chore: Add fast feedback loop tooling (#538)

Signed-off-by: phernandez <paul@basicmachines.co>
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Paul Hernandez
2026-02-01 23:04:59 -06:00
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# AGENTS.md - Basic Memory Project Guide
## Project Overview
Basic Memory is a local-first knowledge management system built on the Model Context Protocol (MCP). It enables
bidirectional communication between LLMs (like Claude) and markdown files, creating a personal knowledge graph that can
be traversed using links between documents.
## CODEBASE DEVELOPMENT
### Project information
See the [README.md](README.md) file for a project overview.
### Build and Test Commands
- Install: `just install` or `pip install -e ".[dev]"`
- Run all tests (SQLite + Postgres): `just test`
- Run all tests against SQLite: `just test-sqlite`
- Run all tests against Postgres: `just test-postgres` (uses testcontainers)
- Run unit tests (SQLite): `just test-unit-sqlite`
- Run unit tests (Postgres): `just test-unit-postgres`
- Run integration tests (SQLite): `just test-int-sqlite`
- Run integration tests (Postgres): `just test-int-postgres`
- Run impacted tests: `just testmon` (pytest-testmon)
- Run MCP smoke test: `just test-smoke`
- Fast local loop: `just fast-check`
- Local consistency check: `just doctor`
- Generate HTML coverage: `just coverage`
- Single test: `pytest tests/path/to/test_file.py::test_function_name`
- Run benchmarks: `pytest test-int/test_sync_performance_benchmark.py -v -m "benchmark and not slow"`
- Lint: `just lint` or `ruff check . --fix`
- Type check: `just typecheck` or `uv run pyright`
- Format: `just format` or `uv run ruff format .`
- Run all code checks: `just check` (runs lint, format, typecheck, test)
- Create db migration: `just migration "Your migration message"`
- Run development MCP Inspector: `just run-inspector`
**Note:** Project requires Python 3.12+ (uses type parameter syntax and `type` aliases introduced in 3.12)
**Postgres Testing:** Uses [testcontainers](https://testcontainers-python.readthedocs.io/) which automatically spins up a Postgres instance in Docker. No manual database setup required - just have Docker running.
**Doctor Note:** `just doctor` runs with a temporary HOME/config so it won't touch your local Basic Memory settings. It leaves temp dirs in `/tmp` (safe to ignore or remove).
**Testmon Note:** When no files have changed, `just testmon` may collect 0 tests. That's expected and means no impacted tests were detected.
### Code/Test/Verify Loop (fast path)
1) **Code:** make changes.
2) **Test:** `just fast-check` (lint/format/typecheck + impacted tests + MCP smoke).
3) **Verify:** `just doctor` (end-to-end file ↔ DB loop in a temp project).
4) **Full gate (when needed):** `just test` or `just check` for SQLite + Postgres.
If testmon is “cold,” the first run may be long. Subsequent runs get much faster.
### Test Structure
- `tests/` - Unit tests for individual components (mocked, fast)
- `test-int/` - Integration tests for real-world scenarios (no mocks, realistic)
- Both directories are covered by unified coverage reporting
- Benchmark tests in `test-int/` are marked with `@pytest.mark.benchmark`
- Slow tests are marked with `@pytest.mark.slow`
- Smoke tests are marked with `@pytest.mark.smoke`
### Code Style Guidelines
- Line length: 100 characters max
- Python 3.12+ with full type annotations (uses type parameters and type aliases)
- Format with ruff (consistent styling)
- Import order: standard lib, third-party, local imports
- Naming: snake_case for functions/variables, PascalCase for classes
- Prefer async patterns with SQLAlchemy 2.0
- Use Pydantic v2 for data validation and schemas
- CLI uses Typer for command structure
- API uses FastAPI for endpoints
- Follow the repository pattern for data access
- Tools communicate to api routers via the httpx ASGI client (in process)
### Code Change Guidelines
- **Full file read before edits**: Before editing any file, read it in full first to ensure complete context; partial reads lead to corrupted edits
- **Minimize diffs**: Prefer the smallest change that satisfies the request. Avoid unrelated refactors or style rewrites unless necessary for correctness
- **No speculative getattr**: Never use `getattr(obj, "attr", default)` when unsure about attribute names. Check the class definition or source code first
- **Fail fast**: Write code with fail-fast logic by default. Do not swallow exceptions with errors or warnings
- **No fallback logic**: Do not add fallback logic unless explicitly told to and agreed with the user
- **No guessing**: Do not say "The issue is..." before you actually know what the issue is. Investigate first.
### Literate Programming Style
Code should tell a story. Comments must explain the "why" and narrative flow, not just the "what".
**Section Headers:**
For files with multiple phases of logic, add section headers so the control flow reads like chapters:
```python
# --- Authentication ---
# ... auth logic ...
# --- Data Validation ---
# ... validation logic ...
# --- Business Logic ---
# ... core logic ...
```
**Decision Point Comments:**
For conditionals that materially change behavior (gates, fallbacks, retries, feature flags), add comments with:
- **Trigger**: what condition causes this branch
- **Why**: the rationale (cost, correctness, UX, determinism)
- **Outcome**: what changes downstream
```python
# Trigger: project has no active sync watcher
# Why: avoid duplicate file system watchers consuming resources
# Outcome: starts new watcher, registers in active_watchers dict
if project_id not in active_watchers:
start_watcher(project_id)
```
**Constraint Comments:**
If code exists because of a constraint (async requirements, rate limits, schema compatibility), explain the constraint near the code:
```python
# SQLite requires WAL mode for concurrent read/write access
connection.execute("PRAGMA journal_mode=WAL")
```
**What NOT to Comment:**
Avoid comments that restate obvious code:
```python
# Bad - restates code
counter += 1 # increment counter
# Good - explains why
counter += 1 # track retries for backoff calculation
```
### Codebase Architecture
See [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md) for detailed architecture documentation.
**Directory Structure:**
- `/alembic` - Alembic db migrations
- `/api` - FastAPI REST endpoints + `container.py` composition root
- `/cli` - Typer CLI + `container.py` composition root
- `/deps` - Feature-scoped FastAPI dependencies (config, db, projects, repositories, services, importers)
- `/importers` - Import functionality for Claude, ChatGPT, and other sources
- `/markdown` - Markdown parsing and processing
- `/mcp` - MCP server + `container.py` composition root + `clients/` typed API clients
- `/models` - SQLAlchemy ORM models
- `/repository` - Data access layer
- `/schemas` - Pydantic models for validation
- `/services` - Business logic layer
- `/sync` - File synchronization services + `coordinator.py` for lifecycle management
**Composition Roots:**
Each entrypoint (API, MCP, CLI) has a composition root that:
- Reads `ConfigManager` (the only place that reads global config)
- Resolves runtime mode via `RuntimeMode` enum (TEST > CLOUD > LOCAL)
- Provides dependencies to downstream code explicitly
**Typed API Clients (MCP):**
MCP tools use typed clients in `mcp/clients/` to communicate with the API:
- `KnowledgeClient` - Entity CRUD operations
- `SearchClient` - Search operations
- `MemoryClient` - Context building
- `DirectoryClient` - Directory listing
- `ResourceClient` - Resource reading
- `ProjectClient` - Project management
Flow: MCP Tool → Typed Client → HTTP API → Router → Service → Repository
### Development Notes
- MCP tools are defined in src/basic_memory/mcp/tools/
- MCP prompts are defined in src/basic_memory/mcp/prompts/
- MCP tools should be atomic, composable operations
- Use `textwrap.dedent()` for multi-line string formatting in prompts and tools
- MCP Prompts are used to invoke tools and format content with instructions for an LLM
- Schema changes require Alembic migrations
- SQLite is used for indexing and full text search, files are source of truth
- Testing uses pytest with asyncio support (strict mode)
- Unit tests (`tests/`) use mocks when necessary; integration tests (`test-int/`) use real implementations
- By default, tests run against SQLite (fast, no Docker needed)
- Set `BASIC_MEMORY_TEST_POSTGRES=1` to run against Postgres (uses testcontainers - Docker required)
- Each test runs in a standalone environment with isolated database and tmp_path directory
- CI runs SQLite and Postgres tests in parallel for faster feedback
- Performance benchmarks are in `test-int/test_sync_performance_benchmark.py`
- Use pytest markers: `@pytest.mark.benchmark` for benchmarks, `@pytest.mark.slow` for slow tests
- **Coverage must stay at 100%**: Write tests for new code. Only use `# pragma: no cover` when tests would require excessive mocking (e.g., TYPE_CHECKING blocks, error handlers that need failure injection, runtime-mode-dependent code paths)
### Async Client Pattern (Important!)
**All MCP tools and CLI commands use the context manager pattern for HTTP clients:**
```python
from basic_memory.mcp.async_client import get_client
async def my_mcp_tool():
async with get_client() as client:
# Use client for API calls
response = await call_get(client, "/path")
return response
```
**Do NOT use:**
-`from basic_memory.mcp.async_client import client` (deprecated module-level client)
- ❌ Manual auth header management
-`inject_auth_header()` (deleted)
**Key principles:**
- Auth happens at client creation, not per-request
- Proper resource management via context managers
- Supports three modes: Local (ASGI), CLI cloud (HTTP + auth), Cloud app (factory injection)
- Factory pattern enables dependency injection for cloud consolidation
**For cloud app integration:**
```python
from basic_memory.mcp import async_client
# Set custom factory before importing tools
async_client.set_client_factory(your_custom_factory)
```
See SPEC-16 for full context manager refactor details.
## BASIC MEMORY PRODUCT USAGE
### Knowledge Structure
- Entity: Any concept, document, or idea represented as a markdown file
- Observation: A categorized fact about an entity (`- [category] content`)
- Relation: A directional link between entities (`- relation_type [[Target]]`)
- Frontmatter: YAML metadata at the top of markdown files
- Knowledge representation follows precise markdown format:
- Observations with [category] prefixes
- Relations with WikiLinks [[Entity]]
- Frontmatter with metadata
### Basic Memory Commands
**Local Commands:**
- 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`
- 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: `basic-memory cloud login`
- Logout: `basic-memory cloud logout`
- Check cloud status: `basic-memory cloud status`
- Setup cloud sync: `basic-memory cloud setup`
- Manage snapshots: `basic-memory cloud snapshot [create|list|delete|show|browse]`
- Restore from snapshot: `basic-memory cloud restore <path> --snapshot <id>`
### MCP Capabilities
- Basic Memory exposes these MCP tools to LLMs:
**Content Management:**
- `write_note(title, content, directory, tags)` - Create/update markdown notes with semantic observations and relations
- `read_note(identifier, page, page_size)` - Read notes by title, permalink, or memory:// URL with knowledge graph awareness
- `read_content(path)` - Read raw file content (text, images, binaries) without knowledge graph processing
- `view_note(identifier, page, page_size)` - View notes as formatted artifacts for better readability
- `edit_note(identifier, operation, content)` - Edit notes incrementally (append, prepend, find/replace, replace_section)
- `move_note(identifier, destination_path, is_directory)` - Move notes or directories to new locations, updating database and maintaining links
- `delete_note(identifier, is_directory)` - Delete notes or directories from the knowledge base
**Knowledge Graph Navigation:**
- `build_context(url, depth, timeframe)` - Navigate the knowledge graph via memory:// URLs for conversation continuity
- `recent_activity(type, depth, timeframe)` - Get recently updated information with specified timeframe (e.g., "1d", "1 week")
- `list_directory(dir_name, depth, file_name_glob)` - Browse directory contents with filtering and depth control
**Search & Discovery:**
- `search_notes(query, page, page_size, search_type, types, entity_types, after_date)` - Full-text search across all content with advanced filtering options
**Project Management:**
- `list_memory_projects()` - List all available projects with their status
- `create_memory_project(project_name, project_path, set_default)` - Create new Basic Memory projects
- `delete_project(project_name)` - Delete a project from configuration
**Visualization:**
- `canvas(nodes, edges, title, directory)` - Generate Obsidian canvas files for knowledge graph visualization
**ChatGPT-Compatible Tools:**
- `search(query)` - Search across knowledge base (OpenAI actions compatible)
- `fetch(id)` - Fetch full content of a search result document
- MCP Prompts for better AI interaction:
- `ai_assistant_guide()` - Guidance on effectively using Basic Memory tools for AI assistants
- `continue_conversation(topic, timeframe)` - Continue previous conversations with relevant historical context
- `search(query, after_date)` - Search with detailed, formatted results for better context understanding
- `recent_activity(timeframe)` - View recently changed items with formatted output
### Cloud Features (v0.15.0+)
Basic Memory now supports cloud synchronization and storage (requires active subscription):
**Authentication:**
- JWT-based authentication with subscription validation
- Secure session management with token refresh
- Support for multiple cloud projects
**Bidirectional Sync:**
- rclone bisync integration for two-way synchronization
- Conflict resolution and integrity verification
- Real-time sync with change detection
- Mount/unmount cloud storage for direct file access
**Cloud Project Management:**
- Create and manage projects in the cloud
- Toggle between local and cloud modes
- Per-project sync configuration
- Subscription-based access control
**Security & Performance:**
- Removed .env file loading for improved security
- .gitignore integration (respects gitignored files)
- WAL mode for SQLite performance
- Background relation resolution (non-blocking startup)
- API performance optimizations (SPEC-11)
**CLI Routing Flags:**
When 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
## AI-Human Collaborative Development
Basic Memory emerged from and enables a new kind of development process that combines human and AI capabilities. Instead
of using AI just for code generation, we've developed a true collaborative workflow:
1. AI (LLM) writes initial implementation based on specifications and context
2. Human reviews, runs tests, and commits code with any necessary adjustments
3. Knowledge persists across conversations using Basic Memory's knowledge graph
4. Development continues seamlessly across different AI sessions with consistent context
5. Results improve through iterative collaboration and shared understanding
This approach has allowed us to tackle more complex challenges and build a more robust system than either humans or AI
could achieve independently.
**Problem-Solving Guidance:**
- If a solution isn't working after reasonable effort, suggest alternative approaches
- Don't persist with a problematic library or pattern when better alternatives exist
- Example: When py-pglite caused cascading test failures, switching to testcontainers-postgres was the right call
## GitHub Integration
Basic Memory has taken AI-Human collaboration to the next level by integrating Claude directly into the development workflow through GitHub:
### GitHub MCP Tools
Using the GitHub Model Context Protocol server, Claude can now:
- **Repository Management**:
- View repository files and structure
- Read file contents
- Create new branches
- Create and update files
- **Issue Management**:
- Create new issues
- Comment on existing issues
- Close and update issues
- Search across issues
- **Pull Request Workflow**:
- Create pull requests
- Review code changes
- Add comments to PRs
This integration enables Claude to participate as a full team member in the development process, not just as a code generation tool. Claude's GitHub account ([bm-claudeai](https://github.com/bm-claudeai)) is a member of the Basic Machines organization with direct contributor access to the codebase.
### Collaborative Development Process
With GitHub integration, the development workflow includes:
1. **Direct code review** - Claude can analyze PRs and provide detailed feedback
2. **Contribution tracking** - All of Claude's contributions are properly attributed in the Git history
3. **Branch management** - Claude can create feature branches for implementations
4. **Documentation maintenance** - Claude can keep documentation updated as the code evolves
5. **Code Commits**: ALWAYS sign off commits with `git commit -s`
This level of integration represents a new paradigm in AI-human collaboration, where the AI assistant becomes a full-fledged team member rather than just a tool for generating code snippets.
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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 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`
- Generate HTML coverage: `just coverage`
- Single test: `pytest tests/path/to/test_file.py::test_function_name`
- Run benchmarks: `pytest test-int/test_sync_performance_benchmark.py -v -m "benchmark and not slow"`
- Lint: `just lint` or `ruff check . --fix`
- Type check: `just typecheck` or `uv run pyright`
- 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.
### Test Structure
- `tests/` - Unit tests for individual components (mocked, fast)
- `test-int/` - Integration tests for real-world scenarios (no mocks, realistic)
- Both directories are covered by unified coverage reporting
- Benchmark tests in `test-int/` are marked with `@pytest.mark.benchmark`
- Slow tests are marked with `@pytest.mark.slow`
### Code Style Guidelines
- Line length: 100 characters max
- Python 3.12+ with full type annotations (uses type parameters and type aliases)
- Format with ruff (consistent styling)
- Import order: standard lib, third-party, local imports
- Naming: snake_case for functions/variables, PascalCase for classes
- Prefer async patterns with SQLAlchemy 2.0
- Use Pydantic v2 for data validation and schemas
- CLI uses Typer for command structure
- API uses FastAPI for endpoints
- Follow the repository pattern for data access
- Tools communicate to api routers via the httpx ASGI client (in process)
### Code Change Guidelines
- **Full file read before edits**: Before editing any file, read it in full first to ensure complete context; partial reads lead to corrupted edits
- **Minimize diffs**: Prefer the smallest change that satisfies the request. Avoid unrelated refactors or style rewrites unless necessary for correctness
- **No speculative getattr**: Never use `getattr(obj, "attr", default)` when unsure about attribute names. Check the class definition or source code first
- **Fail fast**: Write code with fail-fast logic by default. Do not swallow exceptions with errors or warnings
- **No fallback logic**: Do not add fallback logic unless explicitly told to and agreed with the user
- **No guessing**: Do not say "The issue is..." before you actually know what the issue is. Investigate first.
### Literate Programming Style
Code should tell a story. Comments must explain the "why" and narrative flow, not just the "what".
**Section Headers:**
For files with multiple phases of logic, add section headers so the control flow reads like chapters:
```python
# --- Authentication ---
# ... auth logic ...
# --- Data Validation ---
# ... validation logic ...
# --- Business Logic ---
# ... core logic ...
```
**Decision Point Comments:**
For conditionals that materially change behavior (gates, fallbacks, retries, feature flags), add comments with:
- **Trigger**: what condition causes this branch
- **Why**: the rationale (cost, correctness, UX, determinism)
- **Outcome**: what changes downstream
```python
# Trigger: project has no active sync watcher
# Why: avoid duplicate file system watchers consuming resources
# Outcome: starts new watcher, registers in active_watchers dict
if project_id not in active_watchers:
start_watcher(project_id)
```
**Constraint Comments:**
If code exists because of a constraint (async requirements, rate limits, schema compatibility), explain the constraint near the code:
```python
# SQLite requires WAL mode for concurrent read/write access
connection.execute("PRAGMA journal_mode=WAL")
```
**What NOT to Comment:**
Avoid comments that restate obvious code:
```python
# Bad - restates code
counter += 1 # increment counter
# Good - explains why
counter += 1 # track retries for backoff calculation
```
### Codebase Architecture
See [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md) for detailed architecture documentation.
**Directory Structure:**
- `/alembic` - Alembic db migrations
- `/api` - FastAPI REST endpoints + `container.py` composition root
- `/cli` - Typer CLI + `container.py` composition root
- `/deps` - Feature-scoped FastAPI dependencies (config, db, projects, repositories, services, importers)
- `/importers` - Import functionality for Claude, ChatGPT, and other sources
- `/markdown` - Markdown parsing and processing
- `/mcp` - MCP server + `container.py` composition root + `clients/` typed API clients
- `/models` - SQLAlchemy ORM models
- `/repository` - Data access layer
- `/schemas` - Pydantic models for validation
- `/services` - Business logic layer
- `/sync` - File synchronization services + `coordinator.py` for lifecycle management
**Composition Roots:**
Each entrypoint (API, MCP, CLI) has a composition root that:
- Reads `ConfigManager` (the only place that reads global config)
- Resolves runtime mode via `RuntimeMode` enum (TEST > CLOUD > LOCAL)
- Provides dependencies to downstream code explicitly
**Typed API Clients (MCP):**
MCP tools use typed clients in `mcp/clients/` to communicate with the API:
- `KnowledgeClient` - Entity CRUD operations
- `SearchClient` - Search operations
- `MemoryClient` - Context building
- `DirectoryClient` - Directory listing
- `ResourceClient` - Resource reading
- `ProjectClient` - Project management
Flow: MCP Tool → Typed Client → HTTP API → Router → Service → Repository
### Development Notes
- MCP tools are defined in src/basic_memory/mcp/tools/
- MCP prompts are defined in src/basic_memory/mcp/prompts/
- MCP tools should be atomic, composable operations
- Use `textwrap.dedent()` for multi-line string formatting in prompts and tools
- MCP Prompts are used to invoke tools and format content with instructions for an LLM
- Schema changes require Alembic migrations
- SQLite is used for indexing and full text search, files are source of truth
- Testing uses pytest with asyncio support (strict mode)
- Unit tests (`tests/`) use mocks when necessary; integration tests (`test-int/`) use real implementations
- By default, tests run against SQLite (fast, no Docker needed)
- Set `BASIC_MEMORY_TEST_POSTGRES=1` to run against Postgres (uses testcontainers - Docker required)
- Each test runs in a standalone environment with isolated database and tmp_path directory
- CI runs SQLite and Postgres tests in parallel for faster feedback
- Performance benchmarks are in `test-int/test_sync_performance_benchmark.py`
- Use pytest markers: `@pytest.mark.benchmark` for benchmarks, `@pytest.mark.slow` for slow tests
- **Coverage must stay at 100%**: Write tests for new code. Only use `# pragma: no cover` when tests would require excessive mocking (e.g., TYPE_CHECKING blocks, error handlers that need failure injection, runtime-mode-dependent code paths)
### Async Client Pattern (Important!)
**All MCP tools and CLI commands use the context manager pattern for HTTP clients:**
```python
from basic_memory.mcp.async_client import get_client
async def my_mcp_tool():
async with get_client() as client:
# Use client for API calls
response = await call_get(client, "/path")
return response
```
**Do NOT use:**
-`from basic_memory.mcp.async_client import client` (deprecated module-level client)
- ❌ Manual auth header management
-`inject_auth_header()` (deleted)
**Key principles:**
- Auth happens at client creation, not per-request
- Proper resource management via context managers
- Supports three modes: Local (ASGI), CLI cloud (HTTP + auth), Cloud app (factory injection)
- Factory pattern enables dependency injection for cloud consolidation
**For cloud app integration:**
```python
from basic_memory.mcp import async_client
# Set custom factory before importing tools
async_client.set_client_factory(your_custom_factory)
```
See SPEC-16 for full context manager refactor details.
## BASIC MEMORY PRODUCT USAGE
### Knowledge Structure
- Entity: Any concept, document, or idea represented as a markdown file
- Observation: A categorized fact about an entity (`- [category] content`)
- Relation: A directional link between entities (`- relation_type [[Target]]`)
- Frontmatter: YAML metadata at the top of markdown files
- Knowledge representation follows precise markdown format:
- Observations with [category] prefixes
- Relations with WikiLinks [[Entity]]
- Frontmatter with metadata
### Basic Memory Commands
**Local Commands:**
- Check sync status: `basic-memory status`
- 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`
- 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: `basic-memory cloud login`
- Logout: `basic-memory cloud logout`
- Check cloud status: `basic-memory cloud status`
- Setup cloud sync: `basic-memory cloud setup`
- Manage snapshots: `basic-memory cloud snapshot [create|list|delete|show|browse]`
- Restore from snapshot: `basic-memory cloud restore <path> --snapshot <id>`
### MCP Capabilities
- Basic Memory exposes these MCP tools to LLMs:
**Content Management:**
- `write_note(title, content, directory, tags)` - Create/update markdown notes with semantic observations and relations
- `read_note(identifier, page, page_size)` - Read notes by title, permalink, or memory:// URL with knowledge graph awareness
- `read_content(path)` - Read raw file content (text, images, binaries) without knowledge graph processing
- `view_note(identifier, page, page_size)` - View notes as formatted artifacts for better readability
- `edit_note(identifier, operation, content)` - Edit notes incrementally (append, prepend, find/replace, replace_section)
- `move_note(identifier, destination_path, is_directory)` - Move notes or directories to new locations, updating database and maintaining links
- `delete_note(identifier, is_directory)` - Delete notes or directories from the knowledge base
**Knowledge Graph Navigation:**
- `build_context(url, depth, timeframe)` - Navigate the knowledge graph via memory:// URLs for conversation continuity
- `recent_activity(type, depth, timeframe)` - Get recently updated information with specified timeframe (e.g., "1d", "1 week")
- `list_directory(dir_name, depth, file_name_glob)` - Browse directory contents with filtering and depth control
**Search & Discovery:**
- `search_notes(query, page, page_size, search_type, types, entity_types, after_date)` - Full-text search across all content with advanced filtering options
**Project Management:**
- `list_memory_projects()` - List all available projects with their status
- `create_memory_project(project_name, project_path, set_default)` - Create new Basic Memory projects
- `delete_project(project_name)` - Delete a project from configuration
**Visualization:**
- `canvas(nodes, edges, title, directory)` - Generate Obsidian canvas files for knowledge graph visualization
**ChatGPT-Compatible Tools:**
- `search(query)` - Search across knowledge base (OpenAI actions compatible)
- `fetch(id)` - Fetch full content of a search result document
- MCP Prompts for better AI interaction:
- `ai_assistant_guide()` - Guidance on effectively using Basic Memory tools for AI assistants
- `continue_conversation(topic, timeframe)` - Continue previous conversations with relevant historical context
- `search(query, after_date)` - Search with detailed, formatted results for better context understanding
- `recent_activity(timeframe)` - View recently changed items with formatted output
### Cloud Features (v0.15.0+)
Basic Memory now supports cloud synchronization and storage (requires active subscription):
**Authentication:**
- JWT-based authentication with subscription validation
- Secure session management with token refresh
- Support for multiple cloud projects
**Bidirectional Sync:**
- rclone bisync integration for two-way synchronization
- Conflict resolution and integrity verification
- Real-time sync with change detection
- Mount/unmount cloud storage for direct file access
**Cloud Project Management:**
- Create and manage projects in the cloud
- Toggle between local and cloud modes
- Per-project sync configuration
- Subscription-based access control
**Security & Performance:**
- Removed .env file loading for improved security
- .gitignore integration (respects gitignored files)
- WAL mode for SQLite performance
- Background relation resolution (non-blocking startup)
- API performance optimizations (SPEC-11)
**CLI Routing Flags:**
When 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
## AI-Human Collaborative Development
Basic Memory emerged from and enables a new kind of development process that combines human and AI capabilities. Instead
of using AI just for code generation, we've developed a true collaborative workflow:
1. AI (LLM) writes initial implementation based on specifications and context
2. Human reviews, runs tests, and commits code with any necessary adjustments
3. Knowledge persists across conversations using Basic Memory's knowledge graph
4. Development continues seamlessly across different AI sessions with consistent context
5. Results improve through iterative collaboration and shared understanding
This approach has allowed us to tackle more complex challenges and build a more robust system than either humans or AI
could achieve independently.
**Problem-Solving Guidance:**
- If a solution isn't working after reasonable effort, suggest alternative approaches
- Don't persist with a problematic library or pattern when better alternatives exist
- Example: When py-pglite caused cascading test failures, switching to testcontainers-postgres was the right call
## GitHub Integration
Basic Memory has taken AI-Human collaboration to the next level by integrating Claude directly into the development workflow through GitHub:
### GitHub MCP Tools
Using the GitHub Model Context Protocol server, Claude can now:
- **Repository Management**:
- View repository files and structure
- Read file contents
- Create new branches
- Create and update files
- **Issue Management**:
- Create new issues
- Comment on existing issues
- Close and update issues
- Search across issues
- **Pull Request Workflow**:
- Create pull requests
- Review code changes
- Add comments to PRs
This integration enables Claude to participate as a full team member in the development process, not just as a code generation tool. Claude's GitHub account ([bm-claudeai](https://github.com/bm-claudeai)) is a member of the Basic Machines organization with direct contributor access to the codebase.
### Collaborative Development Process
With GitHub integration, the development workflow includes:
1. **Direct code review** - Claude can analyze PRs and provide detailed feedback
2. **Contribution tracking** - All of Claude's contributions are properly attributed in the Git history
3. **Branch management** - Claude can create feature branches for implementations
4. **Documentation maintenance** - Claude can keep documentation updated as the code evolves
5. **Code Commits**: ALWAYS sign off commits with `git commit -s`
This level of integration represents a new paradigm in AI-human collaboration, where the AI assistant becomes a full-fledged team member rather than just a tool for generating code snippets.
Symlink
+1
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@@ -0,0 +1 @@
AGENTS.md
+14
View File
@@ -496,16 +496,23 @@ just test
- `just test-int-postgres` - Run integration tests against Postgres
- `just test-windows` - Run Windows-specific tests (auto-skips on other platforms)
- `just test-benchmark` - Run performance benchmark tests
- `just testmon` - Run tests impacted by recent changes (pytest-testmon)
- `just test-smoke` - Run fast MCP end-to-end smoke test
- `just fast-check` - Run fix/format/typecheck + impacted tests + smoke test
- `just doctor` - Run local file <-> DB consistency checks with temp config
**Postgres Testing:**
Postgres tests use [testcontainers](https://testcontainers-python.readthedocs.io/) which automatically spins up a Postgres instance in Docker. No manual database setup required - just have Docker running.
**Testmon Note:** When no files have changed, `just testmon` may collect 0 tests. That's expected and means no impacted tests were detected.
**Test Markers:**
Tests use pytest markers for selective execution:
- `windows` - Windows-specific database optimizations
- `benchmark` - Performance tests (excluded from default runs)
- `smoke` - Fast MCP end-to-end smoke tests
**Other Development Commands:**
```bash
@@ -513,10 +520,17 @@ just install # Install with dev dependencies
just lint # Run linting checks
just typecheck # Run type checking
just format # Format code with ruff
just fast-check # Fast local loop (fix/format/typecheck + testmon + smoke)
just doctor # Local consistency check (temp config)
just check # Run all quality checks
just migration "msg" # Create database migration
```
**Local Consistency Check:**
```bash
basic-memory doctor # Verifies file <-> database sync in a temp project
```
See the [justfile](justfile) for the complete list of development commands.
## License
+28
View File
@@ -62,6 +62,22 @@ test-int-postgres:
BASIC_MEMORY_TEST_POSTGRES=1 uv run pytest -p pytest_mock -v --no-cov test-int
fi
# Run tests impacted by recent changes (requires pytest-testmon)
testmon *args:
BASIC_MEMORY_ENV=test uv run pytest -p pytest_mock -v --no-cov --testmon --testmon-forceselect {{args}}
# Run MCP smoke test (fast end-to-end loop)
test-smoke:
BASIC_MEMORY_ENV=test uv run pytest -p pytest_mock -v --no-cov -m smoke test-int/mcp/test_smoke_integration.py
# Fast local loop: lint, format, typecheck, impacted tests
fast-check:
just fix
just format
just typecheck
just testmon
just test-smoke
# Reset Postgres test database (drops and recreates schema)
# Useful when Alembic migration state gets out of sync during development
# Uses credentials from docker-compose-postgres.yml
@@ -149,6 +165,18 @@ format:
run-inspector:
npx @modelcontextprotocol/inspector
# Run doctor checks in an isolated temp home/config
doctor:
#!/usr/bin/env bash
set -euo pipefail
TMP_HOME=$(mktemp -d)
TMP_CONFIG=$(mktemp -d)
HOME="$TMP_HOME" \
BASIC_MEMORY_ENV=test \
BASIC_MEMORY_HOME="$TMP_HOME/basic-memory" \
BASIC_MEMORY_CONFIG_DIR="$TMP_CONFIG" \
./.venv/bin/python -m basic_memory.cli.main doctor --local
# Update all dependencies to latest versions
update-deps:
+2
View File
@@ -71,6 +71,7 @@ markers = [
"slow: Slow-running tests (deselect with '-m \"not slow\"')",
"postgres: Tests that run against Postgres backend (deselect with '-m \"not postgres\"')",
"windows: Windows-specific tests (deselect with '-m \"not windows\"')",
"smoke: Fast end-to-end smoke tests for MCP flows",
]
[tool.ruff]
@@ -91,6 +92,7 @@ dev = [
"testcontainers[postgres]>=4.0.0",
"psycopg>=3.2.0",
"pyright>=1.1.408",
"pytest-testmon>=2.2.0",
]
[tool.hatch.version]
+1 -1
View File
@@ -50,7 +50,7 @@ def app_callback(
# Skip for 'mcp' command - it has its own lifespan that handles initialization
# Skip for API-using commands (status, sync, etc.) - they handle initialization via deps.py
# Skip for 'reset' command - it manages its own database lifecycle
skip_init_commands = {"mcp", "status", "sync", "project", "tool", "reset"}
skip_init_commands = {"doctor", "mcp", "status", "sync", "project", "tool", "reset"}
if (
not version
and ctx.invoked_subcommand is not None
+2 -1
View File
@@ -1,11 +1,12 @@
"""CLI commands for basic-memory."""
from . import status, db, import_memory_json, mcp, import_claude_conversations
from . import status, db, doctor, import_memory_json, mcp, import_claude_conversations
from . import import_claude_projects, import_chatgpt, tool, project, format
__all__ = [
"status",
"db",
"doctor",
"import_memory_json",
"mcp",
"import_claude_conversations",
+153
View File
@@ -0,0 +1,153 @@
"""Doctor command for local consistency checks."""
from __future__ import annotations
import tempfile
import uuid
from pathlib import Path
from loguru import logger
from mcp.server.fastmcp.exceptions import ToolError
from rich.console import Console
import typer
from basic_memory.cli.app import app
from basic_memory.cli.commands.command_utils import run_with_cleanup
from basic_memory.cli.commands.routing import force_routing, validate_routing_flags
from basic_memory.markdown.entity_parser import EntityParser
from basic_memory.markdown.markdown_processor import MarkdownProcessor
from basic_memory.markdown.schemas import EntityFrontmatter, EntityMarkdown
from basic_memory.mcp.async_client import get_client
from basic_memory.mcp.clients import KnowledgeClient, ProjectClient, SearchClient
from basic_memory.mcp.tools.utils import call_post
from basic_memory.schemas.base import Entity
from basic_memory.schemas.project_info import ProjectInfoRequest
from basic_memory.schemas.search import SearchQuery
from basic_memory.schemas import SyncReportResponse
console = Console()
async def run_doctor() -> None:
"""Run local consistency checks for file <-> database flows."""
console.print("[blue]Running Basic Memory doctor checks...[/blue]")
project_name = f"doctor-{uuid.uuid4().hex[:8]}"
api_note_title = "Doctor API Note"
manual_note_title = "Doctor Manual Note"
manual_permalink = "doctor/manual-note"
with tempfile.TemporaryDirectory() as temp_dir:
temp_path = Path(temp_dir)
async with get_client() as client:
project_client = ProjectClient(client)
project_request = ProjectInfoRequest(
name=project_name,
path=str(temp_path),
set_default=False,
)
project_id: str | None = None
try:
status = await project_client.create_project(project_request.model_dump())
if not status.new_project:
raise ValueError("Failed to create doctor project")
project_id = status.new_project.external_id
console.print(f"[green]OK[/green] Created doctor project: {project_name}")
# --- DB -> File: create an entity via API ---
knowledge_client = KnowledgeClient(client, project_id)
api_note = Entity(
title=api_note_title,
directory="doctor",
entity_type="note",
content_type="text/markdown",
content=f"# {api_note_title}\n\n- [note] API to file check",
entity_metadata={"tags": ["doctor"]},
)
api_result = await knowledge_client.create_entity(api_note.model_dump(), fast=False)
api_file = temp_path / api_result.file_path
if not api_file.exists():
raise ValueError(f"API note file missing: {api_result.file_path}")
api_text = api_file.read_text(encoding="utf-8")
if api_note_title not in api_text:
raise ValueError("API note content missing from file")
console.print("[green]OK[/green] API write created file")
# --- File -> DB: write markdown file directly, then sync ---
parser = EntityParser(temp_path)
processor = MarkdownProcessor(parser)
manual_markdown = EntityMarkdown(
frontmatter=EntityFrontmatter(
metadata={
"title": manual_note_title,
"type": "note",
"permalink": manual_permalink,
"tags": ["doctor"],
}
),
content=f"# {manual_note_title}\n\n- [note] File to DB check",
)
manual_path = temp_path / "doctor" / "manual-note.md"
await processor.write_file(manual_path, manual_markdown)
console.print("[green]OK[/green] Manual file written")
sync_response = await call_post(
client,
f"/v2/projects/{project_id}/sync?force_full=true&run_in_background=false",
)
sync_report = SyncReportResponse.model_validate(sync_response.json())
if sync_report.total == 0:
raise ValueError("Sync did not detect any changes")
console.print("[green]OK[/green] Sync indexed manual file")
search_client = SearchClient(client, project_id)
search_query = SearchQuery(title=manual_note_title)
search_results = await search_client.search(
search_query.model_dump(), page=1, page_size=5
)
if not any(result.title == manual_note_title for result in search_results.results):
raise ValueError("Manual note not found in search index")
console.print("[green]OK[/green] Search confirmed manual file")
status_response = await call_post(client, f"/v2/projects/{project_id}/status")
status_report = SyncReportResponse.model_validate(status_response.json())
if status_report.total != 0:
raise ValueError("Project status not clean after sync")
console.print("[green]OK[/green] Status clean after sync")
finally:
if project_id:
await project_client.delete_project(project_id)
console.print("[green]Doctor checks passed.[/green]")
@app.command()
def doctor(
local: bool = typer.Option(
False, "--local", help="Force local API routing (ignore cloud mode)"
),
cloud: bool = typer.Option(False, "--cloud", help="Force cloud API routing"),
) -> None:
"""Run local consistency checks to verify file/database sync."""
try:
validate_routing_flags(local, cloud)
with force_routing(local=local, cloud=cloud):
run_with_cleanup(run_doctor())
except (ToolError, ValueError) as e:
console.print(f"[red]Doctor failed: {e}[/red]")
raise typer.Exit(code=1)
except Exception as e:
logger.error(f"Doctor failed: {e}")
typer.echo(f"Doctor failed: {e}", err=True)
raise typer.Exit(code=1) # pragma: no cover
+1
View File
@@ -6,6 +6,7 @@ from basic_memory.cli.app import app # pragma: no cover
from basic_memory.cli.commands import ( # noqa: F401 # pragma: no cover
cloud,
db,
doctor,
import_chatgpt,
import_claude_conversations,
import_claude_projects,
+55
View File
@@ -0,0 +1,55 @@
"""Smoke test for MCP end-to-end flow."""
import pytest
from fastmcp import Client
@pytest.mark.asyncio
@pytest.mark.smoke
async def test_mcp_smoke_flow(mcp_server, app, test_project):
"""Verify write -> read -> search -> build_context works end-to-end."""
async with Client(mcp_server) as client:
title = "Smoke Test Note"
content = "# Smoke Test Note\n\n- [note] MCP smoke flow"
await client.call_tool(
"write_note",
{
"project": test_project.name,
"title": title,
"directory": "smoke",
"content": content,
"tags": "smoke,test",
},
)
read_result = await client.call_tool(
"read_note",
{
"project": test_project.name,
"identifier": title,
},
)
assert len(read_result.content) == 1
assert title in read_result.content[0].text
search_result = await client.call_tool(
"search_notes",
{
"project": test_project.name,
"query": "Smoke Test Note",
},
)
assert len(search_result.content) == 1
assert title in search_result.content[0].text
context_result = await client.call_tool(
"build_context",
{
"project": test_project.name,
"url": "smoke/*",
},
)
assert len(context_result.content) == 1
assert title in context_result.content[0].text
@@ -349,7 +349,7 @@ async def test_frontmatter_roundtrip_preserves_user_metadata(tmp_path):
# Create a file with user's custom frontmatter (like the bug report)
test_file = tmp_path / "litnote.md"
content = dedent(
'''
"""
---
title: "My Document Title"
type: litnote
@@ -360,7 +360,7 @@ async def test_frontmatter_roundtrip_preserves_user_metadata(tmp_path):
---
# Content here...
'''
"""
).strip()
test_file.write_text(content)
@@ -379,7 +379,9 @@ async def test_frontmatter_roundtrip_preserves_user_metadata(tmp_path):
output = dump_frontmatter(post)
# The output should NOT have duplicate frontmatter or metadata: {} key
assert output.count("---") == 2, "Should have exactly one frontmatter block (two --- delimiters)"
assert output.count("---") == 2, (
"Should have exactly one frontmatter block (two --- delimiters)"
)
assert "metadata:" not in output, "Should not have 'metadata:' key in output"
assert "citekey: authorTitleYear2024" in output, "User's citekey should be preserved"
assert "type: litnote" in output, "User's type should be preserved"
+88
View File
@@ -0,0 +1,88 @@
"""Tool contract tests for MCP tool signatures."""
from __future__ import annotations
import inspect
from basic_memory.mcp import tools
EXPECTED_TOOL_SIGNATURES: dict[str, list[str]] = {
"build_context": ["url", "project", "depth", "timeframe", "page", "page_size", "max_related"],
"canvas": ["nodes", "edges", "title", "directory", "project"],
"create_memory_project": ["project_name", "project_path", "set_default"],
"delete_note": ["identifier", "is_directory", "project"],
"delete_project": ["project_name"],
"edit_note": [
"identifier",
"operation",
"content",
"project",
"section",
"find_text",
"expected_replacements",
],
"fetch": ["id"],
"list_directory": ["dir_name", "depth", "file_name_glob", "project"],
"list_memory_projects": [],
"move_note": ["identifier", "destination_path", "is_directory", "project"],
"read_content": ["path", "project"],
"read_note": ["identifier", "project", "page", "page_size"],
"recent_activity": ["type", "depth", "timeframe", "project"],
"search": ["query"],
"search_by_metadata": ["filters", "project", "limit", "offset"],
"search_notes": [
"query",
"project",
"page",
"page_size",
"search_type",
"types",
"entity_types",
"after_date",
"metadata_filters",
"tags",
"status",
],
"view_note": ["identifier", "project", "page", "page_size"],
"write_note": ["title", "content", "directory", "project", "tags", "note_type"],
}
TOOL_FUNCTIONS: dict[str, object] = {
"build_context": tools.build_context,
"canvas": tools.canvas,
"create_memory_project": tools.create_memory_project,
"delete_note": tools.delete_note,
"delete_project": tools.delete_project,
"edit_note": tools.edit_note,
"fetch": tools.fetch,
"list_directory": tools.list_directory,
"list_memory_projects": tools.list_memory_projects,
"move_note": tools.move_note,
"read_content": tools.read_content,
"read_note": tools.read_note,
"recent_activity": tools.recent_activity,
"search": tools.search,
"search_by_metadata": tools.search_by_metadata,
"search_notes": tools.search_notes,
"view_note": tools.view_note,
"write_note": tools.write_note,
}
def _signature_params(tool_obj: object) -> list[str]:
fn = tool_obj.fn
params = []
for param in inspect.signature(fn).parameters.values():
if param.name == "context":
continue
params.append(param.name)
return params
def test_mcp_tool_signatures_are_stable():
assert set(TOOL_FUNCTIONS.keys()) == set(EXPECTED_TOOL_SIGNATURES.keys())
for tool_name, tool_obj in TOOL_FUNCTIONS.items():
assert _signature_params(tool_obj) == EXPECTED_TOOL_SIGNATURES[tool_name]
+1 -3
View File
@@ -934,9 +934,7 @@ async def test_search_metadata_filters_numeric_comparisons(search_repository, se
{"schema": {"confidence": 0.4}},
)
results = await search_repository.search(
metadata_filters={"schema.confidence": {"$gt": 0.7}}
)
results = await search_repository.search(metadata_filters={"schema.confidence": {"$gt": 0.7}})
assert {result.id for result in results} == {entity_high.id}
results = await search_repository.search(
Generated
+15
View File
@@ -187,6 +187,7 @@ dev = [
{ name = "pytest-asyncio" },
{ name = "pytest-cov" },
{ name = "pytest-mock" },
{ name = "pytest-testmon" },
{ name = "pytest-xdist" },
{ name = "ruff" },
{ name = "testcontainers" },
@@ -242,6 +243,7 @@ dev = [
{ name = "pytest-asyncio", specifier = ">=0.24.0" },
{ name = "pytest-cov", specifier = ">=4.1.0" },
{ name = "pytest-mock", specifier = ">=3.12.0" },
{ name = "pytest-testmon", specifier = ">=2.2.0" },
{ name = "pytest-xdist", specifier = ">=3.0.0" },
{ name = "ruff", specifier = ">=0.1.6" },
{ name = "testcontainers", extras = ["postgres"], specifier = ">=4.0.0" },
@@ -1723,6 +1725,19 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/5a/cc/06253936f4a7fa2e0f48dfe6d851d9c56df896a9ab09ac019d70b760619c/pytest_mock-3.15.1-py3-none-any.whl", hash = "sha256:0a25e2eb88fe5168d535041d09a4529a188176ae608a6d249ee65abc0949630d", size = 10095, upload-time = "2025-09-16T16:37:25.734Z" },
]
[[package]]
name = "pytest-testmon"
version = "2.2.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "coverage" },
{ name = "pytest" },
]
sdist = { url = "https://files.pythonhosted.org/packages/4d/1d/3e4230cc67cd6205bbe03c3527500c0ccaf7f0c78b436537eac71590ee4a/pytest_testmon-2.2.0.tar.gz", hash = "sha256:01f488e955ed0e0049777bee598bf1f647dd524e06f544c31a24e68f8d775a51", size = 23108, upload-time = "2025-12-01T07:30:24.76Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/61/55/ebb3c2f59fb089f08d00f764830d35780fc4e4c41dffcadafa3264682b65/pytest_testmon-2.2.0-py3-none-any.whl", hash = "sha256:2604ca44a54d61a2e830d9ce828b41a837075e4ebc1f81b148add8e90d34815b", size = 25199, upload-time = "2025-12-01T07:30:23.623Z" },
]
[[package]]
name = "pytest-xdist"
version = "3.8.0"