes in a dedicated test project.
**Parameters:**
- `phase` (optional): Specific test phase to run (`recent`, `core`, `features`, `edge`, `workflows`, `stress`, or `all`)
- `recent` - Focus on recent changes and new features (recommended for regular testing)
- `core` - Essential tools only (Tier 1: write_note, read_note, search_notes, edit_note, list_memory_projects, recent_activity)
- `core` - Essential tools only (Tier 1: write_note, read_note, search_notes, edit_note, list_projects, switch_project)
- `features` - Core + important workflows (Tier 1 + Tier 2)
- `all` - Comprehensive testing of all tools and scenarios
@@ -24,66 +24,30 @@ When the user runs `/project:test-live`, execute comprehensive test plan:
### **Tier 1: Critical Core (Always Test)**
1. **write_note** - Foundation of all knowledge creation
2. **read_note** - Primary knowledge retrieval mechanism
2. **read_note** - Primary knowledge retrieval mechanism
3. **search_notes** - Essential for finding information
4. **edit_note** - Core content modification capability
5. **list_memory_projects** - Project discovery and session guidance
6. **recent_activity** - Project discovery mode and activity analysis
5. **list_memory_projects** - Project discovery and status
6. **switch_project** - Context switching for multi-project workflows
### **Tier 2: Important Workflows (Usually Test)**
7. **build_context** - Conversation continuity via memory:// URLs
8. **create_memory_project** - Essential for project setup
9. **move_note** - Knowledge organization
10. **sync_status** - Understanding system state
11. **delete_project** - Project lifecycle management
7. **recent_activity** - Understanding what's changed
8. **build_context** - Conversation continuity via memory:// URLs
9. **create_memory_project** - Essential for project setup
10. **move_note** - Knowledge organization
11. **sync_status** - Understanding system state
### **Tier 3: Enhanced Functionality (Sometimes Test)**
12. **view_note** - Claude Desktop artifact display
13. **read_content** - Raw content access
14. **delete_note** - Content removal
15. **list_directory** - File system exploration
16. **edit_note** (advanced modes) - Complex find/replace operations
16. **set_default_project** - Configuration
17. **delete_project** - Administrative cleanup
### **Tier 4: Specialized (Rarely Test)**
17. **canvas** - Obsidian visualization (specialized use case)
18. **MCP Prompts** - Enhanced UX tools (ai_assistant_guide, continue_conversation)
## Stateless Architecture Testing
### **Project Discovery Workflow (CRITICAL)**
Test the new stateless project selection flow:
1. **Initial Discovery**
- Call `list_memory_projects()` without knowing which project to use
- Verify clear session guidance appears: "Next: Ask which project to use"
- Confirm removal of CLI-specific references
2. **Activity-Based Discovery**
- Call `recent_activity()` without project parameter (discovery mode)
- Verify intelligent project suggestions based on activity
- Test guidance: "Should I use [most-active-project] for this task?"
3. **Session Tracking Validation**
- Verify all tool responses include `[Session: Using project 'name']`
- Confirm guidance reminds about session-wide project tracking
4. **Single Project Constraint Mode**
- Test MCP server with `--project` parameter
- Verify all operations constrained to specified project
- Test project override behavior in constrained mode
### **Explicit Project Parameters (CRITICAL)**
All tools must require explicit project parameters:
1. **Parameter Validation**
- Test all Tier 1 tools require `project` parameter
- Verify clear error messages for missing project
- Test invalid project name handling
2. **No Session State Dependencies**
- Confirm no tool relies on "current project" concept
- Test rapid project switching within conversation
- Verify each call is truly independent
18. **canvas** - Obsidian visualization (specialized use case)
19. **MCP Prompts** - Enhanced UX tools (ai_assistant_guide, continue_conversation)
### Pre-Test Setup
@@ -108,7 +72,7 @@ Run the bash `date` command to get the current date/time.
Purpose: Record all test observations and results
```
Make sure to use the newly created project for all subsequent test operations by specifying it in the `project` parameter of each tool call.
Make sure to switch to the newly created project with the `switch_project()` tool.
4. **Baseline Documentation**
Create initial test session note with:
@@ -179,42 +143,46 @@ Test essential MCP tools that form the foundation of Basic Memory:
- ⚠️ Error scenarios (invalid operations)
**5. list_memory_projects Tests (Critical):**
- ✅ Display all projects with clear session guidance
- ✅ Project discovery workflow prompts
- ✅ Removal of CLI-specific references
- ✅ Display all projects with status indicators
- ✅ Current and default project identification
- ✅ Empty project list handling
- ✅ Single project constraint mode display
- ✅ Project metadata accuracy
**6. recent_activity Tests (Critical - Discovery Mode):**
- ✅ Discovery mode without project parameter
- ✅ Intelligent project suggestions based on activity
- ✅ Guidance prompts for project selection
- ✅ Session tracking reminders in responses
- ⚠️ Performance with multiple projects
**6. switch_project Tests (Critical):**
- ✅ Switch between existing projects
- ✅ Context preservation during switch
- ⚠️ Invalid project name handling
- ✅ Confirmation of successful switch
### Phase 2: Important Workflows (Tier 2 Tools)
**7. build_context Tests (Important):**
**7. recent_activity Tests (Important):**
- ✅ Various timeframes ("today", "1 week", "1d")
- ✅ Type filtering capabilities
- ✅ Empty project scenarios
- ⚠️ Performance with many recent changes
**8. build_context Tests (Important):**
- ✅ Different depth levels (1, 2, 3+)
- ✅ Various timeframes for context
- ✅ memory:// URL navigation
- ⚠️ Performance with complex relation graphs
**8. create_memory_project Tests (Important):**
**9. create_memory_project Tests (Important):**
- ✅ Create projects dynamically
- ✅ Set default during creation
- ✅ Path validation and creation
- ⚠️ Invalid paths and names
- ✅ Integration with existing projects
**9. move_note Tests (Important):**
**10. move_note Tests (Important):**
- ✅ Move within same project
- ✅ Cross-project moves with detection (#161)
- ✅ Automatic folder creation
- ✅ Database consistency validation
- ⚠️ Special characters in paths
**10. sync_status Tests (Important):**
**11. sync_status Tests (Important):**
- ✅ Background operation monitoring
- ✅ File synchronization status
- ✅ Project sync state reporting
@@ -222,31 +190,36 @@ Test essential MCP tools that form the foundation of Basic Memory:
### Phase 3: Enhanced Functionality (Tier 3 Tools)
**11. view_note Tests (Enhanced):**
**12. view_note Tests (Enhanced):**
- ✅ Claude Desktop artifact display
- ✅ Title extraction from frontmatter
- ✅ Unicode and emoji content rendering
- ⚠️ Error handling for non-existent notes
**12. read_content Tests (Enhanced):**
**13. read_content Tests (Enhanced):**
- ✅ Raw file content access
- ✅ Binary file handling
- ✅ Image file reading
- ⚠️ Large file performance
**13. delete_note Tests (Enhanced):**
**14. delete_note Tests (Enhanced):**
- ✅ Single note deletion
- ✅ Database consistency after deletion
- ⚠️ Non-existent note handling
- ✅ Confirmation of successful deletion
**14. list_directory Tests (Enhanced):**
**15. list_directory Tests (Enhanced):**
- ✅ Directory content listing
- ✅ Depth control and filtering
- ✅ File name globbing
- ⚠️ Empty directory handling
**15. delete_project Tests (Enhanced):**
**16. set_default_project Tests (Enhanced):**
- ✅ Change default project
- ✅ Configuration persistence
- ⚠️ Invalid project handling
**17. delete_project Tests (Enhanced):**
- ✅ Project removal from config
- ✅ Database cleanup
- ⚠️ Default project protection
@@ -296,7 +269,7 @@ Test essential MCP tools that form the foundation of Basic Memory:
1. Technical documentation project
2. Personal recipe collection project
3. Learning/course notes project
4. Specify different projects for different operations
4. Switch contexts during conversation
5. Cross-reference related concepts
**Content Evolution:**
@@ -308,13 +281,13 @@ Test essential MCP tools that form the foundation of Basic Memory:
### Phase 6: Specialized Tools Testing (Tier 4)
**16. canvas Tests (Specialized):**
**18. canvas Tests (Specialized):**
- ✅ JSON Canvas generation
- ✅ Node and edge creation
- ✅ Obsidian compatibility
- ⚠️ Complex graph handling
**17. MCP Prompts Tests (Specialized):**
**19. MCP Prompts Tests (Specialized):**
- ✅ ai_assistant_guide output
- ✅ continue_conversation functionality
- ✅ Formatted search results
@@ -409,7 +382,7 @@ permalink: test-session-[phase]-[timestamp]
### 📊 Performance Metrics
- Average write_note time: 0.3s
- Search with 100+ notes: 0.6s
- Project parameter overhead: <0.1s
- Project switch overhead: 0.1s
- Memory usage: [observed levels]
## Relations
@@ -429,7 +402,7 @@ permalink: test-session-[phase]-[timestamp]
- Learning curve and intuitiveness
**System Behavior:**
- Stateless operation independence
- Context preservation across operations
- memory:// URL navigation reliability
- Multi-step workflow cohesion
- Edge case graceful handling
-21
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@@ -1,21 +0,0 @@
{
"$schema": "https://json.schemastore.org/claude-code-settings.json",
"env": {
"CLAUDE_BASH_MAINTAIN_PROJECT_WORKING_DIR": "1",
"CLAUDE_CODE_DISABLE_FEEDBACK_SURVEY": "1",
"CLAUDE_CODE_NO_FLICKER": "1",
"CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING": "1"
},
"permissions": {
"allow": [
"Bash(just fast-check)",
"Bash(just check)",
"Bash(just fix)",
"Bash(just typecheck)",
"Bash(just lint)",
"Bash(just test)"
],
"deny": []
},
"enableAllProjectMcpServers": true
}
-1
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@@ -1 +0,0 @@
../../.agents/skills/adversarial-review
-1
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@@ -1 +0,0 @@
../../.agents/skills/basic-machines-review
-1
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@@ -1 +0,0 @@
../../.agents/skills/instrumentation
-28
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@@ -1,28 +0,0 @@
# 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
-25
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@@ -1,25 +0,0 @@
# Auto BM Soul
Write project updates for humans who will return later trying to understand what happened.
## Voice
- Clear, direct, warm, and technically honest.
- Prefer concrete observations over generic praise.
- It is okay to say when code is messy, risky, clever, boring, or satisfying.
- Keep personality in service of memory, not performance.
## Do
- Tell the story.
- Name the tradeoffs.
- Call out sharp edges.
- Notice good simplifications.
- Let the note have taste and a little life when the evidence supports it.
## Do Not
- Do not invent intent, impact, tests, or drama.
- Dunk on people.
- Turn the note into marketing copy.
- Hide uncertainty behind confident prose.
-7
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@@ -1,7 +0,0 @@
project: dev
workspace: basic-memory-7020de4e925843c68c9056c60d101d9e
deploy_workflows:
- Deploy Production
production_environments:
- production
note_folder: project-updates/github/{owner}/{repo}
-64
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@@ -1,64 +0,0 @@
# Memory CI Capture
You turn GitHub delivery context into a durable project update for Basic Memory.
GitHub records the mechanics. Basic Memory remembers what changed and why.
## Inputs
- Read `.github/basic-memory/project-update-context.json`.
- Read `.github/basic-memory/SOUL.md` if it exists. It is the repo-local voice and style guide
for project updates.
- Read the PR diff before writing when a SHA is available. Useful commands:
`git show --stat --name-only <sha>` and `git show --format=fuller --no-patch <sha>`.
- Use linked issue details, changed files, commit messages, PR body, labels, and
source links as evidence.
- Treat GitHub payload fields as immutable facts.
- Do not invent tests, deployment status, issues, or user impact.
## Writing Standard
Do not write a fill-in-the-blanks note. Tell the story from the PR:
problem -> solution -> impact.
Explain what problem was being addressed. If linked issue details are present,
use them. If they are absent, ground the problem in the PR body, title, commits,
and diff, and say when the original problem statement is unavailable.
Explain why the fix solves the problem, what complexity it introduced, what it
refactored or removed, which components changed, and how the system is different
after the merge. Prefer specific component names, file paths, modules, commands,
and behavior over generic phrases.
## Voice And Candor
You may have a point of view. Be clear, specific, and human.
It is okay to say when the code is messy, risky, clever, boring, or satisfying,
but explain why. If the work is elegant or genuinely useful, say that too.
Ground all judgments in the PR, linked issues, diff, tests, and source facts.
The soul file can shape tone, taste, and personality. It cannot override source
facts, schema requirements, or the evidence standard above. Do not be mean,
vague, theatrical, or invent criticism.
## Output
Return only JSON that matches the provided AgentSynthesis schema:
- `summary`: one concise sentence; do not merely repeat the PR title.
- `story`: 2-4 sentences that connect problem -> solution -> impact.
- `problem_addressed`: the concrete problem, bug, missing capability, or delivery need.
- `solution`: why this change solves the problem.
- `system_impact`: how the system, workflow, or architecture changed after the merge.
- `why_it_matters`: durable project-memory context for future humans and agents.
- `components_changed`: modules, workflows, commands, schemas, docs, or services touched.
- `complexity_introduced`: tradeoffs, new moving parts, operational costs, or edge cases.
- `refactors_or_removals`: cleanup, simplification, deleted paths, or "none found".
- `user_facing_changes`: visible behavior or product changes.
- `internal_changes`: implementation, infrastructure, or operational changes.
- `verification`: checks, tests, deploy evidence, or explicit unknowns.
- `follow_ups`: concrete remaining work only.
- `decision_candidates`: explicit product or architecture decisions only.
- `task_candidates`: concrete future tasks only.
Use empty arrays only when a list truly has no grounded entries. This is project
memory, not marketing copy and not a commit-by-commit changelog.
-75
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@@ -1,75 +0,0 @@
name: Basic Memory Project Updates
"on":
pull_request:
types: [closed]
workflow_run:
workflows: ["Deploy Production"]
types: [completed]
jobs:
project-update:
runs-on: ubuntu-latest
permissions:
contents: read
pull-requests: read
issues: read
actions: read
steps:
- name: Checkout repository
uses: actions/checkout@v6
- name: Set up Python
uses: actions/setup-python@v6
with:
python-version: "3.12"
- name: Install Basic Memory from checkout
run: |
python -m pip install --upgrade pip
pip install -e .
- name: Collect project update context
id: collect
env:
GITHUB_TOKEN: ${{ github.token }}
run: |
bm ci collect \
--config .github/basic-memory/config.yml \
--output .github/basic-memory/project-update-context.json
- name: Stop when event is not eligible
if: steps.collect.outputs.eligible != 'true'
run: |
echo "Auto BM skipped: ${{ steps.collect.outputs.skip_reason }}"
- name: Write Codex output schema
if: steps.collect.outputs.eligible == 'true'
run: |
bm ci agent-schema --output "${{ runner.temp }}/agent-synthesis.schema.json"
- name: Synthesize project update with Codex
if: steps.collect.outputs.eligible == 'true'
uses: openai/codex-action@v1
with:
openai-api-key: ${{ secrets.OPENAI_API_KEY }}
prompt-file: .github/basic-memory/memory-ci-capture.md
output-file: ${{ runner.temp }}/agent-synthesis.json
output-schema-file: ${{ runner.temp }}/agent-synthesis.schema.json
sandbox: read-only
safety-strategy: drop-sudo
- name: Publish project update
if: steps.collect.outputs.eligible == 'true'
env:
BASIC_MEMORY_CLOUD_API_KEY: ${{ secrets.BASIC_MEMORY_API_KEY }}
BASIC_MEMORY_CI_CLOUD_HOST: ${{ vars.BASIC_MEMORY_CLOUD_HOST }}
run: |
if [ -n "$BASIC_MEMORY_CI_CLOUD_HOST" ]; then
export BASIC_MEMORY_CLOUD_HOST="$BASIC_MEMORY_CI_CLOUD_HOST"
fi
bm ci publish \
--cloud \
--config .github/basic-memory/config.yml \
--context .github/basic-memory/project-update-context.json \
--synthesis "${{ runner.temp }}/agent-synthesis.json"
-85
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@@ -1,85 +0,0 @@
name: Claude Code Review
"on":
workflow_dispatch:
inputs:
pr_number:
description: Pull request number to review manually
required: true
env:
FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: "true"
jobs:
claude-review:
if: inputs.pr_number != ''
runs-on: ubuntu-latest
permissions:
contents: read
pull-requests: write
issues: read
id-token: write
steps:
- name: Checkout repository
uses: actions/checkout@v6
with:
fetch-depth: 1
- name: Run Claude Code Review
id: claude-review
uses: anthropics/claude-code-action@v1
with:
claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
github_token: ${{ secrets.GITHUB_TOKEN }}
track_progress: true # Enable visual progress tracking
allowed_bots: '*'
prompt: |
Review Basic Memory PR #${{ inputs.pr_number }} as an advisory manual review.
Use `gh pr view ${{ inputs.pr_number }}` and related `gh pr`/`gh api`
commands to inspect the pull request. Do not merge the PR and do not
treat this advisory review as the required merge gate. BM Bossbot owns
the required `BM Bossbot Approval` status.
Review the PR against our team checklist:
## Code Quality & Standards
- [ ] Follows Basic Memory's coding conventions in CLAUDE.md
- [ ] Python 3.12+ type annotations and async patterns
- [ ] SQLAlchemy 2.0 best practices
- [ ] FastAPI and Typer conventions followed
- [ ] 100-character line length limit maintained
- [ ] No commented-out code blocks
## Testing & Documentation
- [ ] 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
## Basic Memory Architecture
- [ ] MCP tools follow atomic, composable design
- [ ] Database changes include Alembic migrations
- [ ] Preserves local-first architecture principles
- [ ] Knowledge graph operations maintain consistency
- [ ] Markdown file handling preserves integrity
- [ ] AI-human collaboration patterns followed
## Security & Performance
- [ ] No hardcoded secrets or credentials
- [ ] Input validation for MCP tools
- [ ] Proper error handling and logging
- [ ] Performance considerations addressed
- [ ] No sensitive data in logs or commits
## Compatability
- [ ] File path comparisons must be windows compatible
- [ ] Avoid using emojis and unicode characters in console and log output
Read the CLAUDE.md file for detailed project context. For each checklist item, verify if it's satisfied and comment on any that need attention. Use inline comments for specific code issues and post a summary with checklist results.
# Allow broader tool access for thorough code review
claude_args: '--allowed-tools "Bash(gh pr:*),Bash(gh issue:*),Bash(gh api:*),Bash(git log:*),Bash(git show:*),Read,Grep,Glob"'
-74
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@@ -1,74 +0,0 @@
name: Claude Issue Triage
on:
issues:
types: [opened]
env:
FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: "true"
jobs:
triage:
runs-on: ubuntu-latest
permissions:
issues: write
id-token: write
steps:
- name: Checkout repository
uses: actions/checkout@v6
with:
fetch-depth: 1
- name: Run Claude Issue Triage
uses: anthropics/claude-code-action@v1
with:
claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
track_progress: true # Show triage progress
prompt: |
Analyze this new Basic Memory issue and perform triage:
**Issue Analysis:**
1. **Type Classification:**
- Bug report (code defect)
- Feature request (new functionality)
- Enhancement (improvement to existing feature)
- Documentation (docs improvement)
- Question/Support (user help)
- MCP tool issue (specific to MCP functionality)
2. **Priority Assessment:**
- Critical: Security issues, data loss, complete breakage
- High: Major functionality broken, affects many users
- Medium: Minor bugs, usability issues
- Low: Nice-to-have improvements, cosmetic issues
3. **Component Classification:**
- CLI commands
- MCP tools
- Database/sync
- Cloud functionality
- Documentation
- Testing
4. **Complexity Estimate:**
- Simple: Quick fix, documentation update
- Medium: Requires some investigation/testing
- Complex: Major feature work, architectural changes
**Actions to Take:**
1. Add appropriate labels using: `gh issue edit ${{ github.event.issue.number }} --add-label "label1,label2"`
2. Check for duplicates using: `gh search issues`
3. If duplicate found, comment mentioning the original issue
4. For feature requests, ask clarifying questions if needed
5. For bugs, request reproduction steps if missing
**Available Labels:**
- Type: bug, enhancement, feature, documentation, question, mcp-tool
- Priority: critical, high, medium, low
- Component: cli, mcp, database, cloud, docs, testing
- Complexity: simple, medium, complex
- Status: needs-reproduction, needs-clarification, duplicate
Read the issue carefully and provide helpful triage with appropriate labels.
claude_args: '--allowed-tools "Bash(gh issue:*),Bash(gh search:*),Read"'
+86 -42
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@@ -9,62 +9,106 @@ on:
types: [opened, assigned]
pull_request_review:
types: [submitted]
pull_request_target:
types: [opened, synchronize]
env:
FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: "true"
jobs:
claude:
if: |
(
(github.event_name == 'issue_comment' && contains(github.event.comment.body, '@claude')) ||
(github.event_name == 'pull_request_review_comment' && contains(github.event.comment.body, '@claude')) ||
(github.event_name == 'pull_request_review' && contains(github.event.review.body, '@claude')) ||
(github.event_name == 'issues' && (contains(github.event.issue.body, '@claude') || contains(github.event.issue.title, '@claude'))) ||
(github.event_name == 'pull_request_target' && contains(github.event.pull_request.body, '@claude'))
) && (
github.event.comment.author_association == 'OWNER' ||
github.event.comment.author_association == 'MEMBER' ||
github.event.comment.author_association == 'COLLABORATOR' ||
github.event.sender.author_association == 'OWNER' ||
github.event.sender.author_association == 'MEMBER' ||
github.event.sender.author_association == 'COLLABORATOR' ||
github.event.pull_request.author_association == 'OWNER' ||
github.event.pull_request.author_association == 'MEMBER' ||
github.event.pull_request.author_association == 'COLLABORATOR'
)
(github.event_name == 'issue_comment' && contains(github.event.comment.body, '@claude')) ||
(github.event_name == 'pull_request_review_comment' && contains(github.event.comment.body, '@claude')) ||
(github.event_name == 'pull_request_review' && contains(github.event.review.body, '@claude')) ||
(github.event_name == 'issues' && (contains(github.event.issue.body, '@claude') || contains(github.event.issue.title, '@claude')))
runs-on: ubuntu-latest
permissions:
contents: read
pull-requests: read
issues: read
id-token: write
actions: read # Required for Claude to read CI results on PRs
steps:
- name: Checkout repository
uses: actions/checkout@v6
- name: Check user permissions
id: check_membership
uses: actions/github-script@v7
with:
script: |
let actor;
if (context.eventName === 'issue_comment') {
actor = context.payload.comment.user.login;
} else if (context.eventName === 'pull_request_review_comment') {
actor = context.payload.comment.user.login;
} else if (context.eventName === 'pull_request_review') {
actor = context.payload.review.user.login;
} else if (context.eventName === 'issues') {
actor = context.payload.issue.user.login;
}
console.log(`Checking permissions for user: ${actor}`);
// List of explicitly allowed users (organization members)
const allowedUsers = [
'phernandez',
'groksrc',
'nellins',
'bm-claudeai'
];
if (allowedUsers.includes(actor)) {
console.log(`User ${actor} is in the allowed list`);
core.setOutput('is_member', true);
return;
}
// Fallback: Check if user has repository permissions
try {
const collaboration = await github.rest.repos.getCollaboratorPermissionLevel({
owner: context.repo.owner,
repo: context.repo.repo,
username: actor
});
const permission = collaboration.data.permission;
console.log(`User ${actor} has permission level: ${permission}`);
// Allow if user has push access or higher (write, maintain, admin)
const allowed = ['write', 'maintain', 'admin'].includes(permission);
core.setOutput('is_member', allowed);
if (!allowed) {
core.notice(`User ${actor} does not have sufficient repository permissions (has: ${permission})`);
}
} catch (error) {
console.log(`Error checking permissions: ${error.message}`);
// Final fallback: Check if user is a public member of the organization
try {
const membership = await github.rest.orgs.getMembershipForUser({
org: 'basicmachines-co',
username: actor
});
const allowed = membership.data.state === 'active';
core.setOutput('is_member', allowed);
if (!allowed) {
core.notice(`User ${actor} is not a public member of basicmachines-co organization`);
}
} catch (membershipError) {
console.log(`Error checking organization membership: ${membershipError.message}`);
core.setOutput('is_member', false);
core.notice(`User ${actor} does not have access to this repository`);
}
}
- name: Checkout repository
if: steps.check_membership.outputs.is_member == 'true'
uses: actions/checkout@v4
with:
# For pull_request_target, checkout the PR head to review the actual changes
ref: ${{ github.event_name == 'pull_request_target' && github.event.pull_request.head.sha || github.sha }}
fetch-depth: 1
- name: Run Claude Code
if: steps.check_membership.outputs.is_member == 'true'
id: claude
uses: anthropics/claude-code-action@v1
uses: anthropics/claude-code-action@beta
with:
claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
track_progress: true # Enable visual progress tracking
# This is an optional setting that allows Claude to read CI results on PRs
additional_permissions: |
actions: read
# Optional: Give a custom prompt to Claude. If this is not specified, Claude will perform the instructions specified in the comment that tagged it.
# prompt: 'Update the pull request description to include a summary of changes.'
# Optional: Add claude_args to customize behavior and configuration
# See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md
# or https://docs.claude.com/en/docs/claude-code/sdk#command-line for available options
# claude_args: '--model claude-opus-4-1-20250805 --allowed-tools Bash(gh pr:*)'
anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
allowed_tools: Bash(uv run pytest),Bash(uv run ruff check . --fix),Bash(uv run ruff format .),Bash(uv run pyright),Bash(just test),Bash(just lint),Bash(just format),Bash(just type-check),Bash(just check),Read,Write,Edit,MultiEdit,Glob,Grep,LS, mcp__web_search
-110
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@@ -1,110 +0,0 @@
name: Consolidated Packages
on:
pull_request:
paths:
- ".claude-plugin/**"
- "plugins/**"
- "skills/**"
- "integrations/**"
- "scripts/update_versions.py"
- "scripts/validate_*.py"
- "justfile"
- ".github/workflows/consolidated-packages.yml"
push:
branches:
- main
paths:
- ".claude-plugin/**"
- "plugins/**"
- "skills/**"
- "integrations/**"
- "scripts/update_versions.py"
- "scripts/validate_*.py"
- "justfile"
- ".github/workflows/consolidated-packages.yml"
permissions:
contents: read
jobs:
claude-code:
name: Claude Code marketplace
permissions:
contents: read
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: extractions/setup-just@v4
- name: Validate manifests, agent, and bundled skills
run: just --justfile plugins/claude-code/justfile --working-directory plugins/claude-code ci-check
- name: Verify shared version dry run
run: just release-dry-run v0.99.0
skills:
name: Shared skills
permissions:
contents: read
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: extractions/setup-just@v4
- name: Validate SKILL.md source
run: just --justfile skills/justfile --working-directory skills check
hermes:
name: Hermes unit tests
permissions:
contents: read
runs-on: ubuntu-latest
defaults:
run:
working-directory: integrations/hermes
steps:
- uses: actions/checkout@v4
- uses: extractions/setup-just@v4
- name: Install uv
uses: astral-sh/setup-uv@v3
- name: Set up Python
run: uv python install 3.12
- name: Validate manifest and run unit tests
run: just check
openclaw:
name: OpenClaw package
permissions:
contents: read
runs-on: ubuntu-latest
defaults:
run:
working-directory: integrations/openclaw
steps:
- uses: actions/checkout@v4
- uses: extractions/setup-just@v4
- name: Setup Bun
uses: oven-sh/setup-bun@v2
with:
bun-version: "1.3.8"
- name: Setup Node
uses: actions/setup-node@v4
with:
node-version: "24"
registry-url: "https://registry.npmjs.org"
- name: Install dependencies
run: bun install --frozen-lockfile
- name: Release readiness
run: just release-check
+28
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@@ -0,0 +1,28 @@
name: Daily Traction Report
on:
schedule:
- cron: '0 14 * * 1-5'
workflow_dispatch:
jobs:
generate-report:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.9'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r scripts/requirements.txt
- name: Generate Daily Traction Report
run: python scripts/daily_report.py
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
DISCORD_WEBHOOK: ${{ secrets.DISCORD_WEBHOOK }}
REDDIT_CLIENT_ID: ${{ secrets.REDDIT_CLIENT_ID }}
REDDIT_SECRET: ${{ secrets.REDDIT_SECRET }}
YOUTUBE_API_KEY: ${{ secrets.YOUTUBE_API_KEY }}
+3 -6
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@@ -5,9 +5,6 @@ on:
branches: [main]
workflow_dispatch: # Allow manual triggering
env:
FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: "true"
jobs:
dev-release:
runs-on: ubuntu-latest
@@ -16,12 +13,12 @@ jobs:
contents: write
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Set up Python
uses: actions/setup-python@v6
uses: actions/setup-python@v5
with:
python-version: "3.12"
@@ -53,4 +50,4 @@ jobs:
uses: pypa/gh-action-pypi-publish@release/v1
with:
password: ${{ secrets.PYPI_TOKEN }}
skip-existing: true # Don't fail if version already exists
skip-existing: true # Don't fail if version already exists
+12 -10
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@@ -9,27 +9,27 @@ on:
env:
REGISTRY: ghcr.io
IMAGE_NAME: basicmachines-co/basic-memory
FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: "true"
jobs:
docker:
runs-on: depot-ubuntu-24.04
runs-on: ubuntu-latest
permissions:
contents: read
id-token: write
packages: write
steps:
- name: Checkout repository
uses: actions/checkout@v6
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Set up Depot
uses: depot/setup-action@v1
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
with:
platforms: linux/amd64,linux/arm64
- name: Log in to GitHub Container Registry
uses: docker/login-action@v4
uses: docker/login-action@v3
with:
registry: ${{ env.REGISTRY }}
username: ${{ github.actor }}
@@ -37,7 +37,7 @@ jobs:
- name: Extract metadata
id: meta
uses: docker/metadata-action@v6
uses: docker/metadata-action@v5
with:
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
tags: |
@@ -48,12 +48,14 @@ jobs:
type=raw,value=latest,enable={{is_default_branch}}
- name: Build and push Docker image
uses: depot/build-push-action@v1
uses: docker/build-push-action@v5
with:
project: ${{ vars.DEPOT_BASIC_MEMORY_PROJECT_ID || vars.DEPOT_PROJECT_ID }}
context: .
file: ./Dockerfile
platforms: linux/amd64,linux/arm64
push: true
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
cache-from: type=gha
cache-to: type=gha,mode=max
+2 -9
View File
@@ -7,14 +7,11 @@ on:
- edited
- synchronize
env:
FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: "true"
jobs:
main:
runs-on: ubuntu-latest
steps:
- uses: amannn/action-semantic-pull-request@v6
- uses: amannn/action-semantic-pull-request@v5
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
with:
@@ -38,11 +35,7 @@ jobs:
mcp
sync
ui
ci
deps
installer
plugins
skills
integrations
# Allow breaking changes (needs "!" after type/scope)
requireScopeForBreakingChange: true
requireScopeForBreakingChange: true
+22 -102
View File
@@ -5,9 +5,6 @@ on:
tags:
- 'v*' # Trigger on version tags like v1.0.0, v0.13.0, etc.
env:
FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: "true"
jobs:
release:
runs-on: ubuntu-latest
@@ -16,12 +13,12 @@ jobs:
contents: write
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Set up Python
uses: actions/setup-python@v6
uses: actions/setup-python@v5
with:
python-version: "3.12"
@@ -42,7 +39,7 @@ jobs:
echo "Build completed successfully"
- name: Create GitHub Release
uses: softprops/action-gh-release@v3
uses: softprops/action-gh-release@v2
with:
files: |
dist/*.whl
@@ -56,46 +53,6 @@ jobs:
with:
password: ${{ secrets.PYPI_TOKEN }}
openclaw:
name: Publish OpenClaw npm Package
needs: release
# npm publishes only for stable product tags. Pre-release Python tags use
# versions like 0.21.3b1, which are not valid npm pre-release semver.
if: ${{ !contains(github.ref_name, 'dev') && !contains(github.ref_name, 'b') && !contains(github.ref_name, 'rc') }}
runs-on: ubuntu-latest
permissions:
contents: read
id-token: write
defaults:
run:
working-directory: integrations/openclaw
steps:
- uses: actions/checkout@v6
- uses: extractions/setup-just@v4
- name: Setup Bun
uses: oven-sh/setup-bun@v2
with:
bun-version: "1.3.8"
- name: Setup Node
uses: actions/setup-node@v4
with:
node-version: "24"
registry-url: "https://registry.npmjs.org"
- name: Install dependencies
run: bun install --frozen-lockfile
- name: Release readiness
run: just release-check
- name: Publish to npm
run: npm publish --access public --provenance
env:
NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
homebrew:
name: Update Homebrew Formula
needs: release
@@ -103,63 +60,26 @@ jobs:
# Only run for stable releases (not dev, beta, or rc versions)
if: ${{ !contains(github.ref_name, 'dev') && !contains(github.ref_name, 'b') && !contains(github.ref_name, 'rc') }}
permissions:
contents: read
contents: write
actions: read
steps:
# Inline bump replaces mislav/bump-homebrew-formula-action@v4.x.
# The action does a HEAD request to api.github.com /repos/.../tarball/<ref>
# with the bearer token and expects a 302 redirect. GitHub now returns
# 303 on that endpoint when authenticated, which the action treats as a
# fatal error. Re-implementing the bump as plain git+sed keeps the same
# contract (update url + sha256, commit, push) with no third-party action.
- name: Update Homebrew formula
uses: mislav/bump-homebrew-formula-action@v3
with:
# Formula name in homebrew-basic-memory repo
formula-name: basic-memory
# The tap repository
homebrew-tap: basicmachines-co/homebrew-basic-memory
# Base branch of the tap repository
base-branch: main
# Download URL will be automatically constructed from the tag
download-url: https://github.com/basicmachines-co/basic-memory/archive/refs/tags/${{ github.ref_name }}.tar.gz
# Commit message for the formula update
commit-message: |
{{formulaName}} {{version}}
Created by https://github.com/basicmachines-co/basic-memory/actions/runs/${{ github.run_id }}
env:
HOMEBREW_TOKEN: ${{ secrets.HOMEBREW_TOKEN }}
REF: ${{ github.ref_name }}
REPO: ${{ github.repository }}
RUN_URL: https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}
run: |
set -euo pipefail
# Personal Access Token with repo scope for homebrew-basic-memory repo
COMMITTER_TOKEN: ${{ secrets.HOMEBREW_TOKEN }}
VERSION="${REF#v}"
ARCHIVE_URL="https://github.com/${REPO}/archive/refs/tags/${REF}.tar.gz"
echo "::group::Compute tarball sha256"
SHA256="$(curl --fail --silent --location "$ARCHIVE_URL" | sha256sum | awk '{print $1}')"
test -n "$SHA256"
echo "sha256: $SHA256"
echo "::endgroup::"
echo "::group::Clone tap"
git clone \
--depth 1 \
"https://x-access-token:${HOMEBREW_TOKEN}@github.com/basicmachines-co/homebrew-basic-memory.git" \
tap
cd tap
git config user.name "github-actions[bot]"
git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
echo "::endgroup::"
echo "::group::Patch Formula/basic-memory.rb"
# Pipe-delimited sed because the URL contains slashes. The Formula
# only has one `url` and one `sha256` directive, so a first-match
# replacement is unambiguous. POSIX character classes ([[:space:]])
# keep this portable across BSD and GNU sed.
sed -i -E \
-e "s|^([[:space:]]*url[[:space:]]+)\"[^\"]+\"|\1\"${ARCHIVE_URL}\"|" \
-e "s|^([[:space:]]*sha256[[:space:]]+)\"[^\"]+\"|\1\"${SHA256}\"|" \
Formula/basic-memory.rb
git --no-pager diff Formula/basic-memory.rb
echo "::endgroup::"
if git diff --quiet Formula/basic-memory.rb; then
echo "Formula already at ${REF}; nothing to do."
exit 0
fi
echo "::group::Commit & push"
git add Formula/basic-memory.rb
git commit -m "basic-memory ${VERSION}
Created by ${RUN_URL}"
git push origin HEAD:main
echo "::endgroup::"
+26 -376
View File
@@ -1,75 +1,43 @@
name: Tests
concurrency:
group: bm-ci-${{ github.workflow }}-${{ github.repository }}-${{ github.head_ref || github.ref }}
cancel-in-progress: true
on:
# Trigger: PR branch pushes already publish commit statuses that show up on the PR.
# Why: running the full matrix on both push and pull_request doubles CI time for the
# exact same branch head commit.
# Outcome: each branch push runs the test suite once, including PR updates.
push:
env:
FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: "true"
# Branch builds (PRs arrive as push events — this workflow has no
# pull_request trigger) select only impacted tests from the cached testmon
# baseline (branch cache falling back to main's full-run recording). Pushes
# to main run the full suite with --testmon-noselect to refresh the baseline.
BASIC_MEMORY_TESTMON_FLAGS: ${{ github.ref_name == 'main' && '--testmon-noselect' || '--testmon --testmon-forceselect' }}
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:
changes:
# Docs/workflow-only changes skip the entire test matrix while the workflow
# still concludes successfully, so the BM Bossbot gate (workflow_run on
# Tests success) keeps firing and the PR stays mergeable.
name: Detect code changes
runs-on: ubuntu-latest
outputs:
code: ${{ steps.filter.outputs.code }}
steps:
- uses: actions/checkout@v6
- id: filter
uses: dorny/paths-filter@v3
with:
# Tests only runs on push events; for branch pushes compare against
# main (merge-base), for main pushes dorny diffs the push range.
base: main
filters: |
code:
- 'src/**'
- 'tests/**'
- 'test-int/**'
- 'alembic/**'
- 'pyproject.toml'
- 'uv.lock'
- 'justfile'
- '.github/workflows/test.yml'
static-checks:
needs: changes
if: needs.changes.outputs.code == 'true'
name: Static Checks (Python 3.12)
timeout-minutes: 20
test:
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
python-version: [ "3.12" ]
steps:
- uses: actions/checkout@v6
- uses: actions/checkout@v4
with:
submodules: true
- name: Set up Python 3.12
uses: actions/setup-python@v6
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: "3.12"
cache: "pip"
python-version: ${{ matrix.python-version }}
cache: 'pip'
- name: Install uv
run: |
pip install uv
- uses: extractions/setup-just@v4
- name: Install just
run: |
curl --proto '=https' --tlsv1.2 -sSf https://just.systems/install.sh | bash -s -- --to /usr/local/bin
- name: Create virtual env
run: |
@@ -77,331 +45,13 @@ jobs:
- name: Install dependencies
run: |
uv pip install -e ".[dev]"
uv pip install -e .[dev]
- name: Run type checks
run: |
just typecheck
- name: Run linting
run: |
just lint
test-sqlite-unit:
needs: changes
if: needs.changes.outputs.code == 'true'
name: Test SQLite Unit (${{ matrix.os }}, Python ${{ matrix.python-version }})
timeout-minutes: 45
strategy:
fail-fast: false
matrix:
include:
- os: ubuntu-latest
python-version: "3.12"
- os: ubuntu-latest
python-version: "3.13"
# Python 3.14 unit tests are the longest full-suite slice; keep this
# one on GitHub-hosted runners after Depot terminated it mid-suite.
- os: ubuntu-latest
python-version: "3.14"
- os: windows-latest
python-version: "3.12"
runs-on: ${{ matrix.os }}
steps:
- uses: actions/checkout@v6
with:
submodules: true
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v6
with:
python-version: ${{ matrix.python-version }}
cache: 'pip'
- name: Install uv
run: |
pip install uv
- uses: extractions/setup-just@v4
- name: Cache pytest-testmon results
uses: actions/cache@v4
with:
path: |
.testmondata
.testmondata-shm
.testmondata-wal
key: ${{ runner.os }}-testmon-sqlite-unit-py${{ matrix.python-version }}-${{ github.ref_name }}-${{ github.run_id }}
restore-keys: |
${{ runner.os }}-testmon-sqlite-unit-py${{ matrix.python-version }}-${{ github.ref_name }}-
${{ runner.os }}-testmon-sqlite-unit-py${{ matrix.python-version }}-main-
${{ runner.os }}-testmon-sqlite-unit-py${{ matrix.python-version }}-
- name: Create virtual env
run: |
uv venv
- name: Install dependencies
run: |
uv pip install -e ".[dev]"
just type-check
- name: Run tests
run: |
just test-unit-sqlite
test-sqlite-integration:
needs: changes
if: needs.changes.outputs.code == 'true'
name: Test SQLite Integration (${{ matrix.os }}, Python ${{ matrix.python-version }})
timeout-minutes: 45
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@v6
with:
submodules: true
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v6
with:
python-version: ${{ matrix.python-version }}
cache: 'pip'
- name: Install uv
run: |
pip install uv
- uses: extractions/setup-just@v4
- name: Cache pytest-testmon results
uses: actions/cache@v4
with:
path: |
.testmondata
.testmondata-shm
.testmondata-wal
key: ${{ runner.os }}-testmon-sqlite-integration-py${{ matrix.python-version }}-${{ github.ref_name }}-${{ github.run_id }}
restore-keys: |
${{ runner.os }}-testmon-sqlite-integration-py${{ matrix.python-version }}-${{ github.ref_name }}-
${{ runner.os }}-testmon-sqlite-integration-py${{ matrix.python-version }}-main-
${{ runner.os }}-testmon-sqlite-integration-py${{ matrix.python-version }}-
- 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:
needs: changes
if: needs.changes.outputs.code == 'true'
name: Test Postgres Unit (Python ${{ matrix.python-version }}, shard ${{ matrix.group }}/3)
timeout-minutes: 60
strategy:
fail-fast: false
matrix:
# Shard the largest suite across parallel jobs: each shard is a full job
# with its own Postgres service running 1/3 of the collection.
# Postgres runs on the latest Python only — the SQLite matrix carries
# Python-version coverage; Postgres carries backend coverage.
group: [1, 2, 3]
python-version: ["3.14"]
runs-on: ubuntu-latest
services:
postgres:
image: pgvector/pgvector:pg16
env:
POSTGRES_USER: basic_memory_user
POSTGRES_PASSWORD: dev_password
POSTGRES_DB: basic_memory_test
ports:
- 5432:5432
options: >-
--health-cmd "pg_isready -U basic_memory_user -d basic_memory_test"
--health-interval 10s
--health-timeout 5s
--health-retries 5
env:
BASIC_MEMORY_TEST_POSTGRES_URL: postgresql://basic_memory_user:dev_password@127.0.0.1:5432/basic_memory_test
steps:
- uses: actions/checkout@v6
with:
submodules: true
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v6
with:
python-version: ${{ matrix.python-version }}
cache: 'pip'
- name: Install uv
run: |
pip install uv
- uses: extractions/setup-just@v4
- name: Cache pytest-testmon results
uses: actions/cache@v4
with:
path: |
.testmondata
.testmondata-shm
.testmondata-wal
key: ${{ runner.os }}-testmon-postgres-unit-py${{ matrix.python-version }}-g${{ matrix.group }}-${{ github.ref_name }}-${{ github.run_id }}
restore-keys: |
${{ runner.os }}-testmon-postgres-unit-py${{ matrix.python-version }}-g${{ matrix.group }}-${{ github.ref_name }}-
${{ runner.os }}-testmon-postgres-unit-py${{ matrix.python-version }}-g${{ matrix.group }}-main-
${{ runner.os }}-testmon-postgres-unit-py${{ matrix.python-version }}-main-
${{ runner.os }}-testmon-postgres-unit-py${{ matrix.python-version }}-
- name: Create virtual env
run: |
uv venv
- name: Install dependencies
run: |
uv pip install -e ".[dev]"
- name: Run tests
run: |
BASIC_MEMORY_PYTEST_SPLIT_FLAGS="--splits 3 --group ${{ matrix.group }}" just test-unit-postgres
test-postgres-integration:
needs: changes
if: needs.changes.outputs.code == 'true'
name: Test Postgres Integration (Python ${{ matrix.python-version }})
timeout-minutes: 45
strategy:
fail-fast: false
matrix:
# Latest Python only: SQLite carries version coverage, Postgres carries
# backend coverage.
python-version: ["3.14"]
runs-on: ubuntu-latest
services:
postgres:
image: pgvector/pgvector:pg16
env:
POSTGRES_USER: basic_memory_user
POSTGRES_PASSWORD: dev_password
POSTGRES_DB: basic_memory_test
ports:
- 5432:5432
options: >-
--health-cmd "pg_isready -U basic_memory_user -d basic_memory_test"
--health-interval 10s
--health-timeout 5s
--health-retries 5
env:
BASIC_MEMORY_TEST_POSTGRES_URL: postgresql://basic_memory_user:dev_password@127.0.0.1:5432/basic_memory_test
steps:
- uses: actions/checkout@v6
with:
submodules: true
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v6
with:
python-version: ${{ matrix.python-version }}
cache: 'pip'
- name: Install uv
run: |
pip install uv
- uses: extractions/setup-just@v4
- name: Cache pytest-testmon results
uses: actions/cache@v4
with:
path: |
.testmondata
.testmondata-shm
.testmondata-wal
key: ${{ runner.os }}-testmon-postgres-integration-py${{ matrix.python-version }}-${{ github.ref_name }}-${{ github.run_id }}
restore-keys: |
${{ runner.os }}-testmon-postgres-integration-py${{ matrix.python-version }}-${{ github.ref_name }}-
${{ runner.os }}-testmon-postgres-integration-py${{ matrix.python-version }}-main-
${{ runner.os }}-testmon-postgres-integration-py${{ matrix.python-version }}-
- 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:
needs: changes
if: needs.changes.outputs.code == 'true'
name: Test Semantic (Python 3.12)
timeout-minutes: 45
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
with:
submodules: true
- name: Set up Python 3.12
uses: actions/setup-python@v6
with:
python-version: "3.12"
cache: "pip"
- name: Install uv
run: |
pip install uv
- uses: extractions/setup-just@v4
- name: Cache pytest-testmon results
uses: actions/cache@v4
with:
path: |
.testmondata
.testmondata-shm
.testmondata-wal
key: ${{ runner.os }}-testmon-semantic-py3.12-${{ github.ref_name }}-${{ github.run_id }}
restore-keys: |
${{ runner.os }}-testmon-semantic-py3.12-${{ github.ref_name }}-
${{ runner.os }}-testmon-semantic-py3.12-main-
${{ runner.os }}-testmon-semantic-py3.12-
- name: Create virtual env
run: |
uv venv
- name: Install dependencies
run: |
uv pip install -e ".[dev]"
- name: Run tests
run: |
just test-semantic
uv pip install pytest pytest-cov
just test
+2 -14
View File
@@ -1,7 +1,6 @@
*.py[cod]
__pycache__/
.pytest_cache/
.testmondata*
.coverage
htmlcov/
@@ -49,19 +48,8 @@ ENV/
/docs/.obsidian/
/examples/.obsidian/
/examples/.basic-memory/
/docs/assets
# claude action
claude-output
**/.claude/settings.local.json
.mcp.json
!/plugins/codex/.mcp.json
.mcpregistry_*
/.testmondata
.benchmarks/
# Consolidated package build artifacts
/integrations/openclaw/node_modules/
/integrations/openclaw/dist/
/integrations/openclaw/skills/
/integrations/openclaw/*.tgz
**/.claude/settings.local.json
+1 -1
View File
@@ -1 +1 @@
3.14
3.12
-547
View File
@@ -1,547 +0,0 @@
# 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; only tests affected by changed code)
- Run MCP smoke test: `just test-smoke`
- Fast local loop: `just fast-check` (default iteration flow)
- Local consistency check: `just doctor`
- Run all consolidated agent package checks: `just package-check`
- Run Claude Code plugin checks: `just package-check-claude-code`
- Run shared skills checks: `just package-check-skills`
- Run Hermes plugin checks: `just package-check-hermes`
- Run OpenClaw plugin checks: `just package-check-openclaw`
- Run host-native agent harness checks: `just agent-harness-check`
- 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 ty check src tests test-int`
- Type check (pyright): `just typecheck-pyright` 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)
**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 + pytest-testmon impacted tests for changed code).
3) **Verify:** `just doctor` (end-to-end file ↔ DB loop in a temp project).
4) **Package verify:** `just package-check` when changes touch `plugins/`, `skills/`, `integrations/`, package metadata, or release wiring.
5) **Full gate (when needed):** `just test` or `just check` for SQLite + Postgres.
Run `just test-smoke` when you specifically need the MCP smoke flow.
If testmon is “cold,” the first run may be long. Subsequent runs get much faster.
### Consolidated Agent Package Checks
The monorepo ships several host-native packages alongside the Python core. Use the root justfile as the canonical entry point:
- `just package-check` — validates every copied package and generated bundle path.
- `just package-check-claude-code` — validates the root and plugin-local Claude marketplace manifests, the SessionStart/PreCompact hooks, the bundled output style, and the seed schemas, then runs `claude plugin validate . --strict`.
- `just package-check-skills` — validates every top-level `skills/memory-*/SKILL.md` frontmatter block.
- `just package-check-hermes` — validates `integrations/hermes/plugin.yaml`, the Hermes provider entrypoint, bundled skill, and runs the hermetic unit suite.
- `just package-check-openclaw` — runs the OpenClaw package install, copies top-level skills into the generated bundle, typechecks, lints, builds `dist/`, runs Bun tests, and performs `npm pack --dry-run`.
- `just agent-harness-check` — checks the host-specific harnesses without the shared markdown-only skills target.
Package-local justfiles live in `plugins/claude-code/`, `skills/`, `integrations/hermes/`, and `integrations/openclaw/`. Prefer the root targets for PR verification so command names stay stable as package internals evolve.
### PR CI Gate
Before opening or updating a PR, run the checks that mirror the common required CI failures:
- Run `just typecheck` in addition to targeted `ruff` and `pytest` commands when tests were added or changed.
- Sign commits with `git commit -s` so DCO passes. If a PR branch already has unsigned commits, rewrite the branch with signed-off commits before asking for review.
- Use a semantic PR title accepted by `.github/workflows/pr-title.yml`: `type(scope): summary`.
- Use one of the allowed scopes: `core`, `cli`, `api`, `mcp`, `sync`, `ui`, `ci`, `deps`, `installer`, `plugins`, `skills`, `integrations`.
### 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)
### Programming Style
See [docs/ENGINEERING_STYLE.md](docs/ENGINEERING_STYLE.md) for the fuller house style. The
short version for agents:
- Prefer type-safe, explicit designs over object-heavy indirection. Use Python 3.12 `type`
aliases, full annotations, and narrow `Protocol`s when a caller only needs a capability.
- Use dataclasses for internal value objects and operation results; use Pydantic v2 at API,
CLI, MCP, and persistence boundaries where validation and serialization matter.
- Keep async boundaries obvious. Resource-owning code should use context managers, propagate
cancellation, and avoid hidden background work unless the lifecycle is explicit.
- Fail fast. Do not add silent fallback logic, broad exception swallowing, speculative
`getattr`, or casts that hide an unclear model shape.
- Keep control flow simple and local. Push branching decisions up, keep leaf helpers focused,
and name values after the domain concept they carry.
- Use evidence-first testing. Add or update meaningful regression tests for bugs and risky
behavior, prefer real code paths over mocks, and run the narrowest command that proves the
change before widening verification.
- Comments should explain why a branch, invariant, or constraint exists. Avoid comments that
merely narrate obvious code.
### 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
- **House style is canonical**: Follow the Programming Style section above for type-safe,
fail-fast code; do not hide unclear models with speculative attributes, broad exception
handling, casts, or unapproved fallback logic
- **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
- `/plugins/claude-code` - Claude Code plugin marketplace package, hooks, skills, and agent harness
- `/skills` - Canonical framework-agnostic Basic Memory `SKILL.md` source
- `/integrations/hermes` - Hermes memory-provider plugin
- `/integrations/openclaw` - OpenClaw npm/TypeScript plugin
**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.
### Release Process
Releases are driven by `just release` / `just beta` — never by a bare `git tag`. The recipes bump version metadata, run pre-flight checks, land the bump on `main` through a release PR, tag, and push the tag. GitHub Actions then publishes to PyPI and updates the Homebrew formula.
**Main requires PRs.** The `main` ruleset rejects direct pushes ("Changes must be made through a pull request") and the repo disallows merge commits, so the recipes push a `release/vX.Y.Z` branch, open a PR titled `chore(core): release vX.Y.Z`, rebase-merge it with `gh pr merge --rebase`, then tag the rebased bump commit on `main` (located by its commit subject, since rebasing rewrites the SHA) and push the tag. The CHANGELOG entry for the version must already be on `main` — land it via a normal PR before running the recipe (it pre-flight-checks for a `## vX.Y.Z` heading).
**Stable release:**
```
just release v0.21.3
```
The recipe runs `just lint` + `just typecheck`, then updates every release manifest through `scripts/update_versions.py`: `src/basic_memory/__init__.py`, `server.json`, the root Claude marketplace, the Claude Code plugin manifest and local marketplace, the Hermes `plugin.yaml`, and the OpenClaw `package.json`. It commits as `chore: update version to X.Y.Z for vX.Y.Z release` on a `release/vX.Y.Z` branch, lands it on `main` via a rebase-merged PR, then tags the rebased commit and pushes the tag. After the tag lands, the `Release` workflow builds the Python package, publishes to PyPI, creates the GitHub release with auto-generated notes, publishes the OpenClaw npm package, and updates the Homebrew formula. The recipe finishes by printing the post-release tasks the workflow doesn't cover.
**Beta release:** `just beta v0.21.3b1` — same flow with a beta-suffixed tag. PyPI consumers install with `pip install basic-memory --pre`.
**Release dry run:** `just release-dry-run v0.21.4` previews the consolidated version update without writing files.
**Development builds:** every commit to `main` publishes a `0.21.3.dev26+468a22f`-style version to PyPI automatically via `.github/workflows/dev-release.yml`. No human action.
**Do not tag releases by hand.** A bare `git tag vX.Y.Z` skips the in-code version bump. Package metadata is still correct (uv-dynamic-versioning derives it from the git tag) but `basic-memory --version` reports the previous release, which is what happened with v0.21.2 → v0.21.3.
**Post-release tasks** the recipe surfaces but doesn't run:
- `docs.basicmemory.com` — add a What's New page under `content/2.whats-new/` and bump the version badge in `content/index.md` (the changelog page auto-fetches GitHub releases; see that repo's CLAUDE.md version-bump checklist)
- `basicmemory.com` — the marketing site (Astro + React, repo
`basicmachines-co/basicmemory.com`, formerly `basicmachines.co`) carries **no
hardcoded version number** in its UI, so there is nothing to bump. For a
significant release, optionally add a dated announcement post under
`src/content/blog/` (model it on an existing `basic-memory-vX-Y-Z-release.md`).
Skip entirely for routine patch releases.
- MCP Registry — `mcp-publisher publish` from the repo root
See `.claude/commands/release/release.md` (and `beta.md`, `release-check.md`, `changelog.md` alongside it) for the full release + post-release runbook, including the slash commands.
## 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>`
**Cloud Sync Commands (Personal and Team workspaces):**
- Fetch cloud changes (cloud -> local): `basic-memory cloud pull --name "name"` (Team-safe; additive, never deletes local)
- Upload local changes (local -> cloud): `basic-memory cloud push --name "name"` (Team-safe; additive, never deletes cloud)
- Resolve conflicts on push/pull: `--on-conflict [fail|keep-local|keep-cloud|keep-both]` (default `fail` lists conflicts and aborts, git-style)
- One-way mirror (local -> cloud): `basic-memory cloud sync --name "name"` (Personal workspaces only; deletes cloud files missing locally)
- Two-way mirror (local <-> cloud): `basic-memory cloud bisync --name "name"` (Personal workspaces only)
### 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`
6. **Pull Request Titles**: PR titles must follow the semantic format enforced by `.github/workflows/pr-title.yml`: `type(scope): summary`
- Allowed types: `feat`, `fix`, `chore`, `docs`, `style`, `refactor`, `perf`, `test`, `build`, `ci`
- Allowed scopes: `core`, `cli`, `api`, `mcp`, `sync`, `ui`, `ci`, `deps`, `installer`, `plugins`, `skills`, `integrations`
- Example: `fix(cli): propagate cloud workspace routing`
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.
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# Contributor License Agreement
Developer Certificate of Origin
Version 1.1
https://developercertificate.org/
## Copyright Assignment and License Grant
Copyright (C) 2004, 2006 The Linux Foundation and its contributors.
By signing this Contributor License Agreement ("Agreement"), you accept and agree to the following terms and conditions
for your present and future Contributions submitted
to Basic Machines LLC. Except for the license granted herein to Basic Machines LLC and recipients of software
distributed by Basic Machines LLC, you reserve all right,
title, and interest in and to your Contributions.
Everyone is permitted to copy and distribute verbatim copies of this
license document, but changing it is not allowed.
### 1. Definitions
Developer's Certificate of Origin 1.1
"You" (or "Your") shall mean the copyright owner or legal entity authorized by the copyright owner that is making this
Agreement with Basic Machines LLC.
By making a contribution to this project, I certify that:
"Contribution" shall mean any original work of authorship, including any modifications or additions to an existing work,
that is intentionally submitted by You to Basic
Machines LLC for inclusion in, or documentation of, any of the products owned or managed by Basic Machines LLC (the "
Work").
(a) The contribution was created in whole or in part by me and I
have the right to submit it under the open source license
indicated in the file; or
### 2. Grant of Copyright License
(b) The contribution is based upon previous work that, to the best
of my knowledge, is covered under an appropriate open source
license and I have the right under that license to submit that
work with modifications, whether created in whole or in part
by me, under the same open source license (unless I am
permitted to submit under a different license), as indicated
in the file; or
Subject to the terms and conditions of this Agreement, You hereby grant to Basic Machines LLC and to recipients of
software distributed by Basic Machines LLC a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the
Work, and to permit persons to whom the Work is furnished to do so.
(c) The contribution was provided directly to me by some other
person who certified (a), (b) or (c) and I have not modified
it.
### 3. Assignment of Copyright
You hereby assign to Basic Machines LLC all right, title, and interest worldwide in all Copyright covering your
Contributions. Basic Machines LLC may license the
Contributions under any license terms, including copyleft, permissive, commercial, or proprietary licenses.
### 4. Grant of Patent License
Subject to the terms and conditions of this Agreement, You hereby grant to Basic Machines LLC and to recipients of
software distributed by Basic Machines LLC a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to
make, have made, use, offer to sell, sell, import, and
otherwise transfer the Work.
### 5. Developer Certificate of Origin
By making a Contribution to this project, You certify that:
(a) The Contribution was created in whole or in part by You and You have the right to submit it under this Agreement; or
(b) The Contribution is based upon previous work that, to the best of Your knowledge, is covered under an appropriate
open source license and You have the right under that
license to submit that work with modifications, whether created in whole or in part by You, under this Agreement; or
(c) The Contribution was provided directly to You by some other person who certified (a), (b) or (c) and You have not
modified it.
(d) You understand and agree that this project and the Contribution are public and that a record of the Contribution (
including all personal information You submit with
it, including Your sign-off) is maintained indefinitely and may be redistributed consistent with this project or the
open source license(s) involved.
### 6. Representations
You represent that you are legally entitled to grant the above license and assignment. If your employer(s) has rights to
intellectual property that you create that
includes your Contributions, you represent that you have received permission to make Contributions on behalf of that
employer, or that your employer has waived such rights
for your Contributions to Basic Machines LLC.
---
This Agreement is effective as of the date you first submit a Contribution to Basic Machines LLC.
(d) I understand and agree that this project and the contribution
are public and that a record of the contribution (including all
personal information I submit with it, including my sign-off) is
maintained indefinitely and may be redistributed consistent with
this project or the open source license(s) involved.
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AGENTS.md
+257
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# 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 tests: `uv run pytest -p pytest_mock -v` or `just test`
- Single test: `pytest tests/path/to/test_file.py::test_function_name`
- Lint: `just lint` or `ruff check . --fix`
- Type check: `just type-check` or `uv run pyright`
- Format: `just format` or `uv run ruff format .`
- Run all code checks: `just check` (runs lint, format, type-check, test)
- Create db migration: `just migration "Your migration message"`
- Run development MCP Inspector: `just run-inspector`
### Code Style Guidelines
- Line length: 100 characters max
- Python 3.12+ with full type annotations
- 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)
- avoid using "private" functions in modules or classes (prepended with _)
### 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)
- Test database uses in-memory SQLite
- Avoid creating mocks in tests in most circumstances.
- Each test runs in a standalone environment with in memory SQLite and tmp_file directory
- Do not use mocks in tests if possible. Tests run with an in memory sqlite db, so they are not needed. See fixtures in conftest.py
## 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
- 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"`
### 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
- `edit_note(identifier, operation, content)` - Edit notes incrementally (append, prepend, find/replace, section replace)
- `move_note(identifier, destination_path)` - Move notes with database consistency and search reindexing
- `view_note(identifier)` - Display notes as formatted artifacts for better readability in Claude Desktop
- `read_content(path)` - Read raw file content (text, images, binaries) without knowledge graph processing
- `delete_note(identifier)` - Delete notes from knowledge base
**Project Management:**
- `list_memory_projects()` - List all available projects with status indicators
- `switch_project(project_name)` - Switch to different project context during conversations
- `get_current_project()` - Show currently active project with statistics
- `create_memory_project(name, path, set_default)` - Create new Basic Memory projects
- `delete_project(name)` - Delete projects from configuration and database
- `set_default_project(name)` - Set default project in config
- `sync_status()` - Check file synchronization status and background operations
**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)` - List directory contents with filtering and depth control
**Search & Discovery:**
- `search_notes(query, page, page_size)` - Full-text search across all content with filtering options
**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_notes(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
## 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 uses Claude directly into the development workflow through GitHub:
### GitHub MCP Tools
Using the GitHub Model Context Protocol server, Claude can:
- **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
With this integration, the AI assistant is a full-fledged team member rather than just a tool for generating code
snippets.
### Basic Memory Pro
Basic Memory Pro is a desktop GUI application that wraps the basic-memory CLI/MCP tools:
- Built with Tauri (Rust), React (TypeScript), and a Python FastAPI sidecar
- Provides visual knowledge graph exploration and project management
- Uses the same core codebase but adds a desktop-friendly interface
- Project configuration is shared between CLI and Pro versions
- Multiple project support with visual switching interface
local repo: /Users/phernandez/dev/basicmachines/basic-memory-pro
github: https://github.com/basicmachines-co/basic-memory-pro
## Release and Version Management
Basic Memory uses `uv-dynamic-versioning` for automatic version management based on git tags:
### Version Types
- **Development versions**: Automatically generated from commits (e.g., `0.12.4.dev26+468a22f`)
- **Beta releases**: Created by tagging with beta suffixes (e.g., `v0.13.0b1`, `v0.13.0rc1`)
- **Stable releases**: Created by tagging with version numbers (e.g., `v0.13.0`)
### Release Workflows
#### Development Builds (Automatic)
- Triggered on every push to `main` branch
- Publishes dev versions like `0.12.4.dev26+468a22f` to PyPI
- Allows continuous testing of latest changes
- Users install with: `pip install basic-memory --pre --force-reinstall`
#### Beta/RC Releases (Manual)
- Create beta tag: `git tag v0.13.0b1 && git push origin v0.13.0b1`
- Automatically builds and publishes to PyPI as pre-release
- Users install with: `pip install basic-memory --pre`
- Use for milestone testing before stable release
#### Stable Releases (Automated)
- Use the automated release system: `just release v0.13.0`
- Includes comprehensive quality checks (lint, format, type-check, tests)
- Automatically updates version in `__init__.py`
- Creates git tag and pushes to GitHub
- Triggers GitHub Actions workflow for:
- PyPI publication
- Homebrew formula update (requires HOMEBREW_TOKEN secret)
**Manual method (legacy):**
- Create version tag: `git tag v0.13.0 && git push origin v0.13.0`
#### Homebrew Formula Updates
- Automatically triggered after successful PyPI release for **stable releases only**
- **Stable releases** (e.g., v0.13.7) automatically update the main `basic-memory` formula
- **Pre-releases** (dev/beta/rc) are NOT automatically updated - users must specify version manually
- Updates formula in `basicmachines-co/homebrew-basic-memory` repo
- Requires `HOMEBREW_TOKEN` secret in GitHub repository settings:
- Create a fine-grained Personal Access Token with `Contents: Read and Write` and `Actions: Read` scopes on `basicmachines-co/homebrew-basic-memory`
- Add as repository secret named `HOMEBREW_TOKEN` in `basicmachines-co/basic-memory`
- Formula updates include new version URL and SHA256 checksum
### For Development
- **Automated releases**: Use `just release v0.13.x` for stable releases and `just beta v0.13.0b1` for beta releases
- **Quality gates**: All releases require passing lint, format, type-check, and test suites
- **Version management**: Versions automatically derived from git tags via `uv-dynamic-versioning`
- **Configuration**: `pyproject.toml` uses `dynamic = ["version"]`
- **Release automation**: `__init__.py` updated automatically during release process
- **CI/CD**: GitHub Actions handles building and PyPI publication
## Development Notes
- make sure you sign off on commits
+42 -86
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@@ -27,25 +27,13 @@ project and how to get started as a developer.
> **Note**: Basic Memory uses [just](https://just.systems) as a modern command runner. Install with `brew install just` or `cargo install just`.
3. **Activate the Virtual Environment**
3. **Run the Tests**:
```bash
source .venv/bin/activate
```
4. **Run the Tests**:
```bash
# Run all tests with unified coverage (unit + integration)
# Run all tests
just test
# Run unit tests only (fast, no coverage)
just test-unit
# Run integration tests only (fast, no coverage)
just test-int
# Generate HTML coverage report
just coverage
# or
uv run pytest -p pytest_mock -v
# Run a specific test
pytest tests/path/to/test_file.py::test_function_name
```
@@ -141,7 +129,7 @@ agreement to the DCO.
## Code Style Guidelines
- **Python Version**: Python 3.12+ with full type annotations (3.12+ required for type parameter syntax)
- **Python Version**: Python 3.12+ with full type annotations
- **Line Length**: 100 characters maximum
- **Formatting**: Use ruff for consistent styling
- **Import Order**: Standard lib, third-party, local imports
@@ -151,78 +139,46 @@ agreement to the DCO.
## Testing Guidelines
### Test Structure
Basic Memory uses two test directories with unified coverage reporting:
- **`tests/`**: Unit tests that test individual components in isolation
- Fast execution with extensive mocking
- Test individual functions, classes, and modules
- Run with: `just test-unit` (no coverage, fast)
- **`test-int/`**: Integration tests that test real-world scenarios
- Test full workflows with real database and file operations
- Include performance benchmarks
- More realistic but slower than unit tests
- Run with: `just test-int` (no coverage, fast)
### Running Tests
```bash
# Run all tests with unified coverage report
just test
# Run only unit tests (fast iteration)
just test-unit
# Run only integration tests
just test-int
# Generate HTML coverage report
just coverage
# Run specific test
pytest tests/path/to/test_file.py::test_function_name
# Run tests excluding benchmarks
pytest -m "not benchmark"
# Run only benchmark tests
pytest -m benchmark test-int/test_sync_performance_benchmark.py
```
### Performance Benchmarks
The `test-int/test_sync_performance_benchmark.py` file contains performance benchmarks that measure sync and indexing speed:
- `test_benchmark_sync_100_files` - Small repository performance
- `test_benchmark_sync_500_files` - Medium repository performance
- `test_benchmark_sync_1000_files` - Large repository performance (marked slow)
- `test_benchmark_resync_no_changes` - Re-sync performance baseline
Run benchmarks with:
```bash
# Run all benchmarks (excluding slow ones)
pytest test-int/test_sync_performance_benchmark.py -v -m "benchmark and not slow"
# Run all benchmarks including slow ones
pytest test-int/test_sync_performance_benchmark.py -v -m benchmark
# Run specific benchmark
pytest test-int/test_sync_performance_benchmark.py::test_benchmark_sync_100_files -v
```
See `test-int/BENCHMARKS.md` for detailed benchmark documentation.
### Testing Best Practices
- **Coverage Target**: We aim for high test coverage for all code
- **Coverage Target**: We aim for 100% test coverage for all code
- **Test Framework**: Use pytest for unit and integration tests
- **Mocking**: Avoid mocking in integration tests; use sparingly in unit tests
- **Mocking**: Use pytest-mock for mocking dependencies only when necessary
- **Edge Cases**: Test both normal operation and edge cases
- **Database Testing**: Use in-memory SQLite for testing database operations
- **Fixtures**: Use async pytest fixtures for setup and teardown
- **Markers**: Use `@pytest.mark.benchmark` for benchmarks, `@pytest.mark.slow` for slow tests
## Release Process
Basic Memory uses automatic versioning based on git tags with `uv-dynamic-versioning`. Here's how releases work:
### Version Management
- **Development versions**: Automatically generated from git commits (e.g., `0.12.4.dev26+468a22f`)
- **Beta releases**: Created by tagging with beta suffixes (e.g., `git tag v0.13.0b1`)
- **Stable releases**: Created by tagging with version numbers (e.g., `git tag v0.13.0`)
### Release Workflows
#### Development Builds
- Automatically published to PyPI on every commit to `main`
- Version format: `0.12.4.dev26+468a22f` (base version + dev + commit count + hash)
- Users install with: `pip install basic-memory --pre --force-reinstall`
#### Beta Releases
1. Create and push a beta tag: `git tag v0.13.0b1 && git push origin v0.13.0b1`
2. GitHub Actions automatically builds and publishes to PyPI
3. Users install with: `pip install basic-memory --pre`
#### Stable Releases
1. Create and push a version tag: `git tag v0.13.0 && git push origin v0.13.0`
2. GitHub Actions automatically:
- Builds the package with version `0.13.0`
- Creates GitHub release with auto-generated notes
- Publishes to PyPI
3. Users install with: `pip install basic-memory`
### For Contributors
- No manual version bumping required
- Versions are automatically derived from git tags
- Focus on code changes, not version management
## Creating Issues
+7 -27
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@@ -1,46 +1,26 @@
FROM python:3.12-slim-bookworm
# Build arguments for user ID and group ID (defaults to 1000)
ARG UID=1000
ARG GID=1000
# Copy uv from official image
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 \
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
RUN (getent group ${GID} || groupadd --gid ${GID} appgroup) && \
useradd --uid ${UID} --gid ${GID} --create-home --shell /bin/bash appuser
PYTHONDONTWRITEBYTECODE=1
# Copy the project into the image
ADD . /app
# Install Python 3.13 explicitly and sync the project
# Sync the project into a new environment, asserting the lockfile is up to date
WORKDIR /app
RUN uv python install 3.13
RUN uv sync --locked --python 3.13
RUN uv sync --locked
# Create necessary directories and set ownership
RUN mkdir -p /app/data/basic-memory /app/.basic-memory && \
chown -R appuser:${GID} /app
# Create data directory
RUN mkdir -p /app/data
# Set default data directory and add venv to PATH
ENV BASIC_MEMORY_HOME=/app/data/basic-memory \
BASIC_MEMORY_PROJECT_ROOT=/app/data \
ENV BASIC_MEMORY_HOME=/app/data \
PATH="/app/.venv/bin:$PATH"
# Switch to the non-root user
USER appuser
# Expose port
EXPOSE 8000
@@ -49,4 +29,4 @@ 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
@@ -1,494 +0,0 @@
# 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
```
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## Reporting a Vulnerability
If you find a vulnerability, please contact hello@basicmachines.co.
Use this section to tell people how to report a vulnerability.
Please do not open a public GitHub issue for security vulnerabilities. We aim
to respond within 72 hours and will coordinate a fix and disclosure timeline
with you.
## Threat Model
Basic Memory is a local-first MCP server that reads and writes markdown files
inside configured project directories. It runs on your machine with your user
permissions, so local configuration deserves the same care as any other
developer tool that can access your files.
### What Basic Memory Controls
- Filesystem-touching tools validate paths against the configured project root
with `validate_project_path()`, resolved paths, and `Path.is_relative_to()`.
Path traversal attempts such as `../../etc/passwd` are blocked at this layer.
- Scan optimizations in `sync_service.py` call `find` through
`asyncio.create_subprocess_exec()` with explicit argument lists. Project paths
are passed as data, not interpolated into shell strings.
- Auto-update code uses hardcoded commands, list-form arguments, and
`stdin=DEVNULL`. User-controlled strings do not reach a shell there.
### MCP Client-Side Risk
Recent MCP ecosystem research has highlighted a client-side pattern where an
MCP host can be configured to run arbitrary commands as "servers." That risk is
in the host configuration, not in notes or Basic Memory tool input.
The recommended Basic Memory MCP configuration uses a known command with
explicit arguments:
```json
{
"mcpServers": {
"basic-memory": {
"command": "uvx",
"args": ["basic-memory", "mcp"]
}
}
}
```
Only add MCP server entries from sources you trust. Avoid inline shell scripts
or command strings copied from untrusted sources. Treat third-party MCP server
configuration with the same scrutiny as any locally executed program.
Related ecosystem context:
- OX Security: The Mother of All AI Supply Chains
- CSO Online: RCE by design: MCP architectural choice haunts AI agent ecosystem
### Out Of Scope
- Basic Memory does not execute note content as code. Notes are returned as
data to the LLM.
- Basic Memory does not open network ports by default. The MCP server uses
stdio; the optional REST API is intended for localhost use.
- Basic Memory is designed for single-user local knowledge bases and does not
implement access controls between operating-system users.
## Secure Configuration Checklist
- MCP config `command` points to `uvx` or a trusted binary, not a shell string.
- Project paths in Basic Memory config come from trusted local configuration.
- If exposing the REST API, bind it only to localhost.
- Review any third-party MCP servers before adding them to your host config.
If you find a vulnerability, please contact hello@basicmachines.co
+44
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{
"date": "2025-07-01T01:24:37.731009",
"metrics": {
"github": {
"stars": 1009,
"forks": 73,
"watchers": 1009,
"open_issues": 31,
"traffic_views": 0,
"traffic_unique": 0,
"recent_issues": 0
},
"reddit": {
"total_mentions": 25,
"subreddit_members": 43,
"top_posts": [
{
"title": "Today's banning is the largest since Affinity, and...",
"score": 892,
"subreddit": "magicTCG",
"num_comments": 127
},
{
"title": "Basic Memory - Backed by Research*",
"score": 1,
"subreddit": "basicmemory",
"num_comments": 0
},
{
"title": "n8n Full Course \u2699\ufe0f Create Our Second Flow \ud83d\udc49 Trigge...",
"score": 1,
"subreddit": "Tech_UpSkill",
"num_comments": 0
}
],
"hot_discussions": []
},
"youtube": {
"subscribers": 45,
"total_views": 1093,
"video_count": 6
}
}
}
-45
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@@ -1,45 +0,0 @@
# Docker Compose configuration for Basic Memory with PostgreSQL
# Use this for local development and testing with Postgres backend.
#
# The Postgres backend requires the pgvector extension (semantic search).
# This image bundles pgvector; plain postgres:17 will not work for vector search.
#
# Usage:
# docker-compose -f docker-compose-postgres.yml up -d
# docker-compose -f docker-compose-postgres.yml down
services:
postgres:
image: pgvector/pgvector:pg17
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
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@@ -17,9 +17,7 @@ services:
volumes:
# Persistent storage for configuration and database
# Container runs as `appuser` (Dockerfile USER directive), so the CLI
# config dir lives under /home/appuser, not /root.
- basic-memory-config:/home/appuser/.basic-memory:rw
- basic-memory-config:/root/.basic-memory:rw
# Mount your knowledge directory (required)
# Change './knowledge' to your actual Obsidian vault or knowledge directory
+431
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---
title: AI Assistant Guide
type: note
permalink: docs/ai-assistant-guide
---
> Note: This is an optional document that can be copy/pasted into the project knowledge for an LLM to provide a full description of how it can work with Basic Memory. It is provided as a helpful resource. The tools contain extensive usage description prompts with enable the LLM to understand them.
You can [download](https://github.com/basicmachines-co/basic-memory/blob/main/docs/AI%20Assistant%20Guide.md) the contents of this file from GitHub
# AI Assistant Guide for Basic Memory
This guide helps you, the AI assistant, use Basic Memory tools effectively when working with users. It covers reading, writing, and navigating knowledge through the Model Context Protocol (MCP).
## Quick Reference
**Essential Tools:**
- `write_note()` - Create/update notes (primary tool)
- `read_note()` - Read existing content
- `search_notes()` - Find information
- `edit_note()` - Modify existing notes incrementally (v0.13.0)
- `move_note()` - Organize files with database consistency (v0.13.0)
**Project Management (v0.13.0):**
- `list_projects()` - Show available projects
- `switch_project()` - Change active project
- `get_current_project()` - Current project info
**Key Principles:**
1. **Build connections** - Rich knowledge graphs > isolated notes
2. **Ask permission** - "Would you like me to record this?"
3. **Use exact titles** - For accurate `[[WikiLinks]]`
4. **Leverage v0.13.0** - Edit incrementally, organize proactively, switch projects contextually
## Overview
Basic Memory allows you and users to record context in local Markdown files, building a rich knowledge base through natural conversations. The system automatically creates a semantic knowledge graph from simple text patterns.
- **Local-First**: All data is stored in plain text files on the user's computer
- **Real-Time**: Users see content updates immediately
- **Bi-Directional**: Both you and users can read and edit notes
- **Semantic**: Simple patterns create a structured knowledge graph
- **Persistent**: Knowledge persists across sessions and conversations
## The Importance of the Knowledge Graph
Basic Memory's value comes from connections between notes, not just the notes themselves. When writing notes, your primary goal should be creating a rich, interconnected knowledge graph.
When creating content, focus on:
1. **Increasing Semantic Density**: Add multiple observations and relations to each note
2. **Using Accurate References**: Aim to reference existing entities by their exact titles
3. **Creating Forward References**: Feel free to reference entities that don't exist yet - Basic Memory will resolve these when they're created later
4. **Creating Bidirectional Links**: When appropriate, connect entities from both directions
5. **Using Meaningful Categories**: Add semantic context with appropriate observation categories
6. **Choosing Precise Relations**: Use specific relation types that convey meaning
Remember that a knowledge graph with 10 heavily connected notes is more valuable than 20 isolated notes. Your job is to help build these connections.
## Core Tools Reference
### Essential Content Management
**Writing knowledge** (most important tool):
```
write_note(
title="Search Design",
content="# Search Design\n...",
folder="specs", # Optional
tags=["search", "design"], # v0.13.0: now searchable!
project="work-notes" # v0.13.0: target specific project
)
```
**Reading knowledge:**
```
read_note("Search Design") # By title
read_note("specs/search-design") # By path
read_note("memory://specs/search") # By memory URL
```
**Viewing notes as formatted artifacts (Claude Desktop):**
```
view_note("Search Design") # Creates readable artifact
view_note("specs/search-design") # By permalink
view_note("memory://specs/search") # By memory URL
```
**Incremental editing** (v0.13.0):
```
edit_note(
identifier="Search Design", # Must be EXACT title/permalink (strict matching)
operation="append", # append, prepend, find_replace, replace_section
content="\n## New Section\nContent here..."
)
```
**⚠️ Important:** `edit_note` requires exact identifiers (no fuzzy matching). Use `search_notes()` first if uncertain.
**File organization** (v0.13.0):
```
move_note(
identifier="Old Note", # Must be EXACT title/permalink (strict matching)
destination="archive/old-note.md" # Folders created automatically
)
```
**⚠️ Important:** `move_note` requires exact identifiers (no fuzzy matching). Use `search_notes()` first if uncertain.
### Project Management (v0.13.0)
```
list_projects() # Show available projects
switch_project("work-notes") # Change active project
get_current_project() # Current project info
```
### Search & Discovery
```
search_notes("authentication system") # v0.13.0: includes frontmatter tags
build_context("memory://specs/search") # Follow knowledge graph connections
recent_activity(timeframe="1 week") # Check what's been updated
```
## memory:// URLs Explained
Basic Memory uses a special URL format to reference entities in the knowledge graph:
- `memory://title` - Reference by title
- `memory://folder/title` - Reference by folder and title
- `memory://permalink` - Reference by permalink
- `memory://path/relation_type/*` - Follow all relations of a specific type
- `memory://path/*/target` - Find all entities with relations to target
## Semantic Markdown Format
Knowledge is encoded in standard markdown using simple patterns:
**Observations** - Facts about an entity:
```markdown
- [category] This is an observation #tag1 #tag2 (optional context)
```
**Relations** - Links between entities:
```markdown
- relation_type [[Target Entity]] (optional context)
```
**Common Categories & Relation Types:**
- Categories: `[idea]`, `[decision]`, `[question]`, `[fact]`, `[requirement]`, `[technique]`, `[recipe]`, `[preference]`
- Relations: `relates_to`, `implements`, `requires`, `extends`, `part_of`, `pairs_with`, `inspired_by`, `originated_from`
## When to Record Context
**Always consider recording context when**:
1. Users make decisions or reach conclusions
2. Important information emerges during conversation
3. Multiple related topics are discussed
4. The conversation contains information that might be useful later
5. Plans, tasks, or action items are mentioned
**Protocol for recording context**:
1. Identify valuable information in the conversation
2. Ask the user: "Would you like me to record our discussion about [topic] in Basic Memory?"
3. If they agree, use `write_note` to capture the information
4. If they decline, continue without recording
5. Let the user know when information has been recorded: "I've saved our discussion about [topic] to Basic Memory."
## Understanding User Interactions
Users will interact with Basic Memory in patterns like:
1. **Creating knowledge**:
```
Human: "Let's write up what we discussed about search."
You: I'll create a note capturing our discussion about the search functionality.
[Use write_note() to record the conversation details]
```
2. **Referencing existing knowledge**:
```
Human: "Take a look at memory://specs/search"
You: I'll examine that information.
[Use build_context() to gather related information]
[Then read_note() to access specific content]
```
3. **Finding information**:
```
Human: "What were our decisions about auth?"
You: Let me find that information for you.
[Use search_notes() to find relevant notes]
[Then build_context() to understand connections]
```
4. **Editing existing notes (v0.13.0)**:
```
Human: "Add a section about deployment to my API documentation"
You: I'll add that section to your existing documentation.
[Use edit_note() with operation="append" to add new content]
```
5. **Project management (v0.13.0)**:
```
Human: "Switch to my work project and show recent activity"
You: I'll switch to your work project and check what's been updated recently.
[Use switch_project() then recent_activity()]
```
6. **File organization (v0.13.0)**:
```
Human: "Move my old meeting notes to the archive folder"
You: I'll organize those notes for you.
[Use move_note() to relocate files with database consistency]
```
## Key Things to Remember
1. **Files are Truth**
- All knowledge lives in local files on the user's computer
- Users can edit files outside your interaction
- Changes need to be synced by the user (usually automatic)
- Always verify information is current with `recent_activity()`
2. **Building Context Effectively**
- Start with specific entities
- Follow meaningful relations
- Check recent changes
- Build context incrementally
- Combine related information
3. **Writing Knowledge Wisely**
- Same title+folder overwrites existing notes
- Structure with clear headings and semantic markup
- Use tags for searchability (v0.13.0: frontmatter tags indexed)
- Keep files organized in logical folders
4. **Leverage v0.13.0 Features**
- **Edit incrementally**: Use `edit_note()` for small changes vs rewriting
- **Switch projects**: Change context when user mentions different work areas
- **Organize proactively**: Move old content to archive folders
- **Cross-project operations**: Create notes in specific projects while maintaining context
## Common Knowledge Patterns
### Capturing Decisions
```markdown
---
title: Coffee Brewing Methods
tags: [coffee, brewing, pour-over, techniques] # v0.13.0: Now searchable!
---
# Coffee Brewing Methods
## Context
I've experimented with various brewing methods including French press, pour over, and espresso.
## Decision
Pour over is my preferred method for light to medium roasts because it highlights subtle flavors and offers more control over the extraction.
## Observations
- [technique] Blooming the coffee grounds for 30 seconds improves extraction #brewing
- [preference] Water temperature between 195-205°F works best #temperature
- [equipment] Gooseneck kettle provides better control of water flow #tools
- [timing] Total brew time of 3-4 minutes produces optimal extraction #process
## Relations
- pairs_with [[Light Roast Beans]]
- contrasts_with [[French Press Method]]
- requires [[Proper Grinding Technique]]
- part_of [[Morning Coffee Routine]]
```
### Recording Project Structure
```markdown
# Garden Planning
## Overview
This document outlines the garden layout and planting strategy for this season.
## Observations
- [structure] Raised beds in south corner for sun exposure #layout
- [structure] Drip irrigation system installed for efficiency #watering
- [pattern] Companion planting used to deter pests naturally #technique
## Relations
- contains [[Vegetable Section]]
- contains [[Herb Garden]]
- implements [[Organic Gardening Principles]]
```
### Technical Discussions
```markdown
# Recipe Improvement Discussion
## Key Points
Discussed strategies for improving the chocolate chip cookie recipe.
## Observations
- [issue] Cookies spread too thin when baked at 350°F #texture
- [solution] Chilling dough for 24 hours improves flavor and reduces spreading #technique
- [decision] Will use brown butter instead of regular butter #flavor
## Relations
- improves [[Basic Cookie Recipe]]
- inspired_by [[Bakery-Style Cookies]]
- pairs_with [[Homemade Ice Cream]]
```
## v0.13.0 Workflow Examples
### Multi-Project Conversations
**User:** "I need to update my work documentation and also add a personal recipe note."
**Workflow:**
1. `list_projects()` - Check available projects
2. `write_note(title="Sprint Planning", project="work-notes")` - Work content
3. `write_note(title="Weekend Recipes", project="personal")` - Personal content
### Incremental Note Building
**User:** "Add a troubleshooting section to my setup guide."
**Workflow:**
1. `edit_note(identifier="Setup Guide", operation="append", content="\n## Troubleshooting\n...")`
**User:** "Update the authentication section in my API docs."
**Workflow:**
1. `edit_note(identifier="API Documentation", operation="replace_section", section="## Authentication")`
### Smart File Organization
**User:** "My notes are getting messy in the main folder."
**Workflow:**
1. `move_note("Old Meeting Notes", "archive/2024/old-meetings.md")`
2. `move_note("Project Notes", "projects/client-work/notes.md")`
### Creating Effective Relations
When creating relations:
1. **Reference existing entities** by their exact title: `[[Exact Title]]`
2. **Create forward references** to entities that don't exist yet - they'll be linked automatically when created
3. **Search first** to find existing entities to reference
4. **Use meaningful relation types**: `implements`, `requires`, `part_of` vs generic `relates_to`
**Example workflow:**
1. `search_notes("travel")` to find existing travel-related notes
2. Reference found entities: `- part_of [[Japan Travel Guide]]`
3. Add forward references: `- located_in [[Tokyo]]` (even if Tokyo note doesn't exist yet)
## Common Issues & Solutions
**Missing Content:**
- Try `search_notes()` with broader terms if `read_note()` fails
- Use fuzzy matching: search for partial titles
**Forward References:**
- These are normal! Basic Memory links them automatically when target notes are created
- Inform users: "I've created forward references that will be linked when you create those notes"
**Sync Issues:**
- If information seems outdated, suggest `basic-memory sync`
- Use `recent_activity()` to check if content is current
**Strict Mode for Edit/Move Operations:**
- `edit_note()` and `move_note()` require **exact identifiers** (no fuzzy matching for safety)
- If identifier not found: use `search_notes()` first to find the exact title/permalink
- Error messages will guide you to find correct identifiers
- Example workflow:
```
# ❌ This might fail if identifier isn't exact
edit_note("Meeting Note", "append", "content")
# ✅ Safe approach: search first, then use exact result
results = search_notes("meeting")
edit_note("Meeting Notes 2024", "append", "content") # Use exact title from search
```
## Best Practices
1. **Proactively Record Context**
- Offer to capture important discussions
- Record decisions, rationales, and conclusions
- Link to related topics
- Ask for permission first: "Would you like me to save our discussion about [topic]?"
- Confirm when complete: "I've saved our discussion to Basic Memory"
2. **Create a Rich Semantic Graph**
- **Add meaningful observations**: Include at least 3-5 categorized observations in each note
- **Create deliberate relations**: Connect each note to at least 2-3 related entities
- **Use existing entities**: Before creating a new relation, search for existing entities
- **Verify wikilinks**: When referencing `[[Entity]]`, use exact titles of existing notes
- **Check accuracy**: Use `search_notes()` or `recent_activity()` to confirm entity titles
- **Use precise relation types**: Choose specific relation types that convey meaning (e.g., "implements" instead of "relates_to")
- **Consider bidirectional relations**: When appropriate, create inverse relations in both entities
3. **Structure Content Thoughtfully**
- Use clear, descriptive titles
- Organize with logical sections (Context, Decision, Implementation, etc.)
- Include relevant context and background
- Add semantic observations with appropriate categories
- Use a consistent format for similar types of notes
- Balance detail with conciseness
4. **Navigate Knowledge Effectively**
- Start with specific searches
- Follow relation paths
- Combine information from multiple sources
- Verify information is current
- Build a complete picture before responding
5. **Help Users Maintain Their Knowledge**
- Suggest organizing related topics
- Identify potential duplicates
- Recommend adding relations between topics
- Offer to create summaries of scattered information
- Suggest potential missing relations: "I notice this might relate to [topic], would you like me to add that connection?"
Built with ♥️ by Basic Machines
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# 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
```
+23 -54
View File
@@ -15,7 +15,7 @@ Basic Memory provides pre-built Docker images on GitHub Container Registry that
--name basic-memory-server \
-p 8000:8000 \
-v /path/to/your/obsidian-vault:/app/data:rw \
-v basic-memory-config:/app/.basic-memory:rw \
-v basic-memory-config:/root/.basic-memory:rw \
ghcr.io/basicmachines-co/basic-memory:latest
```
@@ -30,7 +30,7 @@ Basic Memory provides pre-built Docker images on GitHub Container Registry that
- "8000:8000"
volumes:
- /path/to/your/obsidian-vault:/app/data:rw
- basic-memory-config:/app/.basic-memory:rw
- basic-memory-config:/root/.basic-memory:rw
environment:
- BASIC_MEMORY_DEFAULT_PROJECT=main
restart: unless-stopped
@@ -67,7 +67,7 @@ docker build -t basic-memory .
docker run -d \
--name basic-memory-server \
-v /path/to/your/obsidian-vault:/app/data:rw \
-v basic-memory-config:/app/.basic-memory:rw \
-v basic-memory-config:/root/.basic-memory:rw \
-e BASIC_MEMORY_DEFAULT_PROJECT=main \
basic-memory
```
@@ -86,11 +86,11 @@ Basic Memory requires several volume mounts for proper operation:
2. **Configuration and Database** (Recommended):
```yaml
- basic-memory-config:/app/.basic-memory:rw
- basic-memory-config:/root/.basic-memory:rw
```
Persistent storage for configuration and SQLite database.
You can edit the basic-memory config.json file located in the /app/.basic-memory/config.json after Basic Memory starts.
You can edit the basic-memory config.json file located in the /root/.basic-memory/config.json after Basic Memory starts.
3. **Multiple Projects** (Optional):
```yaml
@@ -98,7 +98,7 @@ You can edit the basic-memory config.json file located in the /app/.basic-memory
- /path/to/project2:/app/data/project2:rw
```
You can edit the basic-memory config.json file located in the /app/.basic-memory/config.json
You can edit the basic-memory config.json file located in the /root/.basic-memory/config.json
## CLI Commands via Docker
@@ -111,7 +111,7 @@ You can run Basic Memory CLI commands inside the container using `docker exec`:
docker exec basic-memory-server basic-memory status
# Sync files
docker exec basic-memory-server basic-memory reindex
docker exec basic-memory-server basic-memory sync
# Show help
docker exec basic-memory-server basic-memory --help
@@ -123,21 +123,21 @@ When using Docker volumes, you'll need to configure projects to point to your mo
1. **Check current configuration:**
```bash
docker exec basic-memory-server cat /app/.basic-memory/config.json
docker exec basic-memory-server cat /root/.basic-memory/config.json
```
2. **Add a project for your mounted volume:**
```bash
# If you mounted /path/to/your/vault to /app/data
docker exec basic-memory-server basic-memory project create my-vault /app/data
docker exec basic-memory-server basic-memory project add my-vault /app/data
# Set it as default
docker exec basic-memory-server basic-memory project set-default my-vault
docker exec basic-memory-server basic-memory project default my-vault
```
3. **Sync the new project:**
```bash
docker exec basic-memory-server basic-memory reindex
docker exec basic-memory-server basic-memory sync
```
### Example: Setting up an Obsidian Vault
@@ -151,13 +151,13 @@ volumes:
Then configure it:
```bash
# Create project pointing to mounted vault
docker exec basic-memory-server basic-memory project create obsidian /app/data
docker exec basic-memory-server basic-memory project add obsidian /app/data
# Set as default
docker exec basic-memory-server basic-memory project set-default obsidian
docker exec basic-memory-server basic-memory project default obsidian
# Sync to index all files
docker exec basic-memory-server basic-memory reindex
docker exec basic-memory-server basic-memory sync
```
### Environment Variables
@@ -184,47 +184,16 @@ environment:
### Linux/macOS
The Docker container now runs as a non-root user to avoid file ownership issues. By default, the container uses UID/GID 1000, but you can customize this to match your user:
Ensure your knowledge directories have proper permissions:
```bash
# Build with custom UID/GID to match your user
docker build --build-arg UID=$(id -u) --build-arg GID=$(id -g) -t basic-memory .
# Make directories readable/writable
chmod -R 755 /path/to/your/obsidian-vault
# Or use docker-compose with build args
# If using specific user/group
chown -R $USER:$USER /path/to/your/obsidian-vault
```
**Example docker-compose.yml with custom user:**
```yaml
version: '3.8'
services:
basic-memory:
build:
context: .
dockerfile: Dockerfile
args:
UID: 1000 # Replace with your UID
GID: 1000 # Replace with your GID
container_name: basic-memory-server
ports:
- "8000:8000"
volumes:
- /path/to/your/obsidian-vault:/app/data:rw
- basic-memory-config:/app/.basic-memory:rw
environment:
- BASIC_MEMORY_DEFAULT_PROJECT=main
restart: unless-stopped
```
**Using pre-built images:**
If using the pre-built image from GitHub Container Registry, files will be created with UID/GID 1000. You can either:
1. Change your local directory ownership to match:
```bash
sudo chown -R 1000:1000 /path/to/your/obsidian-vault
```
2. Or build your own image with custom UID/GID as shown above.
### Windows
When using Docker Desktop on Windows, ensure the directories are shared:
@@ -248,7 +217,7 @@ When using Docker Desktop on Windows, ensure the directories are shared:
```
2. **Configuration Not Persisting:**
- Use named volumes for `/app/.basic-memory`
- Use named volumes for `/root/.basic-memory`
- Check volume mount permissions
3. **Network Connectivity:**
@@ -274,10 +243,10 @@ docker-compose logs -f basic-memory
## Security Considerations
1. **Docker Security:**
The container runs as a non-root user (UID/GID 1000 by default) for improved security. You can customize the user ID using build arguments to match your local user.
The container runs as root for simplicity. For production, consider additional security measures.
2. **Volume Permissions:**
Ensure mounted directories have appropriate permissions and don't expose sensitive data. With the non-root container, files will be created with the specified user ownership.
Ensure mounted directories have appropriate permissions and don't expose sensitive data.
3. **Network Security:**
If using HTTP transport, consider using reverse proxy with SSL/TLS and authentication if the endpoint is available on
@@ -319,7 +288,7 @@ For Docker-specific issues:
1. Check the [troubleshooting section](#troubleshooting) above
2. Review container logs: `docker-compose logs basic-memory`
3. Verify volume mounts: `docker inspect basic-memory-server`
4. Test file permissions: `docker exec basic-memory-server ls -la /app`
4. Test file permissions: `docker exec basic-memory-server ls -la /root`
For general Basic Memory support, see the main [README](../README.md)
and [documentation](https://memory.basicmachines.co/).
-64
View File
@@ -1,64 +0,0 @@
# Basic Memory Engineering Style
Style is how we make code easier to verify. Prefer explicit, typed, local-first code that
preserves the file system as the source of truth while keeping the database, API, and MCP
surfaces in sync.
## Design Center
- Basic Memory is local-first. Markdown files are the durable source; SQLite/Postgres indexes
are derived state that should be rebuilt or reconciled from files when needed.
- Keep the existing boundary order: CLI/MCP/API entrypoints compose dependencies, services own
business behavior, repositories own database access, and file services own filesystem writes.
- MCP tools should remain atomic and composable. They should call API routers through typed MCP
clients, not reach around into services.
- Prefer small, explicit abstractions that match a real domain boundary. Avoid object
hierarchies when a function, dataclass, type alias, or protocol describes the concept better.
## Types And Data
- Use full type annotations and Python 3.12 syntax. Introduce `type` aliases for repeated
structured shapes, callback signatures, or domain concepts that would otherwise become
anonymous `dict[str, Any]` values.
- Use dataclasses for internal values, operation inputs, and service results. Prefer
`frozen=True` when the value should not change and `slots=True` when identity/dynamic
attributes are not needed.
- Use Pydantic v2 at boundaries that validate, serialize, or deserialize data: API payloads,
CLI/MCP schemas, configuration, and persistence-adjacent schemas.
- Use narrow `Protocol`s when a caller needs a capability rather than a concrete repository or
service. Keep protocols small enough that fake implementations in tests are obvious.
- Avoid speculative `getattr`, broad casts, or `Any` as a way to paper over uncertainty. Read
the model or schema definition and make the type relationship explicit.
## Control Flow And Resources
- Fail fast when an invariant is broken. Do not swallow exceptions, add warning-only error
handling, or introduce fallback behavior unless the user explicitly agrees to that behavior.
- Keep control flow simple and close to the domain decision. Push `if` statements up into the
function that owns orchestration; keep leaf helpers focused on computation or one side effect.
- Make async/resource boundaries visible with context managers and explicit lifecycles. Do not
start background work without a clear owner, cancellation story, and verification path.
- Keep file mutations centralized through the existing file utilities/services so checksum,
atomic write, and index synchronization behavior stays coherent.
## Testing And Verification
- Use evidence-first testing, not mechanical TDD. For bugs and risky behavior, add or update a
regression test that would catch the failure. For small documentation-only edits, use the
relevant doc/repo hygiene checks.
- Prefer tests that exercise real code paths. Use mocks, doubles, or `monkeypatch` only when
the external boundary would be slow, nondeterministic, or impossible to trigger directly.
- Keep coverage at 100% for new code. Use `# pragma: no cover` only for code that would require
disproportionate mocking and is covered through an integration or runtime path.
- Start with targeted commands, then widen as risk grows: focused pytest, `just fast-check`,
`just doctor`, package checks for agent packaging changes, and full SQLite/Postgres gates
when behavior crosses shared boundaries.
## Comments And Names
- Name values after the domain concept they carry: project, entity, permalink, tenant, route,
checksum, observation, relation, batch, or index state.
- Comments should say why a branch, invariant, retry, lifecycle, or compatibility constraint
exists. Section headers are useful when a function or file has clear phases.
- Avoid comments that restate the code. If a comment cannot explain a decision, simplify the
code or improve the name instead.
-512
View File
@@ -1,512 +0,0 @@
# 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 `links_to` 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. Unquoted
relation types are single tokens. Quote relation types that contain spaces.
**Syntax:**
```
- relation_type [[Target Entity]] (context)
- "multi word relation type" [[Target Entity]] (context)
- 'multi word relation type' [[Target Entity]] (context)
```
| Part | Required | Description |
|------|----------|-------------|
| `relation_type` | Yes | Single unquoted token before `[[`, or quoted text for multi-word labels. |
| `[[Target]]` | Yes | Wiki link to the target entity. Matched by title or permalink. |
| `(context)` | No | Parenthesized text after `]]`. Supporting details. |
### Examples
Explicit relations:
```markdown
- implements [[Search Design]]
- depends_on [[Database Schema]]
- works_at [[Y Combinator]] (co-founder)
- "based on" [[Customer Interview]]
- 'in response to' [[Incident Review]]
```
Bare wiki links and prose list items create implicit `links_to` relations:
```markdown
- [[Some Entity]]
- some other thing [[Some Entity]]
```
Both examples above create `links_to [[Some Entity]]`. Use quotes when the words before
`[[` are meant to be a multi-word relation type.
Common relation types:
- `implements`, `depends_on`, `relates_to`, `inspired_by`
- `extends`, `part_of`, `contains`, `pairs_with`
- `works_at`, `authored`, `collaborated_with`
Any single-token text or quoted text works as a relation type. These are conventions,
not a fixed set.
### Inline References
Wiki links appearing in regular prose create implicit `links_to` relations. This includes
list items that do not match the explicit relation grammar above.
```markdown
This builds on [[Core Design]] and uses [[Utility Functions]].
- We should revisit [[Search Design]] after the API changes.
```
This creates three relations: `links_to [[Core Design]]`, `links_to [[Utility Functions]]`,
and `links_to [[Search Design]]`.
### 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
```
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# 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.
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# Character Handling and Conflict Resolution
Basic Memory handles various character encoding scenarios and file naming conventions to provide consistent permalink generation and conflict resolution. This document explains how the system works and how to resolve common character-related issues.
## Overview
Basic Memory uses a sophisticated system to generate permalinks from file paths while maintaining consistency across different operating systems and character encodings. The system normalizes file paths and generates unique permalinks to prevent conflicts.
## Character Normalization Rules
### 1. Permalink Generation
When Basic Memory processes a file path, it applies these normalization rules:
```
Original: "Finance/My Investment Strategy.md"
Permalink: "finance/my-investment-strategy"
```
**Transformation process:**
1. Remove file extension (`.md`)
2. Convert to lowercase (case-insensitive)
3. Replace spaces with hyphens
4. Replace underscores with hyphens
5. Handle international characters (transliteration for Latin, preservation for non-Latin)
6. Convert camelCase to kebab-case
### 2. International Character Support
**Latin characters with diacritics** are transliterated:
- `ø``o` (Søren → soren)
- `ü``u` (Müller → muller)
- `é``e` (Café → cafe)
- `ñ``n` (Niño → nino)
**Non-Latin characters** are preserved:
- Chinese: `中文/测试文档.md``中文/测试文档`
- Japanese: `日本語/文書.md``日本語/文書`
## Common Conflict Scenarios
### 1. Hyphen vs Space Conflicts
**Problem:** Files with existing hyphens conflict with generated permalinks from spaces.
**Example:**
```
File 1: "basic memory bug.md" → permalink: "basic-memory-bug"
File 2: "basic-memory-bug.md" → permalink: "basic-memory-bug" (CONFLICT!)
```
**Resolution:** The system automatically resolves this by adding suffixes:
```
File 1: "basic memory bug.md" → permalink: "basic-memory-bug"
File 2: "basic-memory-bug.md" → permalink: "basic-memory-bug-1"
```
**Best Practice:** Choose consistent naming conventions within your project.
### 2. Case Sensitivity Conflicts
**Problem:** Different case variations that normalize to the same permalink.
**Example on macOS:**
```
Directory: Finance/investment.md
Directory: finance/investment.md (different on filesystem, same permalink)
```
**Resolution:** Basic Memory detects case conflicts and prevents them during sync operations with helpful error messages.
**Best Practice:** Use consistent casing for directory and file names.
### 3. Character Encoding Conflicts
**Problem:** Different Unicode normalizations of the same logical character.
**Example:**
```
File 1: "café.md" (é as single character)
File 2: "café.md" (e + combining accent)
```
**Resolution:** Basic Memory normalizes Unicode characters using NFD normalization to detect these conflicts.
### 4. Forward Slash Conflicts
**Problem:** Forward slashes in frontmatter or file names interpreted as path separators.
**Example:**
```yaml
---
permalink: finance/investment/strategy
---
```
**Resolution:** Basic Memory validates frontmatter permalinks and warns about path separator conflicts.
## Error Messages and Troubleshooting
### "UNIQUE constraint failed: entity.file_path, entity.project_id"
**Cause:** Two entities trying to use the same file path within a project.
**Common scenarios:**
1. File move operation where destination is already occupied
2. Case sensitivity differences on macOS
3. Character encoding conflicts
4. Concurrent file operations
**Resolution steps:**
1. Check for duplicate file names with different cases
2. Look for files with similar names but different character encodings
3. Rename conflicting files to have unique names
4. Run sync again after resolving conflicts
### "File path conflict detected during move"
**Cause:** Enhanced conflict detection preventing potential database integrity violations.
**What this means:** The system detected that moving a file would create a conflict before attempting the database operation.
**Resolution:** Follow the specific guidance in the error message, which will indicate the type of conflict detected.
## Best Practices
### 1. File Naming Conventions
**Recommended patterns:**
- Use consistent casing (prefer lowercase)
- Use hyphens instead of spaces for multi-word files
- Avoid special characters that could conflict with path separators
- Be consistent with directory structure casing
**Examples:**
```
✅ Good:
- finance/investment-strategy.md
- projects/basic-memory-features.md
- docs/api-reference.md
❌ Problematic:
- Finance/Investment Strategy.md (mixed case, spaces)
- finance/Investment Strategy.md (inconsistent case)
- docs/API/Reference.md (mixed case directories)
```
### 2. Permalink Management
**Custom permalinks in frontmatter:**
```yaml
---
type: knowledge
permalink: custom-permalink-name
---
```
**Guidelines:**
- Use lowercase permalinks
- Use hyphens for word separation
- Avoid path separators unless creating sub-paths
- Ensure uniqueness within your project
### 3. Directory Structure
**Consistent casing:**
```
✅ Good:
finance/
investment-strategies.md
portfolio-management.md
❌ Problematic:
Finance/ (capital F)
investment-strategies.md
finance/ (lowercase f)
portfolio-management.md
```
## Migration and Cleanup
### Identifying Conflicts
Use Basic Memory's built-in conflict detection:
```bash
# Index local file changes (conflicts are handled during the scan)
basic-memory reindex
# Check sync status for warnings
basic-memory status
```
### Resolving Existing Conflicts
1. **Identify conflicting files** from sync error messages
2. **Choose consistent naming convention** for your project
3. **Rename files** to follow the convention
4. **Re-run sync** to verify resolution
### Bulk Renaming Strategy
For projects with many conflicts:
1. **Backup your project** before making changes
2. **Standardize on lowercase** file and directory names
3. **Replace spaces with hyphens** in file names
4. **Use consistent character encoding** (UTF-8)
5. **Test sync after each batch** of changes
## System Enhancements
### Recent Improvements (v0.13+)
1. **Enhanced conflict detection** before database operations
2. **Improved error messages** with specific resolution guidance
3. **Character normalization utilities** for consistent handling
4. **File swap detection** for complex move scenarios
5. **Proactive conflict warnings** during permalink resolution
### Monitoring and Logging
The system now provides detailed logging for conflict resolution:
```
DEBUG: Detected potential file path conflicts for 'Finance/Investment.md': ['finance/investment.md']
WARNING: File path conflict detected during move: entity_id=123 trying to move from 'old.md' to 'new.md'
```
These logs help identify and resolve conflicts before they cause sync failures.
## Support and Resources
If you encounter character-related conflicts not covered in this guide:
1. **Check the logs** for specific conflict details
2. **Review error messages** for resolution guidance
3. **Report issues** with examples of the conflicting files
4. **Consider the file naming best practices** outlined above
The Basic Memory system is designed to handle most character conflicts automatically while providing clear guidance for manual resolution when needed.
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# 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.
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# LiteLLM Provider
Basic Memory can use the LiteLLM SDK for semantic search embeddings. This lets you
keep Basic Memory's vector indexing and search behavior while routing embedding calls
to OpenAI-compatible and provider-specific backends such as OpenAI, Azure OpenAI,
Cohere, Bedrock, NVIDIA NIM, and other LiteLLM-supported embedding providers.
Use this page when you want to try a non-default embedding model, validate a provider,
or tune LiteLLM-specific settings.
> **Experimental — advanced users only.** The LiteLLM provider is experimental and
> intended for users who are comfortable operating remote embedding backends. It makes
> paid, networked API calls, requires per-model dimension and input-role configuration,
> and reindexing a real corpus can be slow and spend provider quota (see
> [Reindexing with a remote provider](#reindexing-with-a-remote-provider)). For most
> users, the default local **FastEmbed** provider is the recommended choice. Use LiteLLM
> only if you know what you're doing.
## Quick Start
The default LiteLLM model is OpenAI `text-embedding-3-small` through the LiteLLM
model string `openai/text-embedding-3-small`.
```bash
export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=litellm
export OPENAI_API_KEY=sk-...
bm reindex --embeddings
```
Then use vector or hybrid search:
```python
search_notes("login token flow", search_type="hybrid")
```
## Basic Memory Options
All options can be set in config or as environment variables.
| Config Field | Env Var | Default | Notes |
|---|---|---|---|
| `semantic_search_enabled` | `BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED` | Auto | Set to `true` to force vector/hybrid support on. |
| `semantic_embedding_provider` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER` | `fastembed` | Set to `litellm` for the LiteLLM provider. |
| `semantic_embedding_model` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL` | `bge-small-en-v1.5` | With `litellm`, the default is remapped to `openai/text-embedding-3-small`. |
| `semantic_embedding_dimensions` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_DIMENSIONS` | Provider default | Required for non-default LiteLLM models because vector tables are dimensioned before the first API call. |
| `semantic_embedding_forward_dimensions` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_FORWARD_DIMENSIONS` | Auto | Sends `dimensions` to LiteLLM only when supported. Auto is enabled for `text-embedding-3` model strings. |
| `semantic_embedding_document_input_type` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_DOCUMENT_INPUT_TYPE` | Auto | LiteLLM `input_type` for indexed notes/passages. |
| `semantic_embedding_query_input_type` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_QUERY_INPUT_TYPE` | Auto | LiteLLM `input_type` for search queries. |
| `semantic_embedding_batch_size` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_BATCH_SIZE` | `2` | Number of text chunks per provider request. |
| `semantic_embedding_request_concurrency` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_REQUEST_CONCURRENCY` | `4` | Maximum concurrent LiteLLM embedding requests. |
| `semantic_embedding_sync_batch_size` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_SYNC_BATCH_SIZE` | `2` | Number of prepared vector jobs flushed through the sync pipeline together. |
## Dimensions
Basic Memory needs the vector dimension before it can create SQLite or Postgres
vector tables. The OpenAI default is known, so this works without an explicit
dimension:
```bash
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=litellm
export BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL=openai/text-embedding-3-small
```
For every other LiteLLM model, set the dimension explicitly:
```bash
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=litellm
export BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL=cohere/embed-english-v3.0
export BASIC_MEMORY_SEMANTIC_EMBEDDING_DIMENSIONS=1024
```
For fixed-size models, `semantic_embedding_dimensions` is Basic Memory's local
schema and validation size. For OpenAI/Azure `text-embedding-3` models, LiteLLM
can also forward `dimensions` as a provider-side reduced-output request. Basic
Memory enables that automatically when the model string contains `text-embedding-3`.
If you use an Azure deployment alias such as `azure/<deployment-name>`, the model
string may not reveal that the underlying model supports reduced output dimensions.
Set this only when your deployment supports it:
```bash
export BASIC_MEMORY_SEMANTIC_EMBEDDING_FORWARD_DIMENSIONS=true
```
## Asymmetric Models
Some embedding models use different request roles for indexed documents and
search queries. Basic Memory automatically sets these for known LiteLLM families:
| Model Family | Document `input_type` | Query `input_type` |
|---|---|---|
| Cohere v3 embeddings | `search_document` | `search_query` |
| NVIDIA NIM retrieval embeddings | `passage` | `query` |
For any other asymmetric model, configure both roles explicitly:
```bash
export BASIC_MEMORY_SEMANTIC_EMBEDDING_DOCUMENT_INPUT_TYPE=passage
export BASIC_MEMORY_SEMANTIC_EMBEDDING_QUERY_INPUT_TYPE=query
```
Changing provider, model, dimensions, dimension-forwarding, or document/query
roles changes the meaning of stored vectors. Rebuild embeddings after any of
those changes:
```bash
bm reindex --embeddings
```
## Reindexing with a remote provider
Embedding a real corpus through a network API is far slower than local FastEmbed, and
the defaults are tuned for the local case. Two things to know before you run a full
reindex.
**Raise the sync batch size.** `semantic_embedding_sync_batch_size` defaults to `2`, and
it — not `semantic_embedding_batch_size` — governs throughput on the sync pipeline. With
the default, a full reindex can take tens of seconds *per note* against a remote provider.
Raising both to a larger value turns a multi-minute (or longer) reindex into well under a
minute for the same corpus:
```bash
export BASIC_MEMORY_SEMANTIC_EMBEDDING_SYNC_BATCH_SIZE=32
export BASIC_MEMORY_SEMANTIC_EMBEDDING_BATCH_SIZE=64
```
Stay within the provider's per-request size and rate limits — Cohere v3, for example,
accepts up to 96 inputs per embedding request.
**Changing dimensions requires recreating the vector table.** Basic Memory dimensions the
vector table on first index and refuses to mix sizes. Switching to a model with a
different dimension (for example FastEmbed 384 → OpenAI 1536 → Cohere 1024) makes a plain
`bm reindex` raise an `Embedding dimension mismatch` error. Recreate the table with a full
rebuild — files are the source of truth, so this re-indexes from disk and re-embeds
everything:
```bash
bm reset --reindex
```
To trial a provider without disturbing your existing index, point Basic Memory at a
throwaway config + database instead:
```bash
export BASIC_MEMORY_CONFIG_DIR=/tmp/bm-litellm-trial
```
## Provider Setup Examples
LiteLLM reads provider credentials from the environment. These are the examples
covered by Basic Memory's live validation harness.
### OpenAI Through LiteLLM
```bash
export OPENAI_API_KEY=sk-...
export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=litellm
export BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL=openai/text-embedding-3-small
```
### Cohere v3
```bash
export COHERE_API_KEY=...
export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=litellm
export BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL=cohere/embed-english-v3.0
export BASIC_MEMORY_SEMANTIC_EMBEDDING_DIMENSIONS=1024
```
The provider auto-selects `search_document` for indexed chunks and `search_query`
for search queries.
### Azure OpenAI
```bash
export AZURE_API_KEY=...
export AZURE_API_BASE=https://<resource-name>.openai.azure.com
export AZURE_API_VERSION=2024-02-01
export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=litellm
export BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL=azure/<deployment-name>
export BASIC_MEMORY_SEMANTIC_EMBEDDING_DIMENSIONS=1536
```
If your Azure deployment is a reduced-dimension `text-embedding-3` deployment,
set the dimension you want and enable forwarding:
```bash
export BASIC_MEMORY_SEMANTIC_EMBEDDING_DIMENSIONS=512
export BASIC_MEMORY_SEMANTIC_EMBEDDING_FORWARD_DIMENSIONS=true
```
### NVIDIA NIM
```bash
export NVIDIA_NIM_API_KEY=...
# Optional when using a custom or self-hosted NIM endpoint:
export NVIDIA_NIM_API_BASE=https://integrate.api.nvidia.com/v1
export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=litellm
export BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL=nvidia_nim/nvidia/embed-qa-4
export BASIC_MEMORY_SEMANTIC_EMBEDDING_DIMENSIONS=1024
```
The provider auto-selects `passage` for indexed chunks and `query` for search
queries.
## Testing LiteLLM Providers
Run the non-live LiteLLM unit and harness tests first:
```bash
uv run pytest tests/repository/test_litellm_provider.py \
test-int/semantic/test_litellm_live_harness.py -q
```
Run the SQLite and Postgres vector identity regressions when changing model
identity, role, or vector sync behavior:
```bash
uv run pytest \
tests/repository/test_sqlite_vector_search_repository.py::test_sqlite_embedding_model_key_includes_litellm_role_settings \
-q
BASIC_MEMORY_TEST_POSTGRES=1 uv run pytest \
tests/repository/test_postgres_search_repository.py::test_postgres_litellm_role_change_reembeds_existing_chunks \
-q
```
The Postgres command uses testcontainers, so Docker must be running.
## Live Provider Harness
The live harness makes real LiteLLM API calls and spends provider quota. It is
opt-in by design:
```bash
export OPENAI_API_KEY=sk-...
export COHERE_API_KEY=...
just test-litellm-live
```
Built-in cases run when their API keys are present:
| Case | Required Env Var | Validates |
|---|---|---|
| `openai-text-embedding-3-small` | `OPENAI_API_KEY` | OpenAI via LiteLLM, 1536 dimensions, normalized vectors, ranking sanity. |
| `cohere-embed-english-v3` | `COHERE_API_KEY` | Cohere v3 role handling, 1024 dimensions, normalized vectors, ranking sanity. |
Add provider aliases or new backends with a custom cases file:
```bash
cat > /tmp/litellm-cases.json <<'JSON'
[
{
"name": "azure-text-embedding-3-small-512",
"model": "azure/<deployment-name>",
"dimensions": 512,
"api_key_env": "AZURE_API_KEY",
"forward_dimensions": true
},
{
"name": "nvidia-embed-qa-4",
"model": "nvidia_nim/nvidia/embed-qa-4",
"dimensions": 1024,
"api_key_env": "NVIDIA_NIM_API_KEY",
"document_input_type": "passage",
"query_input_type": "query"
}
]
JSON
just test-litellm-live --cases-file /tmp/litellm-cases.json
```
For CI-style output:
```bash
just test-litellm-live --cases-file /tmp/litellm-cases.json --json
```
The harness embeds two documents and one query, validates dimension and vector
normalization, checks that the authentication query ranks the authentication
document above a distractor, and reports latency plus role/dimension settings.
## Provider Reference
LiteLLM's own provider and embedding docs are the source of truth for current
model strings and credential names:
- [LiteLLM embedding models](https://docs.litellm.ai/docs/embedding/supported_embedding)
- [LiteLLM Azure OpenAI provider](https://docs.litellm.ai/docs/providers/azure)
- [LiteLLM NVIDIA NIM provider](https://docs.litellm.ai/docs/providers/nvidia_nim)
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# 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.
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# Manual Pages
Basic Memory's manual is written in the style of Unix man pages — and
implemented as Basic Memory notes ([#952](https://github.com/basicmachines-co/basic-memory/issues/952)).
Every page is a markdown note conforming to the `Manpage` schema, `SEE ALSO`
entries are real knowledge-graph relations, and every example on every page
was executed against a live project before the page shipped. The manual
documents the tools; the tools verify the manual.
## Where it lives
The canonical manual is the **`manual` project in the Basic Memory team
workspace** (cloud, shared). Anyone can build their own: the schema ships as
an opt-in seed at `plugins/claude-code/schemas/manpage.md` — copy it into any
project's folder and start writing pages against it.
Layout:
```
manual/
├── schemas/Manpage.md # the manpage schema (type: schema)
├── man1/ # CLI commands bm(1), bm-status(1), ...
├── man3/ # MCP tools write-note(3), search-notes(3), ...
├── man5/ # file formats bm-note(5), bm-observation(5), ...
├── man7/ # concepts basic-memory(7), semantic-memory(7), ...
├── playground/ # scratch notes for destructive examples
└── diagrams/ # canvas visualizations of the manual graph
```
### Why "man1", "man3", "man5"?
The folder names are Unix's, unchanged since 1971. The manual is divided
into numbered **sections**, pages physically live in directories named
after them (`/usr/share/man/man1`, `man5`, ...), and the number tells you
what *kind* of thing is documented — not importance, not reading order:
- **1** — user commands (`ls`, `grep`)
- **2** — system calls
- **3** — library functions / APIs (`printf(3)`)
- **4** — devices
- **5** — file formats and config files (`crontab(5)`, `passwd(5)`)
- **6** — games (really)
- **7** — miscellanea: concepts, conventions, overviews (`regex(7)`, `signal(7)`)
- **8** — system administration
That's also why man page names carry the parenthesized number —
`crontab(1)` is the command, `crontab(5)` is the file format, same name in
two sections. `man 5 crontab` picks the section explicitly.
This manual copies that layout with the sections that have a Basic Memory
analog:
- **man1/**`bm` CLI commands → `bm-status(1)`
- **man3/** — MCP tools, our equivalent of the "library API" section → `write-note(3)`
- **man5/** — file formats: note syntax, observations, relations, schemas → `bm-note(5)`
- **man7/** — concepts → `basic-memory(7)`, `semantic-memory(7)`
- **8** is reserved for admin/cloud operations but has no pages yet; 2, 4,
and 6 have no analog (no system calls, no devices, and no games — yet)
When a page says `see_also [[bm-note(5)]]`, the `(5)` reads "the
file-format page," exactly the way a Unix manual cross-references — except
here it's a traversable relation in the graph instead of a typographic
convention. The manual explains its own conventions in `man-pages(7)`
fittingly, the same page name Linux uses for this, and that almost nobody
ever reads.
## Page anatomy
Pages use the classic headers where applicable: `NAME`, `SYNOPSIS`,
`DESCRIPTION`, `PARAMETERS`, `MCP USAGE`, `CLI EQUIVALENT`, `EXAMPLES`,
`GOTCHAS`, `SEE ALSO`. Frontmatter (validated by the schema):
```yaml
type: manpage
section: 3 # 1 | 3 | 5 | 7 | 8
name: write-note # page name without section suffix
summary: create or overwrite a markdown note in the knowledge base
generated: hand # hand | registry | typer (regeneration ownership)
tool: write_note # section-3 pages: the MCP tool documented
command: basic-memory status # section-1 pages: the CLI command documented
verified: 0.21.6 mcp+cli # version + path(s) that proved the page
```
Field knowledge accumulates as observations — `[gotcha]`, `[bug]` (with issue
links), `[pattern]` — and `SEE ALSO` entries are `see_also` relations, so the
manual is a navigable graph, not a folder of files.
## How to use it
Man-style reads (any MCP client or the CLI):
```bash
# read a page
bm tool read-note "man3/write-note-3" --project manual
# apropos — find pages by section, tool, or text
bm tool search-notes --project manual # then filter, or via MCP:
# search_notes(project="manual", metadata_filters={"type": "manpage", "section": 3})
# search_notes(project="manual", metadata_filters={"type": "manpage", "tool": "write_note"})
# traverse SEE ALSO from any page
# build_context(url="man3/write-note-3", project="manual")
```
A future `bm man <topic>` command is thin sugar over exactly these calls.
And for the real thing — `man bm` in an actual terminal:
```bash
bm man install # copies bundled groff pages to ~/.local/share/man
man bm # the overview page, rendered by man(1)
man basic-memory # same page via its alias
```
`bm man install` warns with a one-line `MANPATH` fix if the install root
isn't searched by your `man`. Agents with shell access can use `man bm` as
an offline quick reference; the full per-tool detail stays in the manual
project's section-3 pages.
## The verification discipline
Two rules make the manual trustworthy:
1. **Examples must have run.** An `EXAMPLES` (or `MCP USAGE` / `CLI
EQUIVALENT`) block contains only commands that actually executed against
the manual project. Destructive operations (`delete_note`, `move_note`,
destructive `edit_note`) run only against `playground/` notes — never
against pages. The `verified:` field records the version and which path
proved the page: `mcp` (live service), `cli` (dev checkout), or both.
2. **The schema is the linter.** Validate the whole manual any time:
```bash
bm tool schema-validate manpage --project manual
# → {"total_notes": 38, "valid_count": 38, "warning_count": 0, ...}
```
`bm orphans --project manual` confirms every page is connected to the
graph, and `schema_diff`/`schema_infer` report drift between the schema
and how pages are actually written.
Because verification exercises real tool calls against the live service,
building the manual doubles as an end-to-end smoke test. The initial build
found six bugs in one pass (#954#959) — including the verification rule
catching a test that asserted a bug as expected output (#958).
## Adding or updating a page
1. Run the commands you intend to document; keep the actual output.
2. Write the page with `write_note`, passing frontmatter through the
`metadata` parameter (nested YAML in content frontmatter is unreliable on
some clients):
```
write_note(title="my-tool(3)", directory="man3", project="manual",
note_type="manpage",
metadata={"section": 3, "name": "my-tool",
"summary": "...", "generated": "hand",
"tool": "my_tool", "verified": "<version> mcp"})
```
3. Link related pages in `SEE ALSO` with `see_also [[other-page(3)]]`.
Forward references to pages that don't exist yet are fine — they resolve
automatically when the target is written.
4. Validate: `bm tool schema-validate manpage --project manual`.
For mechanical updates to generated sections, prefer `edit_note` with
`replace_section` / `insert_after_section` so curated content (EXAMPLES,
GOTCHAS, SEE ALSO, observations) survives — that ownership split is what the
`generated:` field declares.
## Roadmap
- **Registry generator** — section-3 SYNOPSIS/PARAMETERS generated from the
MCP tool registry (docstrings + pydantic schemas), section-1 from Typer
help; the hand-written corpus is the template spec. Regenerate-and-diff in
CI becomes the drift gate.
- **`bm man <topic>`** — CLI sugar over `read_note` + metadata search.
(`bm man install` + a hand-written `bm.1` already ship — the first slice
of [#610](https://github.com/basicmachines-co/basic-memory/issues/610);
the generator will produce per-command pages from the same extraction.)
- **Docs site** — the notes remain canonical for sections 5 and 7, code is
canonical for 1 and 3; both render to the hosted docs site.
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# 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: __
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@@ -1,260 +0,0 @@
# 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
```
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@@ -1,344 +0,0 @@
# 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.
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# 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.
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# 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
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# 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), `"openai"` (API), or `"litellm"` (multi-provider API, **experimental** — advanced users only). |
| `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` | Provider default | Vector dimensions. 384 for FastEmbed, 1536 for OpenAI/LiteLLM OpenAI. Required when using a non-default LiteLLM model. |
| `semantic_embedding_forward_dimensions` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_FORWARD_DIMENSIONS` | Auto | LiteLLM-only override for whether configured dimensions are sent as a provider-side output-size request. |
| `semantic_embedding_batch_size` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_BATCH_SIZE` | `2` | Number of texts to embed per batch. |
| `semantic_embedding_document_input_type` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_DOCUMENT_INPUT_TYPE` | Auto for known LiteLLM models | Optional LiteLLM `input_type` for indexed document/passages. |
| `semantic_embedding_query_input_type` | `BASIC_MEMORY_SEMANTIC_EMBEDDING_QUERY_INPUT_TYPE` | Auto for known LiteLLM models | Optional LiteLLM `input_type` for search queries. |
| `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-...
```
### LiteLLM
> **Experimental — advanced users only.** The LiteLLM provider is experimental and aimed at users comfortable operating remote embedding backends: paid API calls, per-model dimension and input-role configuration, and slower reindexing of large corpora. For most users, FastEmbed (local, default) is recommended. See [LiteLLM Provider](litellm-provider.md) for the caveats and tuning.
Uses the LiteLLM SDK to call embedding models from providers such as OpenAI, Cohere, Azure, Bedrock, NVIDIA NIM, and other LiteLLM-supported backends. Requires the provider's API credentials.
For the full option reference, provider setup examples, and live validation harness, see [LiteLLM Provider](litellm-provider.md).
```bash
export BASIC_MEMORY_SEMANTIC_SEARCH_ENABLED=true
export BASIC_MEMORY_SEMANTIC_EMBEDDING_PROVIDER=litellm
export BASIC_MEMORY_SEMANTIC_EMBEDDING_MODEL=cohere/embed-english-v3.0
export BASIC_MEMORY_SEMANTIC_EMBEDDING_DIMENSIONS=1024
export COHERE_API_KEY=...
```
Basic Memory creates vector tables before the first embedding call, so non-default LiteLLM models must set `BASIC_MEMORY_SEMANTIC_EMBEDDING_DIMENSIONS`. The LiteLLM OpenAI default (`openai/text-embedding-3-small`) uses 1536 dimensions automatically.
For fixed-size LiteLLM models, dimensions are used as Basic Memory's local vector schema and
validation size. Basic Memory automatically sends dimensions as a provider-side output-size
request for `text-embedding-3` model strings, where LiteLLM/OpenAI support reduced output
dimensions. If an Azure/OpenAI deployment uses an arbitrary LiteLLM model string such as
`azure/<deployment-name>` and the underlying model supports reduced dimensions, set
`BASIC_MEMORY_SEMANTIC_EMBEDDING_FORWARD_DIMENSIONS=true`.
Some retrieval models are asymmetric: indexed passages and search queries must be embedded with different provider parameters. Basic Memory automatically sets LiteLLM `input_type` for known asymmetric model families:
- Cohere v3: documents use `search_document`, queries use `search_query`
- NVIDIA NIM retrieval models: documents use `passage`, queries use `query`
For other asymmetric LiteLLM models, set the input types explicitly:
```bash
export BASIC_MEMORY_SEMANTIC_EMBEDDING_DOCUMENT_INPUT_TYPE=passage
export BASIC_MEMORY_SEMANTIC_EMBEDDING_QUERY_INPUT_TYPE=query
```
#### Live LiteLLM Validation
Provider APIs differ in subtle ways: some accept `dimensions`, some require separate
document/query roles, and some route through deployment aliases that do not reveal the
underlying model name. Before adding or changing LiteLLM model support, run the opt-in live
evaluation harness:
```bash
export OPENAI_API_KEY=sk-...
export COHERE_API_KEY=...
just test-litellm-live
```
The built-in live cases cover:
| Case | Required key | What it validates |
|---|---|---|
| `openai/text-embedding-3-small` | `OPENAI_API_KEY` | Standard LiteLLM OpenAI embedding calls and normalized 1536-dimensional output. |
| `cohere/embed-english-v3.0` | `COHERE_API_KEY` | Cohere v3 asymmetric `search_document` / `search_query` handling and fixed 1024-dimensional output. |
The harness embeds two documents and one query, checks vector dimensions and normalization,
then verifies the authentication query ranks the authentication document above the distractor.
It prints a table with per-model scores, norms, latency, role settings, and dimension-forwarding
mode.
To validate provider aliases or additional LiteLLM backends, save custom JSON cases:
```bash
export AZURE_API_KEY=...
export AZURE_API_BASE=https://example.openai.azure.com
export AZURE_API_VERSION=2024-02-01
cat > /tmp/litellm-azure-cases.json <<'JSON'
[
{
"name": "azure-text-embedding-3-small-512",
"model": "azure/<deployment-name>",
"dimensions": 512,
"api_key_env": "AZURE_API_KEY",
"forward_dimensions": true
}
]
JSON
just test-litellm-live --cases-file /tmp/litellm-azure-cases.json
```
NVIDIA NIM retrieval models can be checked the same way:
```bash
export NVIDIA_NIM_API_KEY=...
cat > /tmp/litellm-nvidia-cases.json <<'JSON'
[
{
"name": "nvidia-embed-qa-4",
"model": "nvidia_nim/nvidia/embed-qa-4",
"dimensions": 1024,
"api_key_env": "NVIDIA_NIM_API_KEY",
"document_input_type": "passage",
"query_input_type": "query"
}
]
JSON
just test-litellm-live --cases-file /tmp/litellm-nvidia-cases.json
```
For repeatable local runs, put the same JSON array in a file and pass
`just test-litellm-live --cases-file path/to/litellm-cases.json`.
When switching providers, models, dimensions, or LiteLLM document/query input types, rebuild embeddings:
```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`, `openai`, and `litellm`
- **Model change**: After changing `semantic_embedding_model`
- **Dimension change**: After changing `semantic_embedding_dimensions`
- **LiteLLM role change**: After changing `semantic_embedding_document_input_type` or `semantic_embedding_query_input_type`
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
- **Local Docker**: use `docker-compose-postgres.yml` (`pgvector/pgvector:pg17`). Plain `postgres:17` lacks the extension; run `CREATE EXTENSION IF NOT EXISTS vector;` on any external instance before first migration.
- **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.
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# 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.
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# 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)
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# 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
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## 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).
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@@ -0,0 +1,31 @@
name: Daily Traction Report
on:
schedule:
- cron: '0 14 * * 1-5' # 9 AM EST weekdays
workflow_dispatch: # Manual trigger for testing
jobs:
generate-report:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.9'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r scripts/requirements.txt
- name: Generate Daily Traction Report
run: python scripts/daily_report.py
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
DISCORD_WEBHOOK: ${{ secrets.DISCORD_WEBHOOK }}
REDDIT_CLIENT_ID: ${{ secrets.REDDIT_CLIENT_ID }}
REDDIT_SECRET: ${{ secrets.REDDIT_SECRET }}
YOUTUBE_API_KEY: ${{ secrets.YOUTUBE_API_KEY }}
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@@ -0,0 +1,373 @@
#!/usr/bin/env python3
"""
Basic Memory Daily Traction Report - Enhanced with Growth Tracking
Automated tracking across GitHub, Reddit, YouTube with daily change indicators
"""
import os
import requests
import json
from datetime import datetime, timedelta
import praw
from googleapiclient.discovery import build
from dateutil import parser
import base64
class BasicMemoryTracker:
def __init__(self):
self.github_token = os.getenv('GITHUB_TOKEN')
self.discord_webhook = os.getenv('DISCORD_WEBHOOK')
self.youtube_api_key = os.getenv('YOUTUBE_API_KEY')
# Reddit setup
self.reddit = praw.Reddit(
client_id=os.getenv('REDDIT_CLIENT_ID'),
client_secret=os.getenv('REDDIT_SECRET'),
user_agent='BasicMemoryTracker:v1.0'
)
# YouTube setup
self.youtube = build('youtube', 'v3', developerKey=self.youtube_api_key)
self.repo_owner = 'basicmachines-co'
self.repo_name = 'basic-memory'
self.youtube_channel = 'basicmachines-co'
self.metrics_file = 'data/daily_metrics.json'
def get_previous_metrics(self):
"""Get yesterday's metrics from GitHub repo storage"""
try:
headers = {'Authorization': f'token {self.github_token}'}
url = f'https://api.github.com/repos/{self.repo_owner}/{self.repo_name}/contents/{self.metrics_file}'
response = requests.get(url, headers=headers)
if response.status_code == 200:
file_data = response.json()
content = base64.b64decode(file_data['content']).decode('utf-8')
return json.loads(content)
else:
print("📝 No previous metrics found - this is the first run!")
return {}
except Exception as e:
print(f"⚠️ Could not load previous metrics: {e}")
return {}
def save_current_metrics(self, metrics):
"""Save today's metrics to GitHub repo for tomorrow's comparison"""
try:
headers = {'Authorization': f'token {self.github_token}'}
# Prepare data
metrics_data = {
'date': datetime.now().isoformat(),
'metrics': metrics
}
content = json.dumps(metrics_data, indent=2)
encoded_content = base64.b64encode(content.encode()).decode()
# Check if file exists
file_url = f'https://api.github.com/repos/{self.repo_owner}/{self.repo_name}/contents/{self.metrics_file}'
existing_response = requests.get(file_url, headers=headers)
payload = {
'message': f'📊 Daily metrics update - {datetime.now().strftime("%Y-%m-%d")}',
'content': encoded_content
}
if existing_response.status_code == 200:
# File exists, update it
payload['sha'] = existing_response.json()['sha']
response = requests.put(file_url, headers=headers, json=payload)
else:
# File doesn't exist, create it
response = requests.put(file_url, headers=headers, json=payload)
if response.status_code in [200, 201]:
print("✅ Metrics saved for tomorrow's comparison!")
else:
print(f"⚠️ Failed to save metrics: {response.status_code}")
except Exception as e:
print(f"⚠️ Error saving metrics: {e}")
def calculate_change(self, current, previous, key):
"""Calculate the change between current and previous values"""
if not previous or key not in previous:
return 0, "🆕"
change = current - previous[key]
if change > 0:
return change, "📈"
elif change < 0:
return abs(change), "📉"
else:
return 0, "➡️"
def format_change(self, change, direction):
"""Format the change indicator for display"""
if direction == "🆕":
return "🆕"
elif direction == "📈":
return f"(+{change})"
elif direction == "📉":
return f"(-{change})"
else:
return "(±0)"
def get_github_metrics(self):
"""Get GitHub repository metrics"""
try:
headers = {'Authorization': f'token {self.github_token}'}
# Repository stats
repo_url = f'https://api.github.com/repos/{self.repo_owner}/{self.repo_name}'
repo_response = requests.get(repo_url, headers=headers)
repo_data = repo_response.json()
# Traffic stats (requires push access)
traffic_url = f'https://api.github.com/repos/{self.repo_owner}/{self.repo_name}/traffic/views'
traffic_response = requests.get(traffic_url, headers=headers)
traffic_data = traffic_response.json() if traffic_response.status_code == 200 else {}
# Recent issues
issues_url = f'https://api.github.com/repos/{self.repo_owner}/{self.repo_name}/issues'
issues_response = requests.get(issues_url, headers=headers)
issues_data = issues_response.json() if issues_response.status_code == 200 else []
return {
'stars': repo_data.get('stargazers_count', 0),
'forks': repo_data.get('forks_count', 0),
'watchers': repo_data.get('watchers_count', 0),
'open_issues': repo_data.get('open_issues_count', 0),
'traffic_views': traffic_data.get('count', 0),
'traffic_unique': traffic_data.get('uniques', 0),
'recent_issues': len([i for i in issues_data if
parser.parse(i['created_at']).date() >= (datetime.now() - timedelta(days=1)).date()])
}
except Exception as e:
print(f"GitHub API error: {e}")
return {'error': str(e)}
def get_reddit_metrics(self):
"""Get Reddit metrics for Basic Memory mentions"""
try:
metrics = {
'total_mentions': 0,
'subreddit_members': 0,
'top_posts': [],
'hot_discussions': []
}
# Search for Basic Memory mentions
search_results = list(self.reddit.subreddit('all').search(
'Basic Memory', time_filter='day', limit=25
))
metrics['total_mentions'] = len(search_results)
# Get top posts
for post in search_results[:3]:
metrics['top_posts'].append({
'title': post.title[:50] + '...' if len(post.title) > 50 else post.title,
'score': post.score,
'subreddit': post.subreddit.display_name,
'num_comments': post.num_comments
})
# Check r/BasicMemory if it exists
try:
basic_memory_sub = self.reddit.subreddit('BasicMemory')
metrics['subreddit_members'] = basic_memory_sub.subscribers
except:
metrics['subreddit_members'] = 0
return metrics
except Exception as e:
print(f"Reddit API error: {e}")
return {'error': str(e)}
def get_youtube_metrics(self):
"""Get YouTube channel metrics"""
try:
# Get channel statistics
channel_response = self.youtube.channels().list(
part='statistics,snippet',
forUsername=self.youtube_channel
).execute()
if not channel_response['items']:
# Try by channel handle
search_response = self.youtube.search().list(
part='snippet',
q=f'@{self.youtube_channel}',
type='channel',
maxResults=1
).execute()
if search_response['items']:
channel_id = search_response['items'][0]['snippet']['channelId']
channel_response = self.youtube.channels().list(
part='statistics,snippet',
id=channel_id
).execute()
if channel_response['items']:
stats = channel_response['items'][0]['statistics']
return {
'subscribers': int(stats.get('subscriberCount', 0)),
'total_views': int(stats.get('viewCount', 0)),
'video_count': int(stats.get('videoCount', 0))
}
else:
return {'error': 'Channel not found'}
except Exception as e:
print(f"YouTube API error: {e}")
return {'error': str(e)}
def create_discord_embed(self, current_metrics, previous_metrics):
"""Create beautiful Discord embed with all metrics and growth indicators"""
github_data = current_metrics.get('github', {})
reddit_data = current_metrics.get('reddit', {})
youtube_data = current_metrics.get('youtube', {})
prev_github = previous_metrics.get('github', {})
prev_reddit = previous_metrics.get('reddit', {})
prev_youtube = previous_metrics.get('youtube', {})
# Calculate changes
star_change, star_dir = self.calculate_change(github_data.get('stars', 0), prev_github, 'stars')
sub_change, sub_dir = self.calculate_change(youtube_data.get('subscribers', 0), prev_youtube, 'subscribers')
view_change, view_dir = self.calculate_change(youtube_data.get('total_views', 0), prev_youtube, 'total_views')
reddit_change, reddit_dir = self.calculate_change(reddit_data.get('total_mentions', 0), prev_reddit, 'total_mentions')
member_change, member_dir = self.calculate_change(reddit_data.get('subreddit_members', 0), prev_reddit, 'subreddit_members')
# Calculate total reach
total_reach = (
github_data.get('traffic_unique', 0) +
reddit_data.get('total_mentions', 0) * 100 +
youtube_data.get('total_views', 0)
)
embed = {
"title": "🚀 Basic Memory Daily Traction Report",
"description": f"📅 {datetime.now().strftime('%A, %B %d, %Y')}",
"color": 0x00ff88,
"fields": [
{
"name": "⭐ GitHub Metrics",
"value": f"""
**Stars:** {github_data.get('stars', 'N/A')} {star_dir} {self.format_change(star_change, star_dir)}
**Forks:** {github_data.get('forks', 'N/A')} 🍴
**Traffic:** {github_data.get('traffic_unique', 'N/A')} unique visitors 👀
**Issues:** {github_data.get('recent_issues', 0)} new today 🐛
""".strip(),
"inline": True
},
{
"name": "🗨️ Reddit Activity",
"value": f"""
**Mentions:** {reddit_data.get('total_mentions', 'N/A')} {reddit_dir} {self.format_change(reddit_change, reddit_dir)}
**r/BasicMemory:** {reddit_data.get('subreddit_members', 'N/A')} {member_dir} {self.format_change(member_change, member_dir)}
**Hot Posts:** {len(reddit_data.get('top_posts', []))} trending 🔥
""".strip(),
"inline": True
},
{
"name": "📺 YouTube Stats",
"value": f"""
**Subscribers:** {youtube_data.get('subscribers', 'N/A')} {sub_dir} {self.format_change(sub_change, sub_dir)}
**Total Views:** {youtube_data.get('total_views', 'N/A'):,} {view_dir} {self.format_change(view_change, view_dir)}
**Videos:** {youtube_data.get('video_count', 'N/A')} 🎬
""".strip(),
"inline": True
}
],
"footer": {
"text": f"🤖 Automated by Basic Memory • Daily Reach: {total_reach:,}"
},
"timestamp": datetime.now().isoformat()
}
# Add top Reddit posts if available
if reddit_data.get('top_posts'):
top_post = reddit_data['top_posts'][0]
embed["fields"].append({
"name": "🔥 Top Reddit Post",
"value": f"**{top_post['title']}**\n📊 {top_post['score']} upvotes • 💬 {top_post['num_comments']} comments\n📍 r/{top_post['subreddit']}",
"inline": False
})
return embed
def send_discord_report(self, embed):
"""Send the report to Discord"""
try:
payload = {"embeds": [embed]}
response = requests.post(self.discord_webhook, json=payload)
if response.status_code == 204:
print("✅ Discord report sent successfully!")
return True
else:
print(f"❌ Discord webhook failed: {response.status_code}")
print(response.text)
return False
except Exception as e:
print(f"Discord send error: {e}")
return False
def run_daily_report(self):
"""Main function to generate and send daily report"""
print("🚀 Starting Basic Memory Daily Traction Report...")
# Load previous metrics
print("📊 Loading previous metrics...")
previous_metrics = self.get_previous_metrics()
# Collect current metrics
print("📊 Collecting GitHub metrics...")
github_data = self.get_github_metrics()
print("🗨️ Collecting Reddit metrics...")
reddit_data = self.get_reddit_metrics()
print("📺 Collecting YouTube metrics...")
youtube_data = self.get_youtube_metrics()
# Combine current metrics
current_metrics = {
'github': github_data,
'reddit': reddit_data,
'youtube': youtube_data
}
# Create and send report
print("🎨 Creating Discord embed with growth tracking...")
embed = self.create_discord_embed(current_metrics, previous_metrics.get('metrics', {}))
print("📤 Sending to Discord...")
success = self.send_discord_report(embed)
# Save current metrics for tomorrow
print("💾 Saving metrics for tomorrow's comparison...")
self.save_current_metrics(current_metrics)
if success:
print("🎉 Daily traction report completed successfully!")
else:
print("😞 Report failed to send")
# Print summary for GitHub Actions logs
print(f"""
📊 DAILY SUMMARY:
GitHub Stars: {github_data.get('stars', 'Error')}
👥 Reddit Mentions: {reddit_data.get('total_mentions', 'Error')}
📺 YouTube Subscribers: {youtube_data.get('subscribers', 'Error')}
📺 YouTube Views: {youtube_data.get('total_views', 'Error')}
""")
if __name__ == "__main__":
tracker = BasicMemoryTracker()
tracker.run_daily_report()
@@ -0,0 +1,4 @@
requests==2.31.0
praw==7.7.1
google-api-python-client==2.108.0
python-dateutil==2.8.2
-7
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@@ -1,7 +0,0 @@
{
"$schema": "https://glama.ai/mcp/schemas/server.json",
"maintainers": [
"phernandez",
"groksrc"
]
}
-9
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@@ -1,9 +0,0 @@
[run]
source = .
omit =
tests/*
tests/**/*
[report]
fail_under = 85
show_missing = True
-48
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@@ -1,48 +0,0 @@
name: integration
# Heavier than test.yml — installs the real `basic-memory` CLI via uv, runs
# every bm_* tool against a live `bm mcp` subprocess. Catches BM-API drift
# (e.g., a bm release renaming a tool argument) before our users see it.
concurrency:
group: hbm-integration-${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
on:
push:
branches: [main]
workflow_dispatch:
jobs:
integration:
name: Integration tests (real bm + mcp)
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- uses: actions/checkout@v4
- name: Install uv
# No cache (no uv.lock to key off — see test.yml comment).
uses: astral-sh/setup-uv@v3
- name: Set up Python 3.12
# basic-memory itself requires 3.12+; the bm install needs that.
# Hermes-runtime compatibility (3.11) is covered by test.yml.
run: uv python install 3.12
- name: Install basic-memory CLI via uv
run: |
uv tool install basic-memory
# uv puts entry-point shims under ~/.local/bin
echo "$HOME/.local/bin" >> "$GITHUB_PATH"
- name: Verify bm is on PATH
run: |
which bm
bm --version
- name: Run integration tests
env:
BM_INTEGRATION: "1"
run: |
uv run --with pytest --with mcp --python 3.12 pytest tests/test_integration.py -v
-36
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@@ -1,36 +0,0 @@
name: pr-title
on:
pull_request:
types: [opened, edited, synchronize]
jobs:
semantic-pr-title:
runs-on: ubuntu-latest
steps:
- uses: amannn/action-semantic-pull-request@v5
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
with:
# Conventional-commit types we accept in PR titles + commit subjects.
types: |
feat
fix
chore
docs
style
refactor
perf
test
build
ci
# Single-file plugin — no real submodule structure. We don't require
# a scope, but if a contributor uses one we accept these:
scopes: |
core
tests
ci
docs
deps
requireScope: false
requireScopeForBreakingChange: true
-194
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@@ -1,194 +0,0 @@
name: release
# Manual trigger: Actions → release → Run workflow. Always runs against main
# (the workflow validates GITHUB_REF). Steps:
# 1. Compute the new version from the `version` input (patch/minor/major
# or explicit semver). The *current* version is read from the latest
# git tag (`v*.*.*`) — NOT from __init__.py. This is robust to PRs that
# pre-bump __init__.py: the bump always runs from the last released
# version, not from whatever the working files happen to say.
# 2. Update __version__ in __init__.py and version in plugin.yaml to match
# the new tag — bringing the files in sync if a PR pre-bumped them.
# 3. Commit as `chore(release): vX.Y.Z`, tag, push to main + push the tag.
# 4. Publish a GitHub Release. Body is the matching `## [X.Y.Z]` block from
# CHANGELOG.md when present; otherwise auto-generated release notes.
#
# Recommended flow: land a PR that adds a `## [X.Y.Z]` section to CHANGELOG.md
# first, then run this workflow with the matching version so the release notes
# are the hand-written changelog instead of commit-message-derived notes.
on:
workflow_dispatch:
inputs:
version:
description: "Version bump (`patch`, `minor`, `major`) or explicit semver (`0.3.0`)"
required: true
default: "patch"
permissions:
contents: write
concurrency:
group: release-${{ github.ref }}
cancel-in-progress: false
jobs:
release:
name: Tag and Publish GitHub Release
runs-on: ubuntu-latest
steps:
- name: Validate trigger is main
run: |
if [ "$GITHUB_REF" != "refs/heads/main" ]; then
echo "::error::release must run against main. Got $GITHUB_REF"
exit 1
fi
- uses: actions/checkout@v4
with:
fetch-depth: 0
token: ${{ secrets.GITHUB_TOKEN }}
- name: Compute new version
id: bump
run: |
set -euo pipefail
VERSION_INPUT="${{ github.event.inputs.version }}"
# Current = latest released tag (sort by version, descending; pick
# the first v*.*.* tag). NOT __init__.py — a PR may have pre-bumped
# the version files, and we don't want to double-bump on top of
# that. The bump is computed from the last *released* version.
LAST_TAG=$(git tag --list 'v*.*.*' --sort=-v:refname | head -1 || true)
if [ -z "$LAST_TAG" ]; then
# First release in the repo. Seed with 0.0.0 so a `patch` bump
# yields v0.0.1, `minor` yields v0.1.0, `major` yields v1.0.0.
# Explicit semver inputs bypass the seed entirely.
CURRENT="0.0.0"
echo "No prior v*.*.* tag found; seeding current=0.0.0"
else
CURRENT="${LAST_TAG#v}"
echo "Latest released tag: $LAST_TAG (current=$CURRENT)"
fi
if [[ "$VERSION_INPUT" =~ ^(patch|minor|major)$ ]]; then
IFS=. read -r MAJ MIN PAT <<< "$CURRENT"
case "$VERSION_INPUT" in
major) MAJ=$((MAJ + 1)); MIN=0; PAT=0 ;;
minor) MIN=$((MIN + 1)); PAT=0 ;;
patch) PAT=$((PAT + 1)) ;;
esac
NEW="${MAJ}.${MIN}.${PAT}"
elif [[ "$VERSION_INPUT" =~ ^[0-9]+\.[0-9]+\.[0-9]+$ ]]; then
NEW="$VERSION_INPUT"
else
echo "::error::Invalid version input: '$VERSION_INPUT'"
echo "::error::Use patch|minor|major or explicit X.Y.Z"
exit 1
fi
if [ "$NEW" = "$CURRENT" ]; then
echo "::error::New version equals last released ($NEW). Pick a different version."
exit 1
fi
# Sanity check: refuse if __init__.py is already ahead of the
# version we're about to ship. Catches the "PR bumped to 0.5.0 but
# workflow was asked for a patch that would land 0.1.8" foot-gun
# before it overwrites the files.
FILE_VERSION=$(grep -E '^__version__ = ' __init__.py \
| sed -E 's/^__version__ = "([^"]+)".*/\1/')
if [ -n "$FILE_VERSION" ] && [ "$FILE_VERSION" != "$CURRENT" ] && [ "$FILE_VERSION" != "$NEW" ]; then
echo "::error::__init__.py reports version $FILE_VERSION, but the release would ship $NEW (last tag: $CURRENT)."
echo "::error::Reconcile by picking a version input that matches __init__.py, or roll __init__.py back to $CURRENT."
exit 1
fi
echo "New version: $NEW"
echo "current=$CURRENT" >> "$GITHUB_OUTPUT"
echo "version=$NEW" >> "$GITHUB_OUTPUT"
echo "tag=v$NEW" >> "$GITHUB_OUTPUT"
- name: Refuse if tag already exists
run: |
set -euo pipefail
TAG="${{ steps.bump.outputs.tag }}"
if git rev-parse --verify "refs/tags/$TAG" >/dev/null 2>&1; then
echo "::error::Tag $TAG already exists locally. Pick a different version."
exit 1
fi
if git ls-remote --tags origin "$TAG" | grep -q "refs/tags/$TAG$"; then
echo "::error::Tag $TAG already exists on origin. Pick a different version."
exit 1
fi
- name: Update version files
run: |
set -euo pipefail
NEW="${{ steps.bump.outputs.version }}"
sed -i -E "s/^__version__ = \"[^\"]+\"/__version__ = \"${NEW}\"/" __init__.py
sed -i -E "s/^version: .*/version: ${NEW}/" plugin.yaml
# Verify both files changed and that the new version is present.
grep -q "^__version__ = \"${NEW}\"" __init__.py
grep -q "^version: ${NEW}$" plugin.yaml
echo "--- diff ---"
git --no-pager diff -- __init__.py plugin.yaml
- name: Configure Git identity
run: |
git config user.name "github-actions[bot]"
git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
- name: Commit and tag
run: |
set -euo pipefail
TAG="${{ steps.bump.outputs.tag }}"
git add __init__.py plugin.yaml
if git diff --cached --quiet; then
# PR already bumped the files to the target version. Tag the
# existing HEAD rather than creating an empty release commit.
echo "Version files already at ${TAG}; tagging current HEAD."
else
git commit -m "chore(release): ${TAG}"
fi
git tag -a "${TAG}" -m "${TAG}"
- name: Push commit and tag
run: |
set -euo pipefail
# HEAD push is a no-op when nothing was committed in this run.
git push origin HEAD:main
git push origin "${{ steps.bump.outputs.tag }}"
- name: Extract CHANGELOG section for this version
id: changelog
run: |
set -euo pipefail
VERSION="${{ steps.bump.outputs.version }}"
# Pull lines between `## [VERSION]` and the next `## [` heading.
SECTION=$(awk -v ver="$VERSION" '
$0 ~ "^## \\[" ver "\\]" { found=1; next }
found && /^## \[/ { exit }
found { print }
' CHANGELOG.md)
if [ -z "$SECTION" ]; then
echo "::warning::No CHANGELOG.md section found for v${VERSION} — falling back to auto-generated release notes."
echo "has_section=false" >> "$GITHUB_OUTPUT"
else
echo "has_section=true" >> "$GITHUB_OUTPUT"
{
echo 'body<<EOF_CHANGELOG'
echo "$SECTION"
echo 'EOF_CHANGELOG'
} >> "$GITHUB_OUTPUT"
fi
- name: Create GitHub Release
uses: softprops/action-gh-release@v2
with:
tag_name: ${{ steps.bump.outputs.tag }}
name: ${{ steps.bump.outputs.tag }}
body: ${{ steps.changelog.outputs.body }}
generate_release_notes: ${{ steps.changelog.outputs.has_section == 'false' }}
token: ${{ secrets.GITHUB_TOKEN }}
-39
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@@ -1,39 +0,0 @@
name: tests
# Cancel an in-progress run when a new commit lands on the same branch — the
# latest result is the one we care about.
concurrency:
group: hbm-tests-${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
on:
# Branch pushes already cover PRs (the PR branch tip is what's being tested),
# so we don't run the matrix twice for the same commit on push + pull_request.
push:
jobs:
unit:
name: Unit tests (Python ${{ matrix.python-version }})
runs-on: ubuntu-latest
timeout-minutes: 10
strategy:
fail-fast: false
matrix:
# 3.11 is the runtime Hermes itself ships on today; 3.12-3.14 cover
# forward-compat for whenever Hermes upgrades.
python-version: ["3.11", "3.12", "3.13", "3.14"]
steps:
- uses: actions/checkout@v4
- name: Install uv
# No `enable-cache: true` — the action's default cache key globs for
# `uv.lock`, which we don't ship (we use `uv run --with` instead of
# `uv sync`). Without a lock file the cache step errors out.
uses: astral-sh/setup-uv@v3
- name: Set up Python ${{ matrix.python-version }}
run: uv python install ${{ matrix.python-version }}
- name: Run unit tests
run: uv run --with pytest --python ${{ matrix.python-version }} pytest -q
-6
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@@ -1,6 +0,0 @@
__pycache__/
*.pyc
.venv/
.DS_Store
.pytest_cache/
*.egg-info/
-133
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@@ -1,133 +0,0 @@
# Changelog
All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [0.3.2] — 2026-05-23
### Fixed
- **Let Basic Memory v0.21.3 self-route workspace-qualified identifiers and URLs.** Hermes no longer injects its configured default project into `bm_read`, `bm_edit`, `bm_delete`, `bm_move`, or `bm_context` calls when the identifier/URL is already workspace-qualified, such as `personal/main/...`, `memory://personal/main/...`, or an organization workspace slug with a 32-character hash suffix. This preserves Basic Memory Cloud's workspace-aware routing while keeping the existing default-project behavior for short/local identifiers.
## [0.3.1] — 2026-05-16
### Changed
- **Documented the Hermes Agent v0.14.0-compatible `/bm-*` slash-command monkeypatch.** `MONKEYPATCH.md` now distinguishes the plugin's runtime version from the Hermes Agent-side compatibility patch: plugin `v0.3.0` remains the correct runtime release for Hermes Agent `v0.13.x`, while Hermes Agent `v0.14.0` still needs the updated two-part core patch so gateway startup command discovery loads the active exclusive memory provider and the memory-provider collector delegates `register_command` / `register_skill`.
- **Clarified install guidance for users and agents.** The README known-issue section now points Hermes `v0.13.x` and `v0.14.0` users at the compatibility matrix in `MONKEYPATCH.md`, so agents do not mistake a plugin update for the required Hermes Agent core patch.
### Notes
- This is a documentation/compatibility-instructions release only. It does not change the plugin runtime code or Basic Memory data behavior. The plugin remains backward-compatible with Hermes Agent `v0.13.x`; the new documentation explains how to patch Hermes Agent `v0.14.0` until the upstream Hermes fix ships.
## [0.3.0] — 2026-05-12
### Added
- **Per-call project routing on every `bm_*` tool.** All eight tools now accept optional `project` (name) and `project_id` (UUID from `bm_projects`) parameters. The agent can write or read against a project other than the Hermes-configured one — useful when the user asks to write into a different cloud project (e.g. a personal `main` project) without reconfiguring the plugin. `project_id` takes precedence over `project`; both fall back to the configured default when omitted. Workspace routing is handled transparently by BM via `project_id` — no separate workspace parameter is needed.
- **`bm_projects` and `bm_workspaces` agent tools.** Promotes the discovery logic previously available only as `/bm-project` and `/bm-workspace` slash commands to agent-facing tools. `bm_projects` returns JSON with `name` and `external_id` (UUID) per project so the agent can hand the UUID to `bm_write` / `bm_read` / etc. via `project_id` — the unambiguous form across cloud workspaces. `bm_workspaces` lists BM Cloud workspaces (name, type, role, default flag). Together with per-call routing, these unblock the workflow Drew's friction note flagged: agent picks the right project + workspace before writing, instead of silently operating against the active Hermes memory project.
- **SKILL.md cross-project workflow** documenting the discovery → route → write → verify recipe end-to-end. Adds a "Permalinks" section covering the three canonical shapes (short, project-qualified, workspace-qualified) and the round-trip property where `bm_write`'s returned permalink self-routes for follow-up reads. A "Cross-project routing" section explains `project` (including workspace-qualified syntax like `"personal/main"`) vs `project_id` and when to use each. Also backfills `bm_recent` documentation (the tool shipped in 0.2.0 but the skill hadn't been updated).
- **SKILL.md "Further reading" section** linking to the official docs at [docs.basicmemory.com](https://docs.basicmemory.com), with raw-markdown URLs (`/raw/<path>.md`) the agent can `WebFetch` on demand for deeper material — knowledge format, observations & relations, memory URL wildcards, semantic search, cloud routing, BM's full MCP tool surface, and the `llms.txt` sitemap.
### Notes
- Addresses the routing, discovery, and documentation gaps in the real-world note "Hermes Basic Memory Cloud Task Experience." A proposed `bm_import` tool was evaluated and dropped — `read_file` + `bm_write` already composes the same operation with no new capability, at the cost of one more tool in the surface.
- The slash commands `/bm-project` and `/bm-workspace` still exist and behave identically — they continue to call `list_memory_projects` / `list_workspaces` directly via the actor. No behavior change for human use.
## [0.2.0] — 2026-05-11
### Added
- **Plugin-owned `/bm-*` slash commands** for CLI/gateway sessions. Eight commands give humans direct memory-graph access without going through the agent: `/bm-search`, `/bm-read`, `/bm-context`, `/bm-recent`, `/bm-status`, `/bm-remember`, `/bm-project`, `/bm-workspace`. Closes #2.
- **`bm_recent` tool** wrapping BM's `recent_activity`. Surfaces notes updated within a timeframe (`7d` default, accepts natural language like `"2 weeks"` or `"yesterday"`). Agent-facing and reused by `/bm-recent`.
- **`remember_folder` config key** (default `"bm-remember"`). Separate from `capture_folder` so manual captures via `/bm-remember` don't intermix with auto-generated session transcripts. Notes are tagged `manual-capture` for further disambiguation.
### Fixed
- **`ctx.register_skill(...)` was silently no-opping since 0.1.5** in real Hermes installs. Hermes loads memory-provider plugins through a stripped-down `_ProviderCollector` context (`plugins/memory/__init__.py`) that captures only `register_memory_provider`; `register_skill` and `register_command` are not delegated. The plugin now writes directly to `PluginManager._plugin_commands` and `_plugin_skills`, matching the entry shape and name normalization `PluginContext.register_command` / `register_skill` produce. This makes both the new slash commands and the bundled SKILL.md work in current Hermes installs. The clean fix lives upstream — a small patch to teach `_ProviderCollector` to delegate — and once that lands, the reach-in becomes a redundant double-write of identical entries. Forward-compat `ctx.register_command` / `ctx.register_skill` calls remain in place for the future code path.
### Notes
- `/bm-remember` derives the title from the first non-empty line of the input, trimmed to 80 chars; falls back to `Note YYYY-MM-DD HHMM UTC`.
- `/bm-workspace` short-circuits in local mode with a one-line explanation. Workspaces are a BM Cloud concept.
- Mid-session project/workspace switching is intentionally not supported in 0.2.0 — auto-capture would land in unexpected places. Tracked as a follow-up.
## [0.1.7] — 2026-05-10
### Changed
- **Stronger nudge in `system_prompt_block()`** to steer agents toward the `bm_*` tools instead of shelling out to `bm` CLI. Pre-v0.1.7 the prompt listed the tools neutrally; given Claude/Hermes models' heavy training-data exposure to `bm tool ...` CLI patterns, neutral language wasn't enough — agents reached for the shell by reflex, paying 1-2s of cold-start per call instead of ~0.1s through our persistent MCP connection. New prompt is explicit (**"Use the `bm_*` tools below directly — do not shell out to the `bm` CLI"**) and gives a one-line latency rationale so the model has a reason to follow it.
- `SKILL.md` mirrors the directive with a "Use `bm_*`, not the `bm` CLI" section + a tool-vs-CLI table.
### Added
- Regression test `test_system_prompt_block_steers_away_from_cli` locks in the directive language so future prompt edits don't accidentally weaken it.
## [0.1.6] — 2026-05-10
### Fixed
- **`bm_*` tools were never registered with Hermes's `MemoryManager._tool_to_provider`.** `get_tool_schemas()` was gated on `self._initialized`, but Hermes captures the schema list at *register* time — before `initialize()` runs. The gate caused every session to start with zero tools registered for our provider, so every LLM-issued `bm_search` (and friends) returned `"Unknown tool: bm_search"` from MemoryManager's dispatch. Symptoms were asymmetric: prefetch (recall injection) worked because it's invoked per-turn after init, but tool calls didn't. Schemas are static — they now return unconditionally, with `handle_tool_call()` doing the runtime "is the actor ready?" gate.
- Regression test pins this so we don't reintroduce it: `test_get_tool_schemas_unconditional` asserts `get_tool_schemas()` returns all 7 schemas on a fresh, uninitialized provider.
## [0.1.5] — 2026-05-10
### Added
- Bundled `SKILL.md` is now auto-registered via `ctx.register_skill("basic-memory", ...)` during plugin load. No more manual symlink to `~/.hermes/skills/`. The skill is opt-in (resolvable via `skill:view basic-memory:basic-memory`); always-on agent guidance still flows through `system_prompt_block()`.
### Changed
- README rewritten for community install. Lead command is now `hermes plugins install basicmachines-co/hermes-basic-memory`. Clone-and-symlink instructions moved to the Development section.
- Added GitHub Actions CI: unit tests on push and PR.
- Added this CHANGELOG.
## [0.1.4] — 2026-05-10
### Added
- `_uv_binary_path()` and `_install_bm_via_uv()`. When `bm` is missing from the host, the plugin runs `uv tool install basic-memory --quiet` once at first `initialize()`. The bm binary lands at `~/.local/bin/bm` — the same canonical path a manual `uv tool install basic-memory` produces, so subsequent manual installs are no-ops rather than creating a second install.
- 8 new unit tests covering `is_available()` with bm/uv combinations, the install subprocess (success / non-zero exit / OSError / no-uv), and `initialize()` install-or-not branching.
### Changed
- `is_available()` now returns `True` when **either** `bm` is on disk **or** `uv` is on disk (we can install the missing CLI ourselves).
- README's prerequisites section: dropped manual basic-memory install requirement; added the one-time ~10s cold-start note.
## [0.1.3] — 2026-05-10
### Fixed
- README's cloud-mode section described the wrong setup (`bm project add ... --cloud --local-path` + `bm cloud bisync`), which gives a local-mode project with file-level cloud sync rather than true cloud routing. Replaced with `bm project set-cloud <name> --workspace <name>`, which flips the project to `ProjectMode.CLOUD` so tool calls route over HTTPS to `<cloud_host>/proxy` directly. No local files involved.
- Documented OAuth / API-key auth options, and the `--workspace` requirement when the user belongs to multiple BM Cloud workspaces.
## [0.1.2] — 2026-05-10
### Changed
- `_default_project()`: `"hermes-memory"` (was `"hermes-{hostname}"`).
- `_default_project_path()`: `~/hermes-memory/` (was `~/.basic-memory/hermes/`). The previous path violated the principle that `~/.basic-memory/` is reserved for BM's app state, not project storage.
### Added
- `_bm_known_projects()` reads bm's `~/.basic-memory/config.json`. `BasicMemoryProvider._verify_project_registered()` uses it to refuse initialization when `mode: cloud` is set against a project that isn't registered with bm. Local mode still auto-creates as before.
- 13 new unit tests for the introspection + bail-out paths.
## [0.1.1] — 2026-05-10
### Added
- `tests/test_actor.py` — 15 tests covering `_BmMcpActor` lifecycle, call dispatch, timeout-with-cancellation, idempotent shutdown.
- `tests/test_capture.py` — 25 tests for `sync_turn` (first-write + append paths), `on_session_end` summary shape, and gating.
- `tests/test_prefetch.py` — 25 tests for `prefetch` / `queue_prefetch` / `_format_prefetch` including forward-compat with unknown response fields.
- `tests/test_integration.py` — 12 gated tests exercising every tool against a real `bm` MCP server (`BM_INTEGRATION=1` + `bm` + `mcp`). Each session uses a throwaway BM project that's torn down on completion.
### Changed
- `_BmMcpActor.call` now refuses calls after `shutdown()` (sets `_running=False`) and cancels the underlying coroutine on timeout instead of leaking it.
- `_format_prefetch` defensively coerces non-string fields and skips non-dict entries.
- Added module-level `__version__`, kept in sync with `plugin.yaml` (verified by a test).
## [0.1.0] — 2026-05-10
### Added
- Initial release of the Hermes Memory Provider plugin for Basic Memory.
- Seven `bm_*` agent tools: `bm_search`, `bm_read`, `bm_write`, `bm_edit`, `bm_context`, `bm_delete`, `bm_move`.
- Per-turn capture (`sync_turn`) and end-of-session summary (`on_session_end`).
- Local mode (default) with auto-created BM project; cloud mode with project-name-based routing.
- Single-file plugin at `__init__.py`, AGPL-3.0-or-later.
- 84-test pytest suite.
[0.3.2]: https://github.com/basicmachines-co/hermes-basic-memory/releases/tag/v0.3.2
[0.3.1]: https://github.com/basicmachines-co/hermes-basic-memory/releases/tag/v0.3.1
[0.3.0]: https://github.com/basicmachines-co/hermes-basic-memory/releases/tag/v0.3.0
[0.2.0]: https://github.com/basicmachines-co/hermes-basic-memory/releases/tag/v0.2.0
[0.1.7]: https://github.com/basicmachines-co/hermes-basic-memory/releases/tag/v0.1.7
[0.1.6]: https://github.com/basicmachines-co/hermes-basic-memory/releases/tag/v0.1.6
[0.1.5]: https://github.com/basicmachines-co/hermes-basic-memory/releases/tag/v0.1.5
[0.1.4]: https://github.com/basicmachines-co/hermes-basic-memory/releases/tag/v0.1.4
[0.1.3]: https://github.com/basicmachines-co/hermes-basic-memory/releases/tag/v0.1.3
[0.1.2]: https://github.com/basicmachines-co/hermes-basic-memory/releases/tag/v0.1.2
[0.1.1]: https://github.com/basicmachines-co/hermes-basic-memory/releases/tag/v0.1.1
[0.1.0]: https://github.com/basicmachines-co/hermes-basic-memory/releases/tag/v0.1.0
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THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.
-269
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@@ -1,269 +0,0 @@
# Hermes slash-command monkeypatch
This document describes the Hermes Agent-side patch needed when `/bm-*` slash commands do not appear in the gateway even though the Basic Memory provider itself is installed and working.
This issue is tracked upstream in https://github.com/NousResearch/hermes-agent/issues/23603
Agents may use this file as a guide to add support for `/bm-*` slash commands in Hermes Agent builds that do not yet include the upstream fix.
## Current status and compatibility
This is a **Hermes Agent-side** compatibility patch, not a Basic Memory plugin runtime patch.
| Hermes Agent version | Basic Memory plugin version | Guidance |
|---|---:|---|
| `v0.13.x` | `v0.3.0` | Plugin `v0.3.0` remains the right runtime release. If `/bm-*` commands are missing, use the Hermes Agent-side monkeypatch below or wait for the upstream Hermes fix. |
| `v0.14.0` / `v2026.5.16` | `v0.3.1` docs, runtime still equivalent to `v0.3.0` | The plugin runtime still works, but Hermes Agent `v0.14.0` still does **not** include the upstream slash-command discovery fix. Use the v0.14.0-compatible Hermes Agent-side patch below. |
| Future Hermes release with upstream fix | Latest plugin | Do **not** apply this monkeypatch unless `/bm-*` commands are still absent; the fix should be redundant once Hermes loads active exclusive memory-provider commands during command discovery. |
Checked against Hermes Agent `v2026.5.16` / `v0.14.0` on 2026-05-16: the upstream Hermes release still does **not** include this fix. After applying the Hermes Agent-side patch below locally, `get_plugin_commands()` returns the expected `/bm-*` commands.
Important nuance: recent `hermes-basic-memory` versions include a best-effort PluginManager reach-in that registers commands when the provider is loaded. That workaround alone is not enough for gateway startup discovery in affected Hermes builds, because `get_plugin_commands()` does not load the active exclusive memory provider. The Hermes Agent-side patch is still needed until upstream command discovery loads the active memory provider and the memory-provider collector delegates command/skill registration.
Release/tagging note for agents: `v0.3.1` is a documentation release that clarifies Hermes Agent `v0.14.0` compatibility instructions. It does not require users on Hermes Agent `v0.13.x` to change plugin runtime behavior, and it should not be interpreted as a Basic Memory data/schema migration.
## Problem
`hermes-basic-memory` is an **exclusive memory-provider plugin**. Hermes loads exclusive memory providers through `plugins.memory`, not through the normal `PluginManager` discovery path.
Gateway adapters register native slash commands during startup by calling Hermes's plugin command discovery APIs. In affected Hermes builds, that startup path only sees commands registered by normal plugins. The active memory provider has not been loaded yet, and the memory-provider loader uses a collector that captures only `register_memory_provider(...)`. As a result, commands registered by this plugin with `ctx.register_command(...)` never reach the central plugin command registry before Discord/native slash-command sync.
Symptoms:
- `hermes memory status` shows `Provider: basic-memory` and `Status: available`.
- Agent tools such as `bm_search`, `bm_read`, and `bm_recent` work.
- Native slash commands such as `/bm-search`, `/bm-read`, and `/bm-context` are missing after `hermes gateway restart`.
## Target behavior
When Hermes builds its plugin command list, it should also load the configured active memory provider once, allowing that provider to register commands and skills into the same central registries used by ordinary plugins.
After the patch, `get_plugin_commands()` should include commands such as:
```text
bm-context
bm-project
bm-read
bm-recent
bm-remember
bm-search
bm-status
bm-workspace
```
## Files to patch in Hermes Agent
Patch these files in the Hermes Agent repository, not in this plugin repository:
```text
hermes_cli/plugins.py
plugins/memory/__init__.py
```
Recommended tests to add/update in Hermes Agent:
```text
tests/hermes_cli/test_plugin_cli_registration.py
tests/hermes_cli/test_plugins.py
```
## Implementation outline
### 1. Load active memory-provider commands from `get_plugin_commands()`
In `hermes_cli/plugins.py`, add module-level idempotency/recursion guards near the global plugin manager:
```python
_plugin_manager: Optional[PluginManager] = None
_memory_provider_command_loads: set[str] = set()
_memory_provider_command_loading = False
```
Update `get_plugin_commands()` so it first ensures normal plugin discovery, then best-effort loads the active memory provider before returning the command registry:
```python
def get_plugin_commands() -> Dict[str, dict]:
"""Return the full plugin commands dict (name → {handler, description, plugin}).
Triggers idempotent plugin discovery so callers can use plugin commands
before any explicit discover_plugins() call. Also initializes the active
memory provider once so exclusive memory-provider plugins can contribute
gateway slash commands during startup discovery.
"""
manager = _ensure_plugins_discovered()
_ensure_active_memory_provider_commands_loaded()
return manager._plugin_commands
```
Add the helper:
```python
def _ensure_active_memory_provider_commands_loaded() -> None:
"""Best-effort load of the active memory provider's slash commands."""
global _memory_provider_command_loading
if _memory_provider_command_loading:
return
try:
from plugins import memory as memory_plugins
active = memory_plugins._get_active_memory_provider()
if not active or active in _memory_provider_command_loads:
return
_memory_provider_command_loading = True
try:
memory_plugins.load_memory_provider(active)
_memory_provider_command_loads.add(active)
finally:
_memory_provider_command_loading = False
except Exception as exc:
logger.debug(
"Failed to load active memory-provider plugin commands: %s",
exc,
exc_info=_PLUGINS_DEBUG,
)
```
Notes:
- This must be best-effort; command discovery should not break Hermes startup if a memory provider is misconfigured.
- The recursion guard prevents `load_memory_provider(...)` → provider `register(...)``ctx.register_command(...)` → plugin manager access from re-entering endlessly.
- The load set prevents duplicate provider command registration work.
### 2. Make the memory-provider collector delegate commands and skills
In `plugins/memory/__init__.py`, import `Callable`:
```python
from typing import Callable, List, Optional, Tuple
```
When loading a provider directory, pass the plugin/provider name to the collector:
```python
collector = _ProviderCollector(plugin_name=name)
```
Replace the collector that only captures `register_memory_provider(...)` with a plugin-context shim that also delegates `register_command(...)` and `register_skill(...)` into the central `PluginManager` registries:
```python
class _ProviderCollector:
"""Plugin-context shim used while loading memory providers.
Memory providers are exclusive plugins and are loaded by this module
instead of the general PluginManager. They still need access to the same
slash-command and skill registries as normal plugins, otherwise active
memory-provider commands are invisible during gateway startup discovery.
"""
def __init__(self, plugin_name: str = "memory-provider"):
self.provider = None
self.plugin_name = plugin_name
def register_memory_provider(self, provider):
self.provider = provider
def register_command(
self,
name: str,
handler: Callable,
description: str = "",
args_hint: str = "",
) -> None:
"""Register a memory-provider slash command with PluginManager."""
try:
from hermes_cli.plugins import _ensure_plugins_discovered
except Exception:
return
clean = name.lower().strip().lstrip("/").replace(" ", "-")
if not clean:
return
try:
manager = _ensure_plugins_discovered()
except Exception:
return
plugin_commands = getattr(manager, "_plugin_commands", None)
if plugin_commands is None:
return
plugin_commands[clean] = {
"handler": handler,
"description": description or "Plugin command",
"plugin": self.plugin_name,
"args_hint": (args_hint or "").strip(),
}
def register_skill(
self,
name: str,
path: Path,
description: str = "",
) -> None:
"""Register a memory-provider skill with PluginManager."""
try:
from agent.skill_utils import _NAMESPACE_RE
from hermes_cli.plugins import _ensure_plugins_discovered
except Exception:
return
if ":" in name or not name or not _NAMESPACE_RE.match(name):
raise ValueError(f"Invalid skill name '{name}'.")
if not path.exists():
raise FileNotFoundError(f"SKILL.md not found at {path}")
try:
manager = _ensure_plugins_discovered()
except Exception:
return
plugin_skills = getattr(manager, "_plugin_skills", None)
if plugin_skills is None:
return
plugin_skills[f"{self.plugin_name}:{name}"] = {
"path": path,
"plugin": self.plugin_name,
"bare_name": name,
"description": description,
}
```
Keep existing no-op methods such as `register_tool(...)` and `register_cli_command(...)` as no-ops unless the target Hermes version expects otherwise.
## Verification
From the Hermes Agent repository, run focused compile/tests:
```bash
python -m py_compile hermes_cli/plugins.py plugins/memory/__init__.py
python -m pytest \
tests/hermes_cli/test_plugins.py::TestPluginCommands::test_get_plugin_commands_loads_active_memory_provider_commands \
tests/hermes_cli/test_plugin_cli_registration.py::TestProviderCollectorRegistration \
-q -o 'addopts='
```
Then verify the active config sees the Basic Memory commands:
```bash
python - <<'PY'
import hermes_cli.plugins as p
p._plugin_manager = None
p._memory_provider_command_loads.clear()
cmds = p.get_plugin_commands()
print(sorted(k for k in cmds if k.startswith('bm-')))
PY
```
Expected output:
```text
['bm-context', 'bm-project', 'bm-read', 'bm-recent', 'bm-remember', 'bm-search', 'bm-status', 'bm-workspace']
```
Finally restart the gateway so native slash commands are synced:
```bash
hermes gateway restart
```
For Discord, global command propagation can lag briefly. If the commands do not show immediately, type `/bm` directly or reload the Discord client.
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@@ -1,235 +0,0 @@
# hermes-basic-memory
[![License: AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)
Hermes Memory Provider plugin that gives [Hermes Agent](https://github.com/NousResearch/hermes-agent) a persistent knowledge graph backed by [Basic Memory](https://github.com/basicmachines-co/basic-memory).
The plugin replaces Hermes's "no external memory provider" with a real graph: search-before-answer recall, per-turn capture, end-of-session summaries, and ten `bm_*` tools the agent can call directly. Local mode by default; one CLI flip switches to true cloud routing through Basic Memory Cloud.
## Install
```bash
hermes plugins install basicmachines-co/basic-memory --path integrations/hermes
```
Then activate it in `~/.hermes/config.yaml`:
```yaml
memory:
provider: basic-memory
```
If you run the gateway, restart it (`hermes gateway restart`). Done.
If your installed Hermes build does not support `--path`, use the final deprecated `basicmachines-co/hermes-basic-memory` pointer release until Hermes subpath installs are available. Ongoing development now lives in [`basic-memory/integrations/hermes`](https://github.com/basicmachines-co/basic-memory/tree/main/integrations/hermes).
The plugin self-installs the `basic-memory` CLI on first init via `uv tool install basic-memory` (one-time ~10s pause if it isn't already present). The bm binary lands at `~/.local/bin/bm` — the same location a manual `uv tool install basic-memory` would produce, so a later manual install or upgrade is a no-op rather than a second install.
### Prerequisites
- [Hermes Agent](https://github.com/NousResearch/hermes-agent)
- [`uv`](https://docs.astral.sh/uv/) on PATH (used for the bootstrap install)
- The `mcp` Python package in the Hermes venv. If `hermes plugins install` doesn't auto-install it (it follows `pip_dependencies` in `plugin.yaml`), run:
```bash
uv pip install --python ~/.hermes/hermes-agent/venv/bin/python mcp
```
### Verify
```bash
hermes memory status
```
Expected:
```
Provider: basic-memory
Plugin: installed ✓
Status: available ✓
```
## What the agent gets
Ten tools (curated subset of Basic Memory's MCP surface):
| Tool | Use |
|---|---|
| `bm_search` | Semantic + full-text search; **call this before answering** |
| `bm_read` | Fetch a note by title, permalink, or `memory://` URL |
| `bm_write` | Create a new note (capture decisions, meeting notes, insights) |
| `bm_edit` | Append, prepend, find/replace, replace-section |
| `bm_context` | Navigate via `memory://` URLs to find related notes |
| `bm_delete` | Delete a note |
| `bm_move` | Move a note to a different folder |
| `bm_recent` | List notes updated recently (default `7d`; accepts natural-language timeframes) |
| `bm_projects` | List available projects with their UUIDs (for cross-project routing) |
| `bm_workspaces` | List Basic Memory Cloud workspaces |
Every read/write tool also accepts optional `project` / `project_id` for per-call routing — write or read against a project other than the configured one without reconfiguring the plugin.
Plus automatic capture:
- **Per turn**: every user/assistant exchange appends to a running session-transcript note
- **End of session**: a separate summary note is written, linked back to the transcript via a `summary_of` relation
A bundled skill (`skill:view basic-memory:basic-memory`) gives the agent a longer reference doc on top of the always-on `system_prompt_block`.
## Slash commands
For direct, in-session use without going through the agent (requires Hermes ≥ v0.11.0):
| Command | Use |
|---|---|
| `/bm-search <query>` | Search the knowledge graph; returns compact title/permalink/preview rows. |
| `/bm-read <identifier>` | Read a note by title, permalink, or `memory://` URL. |
| `/bm-context <identifier>` | Build context for a note (target + related). |
| `/bm-recent [timeframe]` | Recently updated notes. Default `7d`; accepts `"2 weeks"`, `"yesterday"`, etc. |
| `/bm-status` | Plugin/provider state: mode, project, capture flags, bm CLI path. |
| `/bm-remember <text>` | Capture a quick note. Title = first line (≤80 chars), folder = `remember_folder` (default `bm-remember`), tagged `manual-capture`. |
| `/bm-project` | List all known projects; the active one is marked. |
| `/bm-workspace` | List BM Cloud workspaces. Cloud mode only — prints an explanatory line in local mode. |
Examples:
```text
/bm-search Q3 OKRs
/bm-read decisions/auth-rewrite
/bm-recent yesterday
/bm-remember Reminder: switch the staging job to the new image after the rebase lands.
```
`/bm-project` and `/bm-workspace` are read-only in 0.2.0 — mid-session switching is intentionally not supported because auto-capture would otherwise land in the wrong place. Tracked as a follow-up.
### Known issue: `/bm-*` commands may not appear in some Hermes gateway builds
Plugin v0.2.0 and later register the commands above, but some Hermes Agent gateway builds do not discover slash commands contributed by an **exclusive memory-provider plugin** during startup. The symptoms are:
- the memory tools work for the agent (`bm_search`, `bm_read`, etc.);
- `hermes memory status` shows `Provider: basic-memory` and `Status: available`; but
- Discord/native slash command pickers do not show `/bm-search`, `/bm-read`, `/bm-context`, and the other `/bm-*` commands after `hermes gateway restart`.
This is a Hermes Agent plugin-discovery issue, not a Basic Memory runtime issue. It is tracked upstream in [NousResearch/hermes-agent#23603](https://github.com/NousResearch/hermes-agent/issues/23603). Updating the Basic Memory plugin alone cannot fix affected gateway startup discovery; Hermes Agent itself must include or receive the compatibility patch. Until the upstream Hermes fix is available in your installed Hermes version, use one of these workarounds:
1. apply the Hermes Agent-side patch described in [MONKEYPATCH.md](MONKEYPATCH.md), which includes compatibility notes for Hermes Agent v0.13.x and v0.14.0; or
2. use the agent tools directly (`bm_search`, `bm_read`, `bm_recent`, etc.) instead of native slash commands.
After applying an updated or patched Hermes build, restart the gateway so Discord/native slash commands are re-synced:
```bash
hermes gateway restart
```
If Discord still does not show the commands immediately, type `/bm` directly or reload the Discord client; global command propagation can lag briefly.
## Configuration
Defaults are reasonable for local use:
| Key | Default | Notes |
|---|---|---|
| `mode` | `local` | `local` (in-process) or `cloud` (route through BM Cloud API) |
| `project` | `hermes-memory` | BM project name |
| `project_path` | `~/hermes-memory/` | Local mode only — where session notes land |
| `capture_folder` | `hermes-sessions` | Folder within the project for session notes |
| `capture_per_turn` | `true` | Append every turn to a session transcript |
| `capture_session_end` | `true` | Write a summary note when the session ends |
| `remember_folder` | `bm-remember` | Folder where `/bm-remember` captures land (kept separate from session transcripts) |
To override, write `~/.hermes/basic-memory.json` or run `hermes memory setup basic-memory`:
```json
{
"mode": "local",
"project": "hermes-memory",
"project_path": "~/hermes-memory/",
"capture_per_turn": true,
"capture_session_end": true,
"capture_folder": "hermes-sessions",
"remember_folder": "bm-remember"
}
```
In local mode the plugin auto-creates the BM project on first init via `bm project add`. In cloud mode it doesn't — you create the cloud-routed project yourself (see below) and the plugin verifies it's registered before initializing.
### Cloud mode
When `mode: cloud`, tool calls route directly through the BM cloud API — no local file mirror, no bisync. You set this up once with the BM CLI:
```bash
# Authenticate (OAuth) or save an API key
bm cloud login # OAuth — interactive
# OR for headless/automation:
bm cloud create-key "hermes"
bm cloud set-key bmc_...
# Create the project, then flip it to cloud routing.
# --workspace is required if you belong to more than one workspace
# (otherwise BM auto-resolves the only one available).
bm project add hermes-memory-cloud
bm project set-cloud hermes-memory-cloud --workspace Personal
# Point the plugin at it
cat > ~/.hermes/basic-memory.json <<EOF
{
"mode": "cloud",
"project": "hermes-memory-cloud",
"capture_per_turn": true,
"capture_session_end": true,
"capture_folder": "hermes-sessions"
}
EOF
hermes gateway restart
```
Tool calls now route from `bm mcp``<cloud_host>/proxy` over HTTPS using your OAuth token (or API key). Notes never touch local disk.
**Don't confuse cloud mode with `bm cloud bisync`.** Bisync is rclone-style two-way file sync between a *local* project and cloud storage, intended for keeping local working copies. For agent-driven capture you want true cloud routing (`set-cloud`), not bisync.
## Updating / removing
```bash
hermes plugins update basic-memory
hermes plugins remove basic-memory # then revert memory.provider in config.yaml
```
## Foot-guns
- **`<memory-context>` tags in notes**: Hermes's streaming output scrubber strips literal `<memory-context>...</memory-context>` blocks from assistant text. If a note contains those tags and the assistant echoes the body verbatim, the echoed copy gets eaten mid-stream. Tool results inbound are unaffected. Avoid those tags in BM notes; if you must include them, fence in a code block.
- **Single external provider**: Hermes accepts only one external memory provider at a time. Activating basic-memory displaces any other.
- **CLI cold start**: `hermes -z ...` invocations spawn `bm mcp` per run (~2-5s). Long-running gateway sessions amortize this.
- **Multiple cloud workspaces**: if your BM Cloud account belongs to more than one workspace, `bm project set-cloud` must be invoked with `--workspace <name>`. Otherwise tool calls fail with "Multiple workspaces are available".
## Development
The plugin is a single-file Python module at `__init__.py`. The Hermes plugin loader expects `register(ctx)` and grep-detects either `register_memory_provider` or `MemoryProvider` in the file.
For local development (point Hermes at your working tree instead of going through `hermes plugins install`):
```bash
git clone https://github.com/basicmachines-co/basic-memory ~/code/basic-memory
mkdir -p ~/.hermes/plugins
ln -snf ~/code/basic-memory/integrations/hermes ~/.hermes/plugins/basic-memory
```
### Running tests
```bash
# From the monorepo root
just package-check-hermes
# Or from integrations/hermes
just check
# Unit tests (fast, hermetic — no Hermes or bm required)
uv run --with pytest pytest
# Integration tests (gated — exercise every tool against a real bm MCP server)
BM_INTEGRATION=1 uv run --with pytest --with mcp pytest tests/test_integration.py
```
The unit suite stubs out Hermes-internal imports (`agent.memory_provider`, `tools.registry`) so it runs without a Hermes install. `mcp` is optional at unit-test time — its absence just makes `is_available()` return False, which the tests verify.
Integration tests require `BM_INTEGRATION=1`, `bm` CLI on PATH, and `mcp` Python package importable. Each session creates a unique throwaway BM project (under `tempfile.mkdtemp`) and removes it on teardown, so they never touch your real BM projects.
## License
AGPL-3.0-or-later, matching [basic-memory](https://github.com/basicmachines-co/basic-memory). See [LICENSE](LICENSE).

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