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Author SHA1 Message Date
smithery-ai[bot] 2493022e23 Update README 2025-03-11 19:12:40 +00:00
smithery-ai[bot] 8aec09ee0b Add Smithery configuration 2025-03-11 19:12:39 +00:00
smithery-ai[bot] 116619dfdb Add Dockerfile 2025-03-11 19:12:38 +00:00
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# /beta - Create Beta Release
Create a new beta release using the automated justfile target with quality checks and tagging.
## Usage
```
/beta <version>
```
**Parameters:**
- `version` (required): Beta version like `v0.13.2b1` or `v0.13.2rc1`
## Implementation
You are an expert release manager for the Basic Memory project. When the user runs `/beta`, execute the following steps:
### Step 1: Pre-flight Validation
1. Verify version format matches `v\d+\.\d+\.\d+(b\d+|rc\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
### Step 2: Use Justfile Automation
Execute the automated beta release process:
```bash
just beta <version>
```
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 in `src/basic_memory/__init__.py`
- ✅ Automatic commit with proper message
- ✅ Tag creation and pushing to GitHub
- ✅ Beta release workflow trigger
### Step 3: Monitor Beta Release
1. Check GitHub Actions workflow starts successfully
2. Monitor workflow at: https://github.com/basicmachines-co/basic-memory/actions
3. Verify PyPI pre-release publication
4. Test beta installation: `uv tool install basic-memory --pre`
### Step 4: Beta Testing Instructions
Provide users with beta testing instructions:
```bash
# Install/upgrade to beta
uv tool install basic-memory --pre
# Or upgrade existing installation
uv tool upgrade basic-memory --prerelease=allow
```
## Version Guidelines
- **First beta**: `v0.13.2b1`
- **Subsequent betas**: `v0.13.2b2`, `v0.13.2b3`, etc.
- **Release candidates**: `v0.13.2rc1`, `v0.13.2rc2`, etc.
- **Final release**: `v0.13.2` (use `/release` command)
## Error Handling
- If `just beta` fails, examine the error output for specific issues
- If quality checks fail, fix issues and retry
- If version format is invalid, correct and retry
- If tag already exists, increment version number
## Success Output
```
✅ Beta Release v0.13.2b1 Created Successfully!
🏷️ Tag: v0.13.2b1
🚀 GitHub Actions: Running
📦 PyPI: Will be available in ~5 minutes as pre-release
Install/test with:
uv tool install basic-memory --pre
Monitor release: https://github.com/basicmachines-co/basic-memory/actions
```
## Beta Testing Workflow
1. **Create beta**: Use `/beta v0.13.2b1`
2. **Test features**: Install and validate new functionality
3. **Fix issues**: Address bugs found during testing
4. **Iterate**: Create `v0.13.2b2` if needed
5. **Release candidate**: Create `v0.13.2rc1` when stable
6. **Final release**: Use `/release v0.13.2` when ready
## Context
- 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 in `__init__.py`
- Ideal for validating changes before stable release
- Supports both beta (b1, b2) and release candidate (rc1, rc2) versions
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# /changelog - Generate or Update Changelog Entry
Analyze commits and generate formatted changelog entry for a version.
## Usage
```
/changelog <version> [type]
```
**Parameters:**
- `version` (required): Version like `v0.14.0` or `v0.14.0b1`
- `type` (optional): `beta`, `rc`, or `stable` (default: `stable`)
## Implementation
You are an expert technical writer for the Basic Memory project. When the user runs `/changelog`, execute the following steps:
### Step 1: Version Analysis
1. **Determine Commit Range**
```bash
# Find last release tag
git tag -l "v*" --sort=-version:refname | grep -v "b\|rc" | head -1
# Get commits since last release
git log --oneline ${last_tag}..HEAD
```
2. **Parse Conventional Commits**
- Extract feat: (features)
- Extract fix: (bug fixes)
- Extract BREAKING CHANGE: (breaking changes)
- Extract chore:, docs:, test: (other improvements)
### Step 2: Categorize Changes
1. **Features (feat:)**
- New MCP tools
- New CLI commands
- New API endpoints
- Major functionality additions
2. **Bug Fixes (fix:)**
- User-facing bug fixes
- Critical issues resolved
- Performance improvements
- Security fixes
3. **Technical Improvements**
- Test coverage improvements
- Code quality enhancements
- Dependency updates
- Documentation updates
4. **Breaking Changes**
- API changes
- Configuration changes
- Behavior changes
- Migration requirements
### Step 3: Generate Changelog Entry
Create formatted entry following existing CHANGELOG.md style:
Example:
```markdown
## <version> (<date>)
### Features
- **Multi-Project Management System** - Switch between projects instantly during conversations
([`993e88a`](https://github.com/basicmachines-co/basic-memory/commit/993e88a))
- Instant project switching with session context
- Project-specific operations and isolation
- Project discovery and management tools
- **Advanced Note Editing** - Incremental editing with append, prepend, find/replace, and section operations
([`6fc3904`](https://github.com/basicmachines-co/basic-memory/commit/6fc3904))
- `edit_note` tool with multiple operation types
- Smart frontmatter-aware editing
- Validation and error handling
### Bug Fixes
- **#118**: Fix YAML tag formatting to follow standard specification
([`2dc7e27`](https://github.com/basicmachines-co/basic-memory/commit/2dc7e27))
- **#110**: Make --project flag work consistently across CLI commands
([`02dd91a`](https://github.com/basicmachines-co/basic-memory/commit/02dd91a))
### Technical Improvements
- **Comprehensive Testing** - 100% test coverage with integration testing
([`468a22f`](https://github.com/basicmachines-co/basic-memory/commit/468a22f))
- MCP integration test suite
- End-to-end testing framework
- Performance and edge case validation
### Breaking Changes
- **Database Migration**: Automatic migration from per-project to unified database.
Data will be re-index from the filesystem, resulting in no data loss.
- **Configuration Changes**: Projects now synced between config.json and database
- **Full Backward Compatibility**: All existing setups continue to work seamlessly
```
### Step 4: Integration
1. **Update CHANGELOG.md**
- Insert new entry at top
- Maintain consistent formatting
- Include commit links and issue references
2. **Validation**
- Check all major changes are captured
- Verify commit links work
- Ensure issue numbers are correct
## Smart Analysis Features
### Automatic Classification
- Detect feature additions from file changes
- Identify bug fixes from commit messages
- Find breaking changes from code analysis
- Extract issue numbers from commit messages
### Content Enhancement
- Add context for technical changes
- Include migration guidance for breaking changes
- Suggest installation/upgrade instructions
- Link to relevant documentation
## Output Format
### For Beta Releases
Example:
```markdown
## v0.13.0b4 (2025-06-03)
### Beta Changes Since v0.13.0b3
- Fix FastMCP API compatibility issues
- Update dependencies to latest versions
- Resolve setuptools import error
### Installation
```bash
uv tool install basic-memory --prerelease=allow
```
### Known Issues
- [List any known issues for beta testing]
```
### For Stable Releases
Full changelog with complete feature list, organized by impact and category.
## Context
- Follows existing CHANGELOG.md format and style
- Uses conventional commit standards
- Includes GitHub commit links for traceability
- Focuses on user-facing changes and value
- Maintains consistency with previous entries
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# /release-check - Pre-flight Release Validation
Comprehensive pre-flight check for release readiness without making any changes.
## Usage
```
/release-check [version]
```
**Parameters:**
- `version` (optional): Version to validate like `v0.13.0`. If not provided, determines from context.
## Implementation
You are an expert QA engineer for the Basic Memory project. When the user runs `/release-check`, execute the following validation steps:
### Step 1: Environment Validation
1. **Git Status Check**
- Verify working directory is clean
- Confirm on `main` branch
- Check if ahead/behind origin
2. **Version Validation**
- Validate version format if provided
- Check for existing tags with same version
- Verify version increments properly from last release
### Step 2: Code Quality Gates
1. **Test Suite Validation**
```bash
just test
```
- All tests must pass
- Check test coverage (target: 95%+)
- Validate no skipped critical tests
2. **Code Quality Checks**
```bash
just lint
just type-check
```
- No linting errors
- No type checking errors
- Code formatting is consistent
### Step 3: Documentation Validation
1. **Changelog Check**
- CHANGELOG.md contains entry for target version
- Entry includes all major features and fixes
- Breaking changes are documented
2. **Documentation Currency**
- README.md reflects current functionality
- CLI reference is up to date
- MCP tools are documented
### Step 4: Dependency Validation
1. **Security Scan**
- No known vulnerabilities in dependencies
- All dependencies are at appropriate versions
- No conflicting dependency versions
2. **Build Validation**
- Package builds successfully
- All required files are included
- No missing dependencies
### Step 5: Issue Tracking Validation
1. **GitHub Issues Check**
- No critical open issues blocking release
- All milestone issues are resolved
- High-priority bugs are fixed
2. **Testing Coverage**
- Integration tests pass
- MCP tool tests pass
- Cross-platform compatibility verified
## Report Format
Generate a comprehensive report:
```
🔍 Release Readiness Check for v0.13.0
✅ PASSED CHECKS:
├── Git status clean
├── On main branch
├── All tests passing (744/744)
├── Test coverage: 98.2%
├── Type checking passed
├── Linting passed
├── CHANGELOG.md updated
└── No critical issues open
⚠️ WARNINGS:
├── 2 medium-priority issues still open
└── Documentation could be updated
❌ BLOCKING ISSUES:
└── None found
🎯 RELEASE READINESS: ✅ READY
Recommended next steps:
1. Address warnings if desired
2. Run `/release v0.13.0` when ready
```
## Validation Criteria
### Must Pass (Blocking)
- [ ] All tests pass
- [ ] No type errors
- [ ] No linting errors
- [ ] Working directory clean
- [ ] On main branch
- [ ] CHANGELOG.md has version entry
- [ ] No critical open issues
### Should Pass (Warnings)
- [ ] Test coverage >95%
- [ ] No medium-priority open issues
- [ ] Documentation up to date
- [ ] No dependency vulnerabilities
## Context
- This is a read-only validation - makes no changes
- Provides confidence before running actual release
- Helps identify issues early in release process
- Can be run multiple times safely
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# /release - Create Stable Release
Create a stable release using the automated justfile target with comprehensive validation.
## Usage
```
/release <version>
```
**Parameters:**
- `version` (required): Release version like `v0.13.2`
## Implementation
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
#### Documentation Validation
1. **Changelog Check**
- CHANGELOG.md contains entry for target version
- Entry includes all major features and fixes
- Breaking changes are documented
### Step 2: Use Justfile Automation
Execute the automated release process:
```bash
just release <version>
```
The justfile target handles:
- ✅ Version format validation
- ✅ Git status and branch checks
- ✅ 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 (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)
### 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
### 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
cd /Users/drew/code/basic-memory
mcp-publisher publish
```
If not authenticated:
```bash
mcp-publisher login github
# Follow device authentication flow
mcp-publisher publish
```
3. **Verify Publication**
```bash
curl "https://registry.modelcontextprotocol.io/v0.1/servers?search=basic-memory"
```
**Note:** The `mcp-publisher` CLI can be installed via Homebrew (`brew install mcp-publisher`) or from GitHub releases.
#### Website Updates
**1. basicmachines.co** (`/Users/drew/code/basicmachines.co`)
- **Goal**: Update version number displayed on the homepage
- **Location**: Search for "Basic Memory v0." in the codebase to find version displays
- **What to update**:
- Hero section heading that shows "Basic Memory v{VERSION}"
- "What's New in v{VERSION}" section heading
- Feature highlights array (look for array of features with title/description)
- **Process**:
1. Pull latest from GitHub: `git pull origin main`
2. Create release branch: `git checkout -b release/v{VERSION}`
3. Search codebase for current version number (e.g., "v0.16.1")
4. Update version numbers to new release version
5. Update feature highlights with 3-5 key features from this release (extract from CHANGELOG.md)
6. Commit changes: `git commit -m "chore: update to v{VERSION}"`
7. Push branch: `git push origin release/v{VERSION}`
- **Deploy**: Follow deployment process for basicmachines.co
**2. docs.basicmemory.com** (`/Users/drew/code/docs.basicmemory.com`)
- **Goal**: Add new release notes section to the latest-releases page
- **File**: `src/pages/latest-releases.mdx`
- **What to do**:
1. Pull latest from GitHub: `git pull origin main`
2. Create release branch: `git checkout -b release/v{VERSION}`
3. Read the existing file to understand the format and structure
4. Read `/Users/drew/code/basic-memory/CHANGELOG.md` to get release content
5. Add new release section **at the top** (after MDX imports, before other releases)
6. Follow the existing pattern:
- Heading: `## [v{VERSION}](github-link) — YYYY-MM-DD`
- Focus statement if applicable
- `<Info>` block with highlights (3-5 key items)
- Sections for Features, Bug Fixes, Breaking Changes, etc.
- Link to full changelog at the end
- Separator `---` between releases
7. Commit changes: `git commit -m "docs: add v{VERSION} release notes"`
8. Push branch: `git push origin release/v{VERSION}`
- **Source content**: Extract and format sections from CHANGELOG.md for this version
- **Deploy**: Follow deployment process for docs.basicmemory.com
**4. Announce Release**
- Post to Discord community if significant changes
- Update social media if major release
- Notify users via appropriate channels
## Pre-conditions Check
Before starting, verify:
- [ ] All beta testing is complete
- [ ] Critical bugs are fixed
- [ ] Breaking changes are documented
- [ ] CHANGELOG.md is updated (if needed)
- [ ] Version number follows semantic versioning
## Error Handling
- If `just release` fails, examine the error output for specific issues
- If quality checks fail, fix issues and retry
- If changelog entry missing, update CHANGELOG.md and commit before retrying
- If GitHub Actions fail, check workflow logs for debugging
## Success Output
```
🎉 Stable Release v0.13.2 Created Successfully!
🏷️ 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
Users can now upgrade:
uv tool upgrade basic-memory
brew 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 in `__init__.py` and `server.json`
- Triggers automated GitHub release with changelog
- Package is published to PyPI for `pip` and `uv` users
- Homebrew formula is automatically updated for stable releases
- MCP Registry is updated manually via `mcp-publisher publish`
- Supports multiple installation methods (uv, pip, Homebrew)
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---
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
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# /project:test-live - Live Basic Memory Testing Suite
Execute comprehensive real-world testing of Basic Memory using the installed version.
All test results are recorded as notes in a dedicated test project.
## Usage
```
/project:test-live [phase]
```
**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)
- `features` - Core + important workflows (Tier 1 + Tier 2)
- `all` - Comprehensive testing of all tools and scenarios
## Implementation
You are an expert QA engineer conducting live testing of Basic Memory.
When the user runs `/project:test-live`, execute comprehensive test plan:
## Tool Testing Priority
### **Tier 1: Critical Core (Always Test)**
1. **write_note** - Foundation of all knowledge creation
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
### **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
### **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
### **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
### Pre-Test Setup
1. **Environment Verification**
- Verify basic-memory is installed and accessible via MCP
- Check version and confirm it's the expected release
- Test MCP connection and tool availability
2. **Recent Changes Analysis** (if phase includes 'recent' or 'all')
- Run `git log --oneline -20` to examine recent commits
- Identify new features, bug fixes, and enhancements
- Generate targeted test scenarios for recent changes
- Prioritize regression testing for recently fixed issues
3. **Test Project Creation**
Run the bash `date` command to get the current date/time.
```
Create project: "basic-memory-testing-[timestamp]"
Location: ~/basic-memory-testing-[timestamp]
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.
4. **Baseline Documentation**
Create initial test session note with:
- Test environment details
- Version being tested
- Recent changes identified (if applicable)
- Test objectives and scope
- Start timestamp
### Phase 0: Recent Changes Validation (if 'recent' or 'all' phase)
Based on recent commit analysis, create targeted test scenarios:
**Recent Changes Test Protocol:**
1. **Feature Addition Tests** - For each new feature identified:
- Test basic functionality
- Test integration with existing tools
- Verify documentation accuracy
- Test edge cases and error handling
2. **Bug Fix Regression Tests** - For each recent fix:
- Recreate the original problem scenario
- Verify the fix works as expected
- Test related functionality isn't broken
- Document the verification in test notes
3. **Performance/Enhancement Validation** - For optimizations:
- Establish baseline timing
- Compare with expected improvements
- Test under various load conditions
- Document performance observations
**Example Recent Changes (Update based on actual git log):**
- Watch Service Restart (#156): Test project creation → file modification → automatic restart
- Cross-Project Moves (#161): Test move_note with cross-project detection
- Docker Environment Support (#174): Test BASIC_MEMORY_HOME behavior
- MCP Server Logging (#164): Verify log level configurations
### Phase 1: Core Functionality Validation (Tier 1 Tools)
Test essential MCP tools that form the foundation of Basic Memory:
**1. write_note Tests (Critical):**
- ✅ Basic note creation with frontmatter
- ✅ Special characters and Unicode in titles
- ✅ Various content types (lists, headings, code blocks)
- ✅ Empty notes and minimal content edge cases
- ⚠️ Error handling for invalid parameters
**2. read_note Tests (Critical):**
- ✅ Read by title, permalink, memory:// URLs
- ✅ Non-existent notes (error handling)
- ✅ Notes with complex markdown formatting
- ⚠️ Performance with large notes (>10MB)
**3. search_notes Tests (Critical):**
- ✅ Simple text queries across content
- ✅ Tag-based searches with multiple tags
- ✅ Boolean operators (AND, OR, NOT)
- ✅ Empty/no results scenarios
- ⚠️ Performance with 100+ notes
**4. edit_note Tests (Critical):**
- ✅ Append operations preserving frontmatter
- ✅ Prepend operations
- ✅ Find/replace with validation
- ✅ Section replacement under headers
- ⚠️ 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
- ✅ Empty project list handling
- ✅ Single project constraint mode display
**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
### Phase 2: Important Workflows (Tier 2 Tools)
**7. 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):**
- ✅ Create projects dynamically
- ✅ Set default during creation
- ✅ Path validation and creation
- ⚠️ Invalid paths and names
- ✅ Integration with existing projects
**9. 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):**
- ✅ Background operation monitoring
- ✅ File synchronization status
- ✅ Project sync state reporting
- ⚠️ Error state handling
### Phase 3: Enhanced Functionality (Tier 3 Tools)
**11. 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):**
- ✅ Raw file content access
- ✅ Binary file handling
- ✅ Image file reading
- ⚠️ Large file performance
**13. delete_note Tests (Enhanced):**
- ✅ Single note deletion
- ✅ Database consistency after deletion
- ⚠️ Non-existent note handling
- ✅ Confirmation of successful deletion
**14. list_directory Tests (Enhanced):**
- ✅ Directory content listing
- ✅ Depth control and filtering
- ✅ File name globbing
- ⚠️ Empty directory handling
**15. delete_project Tests (Enhanced):**
- ✅ Project removal from config
- ✅ Database cleanup
- ⚠️ Default project protection
- ⚠️ Non-existent project handling
### Phase 4: Edge Case Exploration
**Boundary Testing:**
- Very long titles and content (stress limits)
- Empty projects and notes
- Unicode, emojis, special symbols
- Deeply nested folder structures
- Circular relations and self-references
- Maximum relation depths
**Error Scenarios:**
- Invalid memory:// URLs
- Missing files referenced in database
- Invalid project names and paths
- Malformed note structures
- Concurrent operation conflicts
**Performance Testing:**
- Create 100+ notes rapidly
- Complex search queries
- Deep relation chains (5+ levels)
- Rapid successive operations
- Memory usage monitoring
### Phase 5: Real-World Workflow Scenarios
**Meeting Notes Pipeline:**
1. Create meeting notes with action items
2. Extract action items using edit_note
3. Build relations to project documents
4. Update progress incrementally
5. Search and track completion
**Research Knowledge Building:**
1. Create research topic hierarchy
2. Build complex relation networks
3. Add incremental findings over time
4. Search for connections and patterns
5. Reorganize as knowledge evolves
**Multi-Project Workflow:**
1. Technical documentation project
2. Personal recipe collection project
3. Learning/course notes project
4. Specify different projects for different operations
5. Cross-reference related concepts
**Content Evolution:**
1. Start with basic notes
2. Enhance with relations and observations
3. Reorganize file structure using moves
4. Update content with edit operations
5. Validate knowledge graph integrity
### Phase 6: Specialized Tools Testing (Tier 4)
**16. canvas Tests (Specialized):**
- ✅ JSON Canvas generation
- ✅ Node and edge creation
- ✅ Obsidian compatibility
- ⚠️ Complex graph handling
**17. MCP Prompts Tests (Specialized):**
- ✅ ai_assistant_guide output
- ✅ continue_conversation functionality
- ✅ Formatted search results
- ✅ Enhanced activity reports
### Phase 7: Integration & File Watching Tests
**File System Integration:**
- ✅ Watch service behavior with file changes
- ✅ Project creation → watch restart (#156)
- ✅ Multi-project synchronization
- ⚠️ MCP→API→DB→File stack validation
**Real Integration Testing:**
- ✅ End-to-end file watching vs manual operations
- ✅ Cross-session persistence
- ✅ Database consistency across operations
- ⚠️ Performance under real file system changes
### Phase 8: Creative Stress Testing
**Creative Exploration:**
- Rapid project creation/switching patterns
- Unusual but valid markdown structures
- Creative observation categories
- Novel relation types and patterns
- Unexpected tool combinations
**Stress Scenarios:**
- Bulk operations (many notes quickly)
- Complex nested moves and edits
- Deep context building
- Complex boolean search expressions
- Resource constraint testing
## Test Execution Guidelines
### Quick Testing (core/features phases)
- Focus on Tier 1 tools (core) or Tier 1+2 (features)
- Test essential functionality and common edge cases
- Record critical issues immediately
- Complete in 15-20 minutes
### Comprehensive Testing (all phase)
- Cover all tiers systematically
- Include specialized tools and stress testing
- Document performance baselines
- Complete in 45-60 minutes
### Recent Changes Focus (recent phase)
- Analyze git log for recent commits
- Generate targeted test scenarios
- Focus on regression testing for fixes
- Validate new features thoroughly
## Test Observation Format
Record ALL observations immediately as Basic Memory notes:
```markdown
---
title: Test Session [Phase] YYYY-MM-DD HH:MM
tags: [testing, v0.13.0, live-testing, [phase]]
permalink: test-session-[phase]-[timestamp]
---
# Test Session [Phase] - [Date/Time]
## Environment
- Basic Memory version: [version]
- MCP connection: [status]
- Test project: [name]
- Phase focus: [description]
## Test Results
### ✅ Successful Operations
- [timestamp] ✅ write_note: Created note with emoji title 📝 #tier1 #functionality
- [timestamp] ✅ search_notes: Boolean query returned 23 results in 0.4s #tier1 #performance
- [timestamp] ✅ edit_note: Append operation preserved frontmatter #tier1 #reliability
### ⚠️ Issues Discovered
- [timestamp] ⚠️ move_note: Slow with deep folder paths (2.1s) #tier2 #performance
- [timestamp] 🚨 search_notes: Unicode query returned unexpected results #tier1 #bug #critical
- [timestamp] ⚠️ build_context: Context lost for memory:// URLs #tier2 #issue
### 🚀 Enhancements Identified
- edit_note could benefit from preview mode #ux-improvement
- search_notes needs fuzzy matching for typos #feature-idea
- move_note could auto-suggest folder creation #usability
### 📊 Performance Metrics
- Average write_note time: 0.3s
- Search with 100+ notes: 0.6s
- Project parameter overhead: <0.1s
- Memory usage: [observed levels]
## Relations
- tests [[Basic Memory v0.13.0]]
- part_of [[Live Testing Suite]]
- found_issues [[Bug Report: Unicode Search]]
- discovered [[Performance Optimization Opportunities]]
```
## Quality Assessment Areas
**User Experience & Usability:**
- Tool instruction clarity and examples
- Error message actionability
- Response time acceptability
- Tool consistency and discoverability
- Learning curve and intuitiveness
**System Behavior:**
- Stateless operation independence
- memory:// URL navigation reliability
- Multi-step workflow cohesion
- Edge case graceful handling
- Recovery from user errors
**Documentation Alignment:**
- Tool output clarity and helpfulness
- Behavior vs. documentation accuracy
- Example validity and usefulness
- Real-world vs. documented workflows
**Mental Model Validation:**
- Natural user expectation alignment
- Surprising behavior identification
- Mistake recovery ease
- Knowledge graph concept naturalness
**Performance & Reliability:**
- Operation completion times
- Consistency across sessions
- Scaling behavior with growth
- Unexpected slowness identification
## Error Documentation Protocol
For each error discovered:
1. **Immediate Recording**
- Create dedicated error note
- Include exact reproduction steps
- Capture error messages verbatim
- Note system state when error occurred
2. **Error Note Format**
```markdown
---
title: Bug Report - [Short Description]
tags: [bug, testing, v0.13.0, [severity]]
---
# Bug Report: [Description]
## Reproduction Steps
1. [Exact steps to reproduce]
2. [Include all parameters used]
3. [Note any special conditions]
## Expected Behavior
[What should have happened]
## Actual Behavior
[What actually happened]
## Error Messages
```
[Exact error text]
```
## Environment
- Version: [version]
- Project: [name]
- Timestamp: [when]
## Severity
- [ ] Critical (blocks major functionality)
- [ ] High (impacts user experience)
- [ ] Medium (workaround available)
- [ ] Low (minor inconvenience)
## Relations
- discovered_during [[Test Session [Phase]]]
- affects [[Feature Name]]
```
## Success Metrics Tracking
**Quantitative Measures:**
- Test scenario completion rate
- Bug discovery count with severity
- Performance benchmark establishment
- Tool coverage completeness
**Qualitative Measures:**
- Conversation flow naturalness
- Knowledge graph quality
- User experience insights
- System reliability assessment
## Test Execution Flow
1. **Setup Phase** (5 minutes)
- Verify environment and create test project
- Record baseline system state
- Establish performance benchmarks
2. **Core Testing** (15-20 minutes per phase)
- Execute test scenarios systematically
- Record observations immediately
- Note timestamps for performance tracking
- Explore variations when interesting behaviors occur
3. **Documentation** (5 minutes per phase)
- Create phase summary note
- Link related test observations
- Update running issues list
- Record enhancement ideas
4. **Analysis Phase** (10 minutes)
- Review all observations across phases
- Identify patterns and trends
- Create comprehensive summary report
- Generate development recommendations
## Testing Success Criteria
### Core Testing (Tier 1) - Must Pass
- All 6 critical tools function correctly
- No critical bugs in essential workflows
- Acceptable performance for basic operations
- Error handling works as expected
### Feature Testing (Tier 1+2) - Should Pass
- All 11 core + important tools function
- Workflow scenarios complete successfully
- Performance meets baseline expectations
- Integration points work correctly
### Comprehensive Testing (All Tiers) - Complete Coverage
- All tools tested across all scenarios
- Edge cases and stress testing completed
- Performance baselines established
- Full documentation of issues and enhancements
## Expected Outcomes
**System Validation:**
- Feature verification prioritized by tier importance
- Recent changes validated for regression
- Performance baseline establishment
- Bug identification with severity assessment
**Knowledge Base Creation:**
- Prioritized testing documentation
- Real usage examples for user guides
- Recent changes validation records
- Performance insights for optimization
**Development Insights:**
- Tier-based bug priority list
- Recent changes impact assessment
- Enhancement ideas from real usage
- User experience improvement areas
## Post-Test Deliverables
1. **Test Summary Note**
- Overall results and findings
- Critical issues requiring immediate attention
- Enhancement opportunities discovered
- System readiness assessment
2. **Bug Report Collection**
- All discovered issues with reproduction steps
- Severity and impact assessments
- Suggested fixes where applicable
3. **Performance Baseline**
- Timing data for all operations
- Scaling behavior observations
- Resource usage patterns
4. **UX Improvement Recommendations**
- Usability enhancement suggestions
- Documentation improvement areas
- Tool design optimization ideas
5. **Updated TESTING.md**
- Incorporate new test scenarios discovered
- Update based on real execution experience
- Add performance benchmarks and targets
## Context
- Uses real installed basic-memory version
- Tests complete MCP→API→DB→File stack
- Creates living documentation in Basic Memory itself
- Follows integration over isolation philosophy
- Prioritizes testing by tool importance and usage frequency
- Adapts to recent development changes dynamically
- Focuses on real usage patterns over checklist validation
- Generates actionable insights prioritized by impact
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{
"enabledPlugins": {
"basic-memory@basicmachines": true
}
}
-60
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@@ -1,60 +0,0 @@
# Git files
.git/
.gitignore
.gitattributes
# Development files
.vscode/
.idea/
*.swp
*.swo
*~
# Testing files
tests/
test-int/
.pytest_cache/
.coverage
htmlcov/
# Build artifacts
build/
dist/
*.egg-info/
__pycache__/
*.pyc
*.pyo
*.pyd
.Python
# Virtual environments (uv creates these during build)
.venv/
venv/
.env
# CI/CD files
.github/
# Documentation (keep README.md and pyproject.toml)
docs/
CHANGELOG.md
CLAUDE.md
CONTRIBUTING.md
# Example files not needed for runtime
examples/
# Local development files
.basic-memory/
*.db
*.sqlite3
# OS files
.DS_Store
Thumbs.db
# Temporary files
tmp/
temp/
*.tmp
*.log
-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
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# To get started with Dependabot version updates, you'll need to specify which
# package ecosystems to update and where the package manifests are located.
# Please see the documentation for all configuration options:
# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
version: 2
updates:
- package-ecosystem: "" # See documentation for possible values
directory: "/" # Location of package manifests
schedule:
interval: "weekly"
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name: Claude Code Review
on:
pull_request:
types: [opened, synchronize]
# Optional: Only run on specific file changes
# paths:
# - "src/**/*.ts"
# - "src/**/*.tsx"
# - "src/**/*.js"
# - "src/**/*.jsx"
jobs:
claude-review:
# Only run for organization members and collaborators
if: |
github.event.pull_request.author_association == 'OWNER' ||
github.event.pull_request.author_association == 'MEMBER' ||
github.event.pull_request.author_association == 'COLLABORATOR'
runs-on: ubuntu-latest
permissions:
contents: read
pull-requests: write
issues: read
id-token: write
steps:
- name: Checkout repository
uses: actions/checkout@v4
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 this Basic Memory 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"'
-71
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@@ -1,71 +0,0 @@
name: Claude Issue Triage
on:
issues:
types: [opened]
jobs:
triage:
runs-on: ubuntu-latest
permissions:
issues: write
id-token: write
steps:
- name: Checkout repository
uses: actions/checkout@v4
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"'
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name: Claude Code
on:
issue_comment:
types: [created]
pull_request_review_comment:
types: [created]
issues:
types: [opened, assigned]
pull_request_review:
types: [submitted]
pull_request_target:
types: [opened, synchronize]
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'
)
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@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
id: claude
uses: anthropics/claude-code-action@v1
with:
claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
track_progress: true # Enable visual progress tracking
# This is an optional setting that allows Claude to read CI results on PRs
additional_permissions: |
actions: read
# Optional: Give a custom prompt to Claude. If this is not specified, Claude will perform the instructions specified in the comment that tagged it.
# prompt: 'Update the pull request description to include a summary of changes.'
# Optional: Add claude_args to customize behavior and configuration
# See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md
# or https://docs.claude.com/en/docs/claude-code/sdk#command-line for available options
# claude_args: '--model claude-opus-4-1-20250805 --allowed-tools Bash(gh pr:*)'
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name: Dev Release
on:
push:
branches: [main]
workflow_dispatch: # Allow manual triggering
jobs:
dev-release:
runs-on: ubuntu-latest
permissions:
id-token: write
contents: write
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.12"
- name: Install uv
run: |
pip install uv
- name: Install dependencies and build
run: |
uv venv
uv sync
uv build
- name: Check if this is a dev version
id: check_version
run: |
VERSION=$(uv run python -c "import basic_memory; print(basic_memory.__version__)")
echo "version=$VERSION" >> $GITHUB_OUTPUT
if [[ "$VERSION" == *"dev"* ]]; then
echo "is_dev=true" >> $GITHUB_OUTPUT
echo "Dev version detected: $VERSION"
else
echo "is_dev=false" >> $GITHUB_OUTPUT
echo "Release version detected: $VERSION, skipping dev release"
fi
- name: Publish dev version to PyPI
if: steps.check_version.outputs.is_dev == 'true'
uses: pypa/gh-action-pypi-publish@release/v1
with:
password: ${{ secrets.PYPI_TOKEN }}
skip-existing: true # Don't fail if version already exists
-61
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@@ -1,61 +0,0 @@
name: Docker Image CI
on:
push:
tags:
- 'v*' # Trigger on version tags like v1.0.0, v0.13.0, etc.
workflow_dispatch: # Allow manual triggering for testing
env:
REGISTRY: ghcr.io
IMAGE_NAME: basicmachines-co/basic-memory
jobs:
docker:
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- 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@v3
with:
registry: ${{ env.REGISTRY }}
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Extract metadata
id: meta
uses: docker/metadata-action@v5
with:
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
tags: |
type=ref,event=branch
type=ref,event=pr
type=semver,pattern={{version}}
type=semver,pattern={{major}}.{{minor}}
type=raw,value=latest,enable={{is_default_branch}}
- name: Build and push Docker image
uses: docker/build-push-action@v5
with:
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
+66 -55
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@@ -1,85 +1,96 @@
name: Release
on:
push:
tags:
- 'v*' # Trigger on version tags like v1.0.0, v0.13.0, etc.
workflow_dispatch:
inputs:
version_type:
description: 'Type of version bump (major, minor, patch)'
required: true
default: 'patch'
type: choice
options:
- patch
- minor
- major
jobs:
release:
runs-on: ubuntu-latest
concurrency: release
permissions:
id-token: write
contents: write
outputs:
released: ${{ steps.release.outputs.released }}
tag: ${{ steps.release.outputs.tag }}
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Set up Python
uses: actions/setup-python@v5
- name: Python Semantic Release
id: release
uses: python-semantic-release/python-semantic-release@master
with:
github_token: ${{ secrets.GITHUB_TOKEN }}
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@release/v1
if: steps.release.outputs.released == 'true'
with:
password: ${{ secrets.PYPI_TOKEN }}
- name: Publish to GitHub Release Assets
uses: python-semantic-release/publish-action@v9.8.9
if: steps.release.outputs.released == 'true'
with:
github_token: ${{ secrets.GITHUB_TOKEN }}
tag: ${{ steps.release.outputs.tag }}
build-macos:
needs: release
if: needs.release.outputs.released == 'true'
runs-on: macos-latest
steps:
- uses: actions/checkout@v4
with:
ref: ${{ needs.release.outputs.tag }}
- name: Set up Python "3.12"
uses: actions/setup-python@v4
with:
python-version: "3.12"
cache: 'pip'
- name: Install librsvg
run: brew install librsvg
- name: Install uv
run: |
pip install uv
- name: Install dependencies and build
- name: Create virtual env
run: |
uv venv
uv sync
uv build
- name: Verify build succeeded
- name: Install dependencies
run: |
# Verify that build artifacts exist
ls -la dist/
echo "Build completed successfully"
uv sync
- name: Create GitHub Release
uses: softprops/action-gh-release@v2
- name: Build macOS installer
run: |
make installer-mac
xattr -dr com.apple.quarantine "installer/build/Basic Memory Installer.app"
- name: Zip macOS installer
run: |
cd installer/build
zip -ry "Basic-Memory-Installer-${{ needs.release.outputs.tag }}.zip" "Basic Memory Installer.app"
- name: Upload macOS installer
uses: softprops/action-gh-release@v1
with:
files: |
dist/*.whl
dist/*.tar.gz
generate_release_notes: true
tag_name: ${{ github.ref_name }}
files: installer/build/Basic-Memory-Installer-${{ needs.release.outputs.tag }}.zip
tag_name: ${{ needs.release.outputs.tag }}
token: ${{ secrets.GITHUB_TOKEN }}
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@release/v1
with:
password: ${{ secrets.PYPI_TOKEN }}
homebrew:
name: Update Homebrew Formula
needs: release
runs-on: ubuntu-latest
# Only run for stable releases (not dev, beta, or rc versions)
if: ${{ !contains(github.ref_name, 'dev') && !contains(github.ref_name, 'b') && !contains(github.ref_name, 'rc') }}
permissions:
contents: write
actions: read
steps:
- name: Update Homebrew formula
uses: mislav/bump-homebrew-formula-action@v3
with:
# Formula name in homebrew-basic-memory repo
formula-name: basic-memory
# The tap repository
homebrew-tap: basicmachines-co/homebrew-basic-memory
# Base branch of the tap repository
base-branch: main
# Download URL will be automatically constructed from the tag
download-url: https://github.com/basicmachines-co/basic-memory/archive/refs/tags/${{ 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:
# Personal Access Token with repo scope for homebrew-basic-memory repo
COMMITTER_TOKEN: ${{ secrets.HOMEBREW_TOKEN }}
+6 -126
View File
@@ -5,22 +5,14 @@ on:
branches: [ "main" ]
pull_request:
branches: [ "main" ]
# pull_request_target runs on the BASE of the PR, not the merge result.
# It has write permissions and access to secrets.
# It's useful for PRs from forks or automated PRs but requires careful use for security reasons.
# See: https://docs.github.com/en/actions/using-workflows/events-that-trigger-workflows#pull_request_target
pull_request_target:
branches: [ "main" ]
jobs:
test-sqlite:
name: Test SQLite (${{ matrix.os }}, Python ${{ matrix.python-version }})
test:
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
os: [ubuntu-latest, windows-latest]
python-version: [ "3.12", "3.13", "3.14" ]
runs-on: ${{ matrix.os }}
python-version: [ "3.12" ]
steps:
- uses: actions/checkout@v4
@@ -37,18 +29,6 @@ jobs:
run: |
pip install uv
- name: Install just (Linux/macOS)
if: runner.os != 'Windows'
run: |
curl --proto '=https' --tlsv1.2 -sSf https://just.systems/install.sh | bash -s -- --to /usr/local/bin
- name: Install just (Windows)
if: runner.os == 'Windows'
run: |
# Install just using Chocolatey (pre-installed on GitHub Actions Windows runners)
choco install just --yes
shell: pwsh
- name: Create virtual env
run: |
uv venv
@@ -59,109 +39,9 @@ jobs:
- name: Run type checks
run: |
just typecheck
uv run make type-check
- name: Run linting
run: |
just lint
- name: Run tests (SQLite)
- name: Run tests
run: |
uv pip install pytest pytest-cov
just test-sqlite
test-postgres:
name: Test Postgres (Python ${{ matrix.python-version }})
strategy:
fail-fast: false
matrix:
python-version: [ "3.12", "3.13", "3.14" ]
runs-on: ubuntu-latest
# Note: No services section needed - testcontainers handles Postgres in Docker
steps:
- uses: actions/checkout@v4
with:
submodules: true
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
cache: 'pip'
- name: Install uv
run: |
pip install uv
- name: Install just
run: |
curl --proto '=https' --tlsv1.2 -sSf https://just.systems/install.sh | bash -s -- --to /usr/local/bin
- name: Create virtual env
run: |
uv venv
- name: Install dependencies
run: |
uv pip install -e .[dev]
- name: Run tests (Postgres via testcontainers)
run: |
uv pip install pytest pytest-cov
just test-postgres
coverage:
name: Coverage Summary (combined, Python 3.12)
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
with:
submodules: true
- name: Set up Python 3.12
uses: actions/setup-python@v4
with:
python-version: "3.12"
cache: "pip"
- name: Install uv
run: |
pip install uv
- name: Install just
run: |
curl --proto '=https' --tlsv1.2 -sSf https://just.systems/install.sh | bash -s -- --to /usr/local/bin
- name: Create virtual env
run: |
uv venv
- name: Install dependencies
run: |
uv pip install -e .[dev]
- name: Run combined coverage (SQLite + Postgres)
run: |
uv pip install pytest pytest-cov
just coverage
- name: Add coverage report to job summary
if: always()
run: |
{
echo "## Coverage"
echo ""
echo '```'
uv run coverage report -m
echo '```'
} >> "$GITHUB_STEP_SUMMARY"
- name: Upload HTML coverage report
if: always()
uses: actions/upload-artifact@v4
with:
name: htmlcov
path: htmlcov/
uv run make test
+2 -10
View File
@@ -42,16 +42,8 @@ ENV/
# macOS
.DS_Store
.coverage.*
/.coverage.*
# obsidian docs:
/docs/.obsidian/
/examples/.obsidian/
/examples/.basic-memory/
# claude action
claude-output
**/.claude/settings.local.json
.mcp.json
.mcpregistry_*
/examples/.obsidian/
+1 -1
View File
@@ -1 +1 @@
3.14
3.12
-1926
View File
File diff suppressed because it is too large Load Diff
+26 -63
View File
@@ -1,71 +1,34 @@
# 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.
+33 -289
View File
@@ -14,40 +14,20 @@ 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`
- Install: `make install` or `pip install -e ".[dev]"`
- Run tests: `uv run pytest -p pytest_mock -v` or `make test`
- 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`
- Lint: `make lint` or `ruff check . --fix`
- Type check: `make type-check` or `uv run pyright`
- Format: `make format` or `uv run ruff format .`
- Run all code checks: `make check` (runs lint, format, type-check, test)
- Create db migration: `make migration m="Your migration message"`
- Run development MCP Inspector: `make run-inspector`
### Code Style Guidelines
- Line length: 100 characters max
- Python 3.12+ with full type annotations (uses type parameters and type aliases)
- 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
@@ -58,97 +38,18 @@ See the [README.md](README.md) file for a project overview.
- 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
- `/api` - FastAPI implementation of REST endpoints
- `/cli` - Typer command-line interface
- `/markdown` - Markdown parsing and processing
- `/mcp` - MCP server + `container.py` composition root + `clients/` typed API clients
- `/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 + `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
- `/sync` - File synchronization services
### Development Notes
@@ -160,49 +61,9 @@ Flow: MCP Tool → Typed Client → HTTP API → Router → Service → Reposito
- 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.
- Test database uses in-memory SQLite
- Avoid creating mocks in tests in most circumstances.
- Each test runs in a standalone enviroment with in memory SQLite and tmp_file directory
## BASIC MEMORY PRODUCT USAGE
@@ -219,116 +80,43 @@ See SPEC-16 for full context manager refactor details.
### Basic Memory Commands
**Local Commands:**
- Check sync status: `basic-memory status`
- 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`
- 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>`
- 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, 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
- `write_note(title, content, folder, tags)` - Create/update markdown notes with semantic observations and relations
- `read_note(identifier, page, page_size)` - Read notes by title, permalink, or memory:// URL with knowledge graph
awareness
- `read_file(path)` - Read raw file content (text, images, binaries) without knowledge graph processing
**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
- `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")
**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
- `search(query, page, page_size)` - Full-text search across all content with filtering options
**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
- `canvas(nodes, edges, title, folder)` - Generate Obsidian canvas files for knowledge graph visualization
- MCP Prompts for better AI interaction:
- `ai_assistant_guide()` - Guidance on effectively using Basic Memory tools for AI assistants
- `continue_conversation(topic, timeframe)` - Continue previous conversations with relevant historical context
- `search(query, after_date)` - Search with detailed, formatted results for better context understanding
- `recent_activity(timeframe)` - View recently changed items with formatted output
### 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
- `json_canvas_spec()` - Full JSON Canvas specification for Obsidian visualization
## AI-Human Collaborative Development
@@ -342,48 +130,4 @@ of using AI just for code generation, we've developed a true collaborative workf
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.
could achieve independently.
+49 -167
View File
@@ -1,7 +1,6 @@
# Contributing to Basic Memory
Thank you for considering contributing to Basic Memory! This document outlines the process for contributing to the
project and how to get started as a developer.
Thank you for considering contributing to Basic Memory! This document outlines the process for contributing to the project and how to get started as a developer.
## Getting Started
@@ -15,8 +14,8 @@ project and how to get started as a developer.
2. **Install Dependencies**:
```bash
# Using just (recommended)
just install
# Using make (recommended)
make install
# Or using uv
uv install -e ".[dev]"
@@ -25,27 +24,13 @@ project and how to get started as a developer.
pip install -e ".[dev]"
```
> **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)
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
# Run all tests
make test
# or
uv run pytest -p pytest_mock -v
# Run a specific test
pytest tests/path/to/test_file.py::test_function_name
```
@@ -63,16 +48,16 @@ project and how to get started as a developer.
4. **Check Code Quality**:
```bash
# Run all checks at once
just check
make check
# Or run individual checks
just lint # Run linting
just format # Format code
just type-check # Type checking
make lint # Run linting
make format # Format code
make type-check # Type checking
```
5. **Test Your Changes**: Ensure all tests pass locally and maintain 100% test coverage.
```bash
just test
make test
```
6. **Submit a PR**: Submit a pull request with a detailed description of your changes.
@@ -80,68 +65,65 @@ project and how to get started as a developer.
This project is designed for collaborative development between humans and LLMs (Large Language Models):
1. **CLAUDE.md**: The repository includes a `CLAUDE.md` file that serves as a project guide for both humans and LLMs.
This file contains:
- Key project information and architectural overview
- Development commands and workflows
- Code style guidelines
- Documentation standards
1. **CLAUDE.md**: The repository includes a `CLAUDE.md` file that serves as a project guide for both humans and LLMs. This file contains:
- Key project information and architectural overview
- Development commands and workflows
- Code style guidelines
- Documentation standards
2. **AI-Human Collaborative Workflow**:
- We encourage using LLMs like Claude for code generation, reviews, and documentation
- When possible, save context in markdown files that can be referenced later
- This enables seamless knowledge transfer between different development sessions
- Claude can help with implementation details while you focus on architecture and design
- We encourage using LLMs like Claude for code generation, reviews, and documentation
- When possible, save context in markdown files that can be referenced later
- This enables seamless knowledge transfer between different development sessions
- Claude can help with implementation details while you focus on architecture and design
3. **Adding to CLAUDE.md**:
- If you discover useful project information or common commands, consider adding them to CLAUDE.md
- This helps all contributors (human and AI) maintain consistent knowledge of the project
- If you discover useful project information or common commands, consider adding them to CLAUDE.md
- This helps all contributors (human and AI) maintain consistent knowledge of the project
## Pull Request Process
1. **Create a Pull Request**: Open a PR against the `main` branch with a clear title and description.
2. **Sign the Developer Certificate of Origin (DCO)**: All contributions require signing our DCO, which certifies that
you have the right to submit your contributions. This will be automatically checked by our CLA assistant when you
create a PR.
2. **Sign the Developer Certificate of Origin (DCO)**: All contributions require signing our DCO, which certifies that you have the right to submit your contributions. This will be automatically checked by our CLA assistant when you create a PR.
3. **PR Description**: Include:
- What the PR changes
- Why the change is needed
- How you tested the changes
- Any related issues (use "Fixes #123" to automatically close issues)
- What the PR changes
- Why the change is needed
- How you tested the changes
- Any related issues (use "Fixes #123" to automatically close issues)
4. **Code Review**: Wait for code review and address any feedback.
5. **CI Checks**: Ensure all CI checks pass.
6. **Merge**: Once approved, a maintainer will merge your PR.
## Developer Certificate of Origin
By contributing to this project, you agree to the [Developer Certificate of Origin (DCO)](CLA.md). This means you
certify that:
By contributing to this project, you agree to the [Developer Certificate of Origin (DCO)](CLA.md). This means you certify that:
- You have the right to submit your contributions
- You're not knowingly submitting code with patent or copyright issues
- Your contributions are provided under the project's license (AGPL-3.0)
This is a lightweight alternative to a Contributor License Agreement and helps ensure that all contributions can be
properly incorporated into the project and potentially used in commercial applications.
This is a lightweight alternative to a Contributor License Agreement and helps ensure that all contributions can be properly incorporated into the project and potentially used in commercial applications.
### Signing Your Commits
Sign your commit:
You can sign your commits in one of two ways:
**Using the `-s` or `--signoff` flag**:
1. **Using the `-s` or `--signoff` flag**:
```bash
git commit -s -m "Your commit message"
```
This adds a `Signed-off-by` line to your commit message, certifying that you adhere to the DCO.
```bash
git commit -s -m "Your commit message"
```
2. **Configuring Git to automatically sign off**:
```bash
git config --global alias.cs 'commit -s'
```
Then use `git cs -m "Your commit message"` to commit with sign-off.
This adds a `Signed-off-by` line to your commit message, certifying that you adhere to the DCO.
The sign-off certifies that you have the right to submit your contribution under the project's license and verifies your
agreement to the DCO.
The sign-off certifies that you have the right to submit your contribution under the project's license and verifies your 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
@@ -151,112 +133,12 @@ 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
- **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
- **Fixtures**: Use pytest fixtures for setup and teardown
## Creating Issues
@@ -274,4 +156,4 @@ All contributors must follow the [Code of Conduct](CODE_OF_CONDUCT.md).
## Thank You!
Your contributions help make Basic Memory better. We appreciate your time and effort!
Your contributions help make Basic Memory better for everyone. We appreciate your time and effort!
+10 -46
View File
@@ -1,52 +1,16 @@
FROM python:3.12-slim-bookworm
# Generated by https://smithery.ai. See: https://smithery.ai/docs/config#dockerfile
FROM python:3.12-slim
# 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 ${GID} --create-home --shell /bin/bash appuser
# Copy the project into the image
ADD . /app
# Install Python 3.13 explicitly and sync the project
WORKDIR /app
RUN uv python install 3.13
RUN uv sync --locked --python 3.13
# Create necessary directories and set ownership
RUN mkdir -p /app/data/basic-memory /app/.basic-memory && \
chown -R appuser:${GID} /app
# Copy the project files
COPY . .
# Set default data directory and add venv to PATH
ENV BASIC_MEMORY_HOME=/app/data/basic-memory \
BASIC_MEMORY_PROJECT_ROOT=/app/data \
PATH="/app/.venv/bin:$PATH"
# Install pip and build dependencies
RUN pip install --upgrade pip \
&& pip install . --no-cache-dir --ignore-installed
# Switch to the non-root user
USER appuser
# Expose port if necessary (e.g., uv might use a port, but MCP over stdio so not needed here)
# Expose port
EXPOSE 8000
# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
CMD basic-memory --version || exit 1
# Use the basic-memory entrypoint to run the MCP server with default SSE transport
CMD ["basic-memory", "mcp", "--transport", "sse", "--host", "0.0.0.0", "--port", "8000"]
# Use the basic-memory entrypoint to run the MCP server
CMD ["basic-memory", "mcp"]
+67
View File
@@ -0,0 +1,67 @@
.PHONY: install test test-module lint clean format type-check installer-mac installer-win check
install:
pip install -e ".[dev]"
test:
uv run pytest -p pytest_mock -v
# Run tests for a specific module
# Usage: make test-module m=path/to/module.py [cov=module_path]
test-module:
@if [ -z "$(m)" ]; then \
echo "Usage: make test-module m=path/to/module.py [cov=module_path]"; \
exit 1; \
fi; \
if [ -z "$(cov)" ]; then \
uv run pytest $(m) -v; \
else \
uv run pytest $(m) -v --cov=$(cov); \
fi
lint:
ruff check . --fix
type-check:
uv run pyright
clean:
find . -type f -name '*.pyc' -delete
find . -type d -name '__pycache__' -exec rm -r {} +
rm -rf installer/build/
rm -rf installer/dist/
rm -f rw.*.dmg
rm -rf dist
rm -rf installer/build
rm -rf installer/dist
rm -f .coverage.*
format:
uv run ruff format .
# run inspector tool
run-inspector:
uv run mcp dev src/basic_memory/mcp/main.py
# Build app installer
installer-mac:
cd installer && chmod +x make_icons.sh && ./make_icons.sh
cd installer && uv run python setup.py bdist_mac
installer-win:
cd installer && uv run python setup.py bdist_win32
update-deps:
uv lock --upgrade
check: lint format type-check test
# Target for generating Alembic migrations with a message from command line
migration:
@if [ -z "$(m)" ]; then \
echo "Usage: make migration m=\"Your migration message\""; \
exit 1; \
fi; \
cd src/basic_memory/alembic && alembic revision --autogenerate -m "$(m)"
+380 -214
View File
@@ -1,44 +1,39 @@
<!-- mcp-name: io.github.basicmachines-co/basic-memory -->
# Basic Memory
[![License: AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)
[![PyPI version](https://badge.fury.io/py/basic-memory.svg)](https://badge.fury.io/py/basic-memory)
[![Python 3.12+](https://img.shields.io/badge/python-3.12+-blue.svg)](https://www.python.org/downloads/)
[![Tests](https://github.com/basicmachines-co/basic-memory/workflows/Tests/badge.svg)](https://github.com/basicmachines-co/basic-memory/actions)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
![](https://badge.mcpx.dev?type=server 'MCP Server')
![](https://badge.mcpx.dev?type=dev 'MCP Dev')
## 🚀 Basic Memory Cloud is Live!
- **Cross-device and multi-platform support is here.** Your knowledge graph now works on desktop, web, and mobile - seamlessly synced across all your AI tools (Claude, ChatGPT, Gemini, Claude Code, and Codex)
- **Early Supporter Pricing:** Early users get 25% off forever.
The open source project continues as always. Cloud just makes it work everywhere.
[Sign up now →](https://basicmemory.com)
with a 7 day free trial
# Basic Memory
[![smithery badge](https://smithery.ai/badge/@basicmachines-co/basic-memory)](https://smithery.ai/server/@basicmachines-co/basic-memory)
Basic Memory lets you build persistent knowledge through natural conversations with Large Language Models (LLMs) like
Claude, while keeping everything in simple Markdown files on your computer. It uses the Model Context Protocol (MCP) to
enable any compatible LLM to read and write to your local knowledge base.
- Website: https://basicmemory.com
- Documentation: https://docs.basicmemory.com
- Website: http://basicmachines.co
- Documentation: http://memory.basicmachines.co
## Pick up your conversation right where you left off
![Example Gif](docs/attachments/Obsidian-CoffeeKnowledgeBase-examples-overlays.gif)
- AI assistants can load context from local files in a new conversation
- Notes are saved locally as Markdown files in real time
- No project knowledge or special prompting required
https://github.com/user-attachments/assets/a55d8238-8dd0-454a-be4c-8860dbbd0ddc
Basic Memory provides persistent contextual awareness across sessions through a structured knowledge graph.
The system enables LLMs to access and reference prior conversations, track semantic relationships between concepts, and
incorporate human edits made directly to knowledge files.
## Quick Start
### Installing via Smithery
To install Basic Memory for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@basicmachines-co/basic-memory):
```bash
npx -y @smithery/cli install @basicmachines-co/basic-memory --client claude
```
### Installing Manually
```bash
# Install with uv (recommended)
uv tool install basic-memory
uv install basic-memory
# Configure Claude Desktop (edit ~/Library/Application Support/Claude/claude_desktop_config.json)
# Add this to your config:
@@ -62,6 +57,34 @@ uv tool install basic-memory
You can view shared context via files in `~/basic-memory` (default directory location).
You can also install the cli tools to sync files or manage projects.
```bash
uv tool install basic-memory
# create a new project in a different directory
basic-memory project add coffee ./examples/coffee
# you can set the project to the default
basic-memory project default coffee
```
View available projects
```bash
basic-memory project list
Basic Memory Projects
┏━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┓
┃ Name ┃ Path ┃ Default ┃ Active ┃
┡━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━┩
│ main │ ~/basic-memory │ ✓ │ ✓ │
│ coffee │ ~/dev/basicmachines/basic-memory/examples/coffee │ │ │
└────────┴──────────────────────────────────────────────────┴─────────┴────────┘
```
Basic Memory will write notes in Markdown format. Open you project directory in your text editor to view project files
while you have conversations with an LLM.
## Why Basic Memory?
Most LLM interactions are ephemeral - you ask a question, get an answer, and everything is forgotten. Each conversation
@@ -92,9 +115,6 @@ With Basic Memory, you can:
- Keep everything local and under your control
- Use familiar tools like Obsidian to view and edit notes
- Build a personal knowledge base that grows over time
- Sync your knowledge to the cloud with bidirectional synchronization
- Authenticate and manage cloud projects with subscription validation
- Mount cloud storage for direct file access
## How It Works in Practice
@@ -144,13 +164,36 @@ tags:
- affects [[Flavor Extraction]]
```
The note embeds semantic content and links to other topics via simple Markdown formatting.
The note embeds semantic content and links to other topics via simple Markdown
formatting.
3. You see this file on your computer in real time in the current project directory (default `~/$HOME/basic-memory`).
3. You see this file on your computer in real time in the `~/$HOME/basic-memory` directory:
- Realtime sync can be enabled via running `basic-memory sync --watch`
```markdown
---
title: Coffee Brewing Methods
permalink: coffee-brewing-methods
type: note
---
4. In a chat with the LLM, you can reference a topic:
# Coffee Brewing Methods
## Observations
- [method] Pour over provides more clarity and highlights subtle flavors
- [technique] Water temperature at 205°F (96°C) extracts optimal compounds
- [principle] Freshly ground beans preserve aromatics and flavor
- [preference] Medium-light roasts work best for pour over # Added by you
## Relations
- relates_to [[Coffee Bean Origins]]
- requires [[Proper Grinding Technique]]
- affects [[Flavor Extraction]]
- pairs_with [[Breakfast Pastries]] # Added by you
```
4. In a new chat with the LLM, you can reference this knowledge:
```
Look at `coffee-brewing-methods` for context about pour over coffee
@@ -170,14 +213,15 @@ Following relation 'requires [[Proper Grinding Technique]]':
- Impact of consistent particle size on extraction
```
Each related document can lead to more context, building a rich semantic understanding of your knowledge base.
Each related document can lead to more context, building a rich semantic understanding of your knowledge base. All of
this context comes from standard Markdown files that both humans and LLMs can read and write.
This creates a two-way flow where:
Every time the LLM writes notes,they are saved in local Markdown files that you can:
- Humans write and edit Markdown files
- LLMs read and write through the MCP protocol
- Sync keeps everything consistent
- All knowledge stays in local files.
- Edit in any text editor
- Version via git
- Back up normally
- Share when you want to
## Technical Implementation
@@ -213,7 +257,7 @@ permalink: <a uri slug>
Observations are facts about a topic.
They can be added by creating a Markdown list with a special format that can reference a `category`, `tags` using a
"#" character, and an optional `context`.
"#" charactor, and an optional `context`.
Observation Markdown format:
@@ -257,37 +301,72 @@ Examples of relations:
- documented_in [[Coffee Journal]]
```
## Using with VS Code
### Complete Example
Add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing `Ctrl + Shift + P` and typing `Preferences: Open User Settings (JSON)`.
Here's a complete example of a note with frontmatter, observations, and relations:
```json
{
"mcp": {
"servers": {
"basic-memory": {
"command": "uvx",
"args": ["basic-memory", "mcp"]
}
}
}
}
```markdown
---
title: Pour Over Coffee Method
type: note
permalink: pour-over-coffee-method
tags:
- brewing
- coffee
- techniques
---
# Pour Over Coffee Method
This note documents the pour over brewing method and my experiences with it.
## Overview
The pour over method involves pouring hot water through coffee grounds in a filter. The water drains through the coffee
and filter into a carafe or cup.
## Observations
- [equipment] Hario V60 dripper produces clean, bright cup #gear
- [technique] Pour in concentric circles to ensure even extraction
- [ratio] 1:16 coffee-to-water ratio works best for balanced flavor
- [timing] Total brew time should be 2:30-3:00 minutes for medium roast
- [temperature] Water at 205°F (96°C) extracts optimal flavor compounds
- [grind] Medium-fine grind similar to table salt texture
- [tip] 30-45 second bloom with double the coffee weight in water
- [result] Produces a cleaner cup with more distinct flavor notes than immersion methods
## Relations
- complements [[Light Roast Beans]]
- requires [[Gooseneck Kettle]]
- contrasts_with [[French Press Method]]
- pairs_with [[Breakfast Pastries]]
- documented_in [[Brewing Journal]]
- inspired_by [[Japanese Brewing Techniques]]
- affects [[Flavor Extraction]]
- part_of [[Morning Ritual]]
```
Optionally, you can add it to a file called `.vscode/mcp.json` in your workspace. This will allow you to share the configuration with others.
Basic Memory will parse the Markdown and derive the semantic relationships in the content. When you run
`basic-memory sync`:
```json
{
"servers": {
"basic-memory": {
"command": "uvx",
"args": ["basic-memory", "mcp"]
}
}
}
```
1. New and changed files are detected
2. Markdown patterns become semantic knowledge:
You can use Basic Memory with VS Code to easily retrieve and store information while coding.
- `[tech]` becomes a categorized observation
- `[[WikiLink]]` creates a relation in the knowledge graph
- Tags and metadata are indexed for search
3. A SQLite database maintains these relationships for fast querying
4. MCP-compatible LLMs can access this knowledge via memory:// URLs
This creates a two-way flow where:
- Humans write and edit Markdown files
- LLMs read and write through the MCP protocol
- Sync keeps everything consistent
- All knowledge stays in local files.
## Using with Claude Desktop
@@ -341,73 +420,14 @@ basic-memory sync
basic-memory sync --watch
```
3. Cloud features (optional, requires subscription):
3. In Claude Desktop, the LLM can now use these tools:
```bash
# Authenticate with cloud
basic-memory cloud login
# Bidirectional sync with cloud
basic-memory cloud sync
# Verify cloud integrity
basic-memory cloud check
# Mount cloud storage
basic-memory cloud mount
```
**Routing Flags** (for users with cloud subscriptions):
When cloud mode is enabled, CLI commands communicate with the cloud API by default. Use routing flags to override this:
```bash
# Force local routing (useful for local MCP server while cloud mode is enabled)
basic-memory status --local
basic-memory project list --local
# Force cloud routing (when cloud mode is disabled but you want cloud access)
basic-memory status --cloud
basic-memory project info my-project --cloud
```
The local MCP server (`basic-memory mcp`) automatically uses local routing, so you can use both local Claude Desktop and cloud-based clients simultaneously.
4. In Claude Desktop, the LLM can now use these tools:
**Content Management:**
```
write_note(title, content, folder, tags) - Create or update notes
read_note(identifier, page, page_size) - Read notes by title or permalink
read_content(path) - Read raw file content (text, images, binaries)
view_note(identifier) - View notes as formatted artifacts
edit_note(identifier, operation, content) - Edit notes incrementally
move_note(identifier, destination_path) - Move notes with database consistency
delete_note(identifier) - Delete notes from knowledge base
```
**Knowledge Graph Navigation:**
```
build_context(url, depth, timeframe) - Navigate knowledge graph via memory:// URLs
recent_activity(type, depth, timeframe) - Find recently updated information
list_directory(dir_name, depth) - Browse directory contents with filtering
```
**Search & Discovery:**
```
search(query, page, page_size) - Search across your knowledge base
```
**Project Management:**
```
list_memory_projects() - List all available projects
create_memory_project(project_name, project_path) - Create new projects
get_current_project() - Show current project stats
sync_status() - Check synchronization status
```
**Visualization:**
```
recent_activity(type, depth, timeframe) - Find recently updated information
canvas(nodes, edges, title, folder) - Generate knowledge visualizations
```
@@ -421,117 +441,263 @@ canvas(nodes, edges, title, folder) - Generate knowledge visualizations
"What have I been working on in the past week?"
```
## Futher info
## Multiple Projects
See the [Documentation](https://docs.basicmemory.com) for more info, including:
Basic Memory supports managing multiple separate knowledge bases through projects. This feature allows you to maintain
separate knowledge graphs for different purposes (e.g., personal notes, work projects, research topics).
- [Complete User Guide](https://docs.basicmemory.com/user-guide/)
- [CLI tools](https://docs.basicmemory.com/guides/cli-reference/)
- [Cloud CLI and Sync](https://docs.basicmemory.com/guides/cloud-cli/)
- [Managing multiple Projects](https://docs.basicmemory.com/guides/cli-reference/#project)
- [Importing data from OpenAI/Claude Projects](https://docs.basicmemory.com/guides/cli-reference/#import)
## Logging
Basic Memory uses [Loguru](https://github.com/Delgan/loguru) for logging. The logging behavior varies by entry point:
| Entry Point | Default Behavior | Use Case |
|-------------|------------------|----------|
| CLI commands | File only | Prevents log output from interfering with command output |
| MCP server | File only | Stdout would corrupt the JSON-RPC protocol |
| API server | File (local) or stdout (cloud) | Docker/cloud deployments use stdout |
**Log file location:** `~/.basic-memory/basic-memory.log` (10MB rotation, 10 days retention)
### Environment Variables
| Variable | Default | Description |
|----------|---------|-------------|
| `BASIC_MEMORY_LOG_LEVEL` | `INFO` | Log level: DEBUG, INFO, WARNING, ERROR |
| `BASIC_MEMORY_CLOUD_MODE` | `false` | When `true`, API logs to stdout with structured context |
| `BASIC_MEMORY_FORCE_LOCAL` | `false` | When `true`, forces local API routing (ignores cloud mode) |
| `BASIC_MEMORY_ENV` | `dev` | Set to `test` for test mode (stderr only) |
### Examples
### Managing Projects
```bash
# Enable debug logging
BASIC_MEMORY_LOG_LEVEL=DEBUG basic-memory sync
# List all configured projects
basic-memory project list
# View logs
tail -f ~/.basic-memory/basic-memory.log
# Add a new project
basic-memory project add work ~/work-basic-memory
# Cloud/Docker mode (stdout logging with structured context)
BASIC_MEMORY_CLOUD_MODE=true uvicorn basic_memory.api.app:app
# Set the default project
basic-memory project default work
# Remove a project (doesn't delete files)
basic-memory project remove personal
# Show current project
basic-memory project current
```
## Development
### Using Projects in Commands
### Running Tests
All commands support the `--project` flag to specify which project to use:
Basic Memory supports dual database backends (SQLite and Postgres). By default, tests run against SQLite. Set `BASIC_MEMORY_TEST_POSTGRES=1` to run against Postgres (uses testcontainers - Docker required).
**Quick Start:**
```bash
# Run all tests against SQLite (default, fast)
just test-sqlite
# Sync a specific project
basic-memory --project=work sync
# Run all tests against Postgres (uses testcontainers)
just test-postgres
# Run both SQLite and Postgres tests
just test
# Run MCP server for a specific project
basic-memory --project=personal mcp
```
**Available Test Commands:**
You can also set the `BASIC_MEMORY_PROJECT` environment variable:
- `just test` - Run all tests against both SQLite and Postgres
- `just test-sqlite` - Run all tests against SQLite (fast, no Docker needed)
- `just test-postgres` - Run all tests against Postgres (uses testcontainers)
- `just test-unit-sqlite` - Run unit tests against SQLite
- `just test-unit-postgres` - Run unit tests against Postgres
- `just test-int-sqlite` - Run integration tests against SQLite
- `just test-int-postgres` - Run integration tests against Postgres
- `just test-windows` - Run Windows-specific tests (auto-skips on other platforms)
- `just test-benchmark` - Run performance benchmark tests
**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.
**Test Markers:**
Tests use pytest markers for selective execution:
- `windows` - Windows-specific database optimizations
- `benchmark` - Performance tests (excluded from default runs)
**Other Development Commands:**
```bash
just install # Install with dev dependencies
just lint # Run linting checks
just typecheck # Run type checking
just format # Format code with ruff
just check # Run all quality checks
just migration "msg" # Create database migration
BASIC_MEMORY_PROJECT=work basic-memory sync
```
See the [justfile](justfile) for the complete list of development commands.
### Project Isolation
Each project maintains:
- Its own collection of markdown files in the specified directory
- A separate SQLite database for that project
- Complete knowledge graph isolation from other projects
## Design Philosophy
Basic Memory is built on some key ideas:
- Your knowledge should stay in files you control
- Both humans and AI should use natural formats
- Simple text patterns can capture rich meaning
- Local-first doesn't mean feature-poor
- Knowledge should persist across conversations
- AI assistants should build on past context
- File formats should be human-readable and editable
- Semantic structure should emerge from natural patterns
- Knowledge graphs should be both AI and human navigable
- Systems should augment human memory, not replace it
## Importing Existing Data
Basic Memory provides CLI commands to import data from various sources, converting them into the structured Markdown
format:
### Claude.ai
First, request an export of your data from your Claude account. The data will be emailed to you in several files,
including
`conversations.json` and `projects.json`.
Import Claude.ai conversation data
```bash
basic-memory import claude conversations
```
The conversations will be turned into Markdown files and placed in the "conversations" folder by default (this can be
changed with the --folder arg).
Example:
```bash
Importing chats from conversations.json...writing to .../basic-memory
Reading chat data... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100%
╭────────────────────────────╮
│ Import complete! │
│ │
│ Imported 307 conversations │
│ Containing 7769 messages │
╰────────────────────────────╯
```
Next, you can run the `sync` command to import the data into basic-memory
```bash
basic-memory sync
```
You can also import project data from Claude.ai
```bash
➜ basic-memory import claude projects
Importing projects from projects.json...writing to .../basic-memory/projects
Reading project data... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100%
╭────────────────────────────────╮
│ Import complete! │
│ │
│ Imported 101 project documents │
│ Imported 32 prompt templates │
╰────────────────────────────────╯
Run 'basic-memory sync' to index the new files.
```
### OpenAI ChatGPT
```bash
➜ basic-memory import chatgpt
Importing chats from conversations.json...writing to .../basic-memory/conversations
Reading chat data... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100%
╭────────────────────────────╮
│ Import complete! │
│ │
│ Imported 198 conversations │
│ Containing 11777 messages │
╰────────────────────────────╯
```
### Knowledge Graph Memory Server
From the MCP Server: https://github.com/modelcontextprotocol/servers/tree/main/src/memory
```bash
➜ basic-memory import memory-json
Importing from memory.json...writing to .../basic-memory
Reading memory.json... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100%
Creating entities... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100%
╭──────────────────────╮
│ Import complete! │
│ │
│ Created 126 entities │
│ Added 252 relations │
╰──────────────────────╯
```
## Working with Your Knowledge Base
Once you've built up a knowledge base, you can interact with it in several ways:
### Command Line Interface
Basic Memory provides a powerful CLI for managing your knowledge:
```bash
# See all available commands
basic-memory --help
# Check the status of your knowledge sync
basic-memory status
# Access specific tool functionality directly
basic-memory tools
# Start a continuous sync process
basic-memory sync --watch
```
### Obsidian Integration
Basic Memory works seamlessly with [Obsidian](https://obsidian.md/), a popular knowledge management app:
1. Point Obsidian to your Basic Memory directory
2. Use standard Obsidian features like backlinks and graph view
3. See your knowledge graph visually
4. Use the canvas visualization generated by Basic Memory
### File Organization
Basic Memory is flexible about how you organize your files:
- Group by topic in folders
- Use a flat structure with descriptive filenames
- Add custom metadata in frontmatter
- Tag files for better searchability
The system will build the semantic knowledge graph regardless of your file organization preference.
## Using stdin with Basic Memory's `write_note` Tool
The `write-note` tool supports reading content from standard input (stdin), allowing for more flexible workflows when
creating or updating notes in your Basic Memory knowledge base.
### Use Cases
This feature is particularly useful for:
1. **Piping output from other commands** directly into Basic Memory notes
2. **Creating notes with multi-line content** without having to escape quotes or special characters
3. **Integrating with AI assistants** like Claude Code that can generate content and pipe it to Basic Memory
4. **Processing text data** from files or other sources
## Basic Usage
### Method 1: Using a Pipe
You can pipe content from another command into `write_note`:
```bash
# Pipe output of a command into a new note
echo "# My Note\n\nThis is a test note" | basic-memory tools write-note --title "Test Note" --folder "notes"
# Pipe output of a file into a new note
cat README.md | basic-memory tools write-note --title "Project README" --folder "documentation"
# Process text through other tools before saving as a note
cat data.txt | grep "important" | basic-memory tools write-note --title "Important Data" --folder "data"
```
### Method 2: Using Heredoc Syntax
For multi-line content, you can use heredoc syntax:
```bash
# Create a note with heredoc
cat << EOF | basic-memory tools write_note --title "Project Ideas" --folder "projects"
# Project Ideas for Q2
## AI Integration
- Improve recommendation engine
- Add semantic search to product catalog
## Infrastructure
- Migrate to Kubernetes
- Implement CI/CD pipeline
EOF
```
### Method 3: Input Redirection
You can redirect input from a file:
```bash
# Create a note from file content
basic-memory tools write-note --title "Meeting Notes" --folder "meetings" < meeting_notes.md
```
## License
AGPL-3.0
Contributions are welcome. See the [Contributing](CONTRIBUTING.md) guide for info about setting up the project locally
and submitting PRs.
## Star History
<a href="https://www.star-history.com/#basicmachines-co/basic-memory&Date">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=basicmachines-co/basic-memory&type=Date&theme=dark" />
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=basicmachines-co/basic-memory&type=Date" />
<img alt="Star History Chart" src="https://api.star-history.com/svg?repos=basicmachines-co/basic-memory&type=Date" />
</picture>
</a>
Built with ♥️ by Basic Machines
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# Security Policy
## Supported Versions
| Version | Supported |
| ------- | ------------------ |
| 0.x.x | :white_check_mark: |
## Reporting a Vulnerability
Use this section to tell people how to report a vulnerability.
If you find a vulnerability, please contact hello@basicmachines.co
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# Docker Compose configuration for Basic Memory with PostgreSQL
# Use this for local development and testing with Postgres backend
#
# Usage:
# docker-compose -f docker-compose-postgres.yml up -d
# docker-compose -f docker-compose-postgres.yml down
services:
postgres:
image: postgres:17
container_name: basic-memory-postgres
environment:
# Local development/test credentials - NOT for production
# These values are referenced by tests and justfile commands
POSTGRES_DB: basic_memory
POSTGRES_USER: basic_memory_user
POSTGRES_PASSWORD: dev_password # Simple password for local testing only
ports:
- "5433:5432"
volumes:
- postgres_data:/var/lib/postgresql/data
healthcheck:
test: ["CMD-SHELL", "pg_isready -U basic_memory_user -d basic_memory"]
interval: 10s
timeout: 5s
retries: 5
restart: unless-stopped
volumes:
# Named volume for Postgres data
postgres_data:
driver: local
# Named volume for persistent configuration
# Database will be stored in Postgres, not in this volume
basic-memory-config:
driver: local
# Network configuration (optional)
# networks:
# basic-memory-net:
# driver: bridge
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# Docker Compose configuration for Basic Memory
# See docs/Docker.md for detailed setup instructions
version: '3.8'
services:
basic-memory:
# Use pre-built image (recommended for most users)
image: ghcr.io/basicmachines-co/basic-memory:latest
# Uncomment to build locally instead:
# build: .
container_name: basic-memory-server
# Volume mounts for knowledge directories and persistent data
volumes:
# Persistent storage for configuration and database
- basic-memory-config:/root/.basic-memory:rw
# Mount your knowledge directory (required)
# Change './knowledge' to your actual Obsidian vault or knowledge directory
- ./knowledge:/app/data:rw
# OPTIONAL: Mount additional knowledge directories for multiple projects
# - ./work-notes:/app/data/work:rw
# - ./personal-notes:/app/data/personal:rw
# You can edit the project config manually in the mounted config volume
# The default project will be configured to use /app/data
environment:
# Project configuration
- BASIC_MEMORY_DEFAULT_PROJECT=main
# Enable real-time file synchronization (recommended for Docker)
- BASIC_MEMORY_SYNC_CHANGES=true
# Logging configuration
- BASIC_MEMORY_LOG_LEVEL=INFO
# Sync delay in milliseconds (adjust for performance vs responsiveness)
- BASIC_MEMORY_SYNC_DELAY=1000
# Port exposure for HTTP transport (only needed if not using STDIO)
ports:
- "8000:8000"
# Command with SSE transport (configurable via environment variables above)
# IMPORTANT: The SSE and streamable-http endpoints are not secured
command: ["basic-memory", "mcp", "--transport", "sse", "--host", "0.0.0.0", "--port", "8000"]
# Container management
restart: unless-stopped
# Health monitoring
healthcheck:
test: ["CMD", "basic-memory", "--version"]
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
# Optional: Resource limits
# deploy:
# resources:
# limits:
# memory: 512M
# cpus: '0.5'
# reservations:
# memory: 256M
# cpus: '0.25'
volumes:
# Named volume for persistent configuration and database
# This ensures your configuration and knowledge graph persist across container restarts
basic-memory-config:
driver: local
# Network configuration (optional)
# networks:
# basic-memory-net:
# driver: bridge
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{}
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{
"file-explorer": true,
"global-search": true,
"switcher": true,
"graph": true,
"backlink": true,
"canvas": true,
"outgoing-link": true,
"tag-pane": true,
"properties": false,
"page-preview": true,
"daily-notes": true,
"templates": true,
"note-composer": true,
"command-palette": true,
"slash-command": false,
"editor-status": true,
"bookmarks": true,
"markdown-importer": false,
"zk-prefixer": false,
"random-note": false,
"outline": true,
"word-count": true,
"slides": false,
"audio-recorder": false,
"workspaces": false,
"file-recovery": true,
"publish": true,
"sync": true,
"webviewer": false
}
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{
"siteId": "947ee055a8c6f1a57efa4afa09791e62",
"host": "publish-01.obsidian.md",
"included": [],
"excluded": []
}
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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.
# 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).
## 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
```python
# Writing knowledge - THE MOST IMPORTANT TOOL!
response = await write_note(
title="Search Design", # Required: Note title
content="# Search Design\n...", # Required: Note content
folder="specs", # Optional: Folder to save in
tags=["search", "design"], # Optional: Tags for categorization
verbose=True # Optional: Get parsing details
)
# Reading knowledge
content = await read_note("Search Design") # By title
content = await read_note("specs/search-design") # By path
content = await read_note("memory://specs/search") # By memory URL
# Searching for knowledge
results = await search(
query="authentication system", # Text to search for
page=1, # Optional: Pagination
page_size=10 # Optional: Results per page
)
# Building context from the knowledge graph
context = await build_context(
url="memory://specs/search", # Starting point
depth=2, # Optional: How many hops to follow
timeframe="1 month" # Optional: Recent timeframe
)
# Checking recent changes
activity = await recent_activity(
type="all", # Optional: Entity types to include
depth=1, # Optional: Related items to include
timeframe="1 week" # Optional: Time window
)
# Creating a knowledge visualization
canvas_result = await canvas(
nodes=[{"id": "note1", "label": "Search Design"}], # Nodes to display
edges=[{"from": "note1", "to": "note2"}], # Connections
title="Project Overview", # Canvas title
folder="diagrams" # Storage location
)
```
## 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() to find relevant notes]
[Then build_context() to understand connections]
```
## 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**
- Using the same title+folder will overwrite existing notes
- Structure content with clear headings and sections
- Use semantic markup for observations and relations
- Keep files organized in logical folders
## Common Knowledge Patterns
### Capturing Decisions
```markdown
# 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
## Relations
- pairs_with [[Light Roast Beans]]
- contrasts_with [[French Press Method]]
- requires [[Proper Grinding Technique]]
```
### 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]]
```
### Creating Effective Relations
When creating relations, you can:
1. Reference existing entities by their exact title
2. Create forward references to entities that don't exist yet
```python
# Example workflow for creating notes with effective relations
async def create_note_with_effective_relations():
# Search for existing entities to reference
search_results = await search("travel")
existing_entities = [result.title for result in search_results.primary_results]
# Check if specific entities exist
packing_tips_exists = "Packing Tips" in existing_entities
japan_travel_exists = "Japan Travel Guide" in existing_entities
# Prepare relations section - include both existing and forward references
relations_section = "## Relations\n"
# Existing reference - exact match to known entity
if packing_tips_exists:
relations_section += "- references [[Packing Tips]]\n"
else:
# Forward reference - will be linked when that entity is created later
relations_section += "- references [[Packing Tips]]\n"
# Another possible reference
if japan_travel_exists:
relations_section += "- part_of [[Japan Travel Guide]]\n"
# You can also check recently modified notes to reference them
recent = await recent_activity(timeframe="1 week")
recent_titles = [item.title for item in recent.primary_results]
if "Transportation Options" in recent_titles:
relations_section += "- relates_to [[Transportation Options]]\n"
# Always include meaningful forward references, even if they don't exist yet
relations_section += "- located_in [[Tokyo]]\n"
relations_section += "- visited_during [[Spring 2023 Trip]]\n"
# Now create the note with both verified and forward relations
content = f"""# Tokyo Neighborhood Guide
## Overview
Details about different Tokyo neighborhoods and their unique characteristics.
## Observations
- [area] Shibuya is a busy shopping district #shopping
- [transportation] Yamanote Line connects major neighborhoods #transit
- [recommendation] Visit Shimokitazawa for vintage shopping #unique
- [tip] Get a Suica card for easy train travel #convenience
{relations_section}
"""
result = await write_note(
title="Tokyo Neighborhood Guide",
content=content,
verbose=True
)
# You can check which relations were resolved and which are forward references
if result and 'relations' in result:
resolved = [r['to_name'] for r in result['relations'] if r.get('target_id')]
forward_refs = [r['to_name'] for r in result['relations'] if not r.get('target_id')]
print(f"Resolved relations: {resolved}")
print(f"Forward references that will be resolved later: {forward_refs}")
```
## Error Handling
Common issues to watch for:
1. **Missing Content**
```python
try:
content = await read_note("Document")
except:
# Try search instead
results = await search("Document")
if results and results.primary_results:
# Found something similar
content = await read_note(results.primary_results[0].permalink)
```
2. **Forward References (Unresolved Relations)**
```python
response = await write_note(..., verbose=True)
# Check for forward references (unresolved relations)
forward_refs = []
for relation in response.get('relations', []):
if not relation.get('target_id'):
forward_refs.append(relation.get('to_name'))
if forward_refs:
# This is a feature, not an error! Inform the user about forward references
print(f"Note created with forward references to: {forward_refs}")
print("These will be automatically linked when those notes are created.")
# Optionally suggest creating those entities now
print("Would you like me to create any of these notes now to complete the connections?")
```
3. **Sync Issues**
```python
# If information seems outdated
activity = await recent_activity(timeframe="1 hour")
if not activity or not activity.primary_results:
print("It seems there haven't been recent updates. You might need to run 'basic-memory sync'.")
```
## 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()` 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 (cloud/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(
cloud_mode_enabled=config.cloud_mode_enabled,
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: **TEST > CLOUD > LOCAL**
```python
def resolve_runtime_mode(cloud_mode_enabled: bool, is_test_env: bool) -> RuntimeMode:
if is_test_env:
return RuntimeMode.TEST
if cloud_mode_enabled:
return RuntimeMode.CLOUD
return RuntimeMode.LOCAL
```
## 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) -> SearchResponse:
async with get_client() as client:
active_project = await get_active_project(client, project)
# Import client inside function to avoid circular imports
from basic_memory.mcp.clients import SearchClient
search_client = SearchClient(client, active_project.external_id)
return await search_client.search(query)
```
## 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
```
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---
title: CLI Reference
type: note
permalink: docs/cli-reference
---
# CLI Reference
Basic Memory provides command line tools for managing your knowledge base. This reference covers the available commands and their options.
## Core Commands
### sync
Keeps files and the knowledge graph in sync:
```bash
# Basic sync
basic-memory sync
# Watch for changes
basic-memory sync --watch
# Sync specific folder
basic-memory sync path/to/folder
```
Options:
- `--watch`: Continuously monitor for changes
- `--verbose`: Show detailed output
- `PATH`: Optional path to sync (defaults to ~/basic-memory)
### import
Imports external knowledge sources:
```bash
# Claude conversations
basic-memory import claude conversations
# Claude projects
basic-memory import claude projects
# ChatGPT history
basic-memory import chatgpt
```
Options:
- `--folder PATH`: Target folder for imported content
- `--overwrite`: Replace existing files
- `--skip-existing`: Keep existing files
### status
Shows system status information:
```bash
# Basic status check
basic-memory status
# Detailed status
basic-memory status --verbose
# JSON output
basic-memory status --json
```
### project
Create multiple projects to manage your knowledge.
```bash
# List all configured projects
basic-memory project list
# Add a new project
basic-memory project add work ~/work-basic-memory
# Set the default project
basic-memory project default work
# Remove a project (doesn't delete files)
basic-memory project remove personal
# Show current project
basic-memory project current
```
> Be sure to restart Claude Desktop after changing projects.
#### Using Projects in Commands
All commands support the `--project` flag to specify which project to use:
```bash
# Sync a specific project
basic-memory --project=work sync
# Run MCP server for a specific project
basic-memory --project=personal mcp
```
You can also set the `BASIC_MEMORY_PROJECT` environment variable:
```bash
BASIC_MEMORY_PROJECT=work basic-memory sync
```
### help
The full list of commands and help for each can be viewed with the `--help` argument.
```
✗ basic-memory --help
Usage: basic-memory [OPTIONS] COMMAND [ARGS]...
Basic Memory - Local-first personal knowledge management system.
╭─ Options ─────────────────────────────────────────────────────────────────────────────────╮
│ --project -p TEXT Specify which project to use │
│ [env var: BASIC_MEMORY_PROJECT] │
│ [default: None] │
│ --version -V Show version information and exit. │
│ --install-completion Install completion for the current shell. │
│ --show-completion Show completion for the current shell, to copy it or │
│ customize the installation. │
│ --help Show this message and exit. │
╰───────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Commands ────────────────────────────────────────────────────────────────────────────────╮
│ sync Sync knowledge files with the database. │
│ status Show sync status between files and database. │
│ reset Reset database (drop all tables and recreate). │
│ mcp Run the MCP server for Claude Desktop integration. │
│ import Import data from various sources │
│ tool Direct access to MCP tools via CLI │
│ project Manage multiple Basic Memory projects │
╰───────────────────────────────────────────────────────────────────────────────────────────╯
```
## Initial Setup
```bash
# Install Basic Memory
uv install basic-memory
# First sync
basic-memory sync
# Start watching mode
basic-memory sync --watch
```
> **Important**: You need to install Basic Memory via `uv` or `pip` to use the command line tools, see [[Getting Started with Basic Memory#Installation]].
## Regular Usage
```bash
# Check status
basic-memory status
# Import new content
basic-memory import claude conversations
# Sync changes
basic-memory sync
# Sync changes continuously
basic-memory sync --watch
```
## Maintenance Tasks
```bash
# Check system status in detail
basic-memory status --verbose
# Full resync of all files
basic-memory sync
# Import updates to specific folder
basic-memory import claude conversations --folder new
```
## Using stdin with Basic Memory's `write_note` Tool
The `write-note` tool supports reading content from standard input (stdin), allowing for more flexible workflows when creating or updating notes in your Basic Memory knowledge base.
### Use Cases
This feature is particularly useful for:
1. **Piping output from other commands** directly into Basic Memory notes
2. **Creating notes with multi-line content** without having to escape quotes or special characters
3. **Integrating with AI assistants** like Claude Code that can generate content and pipe it to Basic Memory
4. **Processing text data** from files or other sources
### Basic Usage
#### Method 1: Using a Pipe
You can pipe content from another command into `write_note`:
```bash
# Pipe output of a command into a new note
echo "# My Note\n\nThis is a test note" | basic-memory tool write-note --title "Test Note" --folder "notes"
# Pipe output of a file into a new note
cat README.md | basic-memory tool write-note --title "Project README" --folder "documentation"
# Process text through other tools before saving as a note
cat data.txt | grep "important" | basic-memory tool write-note --title "Important Data" --folder "data"
```
#### Method 2: Using Heredoc Syntax
For multi-line content, you can use heredoc syntax:
```bash
# Create a note with heredoc
cat << EOF | basic-memory tool write_note --title "Project Ideas" --folder "projects"
# Project Ideas for Q2
## AI Integration
- Improve recommendation engine
- Add semantic search to product catalog
## Infrastructure
- Migrate to Kubernetes
- Implement CI/CD pipeline
EOF
```
#### Method 3: Input Redirection
You can redirect input from a file:
```bash
# Create a note from file content
basic-memory tool write-note --title "Meeting Notes" --folder "meetings" < meeting_notes.md
```
#### Integration with Claude Code
This feature works well with Claude Code in the terminal:
In a Claude Code session, let Claude know he can use the basic-memory tools, then he can execute them via the cli:
```
⏺ Bash(echo "# Test Note from Claude\n\nThis is a test note created by Claude to test the stdin functionality." | basic-memory tool write-note --title "Claude Test Note" --folder "test" --tags "test" --tags "claude")…
  ⎿  # Created test/Claude Test Note.md (23e00eec)
permalink: test/claude-test-note
## Tags
- test, claude
```
## Troubleshooting Common Issues
### Sync Conflicts
If you encounter a file changed during sync error:
1. Check the file referenced in the error message
2. Resolve any conflicts manually
3. Run sync again
### Import Errors
If import fails:
1. Check that the source file is in the correct format
2. Verify permissions on the target directory
3. Use --verbose flag for detailed error information
### Status Issues
If status shows problems:
1. Note any unresolved relations or warnings
2. Run a full sync to attempt automatic resolution
3. Check file permissions if database access errors occur
## Relations
- used_by [[Getting Started with Basic Memory]] (Installation instructions)
- complements [[User Guide]] (How to use Basic Memory)
- relates_to [[Introduction to Basic Memory]] (System overview)
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---
title: Canvas Visualizations
type: note
permalink: docs/canvas
tags:
- visualization
- mapping
- obsidian
---
# Canvas Visualizations
Basic Memory can create visual knowledge maps using Obsidian's Canvas feature. These visualizations help you understand relationships between concepts, map out processes, and visualize your knowledge structure.
## Creating Canvas Visualizations
Ask Claude to create a visualization by describing what you want to map:
```
You: "Create a canvas visualization of my project components and their relationships."
You: "Make a concept map showing the main themes from our discussion about climate change."
You: "Can you make a canvas diagram of the perfect pour over method?"
```
![[Canvas.png]]
## Types of Visualizations
Basic Memory can create several types of visual maps:
### Document Maps
Visualize connections between your notes and documents
### Concept Maps
Create visual representations of ideas and their relationships
### Process Diagrams
Map workflows, sequences, and procedures
### Thematic Analysis
Organize ideas around central themes
### Relationship Networks
Show how different entities relate to each other
## Visualization Sources
Claude can create visualizations based on:
### Documents in Your Knowledge Base
```
You: "Create a canvas showing the connections between my project planning documents"
```
### Conversation Content
```
You: "Make a canvas visualization of the main points we just discussed"
```
### Search Results
```
You: "Find all my notes about psychology and create a visual map of the concepts"
```
### Themes and Relationships
```
You: "Create a visual map showing how different philosophical schools relate to each other"
```
## Visualization Workflow
1. **Request a visualization** by describing what you want to see
2. **Claude creates the canvas file** in your Basic Memory directory
3. **Open the file in Obsidian** to view the visualization
4. **Refine the visualization** by asking Claude for adjustments:
```
You: "Could you reorganize the canvas to group related components together?"
You: "Please add more detail about the connection between these two concepts."
```
## Technical Details
Behind the scenes, Claude:
1. Creates a `.canvas` file in JSON format
2. Adds nodes for each concept or document
3. Creates edges to represent relationships
4. Sets positions for visual clarity
5. Includes any relevant metadata
The resulting file is fully compatible with Obsidian's Canvas feature and can be edited directly in Obsidian.
## Tips for Effective Visualizations
- **Be specific** about what you want to visualize
- **Specify the level of detail** you need
- **Mention the visualization type** you want (concept map, process flow, etc.)
- **Start simple** and ask for refinements
- **Provide context** about what documents or concepts to include
## Relations
- enhances [[Obsidian Integration]] (Using Basic Memory with Obsidian)
- visualizes [[Knowledge Format]] (The structure of your knowledge)
- complements [[User Guide]] (Ways to use Basic Memory)
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# Docker Setup Guide
Basic Memory can be run in Docker containers to provide a consistent, isolated environment for your knowledge management
system. This is particularly useful for integrating with existing Dockerized MCP servers or for deployment scenarios.
## Quick Start
### Option 1: Using Pre-built Images (Recommended)
Basic Memory provides pre-built Docker images on GitHub Container Registry that are automatically updated with each release.
1. **Use the official image directly:**
```bash
docker run -d \
--name basic-memory-server \
-p 8000:8000 \
-v /path/to/your/obsidian-vault:/app/data:rw \
-v basic-memory-config:/app/.basic-memory:rw \
ghcr.io/basicmachines-co/basic-memory:latest
```
2. **Or use Docker Compose with the pre-built image:**
```yaml
version: '3.8'
services:
basic-memory:
image: ghcr.io/basicmachines-co/basic-memory:latest
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
```
### Option 2: Using Docker Compose (Building Locally)
1. **Clone the repository:**
```bash
git clone https://github.com/basicmachines-co/basic-memory.git
cd basic-memory
```
2. **Update the docker-compose.yml:**
Edit the volume mount to point to your Obsidian vault:
```yaml
volumes:
# Change './obsidian-vault' to your actual directory path
- /path/to/your/obsidian-vault:/app/data:rw
```
3. **Start the container:**
```bash
docker-compose up -d
```
### Option 3: Using Docker CLI
```bash
# Build the image
docker build -t basic-memory .
# Run with volume mounting
docker run -d \
--name basic-memory-server \
-v /path/to/your/obsidian-vault:/app/data:rw \
-v basic-memory-config:/app/.basic-memory:rw \
-e BASIC_MEMORY_DEFAULT_PROJECT=main \
basic-memory
```
## Configuration
### Volume Mounts
Basic Memory requires several volume mounts for proper operation:
1. **Knowledge Directory** (Required):
```yaml
- /path/to/your/obsidian-vault:/app/data:rw
```
Mount your Obsidian vault or knowledge base directory.
2. **Configuration and Database** (Recommended):
```yaml
- basic-memory-config:/app/.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.
3. **Multiple Projects** (Optional):
```yaml
- /path/to/project1:/app/data/project1:rw
- /path/to/project2:/app/data/project2:rw
```
You can edit the basic-memory config.json file located in the /app/.basic-memory/config.json
## CLI Commands via Docker
You can run Basic Memory CLI commands inside the container using `docker exec`:
### Basic Commands
```bash
# Check status
docker exec basic-memory-server basic-memory status
# Sync files
docker exec basic-memory-server basic-memory sync
# Show help
docker exec basic-memory-server basic-memory --help
```
### Managing Projects with Volume Mounts
When using Docker volumes, you'll need to configure projects to point to your mounted directories:
1. **Check current configuration:**
```bash
docker exec basic-memory-server cat /app/.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
# Set it as default
docker exec basic-memory-server basic-memory project set-default my-vault
```
3. **Sync the new project:**
```bash
docker exec basic-memory-server basic-memory sync
```
### Example: Setting up an Obsidian Vault
If you mounted your Obsidian vault like this in docker-compose.yml:
```yaml
volumes:
- /Users/yourname/Documents/ObsidianVault:/app/data:rw
```
Then configure it:
```bash
# Create project pointing to mounted vault
docker exec basic-memory-server basic-memory project create obsidian /app/data
# Set as default
docker exec basic-memory-server basic-memory project set-default obsidian
# Sync to index all files
docker exec basic-memory-server basic-memory sync
```
### Environment Variables
Configure Basic Memory using environment variables:
```yaml
environment:
# Default project
- BASIC_MEMORY_DEFAULT_PROJECT=main
# Enable real-time sync
- BASIC_MEMORY_SYNC_CHANGES=true
# Logging level
- BASIC_MEMORY_LOG_LEVEL=INFO
# Sync delay in milliseconds
- BASIC_MEMORY_SYNC_DELAY=1000
```
## File Permissions
### 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:
```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 .
# Or use docker-compose with build args
```
**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:
1. Open Docker Desktop
2. Go to Settings → Resources → File Sharing
3. Add your knowledge directory path
4. Apply & Restart
## Troubleshooting
### Common Issues
1. **File Watching Not Working:**
- Ensure volume mounts are read-write (`:rw`)
- Check directory permissions
- On Linux, may need to increase inotify limits:
```bash
echo fs.inotify.max_user_watches=524288 | sudo tee -a /etc/sysctl.conf
sudo sysctl -p
```
2. **Configuration Not Persisting:**
- Use named volumes for `/app/.basic-memory`
- Check volume mount permissions
3. **Network Connectivity:**
- For HTTP transport, ensure port 8000 is exposed
- Check firewall settings
### Debug Mode
Run with debug logging:
```yaml
environment:
- BASIC_MEMORY_LOG_LEVEL=DEBUG
```
View logs:
```bash
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.
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.
3. **Network Security:**
If using HTTP transport, consider using reverse proxy with SSL/TLS and authentication if the endpoint is available on
a network.
4. **IMPORTANT:** The HTTP endpoints have no authorization. They should not be exposed on a public network.
## Integration Examples
### Claude Desktop with Docker
The recommended way to connect Claude Desktop to the containerized Basic Memory is using `mcp-proxy`, which converts the HTTP transport to STDIO that Claude Desktop expects:
1. **Start the Docker container:**
```bash
docker-compose up -d
```
2. **Configure Claude Desktop** to use mcp-proxy:
```json
{
"mcpServers": {
"basic-memory": {
"command": "uvx",
"args": [
"mcp-proxy",
"http://localhost:8000/mcp"
]
}
}
}
```
## Support
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`
For general Basic Memory support, see the main [README](../README.md)
and [documentation](https://memory.basicmachines.co/).
## GitHub Container Registry Images
### Available Images
Pre-built Docker images are available on GitHub Container Registry at [`ghcr.io/basicmachines-co/basic-memory`](https://github.com/basicmachines-co/basic-memory/pkgs/container/basic-memory).
**Supported architectures:**
- `linux/amd64` (Intel/AMD x64)
- `linux/arm64` (ARM64, including Apple Silicon)
**Available tags:**
- `latest` - Latest stable release
- `v0.13.8`, `v0.13.7`, etc. - Specific version tags
- `v0.13`, `v0.12`, etc. - Major.minor tags
### Automated Builds
Docker images are automatically built and published when new releases are tagged:
1. **Release Process:** When a git tag matching `v*` (e.g., `v0.13.8`) is pushed, the CI workflow automatically:
- Builds multi-platform Docker images
- Pushes to GitHub Container Registry with appropriate tags
- Uses native GitHub integration for seamless publishing
2. **CI/CD Pipeline:** The Docker workflow includes:
- Multi-platform builds (AMD64 and ARM64)
- Layer caching for faster builds
- Automatic tagging with semantic versioning
- Security scanning and optimization
### Setup Requirements (For Maintainers)
GitHub Container Registry integration is automatic for this repository:
1. **No external setup required** - GHCR is natively integrated with GitHub
2. **Automatic permissions** - Uses `GITHUB_TOKEN` with `packages: write` permission
3. **Public by default** - Images are automatically public for public repositories
The Docker CI workflow (`.github/workflows/docker.yml`) handles everything automatically when version tags are pushed.
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---
title: Getting Started with Basic Memory
type: note
permalink: docs/getting-started
---
# Getting Started with Basic Memory
This guide will help you install Basic Memory, configure it with Claude Desktop, and create your first knowledge notes
through conversations.
Basic Memory uses the [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP) to connect with LLMs. It can be used with any service that supports the MCP, but Claude Desktop works especially well.
## Installation
### 1. Install Basic Memory
```bash
# Install with uv (recommended)
uv install basic-memory
# Or with pip
pip install basic-memory
```
> **Important**: You need to install Basic Memory using one of the commands above to use the command line tools.
### 2. Configure Claude Desktop
Claude Desktop often has trouble finding executables in your user path. Follow these steps for a reliable setup:
#### Step 1: Find the absolute path to uvx
Open Terminal and run:
```bash
which uvx
```
This will show you the full path (e.g., `/Users/yourusername/.cargo/bin/uvx`).
#### Step 2: Edit Claude Desktop Configuration
Edit the configuration file located at `~/Library/Application Support/Claude/claude_desktop_config.json`:
```json
{
"mcpServers": {
"basic-memory": {
"command": "/absolute/path/to/uvx",
"args": [
"basic-memory",
"mcp"
]
}
}
}
```
Replace `/absolute/path/to/uvx` with the actual path you found in Step 1.
> **Note**: Using absolute paths is necessary because Claude Desktop cannot access binaries in your user PATH.
#### Step 3: Restart Claude Desktop
Close and reopen Claude Desktop for the changes to take effect.
### 3. Start the Sync Service
Start the sync service to monitor your files for changes:
```bash
# One-time sync
basic-memory sync
# For continuous monitoring (recommended)
basic-memory sync --watch
```
The `--watch` flag enables automatic detection of file changes, keeping your knowledge base current.
### 4. Staying Updated
To update Basic Memory when new versions are released:
```bash
# Update with uv (recommended)
uv tool upgrade basic-memory
# Or with pip
pip install --upgrade basic-memory
```
> **Note**: After updating, you'll need to restart Claude Desktop and your sync process for changes to take effect.
## Troubleshooting Installation
### Common Issues
#### Claude Says "No Basic Memory Tools Available"
If Claude cannot find Basic Memory tools:
1. **Check absolute paths**: Ensure you're using complete absolute paths to uvx in the Claude Desktop configuration
2. **Verify installation**: Run `basic-memory --version` in Terminal to confirm Basic Memory is installed
3. **Restart applications**: Restart both Terminal and Claude Desktop after making configuration changes
4. **Check sync status**: Ensure `basic-memory sync --watch` is running
#### Permission Issues
If you encounter permission errors:
1. Check that Basic Memory has access to create files in your home directory
2. Ensure Claude Desktop has permission to execute the uvx command
## Creating Your First Knowledge Note
1. **Start the sync process** in a Terminal window:
```bash
basic-memory sync --watch
```
Keep this running in the background.
2. **Open Claude Desktop** and start a new conversation.
3. **Have a natural conversation** about any topic:
```
You: "Let's talk about coffee brewing methods I've been experimenting with."
Claude: "I'd be happy to discuss coffee brewing methods..."
You: "I've found that pour over gives more flavor clarity than French press..."
```
4. **Ask Claude to create a note**:
```
You: "Could you create a note summarizing what we've discussed about coffee brewing?"
```
5. **Confirm note creation**:
Claude will confirm when the note has been created and where it's stored.
6. **View the created file** in your `~/basic-memory` directory using any text editor or Obsidian.
The file structure will look similar to:
```markdown
---
title: Coffee Brewing Methods
permalink: coffee-brewing-methods
---
# Coffee Brewing Methods
## Observations
- [method] Pour over provides more clarity...
- [technique] Water temperature at 205°F...
## Relations
- relates_to [[Other Coffee Topics]]
```
## Using Special Prompts
Basic Memory includes special prompts that help you start conversations with context from your knowledge base:
### Continue Conversation
To resume a previous topic:
```
You: "Let's continue our conversation about coffee brewing."
```
This prompt triggers Claude to:
1. Search your knowledge base for relevant content about coffee brewing
2. Build context from these documents
3. Resume the conversation with full awareness of previous discussions
### Recent Activity
To see what you've been working on:
```
You: "What have we been discussing recently?"
```
This prompt causes Claude to:
1. Retrieve documents modified in the recent past
2. Summarize the topics and main points
3. Offer to continue any of those discussions
### Search
To find specific information:
```
You: "Find information about pour over coffee methods."
```
Claude will:
1. Search your knowledge base for relevant documents
2. Summarize the key findings
3. Offer to explore specific documents in more detail
See [[User Guide#Using Special Prompts]] for further information.
## Using Your Knowledge Base
### Referencing Knowledge
In future conversations, reference your existing knowledge:
```
You: "What water temperature did we decide was optimal for coffee brewing?"
```
Or directly reference notes using memory:// URLs:
```
You: "Take a look at memory://coffee-brewing-methods and let's discuss how to improve my technique."
```
### Building On Previous Knowledge
Basic Memory enables continuous knowledge building:
1. **Reference previous discussions** in new conversations
2. **Add to existing notes** through conversations
3. **Create connections** between related topics
4. **Follow relationships** to build comprehensive context
## Importing Existing Conversations
Import your existing AI conversations:
```bash
# From Claude
basic-memory import claude conversations
# From ChatGPT
basic-memory import chatgpt
```
After importing, run `basic-memory sync` to index everything.
## Quick Tips
- Keep `basic-memory sync --watch` running in a terminal window
- Use special prompts (Continue Conversation, Recent Activity, Search) to start contextual discussions
- Build connections between notes for a richer knowledge graph
- Use direct memory:// URLs when you need precise context
- Use git to version control your knowledge base
- Review and edit AI-generated notes for accuracy
## Next Steps
After getting started, explore these areas:
1. **Read the [[User Guide]]** for comprehensive usage instructions
2. **Understand the [[Knowledge Format]]** to learn how knowledge is structured
3. **Set up [[Obsidian Integration]]** for visual knowledge navigation
4. **Learn about [[Canvas]]** visualizations for mapping concepts
5. **Review the [[CLI Reference]]** for command line tools
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---
title: Knowledge Format
type: note
permalink: docs/knowledge-format
tags:
- architecture
- patterns
- knowledge
- design
---
# Knowledge Format
Basic Memory uses standard Markdown with simple semantic patterns to create a knowledge graph. This document details the file structure and patterns used to organize knowledge.
## File-First Architecture
All knowledge in Basic Memory is stored in plain text Markdown files:
- Files are the source of truth for all knowledge
- Changes to files automatically update the knowledge graph
- You maintain complete ownership and control
- Files work with git and other version control systems
- Knowledge persists independently of any AI conversation
## Core Document Structure
Every document uses this basic structure:
```markdown
---
title: Document Title
type: note
tags: [tag1, tag2]
permalink: custom-path
---
# Document Title
Regular markdown content...
## Observations
- [category] Content with #tags (optional context)
## Relations
- relation_type [[Other Document]] (optional context)
```
### Frontmatter
The YAML frontmatter at the top of each file defines essential metadata:
```yaml
---
title: Document Title # Used for linking and references
type: note # Document type
tags: [tag1, tag2] # For organization and searching
permalink: custom-link # Optional custom URL path
---
```
The title is particularly important as it's used to create links between documents.
### Observations
Observations are facts or statements about a topic:
```markdown
## Observations
- [tech] Uses SQLite for storage #database
- [design] Follows local-first architecture #architecture
- [decision] Selected bcrypt for passwords #security (Based on audit)
```
Each observation contains:
- **Category** in [brackets] - classifies the information type
- **Content text** - the main information
- Optional **#tags** - additional categorization
- Optional **(context)** - supporting details
Common categories include:
- `[tech]`: Technical details
- `[design]`: Architecture decisions
- `[feature]`: User capabilities
- `[decision]`: Choices that were made
- `[principle]`: Fundamental concepts
- `[method]`: Approaches or techniques
- `[preference]`: Personal opinions
### Relations
Relations connect documents to form the knowledge graph:
```markdown
## Relations
- implements [[Search Design]]
- depends_on [[Database Schema]]
- relates_to [[User Interface]]
```
You can also create inline references:
```markdown
This builds on [[Core Design]] and uses [[Utility Functions]].
```
Common relation types include:
- `implements`: Implementation of a specification
- `depends_on`: Required dependency
- `relates_to`: General connection
- `inspired_by`: Source of ideas
- `extends`: Enhancement
- `part_of`: Component relationship
- `contains`: Hierarchical relationship
- `pairs_with`: Complementary relationship
## Knowledge Graph
Basic Memory automatically builds a knowledge graph from your document connections:
- Each document becomes a node in the graph
- Relations create edges between nodes
- Relation types add semantic meaning to connections
- Forward references can link to documents that don't exist yet
This graph enables rich context building and navigation across your knowledge base.
## Permalinks and memory:// URLs
Every document in Basic Memory has a unique permalink that serves as its stable identifier:
### How Permalinks Work
- **Automatically assigned**: The system generates a permalink for each document
- **Based on title**: By default, derived from the document title
- **Always unique**: If conflicts exist, the system adds a suffix to ensure uniqueness
- **Stable reference**: Remains the same even if the file moves in the directory structure
- **Used in memory:// URLs**: Forms the basis of the memory:// addressing scheme
You can specify a custom permalink in the frontmatter:
```yaml
---
title: Authentication Approaches
permalink: auth-approaches-2024
---
```
If not specified, one will be generated automatically from the title.
### Using memory:// URLs
The memory:// URL scheme provides a reliable way to reference knowledge:
```
memory://auth-approaches-2024 # Direct access by permalink
memory://Authentication Approaches # Access by title (automatically resolves)
memory://project/auth-approaches # Access by path
```
Memory URLs support pattern matching for more powerful queries:
```
memory://auth* # All documents with permalinks starting with "auth"
memory://*/approaches # All documents with permalinks ending with "approaches"
memory://project/*/requirements # All requirements documents in the project folder
memory://docs/search/implements/* # Follow all implements relations from search docs
```
This addressing scheme ensures content remains accessible even as your knowledge base evolves and files are reorganized.
## File Organization
Organize files in any structure that suits your needs:
```
docs/
architecture/
design.md
patterns.md
features/
search.md
auth.md
```
You can:
- Group by topic in folders
- Use a flat structure with descriptive filenames
- Tag files for easier discovery
- Add custom metadata in frontmatter
The system will build the semantic knowledge graph regardless of how you organize your files.
## Relations
- implemented_by [[User Guide]] (How to work with this format)
- relates_to [[Getting Started with Basic Memory]] (Setup instructions)
- explained_in [[Introduction to Basic Memory]] (Overview of the system)
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---
title: Obsidian Integration
type: note
permalink: docs/obsidian-integration
---
# Obsidian Integration
Basic Memory integrates seamlessly with [Obsidian](https://obsidian.md), providing powerful visualization and navigation capabilities for your knowledge graph.
## Setup
### Creating an Obsidian Vault
1. Download and install [Obsidian](https://obsidian.md)
2. Create a new vault
3. Point it to your Basic Memory directory (~/basic-memory by default)
4. Enable core plugins like Graph View, Backlinks, and Tags
## Visualization Features
### Graph View
Obsidian's Graph View provides a visual representation of your knowledge network:
- Each document appears as a node
- Relations appear as connections between nodes
- Colors can be customized to distinguish types
- Filters let you focus on specific aspects
- Local graphs show connections for individual documents
### Backlinks
Obsidian automatically tracks references between documents:
- View all documents that reference the current one
- See the exact context of each reference
- Navigate easily through connections
- Track how concepts relate to each other
### Tag Explorer
Use tags to organize and filter content:
- View all tags in your knowledge base
- See how many documents use each tag
- Filter documents by tag combinations
- Create hierarchical tag structures
## Knowledge Elements
Basic Memory's knowledge format works natively with Obsidian:
### Wiki Links
```markdown
## Relations
- implements [[Search Design]]
- depends_on [[Database Schema]]
```
These display as clickable links in Obsidian and appear in the graph view.
### Observations with Tags
```markdown
## Observations
- [tech] Using SQLite #database
- [design] Local-first #architecture
```
Tags become searchable and filterable in Obsidian's tag pane.
### Frontmatter
```yaml
---
title: Document Title
type: note
tags: [search, design]
---
```
Frontmatter provides metadata for Obsidian to use in search and filtering.
## Canvas Integration
Basic Memory can create [Obsidian Canvas](https://obsidian.md/canvas) files:
1. Ask Claude to create a visualization:
```
You: "Create a canvas showing the structure of our project components."
```
2. Claude generates a .canvas file in your knowledge base
3. Open the file in Obsidian to view and edit the visual representation
4. Canvas files maintain references to your documents
## Recommended Plugins
These Obsidian plugins work especially well with Basic Memory:
- **Dataview**: Query your knowledge base programmatically
- **Kanban**: Organize tasks from knowledge files
- **Calendar**: View and navigate temporal knowledge
- **Templates**: Create consistent knowledge structures
## Workflow Suggestions
### Daily Notes
```markdown
# 2024-01-21
## Progress
- Updated [[Search Design]]
- Fixed [[Bug Report 123]]
## Notes
- [idea] Better indexing #enhancement
- [todo] Update docs #documentation
## Links
- relates_to [[Current Sprint]]
- updates [[Project Status]]
```
### Project Tracking
```markdown
# Current Sprint
## Tasks
- [ ] Update [[Search]]
- [ ] Fix [[Auth Bug]]
## Tags
#sprint #planning #current
```
## Relations
- enhances [[Introduction to Basic Memory]] (Overview of system)
- relates_to [[Canvas]] (Visual knowledge mapping)
- complements [[User Guide]] (Using Basic Memory)
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---
title: Technical Information
type: note
permalink: docs/technical-information
---
# Technical Information
This document provides technical details about Basic Memory's implementation, licensing, and integration with the Model Context Protocol (MCP).
## Architecture
Basic Memory consists of:
1. **Core Knowledge Engine**: Parses and indexes Markdown files
2. **SQLite Database**: Provides fast querying and search
3. **MCP Server**: Implements the Model Context Protocol
4. **CLI Tools**: Command-line utilities for management
5. **Sync Service**: Monitors file changes and updates the database
The system follows a file-first architecture where all knowledge is represented in standard Markdown files and the database serves as a secondary index.
## Model Context Protocol (MCP)
Basic Memory implements the [Model Context Protocol](https://github.com/modelcontextprotocol/spec), an open standard for enabling AI models to access external tools:
- **Standardized Interface**: Common protocol for tool integration
- **Tool Registration**: Basic Memory registers as a tool provider
- **Asynchronous Communication**: Enables efficient interaction with AI models
- **Standardized Schema**: Structured data exchange format
Integration with Claude Desktop uses the MCP to grant Claude access to your knowledge base through a set of specialized tools that search, read, and write knowledge.
## Licensing
Basic Memory is licensed under the [GNU Affero General Public License v3.0 (AGPL-3.0)](https://www.gnu.org/licenses/agpl-3.0.en.html):
- **Free Software**: You can use, study, share, and modify the software
- **Copyleft**: Derivative works must be distributed under the same license
- **Network Use**: Network users must be able to receive the source code
- **Commercial Use**: Allowed, subject to license requirements
The AGPL license ensures Basic Memory remains open source while protecting against proprietary forks.
## Source Code
Basic Memory is developed as an open-source project:
- **GitHub Repository**: [https://github.com/basicmachines-co/basic-memory](https://github.com/basicmachines-co/basic-memory)
- **Issue Tracker**: Report bugs and request features on GitHub
- **Contributions**: Pull requests are welcome following the contributing guidelines
- **Documentation**: Source for this documentation is also available in the repository
## Data Storage and Privacy
Basic Memory is designed with privacy as a core principle:
- **Local-First**: All data remains on your local machine
- **No Cloud Dependency**: No remote servers or accounts required
- **Telemetry**: Optional and disabled by default
- **Standard Formats**: All data is stored in standard file formats you control
## Implementation Details
Knowledge in Basic Memory is organized as a semantic graph:
1. **Entities** - Distinct concepts represented by Markdown documents
2. **Observations** - Categorized facts and information about entities
3. **Relations** - Connections between entities that form the knowledge graph
This structure emerges from simple text patterns in standard Markdown:
```markdown
---
title: Coffee Brewing Methods
type: note
permalink: coffee/coffee-brewing-methods
tags:
- '#coffee'
- '#brewing'
- '#methods'
- '#demo'
---
# Coffee Brewing Methods
An exploration of different coffee brewing techniques, their characteristics, and how they affect flavor extraction.
## Overview
Coffee brewing is both an art and a science. Different brewing methods extract different compounds from coffee beans,
resulting in unique flavor profiles, body, and mouthfeel. The key variables in any brewing method are:
- Grind size
- Water temperature
- Brew time
- Coffee-to-water ratio
- Agitation/turbulence
## Observations
- [principle] Coffee extraction follows a predictable pattern: acids extract first, then sugars, then bitter compounds
#extraction
- [method] Pour over methods generally produce cleaner, brighter cups with more distinct flavor notes #clarity
## Relations
- requires [[Proper Grinding Technique]]
- affects [[Flavor Extraction]]
```
Becomes
```json
{
"entities": [
{
"permalink": "coffee/coffee-brewing-methods",
"title": "Coffee Brewing Methods",
"file_path": "Coffee Notes/Coffee Brewing Methods.md",
"entity_type": "note",
"entity_metadata": {
"title": "Coffee Brewing Methods",
"type": "note",
"permalink": "coffee/coffee-brewing-methods",
"tags": "['#coffee', '#brewing', '#methods', '#demo']"
},
"checksum": "bfa32a0f23fa124b53f0694c344d2788b0ce50bd090b55b6d738401d2a349e4c",
"content_type": "text/markdown",
"observations": [
{
"category": "principle",
"content": "Coffee extraction follows a predictable pattern: acids extract first, then sugars, then bitter compounds #extraction",
"tags": [
"extraction"
],
"permalink": "coffee/coffee-brewing-methods/observations/principle/coffee-extraction-follows-a-predictable-pattern-acids-extract-first-then-sugars-then-bitter-compounds-extraction"
},
{
"category": "method",
"content": "Pour over methods generally produce cleaner, brighter cups with more distinct flavor notes #clarity",
"tags": [
"clarity"
],
"permalink": "coffee/coffee-brewing-methods/observations/method/pour-over-methods-generally-produce-cleaner-brighter-cups-with-more-distinct-flavor-notes-clarity"
}
],
"relations": [
{
"from_id": "coffee/coffee-bean-origins",
"to_id": "coffee/coffee-brewing-methods",
"relation_type": "pairs_with",
"permalink": "coffee/coffee-bean-origins/pairs-with/coffee/coffee-brewing-methods",
"to_name": "Coffee Brewing Methods"
},
{
"from_id": "coffee/flavor-extraction",
"to_id": "coffee/coffee-brewing-methods",
"relation_type": "affected_by",
"permalink": "coffee/flavor-extraction/affected-by/coffee/coffee-brewing-methods",
"to_name": "Coffee Brewing Methods"
}
],
"created_at": "2025-03-06T14:01:23.445071",
"updated_at": "2025-03-06T13:34:48.563606"
}
]
}
```
Basic Memory understands how to build context via its semantic graph.
### Entity Model
Basic Memory's core data model consists of:
- **Entities**: Documents in your knowledge base
- **Observations**: Facts or statements about entities
- **Relations**: Connections between entities
- **Tags**: Additional categorization for entities and observations
The system parses Markdown files to extract this structured information while preserving the human-readable format.
### Files as Source of Truth
Plain Markdown files store all knowledge, making it accessible with any text editor and easy to version with git.
```mermaid
flowchart TD
User((User)) <--> |Conversation| Claude["Claude or other LLM"]
Claude <-->|API Calls| BMCP["Basic Memory MCP Server"]
subgraph "Local Storage"
KnowledgeFiles["Markdown Files - Source of Truth"]
KnowledgeIndex[(Knowledge Graph SQLite Index)]
end
BMCP <-->|"write_note() read_note()"| KnowledgeFiles
BMCP <-->|"search() build_context()"| KnowledgeIndex
KnowledgeFiles <-.->|Sync Process| KnowledgeIndex
KnowledgeFiles <-->|Direct Editing| Editors((Text Editors & Git))
User -.->|"Complete control, Privacy preserved"| KnowledgeFiles
class Claude primary
class BMCP secondary
class KnowledgeFiles tertiary
class KnowledgeIndex quaternary
class User,Editors user`;
```
### Sqlite Database
A local SQLite database maintains the knowledge graph topology for fast queries and semantic traversal without cloud dependencies. It contains:
- db tables for the knowledge graph schema
- a search index table enabling full text search across the knowledge base
### Sync Process
The sync process:
1. Detects changes to files in the knowledge directory
2. Parses modified files to extract structured data
3. Updates the SQLite database with changes
4. Resolves forward references when new entities are created
5. Updates the search index for fast querying
### Search Engine
The search functionality:
1. Uses a combination of full-text search and semantic matching
2. Indexes observations, relations, and content
3. Supports wildcards and pattern matching in memory:// URLs
4. Traverses the knowledge graph to follow relationships
5. Ranks results by relevance to the query
## Relations
- relates_to [[Welcome to Basic memory]] (Overview)
- relates_to [[CLI Reference]] (Command line tools)
- implements [[Knowledge Format]] (File structure and format)
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---
title: User Guide
type: note
permalink: docs/user-guide
---
# User Guide
This guide explains how to effectively use Basic Memory in your daily workflow, from creating knowledge through
conversations to building a rich semantic network.
## Basic Memory Workflow
Using Basic Memory follows a natural cycle:
1. **Have conversations** with AI assistants like Claude
2. **Capture knowledge** in Markdown files
3. **Build connections** between pieces of knowledge
4. **Reference your knowledge** in future conversations
5. **Edit files directly** when needed
6. **Sync changes** automatically
## Creating Knowledge
### Through Conversations
To create knowledge during conversations with Claude:
```
You: We've covered several authentication approaches. Could you create a note summarizing what we've discussed?
Claude: I'll create a note summarizing our authentication discussion.
```
This creates a Markdown file in your `~/basic-memory` directory with semantic markup.
### Direct File Creation
You can create files directly:
1. Create a new Markdown file in your `~/basic-memory` directory
2. Add frontmatter with title, type, and optional tags
3. Structure content with observations and relations
4. Save the file
5. Run `basic-memory sync` if not in watch mode
## Using Special Prompts
Basic Memory includes several special prompts that help you leverage your knowledge base more effectively. In apps like
Claude Desktop, these prompts trigger specific tools to search and analyze your knowledge base.
### Continue Conversation
When you want to pick up where you left off on a topic:
```
You: Let's continue our conversation about authentication systems.
```
Behind the scenes:
- Claude searches your knowledge base for content about "authentication systems"
- It retrieves relevant documents and their relations
- It analyzes the context to understand where you left off
- It builds a comprehensive picture of what you've previously discussed
- It can then resume the conversation with all that context
This is particularly useful when:
- Starting a new session days or weeks after your last discussion
- Switching between multiple ongoing projects
- Building on previous work without repeating yourself
### Recent Activity
To get an overview of what you've been working on:
```
You: What have we been discussing recently?
```
Behind the scenes:
- Claude retrieves documents modified recently
- It analyzes patterns and themes
- It summarizes the key topics and changes
- It offers to continue working on any of those topics
This is useful for:
- Coming back after a break
- Getting a quick reminder of ongoing projects
- Deciding what to work on next
### Search
To find specific information in your knowledge base:
```
You: Find information about JWT authentication in my notes.
```
Behind the scenes:
- Claude performs a semantic search for "JWT authentication"
- It retrieves and ranks the most relevant documents
- It summarizes the key findings
- It offers to explore specific areas in more detail
This is useful for:
- Finding specific information quickly
- Exploring what you know about a topic
- Starting work on an existing topic
### Example
Choose "Continue Conversation"
![[prompt 1.png|500]]
Enter a topic
![[prompt2.png|500]]
Give instructions
![[prompt3.png|500]]
Claude Desktop lets you send a prompt to provide context. You can use this at the beginning of a chat to preload context
without needing to copy paste all the time. By using one of the supplied prompts, Basic Memory will search the knowledge
base and give the AI instructions for how to build context.
Choose "Continue Conversation":
![[prompt 1.png|500]]
Enter a topic:
![[prompt2.png|500]]
Give optional additional instructions:
![[prompt3.png|500]]
Claude can build context from the supplied topic. This works independently of Claude Project information. All the
context comes from your local knowledge base.
![[prompt4.png|500]]
## Searching Your Knowledge Base
Basic Memory provides multiple ways to search and explore your knowledge base:
### Natural Language Search
The simplest way to search is to ask Claude directly:
```
You: What do I know about authentication methods?
```
Claude will search your knowledge base semantically and return relevant information.
### Search Prompt
Use the dedicated search prompt for more focused searches:
```
You: Search for "JWT authentication"
```
This triggers a specialized search that returns precise results with document titles, relevant excerpts, and offers to
explore specific documents.
### Boolean Search
For more precise searches, use boolean operators to refine your queries:
```
You: Search for "authentication AND OAuth NOT basic"
```
Basic Memory supports standard boolean operators:
- **AND**: Find documents containing both terms
```
You: Search for "python AND flask"
```
This finds documents containing both "python" and "flask"
- **OR**: Find documents containing either term
```
You: Search for "python OR javascript"
```
This finds documents containing either "python" or "javascript"
- **NOT**: Exclude documents containing specific terms
```
You: Search for "python NOT django"
```
This finds documents containing "python" but excludes those containing "django"
- **Grouping with parentheses**: Control operator precedence
```
You: Search for "(python OR javascript) AND web"
```
This finds documents about web development that mention either Python or JavaScript
Boolean search is particularly useful for:
- Narrowing down results in large knowledge bases
- Finding specific combinations of concepts
- Excluding irrelevant content from search results
- Creating complex queries for precise information retrieval
### Memory URL Pattern Matching
For advanced searches, use memory:// URL patterns with wildcards:
```
You: Look at memory://auth* and summarize all authentication approaches.
```
Pattern matching supports:
- **Wildcards**: `memory://auth*` matches all permalinks starting with "auth"
- **Path patterns**: `memory://project/*/auth` matches auth documents in any project subfolder
- **Relation traversal**: `memory://auth-system/implements/*` finds all documents that implement the auth system
### Combining Search with Context Building
The most powerful searches build comprehensive context by following relationships:
```
You: Search for JWT authentication and then follow all implementation relations.
```
This builds a complete picture by:
1. Finding documents about JWT authentication
2. Following implementation relationships from those documents
3. Building a complete picture of how JWT is implemented across your system
### Search Best Practices
For effective searching:
1. **Be specific** with search terms and phrases
2. **Use boolean operators** to refine searches and find precise information
3. **Use technical terms** when searching for technical content
4. **Follow up** on search results by asking for more details about specific documents
5. **Combine approaches** by starting with search and then using memory:// URLs for precision
6. **Use relation traversal** to explore connected concepts after finding initial documents
## Referencing Knowledge
### Using memory:// URLs
Reference specific knowledge directly:
```
You: Please look at memory://authentication-approaches and suggest which approach would be best for our mobile app.
```
### Natural Language References
Reference knowledge conversationally:
```
You: What did we decide about authentication for the project?
```
### Advanced References
Follow connections across your knowledge graph:
```
You: Look at memory://project-architecture and check related documents to give me a complete picture.
```
## Working with Files
### File Location and Organization
By default, Basic Memory stores files in `~/basic-memory`:
- Browse this directory in your file explorer
- Organize files into subfolders
- Use git for version control
### File Format
Each knowledge file follows this structure:
```markdown
---
title: Authentication Approaches
type: note
tags: [security, architecture]
permalink: authentication-approaches
---
# Authentication Approaches
A comparison of authentication methods.
## Observations
- [approach] JWT provides stateless authentication #security
- [limitation] Session tokens require server-side storage #infrastructure
## Relations
- implements [[Security Requirements]]
- affects [[User Login Flow]]
```
### Editing Files
Modify files in any text editor:
1. Open the file in your preferred editor
2. Make changes to content, observations, or relations
3. Save the file
4. Basic Memory detects changes automatically when running in watch mode
## Building a Knowledge Graph
The value of Basic Memory comes from connections between pieces of knowledge.
### Creating Relations
When creating or editing notes, build connections:
```markdown
## Relations
- implements [[Security Requirements]]
- depends_on [[User Authentication]]
```
Relations can be:
- Hierarchical (part_of, contains)
- Directional (implements, depends_on)
- Associative (relates_to, similar_to)
- Temporal (precedes, follows)
Relations are also created via regular wiki-link style links within the body text.
### Forward References
Reference documents that don't exist yet:
```markdown
- will_impact [[Future Feature]]
```
These references resolve automatically when you create the referenced document.
## Conversation Continuity
Basic Memory maintains context across different conversations.
### Starting New Sessions with Context
When starting a new conversation with Claude, you can:
1. **Use special prompts** like "Continue conversation about..." or "What were we working on?"
2. **Reference specific documents** with memory:// URLs
3. **Ask about recent work** with "What have we been discussing recently?"
4. **Search for specific topics** with "Find information about..."
### Long-Term Projects
Maintain context for complex projects over time:
1. **Document key decisions** as you make them
2. **Create relationships** between project components
3. **Reference past decisions** when implementing features
4. **Update documentation** as the project evolves
### Tips for Effective Continuity
1. **Be specific about topics** when continuing a conversation
2. **Reference documents directly** with memory:// URLs for precision
3. **Create summary notes** after important discussions
4. **Update existing notes** rather than creating duplicates
5. **Build robust connections** between related topics
## Advanced Features
### Importing External Knowledge
Import existing conversations:
```bash
# From Claude
basic-memory import claude conversations
# From ChatGPT
basic-memory import chatgpt
```
After importing, run `basic-memory sync` to index everything.
### Obsidian Integration
Use with [Obsidian](https://obsidian.md):
1. Point Obsidian to your `~/basic-memory` directory
2. Use Obsidian's graph view to visualize your knowledge network
3. All changes sync back to Basic Memory
### Canvas Visualizations
Create visual knowledge maps:
```
You: Could you create a canvas visualization of our project components?
```
This generates an Obsidian canvas file showing the relationships between concepts.
### Advanced Memory URI Patterns
Use wildcards and patterns:
```
You: Review memory://project/*/requirements to summarize all project requirements.
```
## Command Line Interface
### Sync Commands
```bash
# One-time sync
basic-memory sync
# Watch for changes
basic-memory sync --watch
```
### Status and Information
```bash
# Check system status
basic-memory status
# View CLI help
basic-memory --help
```
### Import Commands
```bash
# Import from Claude
basic-memory import claude conversations
# Import from ChatGPT
basic-memory import chatgpt
```
## Multiple Projects
Basic Memory supports managing multiple separate knowledge bases through projects. This feature allows you to maintain
separate knowledge graphs for different purposes (e.g., personal notes, work projects, research topics).
Basic Memory keeps a list of projects in a config file: ` ~/.basic-memory/config.json`
### Managing Projects
```bash
# List all configured projects
basic-memory project list
# Add a new project
basic-memory project add work ~/work-basic-memory
# Set the default project
basic-memory project default work
# Remove a project (doesn't delete files)
basic-memory project remove personal
# Show current project
basic-memory project current
```
### Using Projects in Commands
All commands support the `--project` flag to specify which project to use:
```bash
# Sync a specific project
basic-memory --project=work sync
# Run MCP server for a specific project
basic-memory --project=personal mcp
```
You can also set the `BASIC_MEMORY_PROJECT` environment variable:
```bash
BASIC_MEMORY_PROJECT=work basic-memory sync
```
### Project Isolation
Each project maintains:
- Its own collection of markdown files in the specified directory
- A separate SQLite database for that project
- Complete knowledge graph isolation from other projects
## Workflow Tips
1. Run sync in watch mode for automatic updates
2. Use git for version control of your knowledge base
3. Review and edit AI-created content for accuracy
4. Periodically organize and refine your knowledge structure
5. Build rich connections between related ideas
6. Use forward references to plan future documentation
7. Start conversations with special prompts to leverage existing knowledge
## Troubleshooting
### Sync Issues
If changes aren't showing up:
1. Verify `basic-memory sync --watch` is running
2. Run `basic-memory status` to check system state
3. Try a manual sync with `basic-memory sync`
### Missing Content
If content isn't found:
1. Check the exact path and permalink
2. Try searching with more general terms
3. Verify the file exists in your knowledge base
### Relation Problems
If relations aren't working:
1. Ensure exact title matching in [[WikiLinks]]
2. Check for typos in relation types
3. Verify both documents exist
## Relations
- implements [[Knowledge Format]] (How knowledge is structured)
- relates_to [[Getting Started with Basic Memory]] (Setup and first steps)
- relates_to [[Canvas]] (Creating visual knowledge maps)
- relates_to [[CLI Reference]] (Command line tools)
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---
title: Introduction to Basic Memory
type: docs
permalink: docs/introduction
tags:
- documentation
- index
- overview
---
# BASIC MEMORY
Basic Memory is a knowledge management system that allows you to build a persistent semantic graph from conversations
with AI assistants. All knowledge is stored in standard Markdown files on your computer, giving you full control and
ownership of your data.
Basic Memory connects you and AI assistants through shared knowledge:
1. **Captures knowledge** from natural conversations with AI assistants
2. **Structures information** using simple semantic patterns in Markdown
3. **Enables knowledge reuse** across different conversations and sessions
4. **Maintains persistence** through local files you control completely
Both you and AI assistants like Claude can read from and write to the same knowledge base, creating a continuous
learning environment where each conversation builds upon previous ones.
![[Obsidian-CoffeeKnowledgeBase-examples-overlays.gif]]
Basic Memory uses:
- **Files as the source of truth** - Everything is stored in plain Markdown files
- **Git-compatible storage** - All knowledge can be versioned, branched, and merged
- **Local SQLite database** - For fast indexing and searching only (not primary storage)
- **Model Context Protocol (MCP)** - For seamless AI assistant integration
Basic Memory gives you complete control over your knowledge:
- **Local-first storage** - All knowledge lives on your computer
- **Standard file formats** - Plain Markdown compatible with any editor
- **Directory organization** - Knowledge stored in `~/basic-memory` by default
- **Version control ready** - Use git for history, branching, and collaboration
- **Edit anywhere** - Modify files with any text editor or Obsidian
Changes to files automatically sync with the knowledge graph, and AI assistants can see your edits in conversations.
## Documentation Map
Continue exploring Basic Memory with these guides:
- Installation and setup [[Getting Started with Basic Memory]]
- Comprehensive usage instructions [[User Guide]]
- Detailed explanation of knowledge structure [[Knowledge Format]]
- Obsidian integration guide [[Obsidian Integration]]
- Canvas visualization guide [[Canvas]]
- Command line tool reference [[CLI Reference]]
- Reference for AI assistants using Basic Memory [[AI Assistant Guide]]
- Technical implementation details [[Technical Information]]
## Next Steps
Start with the [[Getting Started with Basic Memory]] guide to install Basic Memory and configure it with your AI
assistant.
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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
# Sync will report conflicts
basic-memory sync
# 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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# Basic Memory Cloud CLI Guide
The Basic Memory Cloud CLI provides seamless integration between local and cloud knowledge bases using **project-scoped synchronization**. Each project can optionally sync with the cloud, giving you fine-grained control over what syncs and where.
## Overview
The cloud CLI enables you to:
- **Toggle cloud mode** - All regular `bm` commands work with cloud when enabled
- **Project-scoped sync** - Each project independently manages its sync configuration
- **Explicit operations** - Sync only what you want, when you want
- **Bidirectional sync** - Keep local and cloud in sync with rclone bisync
- **Offline access** - Work locally, sync when ready
## Prerequisites
Before using Basic Memory Cloud, you need:
- **Active Subscription**: An active Basic Memory Cloud subscription is required to access cloud features
- **Subscribe**: Visit [https://basicmemory.com/subscribe](https://basicmemory.com/subscribe) to sign up
If you attempt to log in without an active subscription, you'll receive a "Subscription Required" error with a link to subscribe.
## Architecture: Project-Scoped Sync
### The Problem
**Old approach (SPEC-8):** All projects lived in a single `~/basic-memory-cloud-sync/` directory. This caused:
- ❌ Directory conflicts between mount and bisync
- ❌ Auto-discovery creating phantom projects
- ❌ Confusion about what syncs and when
- ❌ All-or-nothing sync (couldn't sync just one project)
**New approach (SPEC-20):** Each project independently configures sync.
### How It Works
**Projects can exist in three states:**
1. **Cloud-only** - Project exists on cloud, no local copy
2. **Cloud + Local (synced)** - Project has a local working directory that syncs
3. **Local-only** - Project exists locally (when cloud mode is disabled)
**Example:**
```bash
# You have 3 projects on cloud:
# - research: wants local sync at ~/Documents/research
# - work: wants local sync at ~/work-notes
# - temp: cloud-only, no local sync needed
bm project add research --local-path ~/Documents/research
bm project add work --local-path ~/work-notes
bm project add temp # No local sync
# Now you can sync individually (after initial --resync):
bm project bisync --name research
bm project bisync --name work
# temp stays cloud-only
```
**What happens under the covers:**
- Config stores `cloud_projects` dict mapping project names to local paths
- Each project gets its own bisync state in `~/.basic-memory/bisync-state/{project}/`
- Rclone syncs using single remote: `basic-memory-cloud`
- Projects can live anywhere on your filesystem, not forced into sync directory
## Quick Start
### 1. Enable Cloud Mode
Authenticate and enable cloud mode:
```bash
bm cloud login
```
**What this does:**
1. Opens browser to Basic Memory Cloud authentication page
2. Stores authentication token in `~/.basic-memory/auth/token`
3. **Enables cloud mode** - all CLI commands now work against cloud
4. Validates your subscription status
**Result:** All `bm project`, `bm tools` commands now work with cloud.
### 2. Set Up Sync
Install rclone and configure credentials:
```bash
bm cloud setup
```
**What this does:**
1. Installs rclone automatically (if needed)
2. Fetches your tenant information from cloud
3. Generates scoped S3 credentials for sync
4. Configures single rclone remote: `basic-memory-cloud`
**Result:** You're ready to sync projects. No sync directories created yet - those come with project setup.
### 3. Add Projects with Sync
Create projects with optional local sync paths:
```bash
# Create cloud project without local sync
bm project add research
# Create cloud project WITH local sync
bm project add research --local-path ~/Documents/research
# Or configure sync for existing project
bm project sync-setup research ~/Documents/research
```
**What happens under the covers:**
When you add a project with `--local-path`:
1. Project created on cloud at `/app/data/research`
2. Local path stored in config: `cloud_projects.research.local_path = "~/Documents/research"`
3. Local directory created if it doesn't exist
4. Bisync state directory created at `~/.basic-memory/bisync-state/research/`
**Result:** Project is ready to sync, but no files synced yet.
### 4. Sync Your Project
Establish the initial sync baseline. **Best practice:** Always preview with `--dry-run` first:
```bash
# Step 1: Preview the initial sync (recommended)
bm project bisync --name research --resync --dry-run
# Step 2: If all looks good, run the actual sync
bm project bisync --name research --resync
```
**What happens under the covers:**
1. Rclone reads from `~/Documents/research` (local)
2. Connects to `basic-memory-cloud:bucket-name/app/data/research` (remote)
3. Creates bisync state files in `~/.basic-memory/bisync-state/research/`
4. Syncs files bidirectionally with settings:
- `conflict_resolve=newer` (most recent wins)
- `max_delete=25` (safety limit)
- Respects `.bmignore` patterns
**Result:** Local and cloud are in sync. Baseline established.
**Why `--resync`?** This is an rclone requirement for the first bisync run. It establishes the initial state that future syncs will compare against. After the first sync, never use `--resync` unless you need to force a new baseline.
See: https://rclone.org/bisync/#resync
```
--resync
This will effectively make both Path1 and Path2 filesystems contain a matching superset of all files. By default, Path2 files that do not exist in Path1 will be copied to Path1, and the process will then copy the Path1 tree to Path2.
```
### 5. Subsequent Syncs
After the first sync, just run bisync without `--resync`:
```bash
bm project bisync --name research
```
**What happens:**
1. Rclone compares local and cloud states
2. Syncs changes in both directions
3. Auto-resolves conflicts (newer file wins)
4. Updates `last_sync` timestamp in config
**Result:** Changes flow both ways - edit locally or in cloud, both stay in sync.
### 6. Verify Setup
Check status:
```bash
bm cloud status
```
You should see:
- `Mode: Cloud (enabled)`
- `Cloud instance is healthy`
- Instructions for project sync commands
## Working with Projects
### Understanding Project Commands
**Key concept:** When cloud mode is enabled, use regular `bm project` commands (not `bm cloud project`).
```bash
# In cloud mode:
bm project list # Lists cloud projects
bm project add research # Creates cloud project
# In local mode:
bm project list # Lists local projects
bm project add research ~/Documents/research # Creates local project
```
### Creating Projects
**Use case 1: Cloud-only project (no local sync)**
```bash
bm project add temp-notes
```
**What this does:**
- Creates project on cloud at `/app/data/temp-notes`
- No local directory created
- No sync configuration
**Result:** Project exists on cloud, accessible via MCP tools, but no local copy.
**Use case 2: Cloud project with local sync**
```bash
bm project add research --local-path ~/Documents/research
```
**What this does:**
- Creates project on cloud at `/app/data/research`
- Creates local directory `~/Documents/research`
- Stores sync config in `~/.basic-memory/config.json`
- Prepares for bisync (but doesn't sync yet)
**Result:** Project ready to sync. Run `bm project bisync --name research --resync` to establish baseline.
**Use case 3: Add sync to existing cloud project**
```bash
# Project already exists on cloud
bm project sync-setup research ~/Documents/research
```
**What this does:**
- Updates existing project's sync configuration
- Creates local directory
- Prepares for bisync
**Result:** Existing cloud project now has local sync path. Run bisync to pull files down.
### Listing Projects
View all projects:
```bash
bm project list
```
**What you see:**
- All projects in cloud (when cloud mode enabled)
- Default project marked
- Project paths shown
**Future:** Will show sync status (synced/not synced, last sync time).
## File Synchronization
### Understanding the Sync Commands
**There are three sync-related commands:**
1. `bm project sync` - One-way: local → cloud (make cloud match local)
2. `bm project bisync` - Two-way: local ↔ cloud (recommended)
3. `bm project check` - Verify files match (no changes)
### One-Way Sync: Local → Cloud
**Use case:** You made changes locally and want to push to cloud (overwrite cloud).
```bash
bm project sync --name research
```
**What happens:**
1. Reads files from `~/Documents/research` (local)
2. Uses rclone sync to make cloud identical to local
3. Respects `.bmignore` patterns
4. Shows progress bar
**Result:** Cloud now matches local exactly. Any cloud-only changes are overwritten.
**When to use:**
- You know local is the source of truth
- You want to force cloud to match local
- You don't care about cloud changes
### Two-Way Sync: Local ↔ Cloud (Recommended)
**Use case:** You edit files both locally and in cloud UI, want both to stay in sync.
```bash
# First time - establish baseline
bm project bisync --name research --resync
# Subsequent syncs
bm project bisync --name research
```
**What happens:**
1. Compares local and cloud states using bisync metadata
2. Syncs changes in both directions
3. Auto-resolves conflicts (newer file wins)
4. Detects excessive deletes and fails safely (max 25 files)
**Conflict resolution example:**
```bash
# Edit locally
echo "Local change" > ~/Documents/research/notes.md
# Edit same file in cloud UI
# Cloud now has: "Cloud change"
# Run bisync
bm project bisync --name research
# Result: Newer file wins (based on modification time)
# If cloud was more recent, cloud version kept
# If local was more recent, local version kept
```
**When to use:**
- Default workflow for most users
- You edit in multiple places
- You want automatic conflict resolution
### Verify Sync Integrity
**Use case:** Check if local and cloud match without making changes.
```bash
bm project check --name research
```
**What happens:**
1. Compares file checksums between local and cloud
2. Reports differences
3. No files transferred
**Result:** Shows which files differ. Run bisync to sync them.
```bash
# One-way check (faster)
bm project check --name research --one-way
```
### Preview Changes (Dry Run)
**Use case:** See what would change without actually syncing.
```bash
bm project bisync --name research --dry-run
```
**What happens:**
1. Runs bisync logic
2. Shows what would be transferred/deleted
3. No actual changes made
**Result:** Safe preview of sync operations.
### Advanced: List Remote Files
**Use case:** See what files exist on cloud without syncing.
```bash
# List all files in project
bm project ls --name research
# List files in subdirectory
bm project ls --name research --path subfolder
```
**What happens:**
1. Connects to cloud via rclone
2. Lists files in remote project path
3. No files transferred
**Result:** See cloud file listing.
## Multiple Projects
### Syncing Multiple Projects
**Use case:** You have several projects with local sync, want to sync all at once.
```bash
# Setup multiple projects
bm project add research --local-path ~/Documents/research
bm project add work --local-path ~/work-notes
bm project add personal --local-path ~/personal
# Establish baselines
bm project bisync --name research --resync
bm project bisync --name work --resync
bm project bisync --name personal --resync
# Daily workflow: sync everything
bm project bisync --name research
bm project bisync --name work
bm project bisync --name personal
```
**Future:** `--all` flag will sync all configured projects:
```bash
bm project bisync --all # Coming soon
```
### Mixed Usage
**Use case:** Some projects sync, some stay cloud-only.
```bash
# Projects with sync
bm project add research --local-path ~/Documents/research
bm project add work --local-path ~/work
# Cloud-only projects
bm project add archive
bm project add temp-notes
# Sync only the configured ones
bm project bisync --name research
bm project bisync --name work
# Archive and temp-notes stay cloud-only
```
**Result:** Fine-grained control over what syncs.
## Disable Cloud Mode
Return to local mode:
```bash
bm cloud logout
```
**What this does:**
1. Disables cloud mode in config
2. All commands now work locally
3. Auth token remains (can re-enable with login)
**Result:** All `bm` commands work with local projects again.
## Filter Configuration
### Understanding .bmignore
**The problem:** You don't want to sync everything (e.g., `.git`, `node_modules`, database files).
**The solution:** `.bmignore` file with gitignore-style patterns.
**Location:** `~/.basic-memory/.bmignore`
**Default patterns:**
```gitignore
# Version control
.git/**
# Python
__pycache__/**
*.pyc
.venv/**
venv/**
# Node.js
node_modules/**
# Basic Memory internals
memory.db/**
memory.db-shm/**
memory.db-wal/**
config.json/**
watch-status.json/**
.bmignore.rclone/**
# OS files
.DS_Store/**
Thumbs.db/**
# Environment files
.env/**
.env.local/**
```
**How it works:**
1. On first sync, `.bmignore` created with defaults
2. Patterns converted to rclone filter format (`.bmignore.rclone`)
3. Rclone uses filters during sync
4. Same patterns used by all projects
**Customizing:**
```bash
# Edit patterns
code ~/.basic-memory/.bmignore
# Add custom patterns
echo "*.tmp/**" >> ~/.basic-memory/.bmignore
# Next sync uses updated patterns
bm project bisync --name research
```
## Troubleshooting
### Authentication Issues
**Problem:** "Authentication failed" or "Invalid token"
**Solution:** Re-authenticate:
```bash
bm cloud logout
bm cloud login
```
### Subscription Issues
**Problem:** "Subscription Required" error
**Solution:**
1. Visit subscribe URL shown in error
2. Sign up for subscription
3. Run `bm cloud login` again
**Note:** Access is immediate when subscription becomes active.
### Bisync Initialization
**Problem:** "First bisync requires --resync"
**Explanation:** Bisync needs a baseline state before it can sync changes.
**Solution:**
```bash
bm project bisync --name research --resync
```
**What this does:**
- Establishes initial sync state
- Creates baseline in `~/.basic-memory/bisync-state/research/`
- Syncs all files bidirectionally
**Result:** Future syncs work without `--resync`.
### Empty Directory Issues
**Problem:** "Empty prior Path1 listing. Cannot sync to an empty directory"
**Explanation:** Rclone bisync doesn't work well with completely empty directories. It needs at least one file to establish a baseline.
**Solution:** Add at least one file before running `--resync`:
```bash
# Create a placeholder file
echo "# Research Notes" > ~/Documents/research/README.md
# Now run bisync
bm project bisync --name research --resync
```
**Why this happens:** Bisync creates listing files that track the state of each side. When both directories are completely empty, these listing files are considered invalid by rclone.
**Best practice:** Always have at least one file (like a README.md) in your project directory before setting up sync.
### Bisync State Corruption
**Problem:** Bisync fails with errors about corrupted state or listing files
**Explanation:** Sometimes bisync state can become inconsistent (e.g., after mixing dry-run and actual runs, or after manual file operations).
**Solution:** Clear bisync state and re-establish baseline:
```bash
# Clear bisync state
bm project bisync-reset research
# Re-establish baseline
bm project bisync --name research --resync
```
**What this does:**
- Removes all bisync metadata from `~/.basic-memory/bisync-state/research/`
- Forces fresh baseline on next `--resync`
- Safe operation (doesn't touch your files)
**Note:** This command also runs automatically when you remove a project to clean up state directories.
### Too Many Deletes
**Problem:** "Error: max delete limit (25) exceeded"
**Explanation:** Bisync detected you're about to delete more than 25 files. This is a safety check to prevent accidents.
**Solution 1:** Review what you're deleting, then force resync:
```bash
# Check what would be deleted
bm project bisync --name research --dry-run
# If correct, establish new baseline
bm project bisync --name research --resync
```
**Solution 2:** Use one-way sync if you know local is correct:
```bash
bm project sync --name research
```
### Project Not Configured for Sync
**Problem:** "Project research has no local_sync_path configured"
**Explanation:** Project exists on cloud but has no local sync path.
**Solution:**
```bash
bm project sync-setup research ~/Documents/research
bm project bisync --name research --resync
```
### Connection Issues
**Problem:** "Cannot connect to cloud instance"
**Solution:** Check status:
```bash
bm cloud status
```
If instance is down, wait a few minutes and retry.
## Security
- **Authentication**: OAuth 2.1 with PKCE flow
- **Tokens**: Stored securely in `~/.basic-memory/basic-memory-cloud.json`
- **Transport**: All data encrypted in transit (HTTPS)
- **Credentials**: Scoped S3 credentials (read-write to your tenant only)
- **Isolation**: Your data isolated from other tenants
- **Ignore patterns**: Sensitive files automatically excluded via `.bmignore`
## Command Reference
### Cloud Mode Management
```bash
bm cloud login # Authenticate and enable cloud mode
bm cloud logout # Disable cloud mode
bm cloud status # Check cloud mode and instance health
```
### Setup
```bash
bm cloud setup # Install rclone and configure credentials
```
### Project Management
When cloud mode is enabled:
```bash
bm project list # List cloud projects
bm project add <name> # Create cloud project (no sync)
bm project add <name> --local-path <path> # Create with local sync
bm project sync-setup <name> <path> # Add sync to existing project
bm project rm <name> # Delete project
```
### File Synchronization
```bash
# One-way sync (local → cloud)
bm project sync --name <project>
bm project sync --name <project> --dry-run
bm project sync --name <project> --verbose
# Two-way sync (local ↔ cloud) - Recommended
bm project bisync --name <project> # After first --resync
bm project bisync --name <project> --resync # First time / force baseline
bm project bisync --name <project> --dry-run
bm project bisync --name <project> --verbose
# Integrity check
bm project check --name <project>
bm project check --name <project> --one-way
# List remote files
bm project ls --name <project>
bm project ls --name <project> --path <subpath>
```
## Summary
**Basic Memory Cloud uses project-scoped sync:**
1. **Enable cloud mode** - `bm cloud login`
2. **Install rclone** - `bm cloud setup`
3. **Add projects with sync** - `bm project add research --local-path ~/Documents/research`
4. **Preview first sync** - `bm project bisync --name research --resync --dry-run`
5. **Establish baseline** - `bm project bisync --name research --resync`
6. **Daily workflow** - `bm project bisync --name research`
**Key benefits:**
- ✅ Each project independently syncs (or doesn't)
- ✅ Projects can live anywhere on disk
- ✅ Explicit sync operations (no magic)
- ✅ Safe by design (max delete limits, conflict resolution)
- ✅ Full offline access (work locally, sync when ready)
**Future enhancements:**
- `--all` flag to sync all configured projects
- Project list showing sync status
- Watch mode for automatic sync
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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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{
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{}
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[
"optimize-canvas-connections"
]
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{
"file-explorer": true,
"global-search": true,
"switcher": true,
"graph": true,
"backlink": true,
"canvas": true,
"outgoing-link": true,
"tag-pane": true,
"properties": false,
"page-preview": true,
"daily-notes": true,
"templates": true,
"note-composer": true,
"command-palette": true,
"slash-command": false,
"editor-status": true,
"bookmarks": true,
"markdown-importer": false,
"zk-prefixer": false,
"random-note": false,
"outline": true,
"word-count": true,
"slides": false,
"audio-recorder": false,
"workspaces": false,
"file-recovery": true,
"publish": true,
"sync": true,
"webviewer": false
}
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/*
THIS IS A GENERATED/BUNDLED FILE BY ESBUILD
if you want to view the source, please visit the github repository of this plugin
*/
var __defProp = Object.defineProperty;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
var __getOwnPropNames = Object.getOwnPropertyNames;
var __hasOwnProp = Object.prototype.hasOwnProperty;
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return to;
};
var __toCommonJS = (mod) => __copyProps(__defProp({}, "__esModule", { value: true }), mod);
// main.ts
var main_exports = {};
__export(main_exports, {
default: () => OptimizeCanvasConnectionsPlugin
});
module.exports = __toCommonJS(main_exports);
var import_obsidian = require("obsidian");
var OptimizeCanvasConnectionsPlugin = class extends import_obsidian.Plugin {
async onload() {
this.addCommand({
id: "optimize-preserve-axes-selection",
name: "Optimize selection (preserve axes)",
checkCallback: (checking) => {
const canvasView = app.workspace.getActiveViewOfType(import_obsidian.ItemView);
if ((canvasView == null ? void 0 : canvasView.getViewType()) == "canvas") {
if (!checking) {
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return true;
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this.addCommand({
id: "optimize-shortest-path-selection",
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const canvasView = app.workspace.getActiveViewOfType(import_obsidian.ItemView);
if ((canvasView == null ? void 0 : canvasView.getViewType()) == "canvas") {
if (!checking) {
this.optimize("shortest-path");
}
return true;
}
return false;
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}
onunload() {
}
async optimize(option) {
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const canvas = canvasView == null ? void 0 : canvasView.canvas;
const currentSelection = canvas == null ? void 0 : canvas.selection;
let selectedIDs = new Array();
currentSelection.forEach(function(selection) {
selectedIDs.push(selection.id);
});
let applyToAll = false;
if (selectedIDs.length == 0) {
applyToAll = true;
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for (let [edgeKey, edge] of canvas["edges"]) {
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let toNode = edge["to"]["node"];
let fromPossibilities = [edge["from"]["side"]];
if (applyToAll || selectedIDs.includes(fromNode["id"])) {
switch (option) {
case "shortest-path":
fromPossibilities = ["top", "bottom", "left", "right"];
break;
case "preserve-axes":
switch (edge["from"]["side"]) {
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case "bottom":
fromPossibilities = ["top", "bottom"];
break;
case "left":
case "right":
fromPossibilities = ["left", "right"];
break;
}
}
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let toPossibilities = [edge["to"]["side"]];
if (applyToAll || selectedIDs.includes(toNode["id"])) {
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case "bottom":
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break;
case "left":
case "right":
toPossibilities = ["left", "right"];
break;
}
}
}
let distances = [];
for (const fromSide of fromPossibilities) {
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} else if (fromSide == "bottom") {
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} else if (fromSide == "left") {
fromPoint = { "x": fromNode["x"], "y": fromNode["y"] + fromNode["height"] / 2 };
} else if (fromSide == "right") {
fromPoint = { "x": fromNode["x"] + fromNode["width"], "y": fromNode["y"] + fromNode["height"] / 2 };
}
for (const toSide of toPossibilities) {
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toPoint = { "x": toNode["x"] + toNode["width"] / 2, "y": toNode["y"] + toNode["height"] };
} else if (toSide == "left") {
toPoint = { "x": toNode["x"], "y": toNode["y"] + toNode["height"] / 2 };
} else if (toSide == "right") {
toPoint = { "x": toNode["x"] + toNode["width"], "y": toNode["y"] + toNode["height"] / 2 };
}
distances.push({
"fromSide": fromSide,
"toSide": toSide,
"distance": (toPoint.x - fromPoint.x) ** 2 + (toPoint.y - fromPoint.y) ** 2
});
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}
distances = distances.sort(function(a, b) {
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edge["from"]["side"] = distances[0]["fromSide"];
edge["to"]["side"] = distances[0]["toSide"];
edge.render();
}
canvas.requestSave();
}
};
/* nosourcemap */
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{
"id": "optimize-canvas-connections",
"name": "Optimize Canvas Connections",
"version": "1.0.0",
"minAppVersion": "1.1.9",
"description": "An Obsidian plugin that declutters a canvas by reconnecting notes using their nearest edges.",
"author": "Félix Chénier",
"authorUrl": "https://felixchenier.uqam.ca",
"isDesktopOnly": false
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---
title: Brewing Equipment
type: note
permalink: coffee/brewing-equipment
tags:
- '#coffee'
- '#equipment'
- '#gear'
- '#brewing'
- '#demo'
---
# Brewing Equipment
Essential tools and equipment for brewing coffee, their characteristics, and how they affect the brewing process.
## Overview
The equipment used to brew coffee plays a crucial role in determining the final cup quality. From grinders to brewers to kettles, each piece of equipment contributes to different aspects of the brewing process.
## Observations
- [principle] Equipment quality often has a bigger impact on consistency than on absolute quality potential #quality
- [principle] Good grind consistency is the most important technical factor in extraction quality #grind
- [investment] A good burr grinder is often the most important investment for improving home coffee #gear
- [technique] Equipment maintenance and cleaning significantly impact flavor consistency over time #maintenance
## Grinders
- [equipment] Burr grinders crush beans between two abrasive surfaces for more consistent particle size #grinders
- [equipment] Blade grinders chop beans unevenly, leading to inconsistent extraction #grinders
- [equipment] Flat burr grinders produce very consistent particle size but generate more heat #burrs
- [equipment] Conical burr grinders create slightly less uniform grounds but with less heat and noise #burrs
- [feature] Grind adjustment mechanisms range from stepped to stepless for different precision levels #adjustment
- [feature] Retention (grounds trapped in grinder) affects dose consistency and freshness #retention
- [price] Hand grinders offer excellent value, with models like Timemore C2 and 1Zpresso JX providing excellent results around $100-150 #budget
- [price] Entry-level electric burr grinders like Baratza Encore start around $170 but provide significant improvement over blade grinders #value
## Brewers
### Pour Over Brewers
- [equipment] Hario V60 uses a conical design with spiral ridges to control flow rate #pourover
- [equipment] Kalita Wave has a flat bottom with three small holes for more consistent extraction #pourover
- [equipment] Chemex combines brewer and server with thick proprietary filters for ultra-clean cup #pourover
- [material] Ceramic brewers retain heat better than plastic but are more fragile #materials
- [material] Glass brewers provide neutral flavor but less heat retention #materials
- [material] Plastic brewers are inexpensive, durable, and surprisingly good for heat retention #materials
### Immersion Brewers
- [equipment] French Press uses a metal mesh to separate grounds, allowing oils and fine particles to pass #immersion
- [equipment] AeroPress uses pressure and paper filter for clean, versatile brewing #immersion
- [equipment] Clever Dripper combines immersion and drip methods with a valve mechanism #hybrid
- [material] Glass French presses look elegant but break easily and have poor heat retention #materials
- [material] Stainless steel or ceramic French presses offer better durability and heat retention #materials
### Pressure Brewers
- [equipment] Espresso machines use 9 bars of pressure, requiring significant investment for good results #espresso
- [equipment] Moka pot uses steam pressure for strong, concentrated coffee at affordable price #moka
- [equipment] Manual lever machines like Flair or Robot provide espresso-style coffee with manual control #manual_espresso
## Kettles
- [equipment] Gooseneck kettles provide precision pouring control essential for pour over methods #kettles
- [feature] Variable temperature kettles allow precise temperature control for different roast levels #temp_control
- [feature] Flow restrictors can help beginners maintain consistent pour rates #pour_control
- [material] Electric kettles offer convenience and temperature stability #convenience
- [material] Stovetop kettles may be more durable but offer less temperature control #durability
## Accessories
- [equipment] Coffee scale with 0.1g precision helps maintain consistent ratios #measurement
- [equipment] Timer ensures consistent extraction times #consistency
- [equipment] Quality filters significantly impact flavor clarity and body #filters
- [equipment] Storage containers with one-way valves help preserve bean freshness #storage
- [equipment] Blind shaker or dosing cup reduces grinder mess and improves workflow #workflow
## Relations
- improves [[Coffee Brewing Methods]]
- affects [[Flavor Extraction]]
- requires [[Proper Maintenance]]
- enhances [[Home Coffee Setup]]
- part_of [[Coffee Knowledge Base]]
@@ -0,0 +1,78 @@
---
title: Coffee Bean Origins
type: note
permalink: coffee/coffee-bean-origins
tags:
- '#coffee'
- '#origins'
- '#beans'
- '#regions'
- '#demo'
---
# Coffee Bean Origins
An exploration of coffee-growing regions around the world and how geography, climate, and processing methods affect flavor profiles.
## Overview
Coffee beans are grown in various regions around the world, primarily in what's known as the "Coffee Belt" - the area between the Tropics of Cancer and Capricorn. The flavor characteristics of coffee beans are influenced by:
- Geographic region and climate
- Altitude
- Soil composition
- Variety of coffee plant
- Processing method
- Harvest and sorting practices
## Observations
- [principle] Higher altitude generally produces harder, denser beans with more complex acidity #altitude
- [region] Ethiopian beans often feature bright, fruity notes with floral aromatics #ethiopia
- [region] Colombian coffee typically offers balanced acidity with caramel sweetness and nutty undertones #colombia
- [region] Guatemalan coffee presents complex acidity with chocolate notes and sometimes spice characteristics #guatemala
- [region] Brazilian coffee tends toward nutty, chocolate notes with lower acidity and fuller body #brazil
- [region] Kenyan coffee is known for bright, wine-like acidity and berry or citrus notes #kenya
- [processing] Natural (dry) processing tends to create fruitier, more fermented flavors #processing
- [processing] Washed (wet) processing generally results in cleaner, brighter cups with more clarity #processing
- [processing] Honey processing creates a middle ground with some fruity notes while maintaining clarity #processing
- [factor] Shade-grown coffee typically develops more slowly, resulting in more complex flavors #cultivation
- [factor] Soil volcanic soil often imparts distinctive mineral characteristics to coffee #terroir
- [variety] Gesha/Geisha variety is known for exceptional floral and tea-like qualities #varieties
- [variety] Bourbon varieties often feature sweet, complex cup profiles #varieties
- [variety] Robusta beans have higher caffeine content but generally less complex flavor than Arabica #varieties
## Major Growing Regions
- [africa] Ethiopian coffees: Yirgacheffe, Sidamo, Harrar regions each with distinctive profiles #ethiopia
- [africa] Kenyan coffees: Often categorized by grade (AA, AB, etc.) based on bean size #kenya
- [americas] Colombian regions: Huila, Nariño, Antioquia each with unique characteristics #colombia
- [americas] Central American producers: Guatemala, Costa Rica, Panama known for balanced profiles #central_america
- [americas] Brazilian regions: Cerrado, Sul de Minas, Mogiana with varying profiles #brazil
- [asia] Indonesian islands: Sumatra, Java, Sulawesi producing earthy, full-bodied coffees #indonesia
- [asia] Vietnamese coffee: World's largest Robusta producer, often used in blends and commercial coffee #vietnam
## Processing Methods
- [natural] Beans dried inside the fruit, creating fruity, fermented notes and heavier body #processing
- [washed] Fruit removed before drying, resulting in cleaner cup with more pronounced acidity #processing
- [honey] Some fruit mucilage left on during drying, creates balanced sweetness and body #processing
- [wet-hulled] Unique to Indonesia, creates earthy, herbal, low-acid profiles #processing
- [experimental] Anaerobic fermentation, wine-yeast inoculation, and other newer methods #innovation
## Tasting Notes by Region
- [ethiopia] Blueberry, jasmine, bergamot, stone fruit, citrus #flavor_notes
- [kenya] Blackcurrant, tomato, tropical fruit, wine-like acidity #flavor_notes
- [colombia] Caramel, nuts, red apple, chocolate, balanced acidity #flavor_notes
- [guatemala] Chocolate, spice, green apple, balanced #flavor_notes
- [brazil] Nuts, chocolate, low acidity, full body #flavor_notes
- [indonesia] Earthy, herbal, spice, cedar, full body, low acidity #flavor_notes
## Relations
- influences [[Flavor Extraction]]
- pairs_with [[Coffee Brewing Methods]]
- affects [[Tasting Notes]]
- relates_to [[Specialty Coffee]]
- part_of [[Coffee Knowledge Base]]
@@ -0,0 +1,70 @@
---
title: Coffee Brewing Methods
type: note
permalink: coffee/coffee-brewing-methods
tags:
- '#coffee'
- '#brewing'
- '#methods'
- '#demo'
---
# Coffee Brewing Methods
An exploration of different coffee brewing techniques, their characteristics, and how they affect flavor extraction.
## Overview
Coffee brewing is both an art and a science. Different brewing methods extract different compounds from coffee beans, resulting in unique flavor profiles, body, and mouthfeel. The key variables in any brewing method are:
- Grind size
- Water temperature
- Brew time
- Coffee-to-water ratio
- Agitation/turbulence
## Observations
- [principle] Coffee extraction follows a predictable pattern: acids extract first, then sugars, then bitter compounds #extraction
- [method] Pour over methods generally produce cleaner, brighter cups with more distinct flavor notes #clarity
- [method] Immersion methods like French press create fuller body and more rounded flavors #body
- [technique] Water at 195-205°F (90-96°C) extracts optimal flavor compounds for most brewing methods #temperature
- [technique] Grind size directly correlates with ideal extraction time (finer = shorter, coarser = longer) #grind
- [preference] Medium-light roasts often showcase more origin characteristics in pour over methods #roast
- [equipment] Burr grinders produce more consistent particle size than blade grinders, resulting in more even extraction #gear
- [ratio] 1:15 to 1:17 coffee-to-water ratio (by weight) works well for most brew methods #brewing
- [science] Different brewing temperatures extract different chemical compounds from the beans #chemistry
- [technique] Bloom phase (pre-infusion with small amount of water) allows CO2 to escape and improves extraction #bloom
## Pour Over Methods
- [method] V60 produces very clean cup with excellent clarity of flavor #pourover
- [method] Chemex uses thicker filter paper, resulting in even cleaner cup with fewer oils #pourover
- [method] Kalita Wave provides more consistent extraction due to flat bottom design #pourover
- [technique] Concentric circular pouring pattern ensures even saturation of grounds #technique
- [timing] Most pour over methods complete in 2:30-3:30 total brew time #brewing
## Immersion Methods
- [method] French Press creates full-bodied cup with rich mouthfeel due to metal filter allowing oils to pass #immersion
- [method] AeroPress is versatile, capable of producing both espresso-like and filter-style coffee #immersion
- [method] Cold brew uses time instead of heat to extract, resulting in lower acidity #immersion
- [technique] French press ideal steep time is 4-5 minutes before plunging #timing
- [technique] AeroPress inverted method prevents dripping during extraction phase #technique
## Pressure Methods
- [method] Espresso uses 9 bars of pressure to force water through finely ground coffee #pressure
- [method] Moka pot uses steam pressure to push water through grounds, creating strong, concentrated coffee #pressure
- [technique] Espresso requires very fine grind, almost powder-like consistency #grind
- [timing] Espresso shots typically extract in 25-30 seconds #timing
- [principle] Pressure methods can extract compounds that aren't soluble in regular brewing methods #extraction
## Relations
- requires [[Proper Grinding Technique]]
- affects [[Flavor Extraction]]
- pairs_with [[Coffee Bean Origins]]
- uses [[Brewing Equipment]]
- influences [[Tasting Notes]]
- part_of [[Coffee Knowledge Base]]
@@ -0,0 +1,89 @@
---
title: Coffee Flavor Map
type: note
permalink: coffee/coffee-flavor-map
tags:
- '#coffee'
- '#visualization'
- '#canvas'
- '#demo'
---
# Coffee Flavor Map
A visual mapping of coffee flavor attributes, brewing methods, and their relationships. This note describes a canvas visualization that could be generated to demonstrate Basic Memory's visualization capabilities.
## Overview
The Coffee Flavor Map provides a visual representation of how different brewing methods, coffee origins, and equipment choices affect flavor outcomes. This canvas visualization helps users understand the complex relationships in coffee brewing and tasting.
## Canvas Visualization Elements
### Core Nodes
- **Flavor Attributes**: Acidity, Sweetness, Body, Clarity, Bitterness, Complexity
- **Brewing Methods**: Pour Over, French Press, AeroPress, Espresso, Moka Pot, Cold Brew
- **Origin Regions**: Ethiopia, Kenya, Colombia, Brazil, Guatemala, Indonesia
- **Equipment Elements**: Grinder Quality, Water Temperature, Brewing Device, Filter Type
### Node Connections
- Lines connecting brewing methods to their typical flavor outcomes
- Arrows showing how equipment choices affect extraction variables
- Connections between origins and their characteristic flavor profiles
- Highlighting of optimal brewing methods for different origins
### Visual Organization
- Flavor outcomes in the center
- Brewing methods on the left side
- Origins on the right side
- Equipment variables at the bottom
- Color coding by category (methods, origins, equipment, flavors)
## Using This Visualization
### For Coffee Exploration
- Identify which brewing methods might highlight the characteristics you prefer
- See which origins naturally pair well with your preferred brewing method
- Understand how equipment changes can modify flavor outcomes
- Visualize the complex interplay between all coffee variables
### As a Basic Memory Demo
- Demonstrates Canvas visualization capabilities
- Shows how relations can be visually mapped
- Illustrates complex knowledge organization
- Provides an intuitive way to navigate coffee knowledge
## How To Generate This Canvas
In a conversation with Claude, you could request:
```
Please create a canvas visualization mapping the relationships between coffee brewing methods, origins, and flavor outcomes. Show how different equipment and techniques influence extraction and resulting flavor profiles.
```
This would generate a `.canvas` file in your Basic Memory directory that could be opened with Obsidian for an interactive visualization of these coffee relationships.
## Example Visualization Snippets
### Pour Over Method Node
- Connected to: High Clarity, Bright Acidity, Medium Body
- Best pairs with: Ethiopian and Kenyan beans
- Equipment dependencies: Gooseneck Kettle, Paper Filter, Burr Grinder
### Ethiopian Coffee Node
- Characteristic flavors: Floral, Fruity, Bright
- Best brewing methods: Pour Over, AeroPress
- Challenging with: French Press (loses clarity of delicate notes)
### Grind Size Node
- Affects: Extraction Rate, Flavor Balance
- Fine grind increases: Extraction Speed, Surface Area
- Coarse grind increases: Flow Rate, Reduces Bitter Compounds
## Relations
- visualizes [[Coffee Knowledge Base]]
- relates_to [[Coffee Brewing Methods]]
- relates_to [[Coffee Bean Origins]]
- relates_to [[Flavor Extraction]]
- relates_to [[Tasting Notes]]
- demonstrates [[Canvas]]
@@ -0,0 +1,73 @@
---
title: Coffee Knowledge Base
type: note
permalink: coffee/coffee-knowledge-base
tags:
- '#coffee'
- '#index'
- '#demo'
- '#knowledge'
---
# Coffee Knowledge Base
A comprehensive collection of coffee knowledge, from bean origins to brewing methods to tasting notes. This knowledge base demonstrates Basic Memory's ability to organize and connect information in a meaningful way.
## Overview
This Coffee Knowledge Base captures key information about coffee, structured with semantic observations and relations that connect different aspects of coffee knowledge. It serves as both a useful reference for coffee enthusiasts and a demonstration of how Basic Memory organizes information.
## Key Topics
### Core Coffee Knowledge
- [[Coffee Brewing Methods]] - Different techniques for preparing coffee
- [[Coffee Bean Origins]] - Where coffee comes from and how region affects flavor
- [[Brewing Equipment]] - Tools and devices used to prepare coffee
- [[Flavor Extraction]] - The science of dissolving flavor compounds from coffee
- [[Tasting Notes]] - How to taste and describe coffee flavors
### Brewing Techniques
- Proper grinding is fundamental to good extraction
- Water quality significantly impacts flavor
- Different brewing methods highlight different characteristics
- Time, temperature, and grind size are the key variables to control
- Freshness of beans dramatically affects quality
### Coffee Preferences
- Light roasts preserve more origin characteristics and acidity
- Dark roasts emphasize body and chocolatey/roasted flavors
- Pour over methods highlight clarity and distinct flavor notes
- Immersion methods create fuller body and rounded flavor
- Personal preference matters more than "correctness"
## Using This Knowledge Base
### For Learning
Use this knowledge base to:
- Understand coffee fundamentals
- Explore connections between brewing methods and flavor outcomes
- Learn how different origins produce distinct flavor profiles
- Discover how equipment affects the brewing process
- Develop a vocabulary for describing coffee experiences
### As a Demo
This knowledge base demonstrates:
- Semantic knowledge organization with categories and relations
- Building connections between related concepts
- Creating a navigable knowledge graph
- Structuring information in a way both humans and AI assistants can understand
- How Basic Memory enables persistent knowledge across conversations
## Relations
- contains [[Coffee Brewing Methods]]
- contains [[Coffee Bean Origins]]
- contains [[Brewing Equipment]]
- contains [[Flavor Extraction]]
- contains [[Tasting Notes]]
- demonstrates [[Basic Memory Capabilities]]
@@ -0,0 +1,79 @@
---
title: Flavor Extraction
type: note
permalink: coffee/flavor-extraction
tags:
- '#coffee'
- '#extraction'
- '#brewing'
- '#science'
- '#demo'
---
# Flavor Extraction
Understanding the science of coffee extraction, how different compounds dissolve at different rates, and how to control extraction to achieve desired flavor profiles.
## Overview
Coffee extraction is the process of dissolving flavor compounds from ground coffee into water. The science of extraction is key to producing a balanced, flavorful cup. Extraction is affected by numerous variables including grind size, water temperature, contact time, agitation, and pressure.
## Observations
- [science] Coffee contains over 1,000 aroma compounds and hundreds of flavor compounds #chemistry
- [principle] Extraction occurs in a predictable sequence: acids → sugars → bitter compounds #extraction_order
- [principle] Under-extraction results in sour, bright, thin coffee lacking sweetness and body #under_extraction
- [principle] Over-extraction results in bitter, hollow, astringent flavors #over_extraction
- [principle] The goal is typically balanced extraction (18-22% of coffee solubles dissolved) #balanced_extraction
- [technique] Finer grind size increases extraction rate due to greater surface area #grind_size
- [technique] Higher water temperature increases extraction rate and solubility of compounds #temperature
- [technique] Longer contact time allows more complete extraction #brew_time
- [technique] Agitation (stirring, turbulence) increases extraction rate by preventing saturation zones #agitation
- [technique] Pressure (as in espresso) can extract compounds that aren't water-soluble at atmospheric pressure #pressure
## Factors Affecting Extraction
- [factor] Grind size: Finer = faster extraction, coarser = slower extraction #grind
- [factor] Water temperature: Higher = faster extraction, lower = slower extraction #temperature
- [factor] Contact time: Longer = more extraction, shorter = less extraction #time
- [factor] Agitation: More = faster extraction, less = slower extraction #agitation
- [factor] Coffee-to-water ratio: More coffee = lower extraction percentage #ratio
- [factor] Water quality: Mineral content affects extraction of different compounds #water
- [factor] Roast level: Darker roasts extract more easily than lighter roasts #roast
- [factor] Bean density: Denser beans (typically high-altitude) require more effort to extract #density
- [factor] Freshness: Freshly roasted coffee extracts differently than aged coffee #freshness
- [factor] Brewing method: Different methods extract different compounds at different rates #method
## Signs of Extraction Levels
- [under] Sour, bright, lack of sweetness, thin body, quick finish #flavor
- [under] Typically from: too coarse grind, too cool water, too short brew time #causes
- [balanced] Sweet, bright but not sour, rich but not bitter, pleasing finish #flavor
- [balanced] Achieved through proper ratio of variables for given coffee #technique
- [over] Bitter, hollow, astringent, dry finish, sometimes papery #flavor
- [over] Typically from: too fine grind, too hot water, too long brew time #causes
## Measuring Extraction
- [method] Total Dissolved Solids (TDS) meters measure concentration of coffee solution #measurement
- [method] Extraction yield = percentage of coffee grounds dissolved in the final brew #calculation
- [preference] Specialty coffee typically targets 18-22% extraction yield #standards
- [preference] Some specialty light roasts may taste best at higher extraction percentages #speciality
## Controlling Extraction
- [technique] Adjust grind size as primary extraction control #basics
- [technique] Use water temperature to fine-tune extraction #fine_tuning
- [technique] Modify pour technique to control agitation level #technique
- [technique] Adjust coffee-to-water ratio to balance strength and extraction #ratio
- [technique] Pre-infusion (blooming) helps achieve even extraction #blooming
- [technique] Pulse pouring creates different extraction dynamics than continuous pour #pour_technique
## Relations
- affected_by [[Coffee Brewing Methods]]
- influenced_by [[Coffee Bean Origins]]
- enhanced_by [[Brewing Equipment]]
- determines [[Tasting Notes]]
- requires [[Water Quality]]
- part_of [[Coffee Knowledge Base]]
@@ -0,0 +1,161 @@
{
"nodes":[
{
"id":"node-5",
"type":"text",
"text":"## Main Pour Phase\n- Use concentric circles from center outward\n- Maintain steady, controlled flow rate\n- Avoid pouring directly on filter walls\n- Keep water level consistent\n- Pulse pour in 2-3 stages (or continuous pour)\n- Total brew time target: 2:30-3:30",
"position":{"x":450,"y":200},
"x":530,
"y":-100,
"width":300,
"height":200,
"color":"1"
},
{
"id":"node-8",
"type":"text",
"text":"## Drawdown\n- Allow water to fully drain\n- Flat bed indicates even extraction\n- Total brew time should be ~2:30-3:30\n- Remove filter promptly after brewing",
"position":{"x":450,"y":700},
"x":540,
"y":375,
"width":300,
"height":150,
"color":"1"
},
{
"id":"node-6",
"type":"text",
"text":"## Pour Pattern\n\nConcentric circles ensure even saturation of coffee grounds. Begin at the center and work outward, avoiding filter edges. Pour height of 1-2 inches above coffee bed.",
"position":{"x":250,"y":450},
"x":960,
"y":25,
"width":300,
"height":150,
"color":"5"
},
{
"id":"node-12",
"type":"text",
"text":"## Tasting Notes\n\n- Balanced extraction: sweet, bright, complex\n- Under-extraction: sour, lacking sweetness\n- Over-extraction: bitter, astringent, hollow\n\nTake notes on each brew to track improvements and preferences.",
"position":{"x":-250,"y":700},
"x":1020,
"y":420,
"width":300,
"height":150,
"color":"6"
},
{
"id":"node-9",
"type":"text",
"text":"## Troubleshooting\n\n- Too sour/weak: Grind finer, water hotter, increase brew time\n- Too bitter/strong: Grind coarser, water cooler, decrease brew time\n- Uneven extraction: Improve pour technique, better grinder\n- Channeling: More careful pouring, better bloom\n- Slow drawdown: Coarser grind, less agitation\n- Fast drawdown: Finer grind, more careful pouring",
"position":{"x":100,"y":700},
"x":30,
"y":570,
"width":300,
"height":200,
"color":"6"
},
{
"id":"node-3",
"type":"text",
"text":"## Preparation\n- Heat water to 195-205°F (90-96°C)\n- Measure coffee (1:15 to 1:17 ratio)\n- Medium-fine grind (sea salt consistency)\n- Rinse filter with hot water\n- Discard rinse water\n- Add ground coffee to filter\n- Level coffee bed",
"position":{"x":-250,"y":200},
"x":30,
"y":-500,
"width":300,
"height":200,
"color":"3"
},
{
"id":"node-1",
"type":"text",
"text":"# Perfect Pour Over Method\n\nA systematic approach to brewing exceptional pour over coffee by controlling key variables and following proper technique.",
"position":{"x":0,"y":0},
"x":-580,
"y":-760,
"width":400,
"height":120,
"color":"4"
},
{
"id":"node-10",
"type":"text",
"text":"## Grinding Parameters\n\n- V60: Medium-fine (sea salt)\n- Chemex: Medium (slightly coarser than V60)\n- Kalita Wave: Medium (between V60 and Chemex)\n\nConsistent particle size is critical; use quality burr grinder.",
"position":{"x":-250,"y":450},
"x":30,
"y":-910,
"width":300,
"height":150,
"color":"5"
},
{
"id":"node-2",
"type":"text",
"text":"## Equipment Setup\n- Clean V60/Chemex/Kalita Wave\n- Paper filter (rinsed)\n- Server/mug\n- Scale with timer\n- Gooseneck kettle\n- Burr grinder\n- Fresh coffee beans",
"position":{"x":-600,"y":200},
"x":-530,
"y":-500,
"width":300,
"height":200,
"color":"3"
},
{
"id":"node-4",
"type":"text",
"text":"## The Bloom\n- Start timer\n- Pour 2-3x coffee weight water\n- Ensure all grounds are saturated\n- Gentle stir or swirl if needed\n- Allow 30-45 seconds for degassing\n- Look for bubbling and dome formation",
"position":{"x":100,"y":200},
"x":530,
"y":-500,
"width":300,
"height":200,
"color":"1"
},
{
"id":"node-13",
"type":"text",
"text":"## Coffee-to-Water Ratio\n\n- Standard: 1:15 to 1:17 (coffee:water)\n- Stronger cup: 1:15 (67g/L)\n- Medium cup: 1:16 (62.5g/L)\n- Lighter cup: 1:17 (58.8g/L)\n\nExample: For 300ml water, use ~18-20g coffee",
"position":{"x":-600,"y":700},
"x":30,
"y":-100,
"width":300,
"height":150,
"color":"6"
},
{
"id":"node-7",
"type":"text",
"text":"## Brew Time Guideline\n\n- Bloom: 30-45 seconds\n- First pour: 1:00-1:15\n- Second pour: 1:45-2:00\n- Final pour: 2:15-2:30\n- Drawdown complete: 2:45-3:30\n\nAdjust for taste: shorter for lighter, longer for stronger",
"position":{"x":600,"y":450},
"x":-80,
"y":220,
"width":300,
"height":200,
"color":"5"
},
{
"id":"node-11",
"type":"text",
"text":"## Water Quality\n\n- Clean, filtered water\n- No strong odors or flavors\n- Ideal TDS: 75-150 ppm\n- Ideal pH: 7.0-7.5\n- Avoid distilled water (lacks minerals)\n- Avoid hard water (scaling issues)",
"position":{"x":-600,"y":450},
"x":-780,
"y":-125,
"width":300,
"height":150,
"color":"5"
}
],
"edges":[
{"id":"edge-1","fromNode":"node-1","fromSide":"bottom","toNode":"node-2","toSide":"top","label":"Step 1"},
{"id":"edge-2","fromNode":"node-2","fromSide":"right","toNode":"node-3","toSide":"left","label":"Step 2"},
{"id":"edge-3","fromNode":"node-3","fromSide":"right","toNode":"node-4","toSide":"left","label":"Step 3"},
{"id":"edge-4","fromNode":"node-4","fromSide":"bottom","toNode":"node-5","toSide":"top","label":"Step 4"},
{"id":"edge-5","fromNode":"node-5","fromSide":"bottom","toNode":"node-8","toSide":"top","label":"Step 5"},
{"id":"edge-6","fromNode":"node-5","fromSide":"left","toNode":"node-7","toSide":"right","label":"Timing"},
{"id":"edge-7","fromNode":"node-5","fromSide":"right","toNode":"node-6","toSide":"left","label":"Technique"},
{"id":"edge-8","fromNode":"node-8","fromSide":"left","toNode":"node-9","toSide":"right","label":"if problems"},
{"id":"edge-9","fromNode":"node-3","fromSide":"top","toNode":"node-10","toSide":"bottom","label":"Grinding details"},
{"id":"edge-10","fromNode":"node-2","fromSide":"bottom","toNode":"node-11","toSide":"right","label":"Water details"},
{"id":"edge-11","fromNode":"node-8","fromSide":"right","toNode":"node-12","toSide":"left","label":"Evaluate"},
{"id":"edge-12","fromNode":"node-3","fromSide":"bottom","toNode":"node-13","toSide":"top","label":"Ratio details"}
]
}
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---
title: Tasting Notes
type: note
permalink: coffee/tasting-notes
tags:
- '#coffee'
- '#tasting'
- '#flavor'
- '#cupping'
- '#demo'
---
# Tasting Notes
How to taste and evaluate coffee, identify flavor characteristics, and develop a personal coffee palate.
## Overview
Coffee tasting, or "cupping" in professional contexts, is the practice of observing the tastes and aromas of brewed coffee. Developing a coffee palate helps identify preferences, communicate about coffee experiences, and better understand how brewing variables affect the cup.
## Observations
- [principle] Flavor perception includes taste, aroma, mouthfeel, and retronasal perception #sensory
- [principle] Our taste buds can only perceive sweet, sour, salty, bitter, and umami #taste
- [principle] Most of what we call "flavor" is actually aroma detected retronasally #aroma
- [technique] Professional coffee tasting (cupping) uses a standardized protocol for consistency #cupping
- [technique] Slurping coffee aerates it and spreads it across all taste receptors #technique
- [technique] Allowing coffee to cool reveals different flavor notes at different temperatures #temperature
## Coffee Flavor Wheel
- [tool] The SCA Coffee Flavor Wheel provides a standardized vocabulary for describing coffee #flavor_wheel
- [category] Primary categories include: Fruity, Floral, Sweet, Nutty/Cocoa, Spice, Roasted, Other #categories
- [subcategory] Fruity breaks down into: Berry, Dried Fruit, Citrus Fruit, Stone Fruit, Tropical Fruit, etc. #fruit_notes
- [subcategory] Floral includes: Floral, Black Tea, Chamomile, Rose, Jasmine, etc. #floral_notes
- [subcategory] Sweet includes: Brown Sugar, Molasses, Honey, Maple Syrup, Vanilla, etc. #sweet_notes
- [subcategory] Nutty/Cocoa includes: Nut, Cocoa, Dark Chocolate, Chocolate, etc. #nutty_notes
- [subcategory] Spice includes: Brown Spice, Pepper, Anise, Nutmeg, Cinnamon, etc. #spice_notes
## Basic Tasting Components
- [component] Acidity: The bright, tangy quality (not sourness from under-extraction) #acidity
- [component] Sweetness: The pleasant, sugary quality balancing other elements #sweetness
- [component] Body: The physical mouthfeel and weight of the coffee #body
- [component] Finish/Aftertaste: The flavor that lingers after swallowing #finish
- [component] Balance: How well all elements work together #balance
- [component] Complexity: The range and layers of distinct flavors #complexity
- [component] Cleanliness: Absence of defects or off-flavors #cleanliness
## Common Flavor Notes by Origin
- [ethiopia] Blueberry, jasmine, bergamot, lemon, tea-like #flavor_notes
- [kenya] Blackcurrant, grapefruit, tomato-like acidity, winey #flavor_notes
- [colombia] Caramel, red apple, nuts, chocolate, balanced acidity #flavor_notes
- [guatemala] Chocolate, spice, apple, medium acidity #flavor_notes
- [brazil] Nuts, chocolate, low-to-medium acidity, full body #flavor_notes
- [indonesia] Earthy, herbal, spice, cedar, full body, low acidity #flavor_notes
- [costa_rica] Clean, bright, citrus, balanced, light chocolate #flavor_notes
## Developing Your Palate
- [technique] Taste coffees side-by-side to identify differences #comparison
- [technique] Try describing flavors before looking at roaster's notes #blind_tasting
- [technique] Keep a coffee journal with detailed notes about each coffee #journaling
- [technique] Explore different processing methods of the same origin #processing
- [technique] Try the same coffee brewed with different methods #brewing_comparison
- [technique] Use reference flavors (actual fruits, chocolates, etc.) to calibrate your palate #calibration
## Personal Coffee Experiences
- [experience] Ethiopian Yirgacheffe prepared as pour over: intense blueberry, jasmine aromatics, tea-like body
- [experience] Sumatra Mandheling in French press: earthy, cedar, herbal, tobacco, full body
- [experience] Panama Gesha as pour over: intense floral notes, jasmine, bergamot, delicate body
- [experience] Brazil Cerrado as espresso: nutty, chocolate, caramel, low acidity, great crema
- [experience] Kenya AA as pour over: bright blackcurrant, tomato-like acidity, winey finish
## Relations
- determined_by [[Flavor Extraction]]
- influenced_by [[Coffee Bean Origins]]
- varies_with [[Coffee Brewing Methods]]
- enhanced_by [[Proper Grinding Technique]]
- documented_in [[Coffee Journal]]
- part_of [[Coffee Knowledge Base]]
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# Basic Memory Installer
This installer configures Basic Memory to work with Claude Desktop.
## Installation
1. Download the latest installer from the [releases page](https://github.com/basicmachines-co/basic-memory/releases)
2. Unzip the downloaded file
3. Since the app is currently unsigned, you'll need to:
On your Mac, choose Apple menu > System Settings, then click Privacy & Security in the sidebar. (You may need to
scroll down.)
Go to Security, then click Open.
Click Open Anyway.
This button is available for about an hour after you try to open the app.
Enter your login password, then click OK.
https://support.apple.com/guide/mac-help/apple-cant-check-app-for-malicious-software-mchleab3a043/mac
5. Restart Claude Desktop
The warning only appears the first time you open the app. Future updates will include proper code signing.
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<?xml version="1.0" encoding="UTF-8"?>
<svg viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg">
<!-- Background -->
<rect x="0" y="0" width="512" height="512" rx="64" fill="#111111"/>
<!-- Define arrowhead marker -->
<defs>
<marker id="arrowhead"
markerWidth="10"
markerHeight="10"
refX="8"
refY="5"
orient="auto">
<path d="M 0 0 L 10 5 L 0 10 Z"
fill="#00cc00"/>
</marker>
</defs>
<!-- State 1 (initial) -->
<circle cx="156" cy="256" r="30" fill="none" stroke="#00cc00" stroke-width="3"/>
<!-- State 2 (accept) -->
<circle cx="356" cy="176" r="34" fill="none" stroke="#00cc00" stroke-width="3"/>
<circle cx="356" cy="176" r="28" fill="none" stroke="#00cc00" stroke-width="3"/>
<!-- State 3 (accept) -->
<circle cx="356" cy="336" r="34" fill="none" stroke="#00cc00" stroke-width="3"/>
<circle cx="356" cy="336" r="28" fill="none" stroke="#00cc00" stroke-width="3"/>
<!-- Initial arrow -->
<path d="M 96 256 L 126 256"
stroke="#00cc00" stroke-width="3" fill="none"
marker-end="url(#arrowhead)"/>
<!-- State transitions -->
<!-- 1 -> 2 -->
<path d="M 180 240
Q 260 200, 320 176"
stroke="#00cc00" stroke-width="3" fill="none"
marker-end="url(#arrowhead)"/>
<!-- 1 -> 3 -->
<path d="M 180 272
Q 260 312, 320 336"
stroke="#00cc00" stroke-width="3" fill="none"
marker-end="url(#arrowhead)"/>
<!-- Self loops -->
<path d="M 356 142
Q 396 142, 396 176
Q 396 210, 356 210
Q 316 210, 316 176
Q 316 142, 356 142"
stroke="#00cc00" stroke-width="2" fill="none"
marker-end="url(#arrowhead)"/>
<path d="M 356 302
Q 396 302, 396 336
Q 396 370, 356 370
Q 316 370, 316 336
Q 316 302, 356 302"
stroke="#00cc00" stroke-width="2" fill="none"
marker-end="url(#arrowhead)"/>
</svg>

After

Width:  |  Height:  |  Size: 2.0 KiB

+93
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@@ -0,0 +1,93 @@
import json
import subprocess
import sys
from pathlib import Path
# Use tkinter for GUI alerts on macOS
if sys.platform == "darwin":
import tkinter as tk
from tkinter import messagebox
def ensure_uv_installed():
"""Check if uv is installed, install if not."""
try:
subprocess.run(["uv", "--version"], capture_output=True, check=True)
except (subprocess.CalledProcessError, FileNotFoundError):
print("Installing uv package manager...")
subprocess.run(
[
"curl",
"-LsSf",
"https://astral.sh/uv/install.sh",
"|",
"sh",
],
shell=True,
)
def get_config_path():
"""Get Claude Desktop config path for current platform."""
if sys.platform == "darwin":
return Path.home() / "Library/Application Support/Claude/claude_desktop_config.json"
elif sys.platform == "win32":
return Path.home() / "AppData/Roaming/Claude/claude_desktop_config.json"
else:
raise RuntimeError(f"Unsupported platform: {sys.platform}")
def update_claude_config():
"""Update Claude Desktop config to include basic-memory."""
config_path = get_config_path()
config_path.parent.mkdir(parents=True, exist_ok=True)
# Load existing config or create new
if config_path.exists():
config = json.loads(config_path.read_text())
else:
config = {"mcpServers": {}}
# Add/update basic-memory config
config["mcpServers"]["basic-memory"] = {
"command": "uvx",
"args": ["basic-memory@latest", "mcp"],
}
# Write back config
config_path.write_text(json.dumps(config, indent=2))
def print_completion_message():
"""Show completion message with helpful tips."""
message = """Installation complete! Basic Memory is now available in Claude Desktop.
Please restart Claude Desktop for changes to take effect.
Quick Start:
1. You can run sync directly using: uvx basic-memory sync
2. Optionally, install globally with: uv pip install basic-memory
Built with ♥️ by Basic Machines."""
if sys.platform == "darwin":
# Show GUI message on macOS
root = tk.Tk()
root.withdraw() # Hide the main window
messagebox.showinfo("Basic Memory", message)
root.destroy()
else:
# Fallback to console output
print(message)
def main():
print("Welcome to Basic Memory installer")
ensure_uv_installed()
print("Configuring Claude Desktop...")
update_claude_config()
print_completion_message()
if __name__ == "__main__":
main()
+27
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@@ -0,0 +1,27 @@
#!/bin/bash
# Convert SVG to PNG at various required sizes
rsvg-convert -h 16 -w 16 icon.svg > icon_16x16.png
rsvg-convert -h 32 -w 32 icon.svg > icon_32x32.png
rsvg-convert -h 128 -w 128 icon.svg > icon_128x128.png
rsvg-convert -h 256 -w 256 icon.svg > icon_256x256.png
rsvg-convert -h 512 -w 512 icon.svg > icon_512x512.png
# Create iconset directory
mkdir -p Basic.iconset
# Move files into iconset with Mac-specific names
cp icon_16x16.png Basic.iconset/icon_16x16.png
cp icon_32x32.png Basic.iconset/icon_16x16@2x.png
cp icon_32x32.png Basic.iconset/icon_32x32.png
cp icon_128x128.png Basic.iconset/icon_32x32@2x.png
cp icon_256x256.png Basic.iconset/icon_128x128.png
cp icon_512x512.png Basic.iconset/icon_256x256.png
cp icon_512x512.png Basic.iconset/icon_512x512.png
# Convert iconset to icns
iconutil -c icns Basic.iconset
# Clean up
rm -rf Basic.iconset
rm icon_*.png
+40
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@@ -0,0 +1,40 @@
from cx_Freeze import setup, Executable
import sys
# Build options for all platforms
build_exe_options = {
"packages": ["json", "pathlib"],
"excludes": ["unittest", "pydoc", "test"],
}
# Platform-specific options
if sys.platform == "win32":
base = "Win32GUI" # Use GUI base for Windows
build_exe_options.update(
{
"include_msvcr": True,
}
)
target_name = "Basic Memory Installer.exe"
else: # darwin
base = None # Don't use GUI base for macOS
target_name = "Basic Memory Installer"
executables = [
Executable(script="installer.py", target_name=target_name, base=base, icon="Basic.icns")
]
setup(
name="basic-memory",
version=open("../pyproject.toml").read().split('version = "', 1)[1].split('"', 1)[0],
description="Basic Memory - Local-first knowledge management",
options={
"build_exe": build_exe_options,
"bdist_mac": {
"bundle_name": "Basic Memory Installer",
"iconfile": "Basic.icns",
"codesign_identity": "-", # Force ad-hoc signing
},
},
executables=executables,
)
-308
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@@ -1,308 +0,0 @@
# Basic Memory - Modern Command Runner
# Install dependencies
install:
uv pip install -e ".[dev]"
uv sync
@echo ""
@echo "💡 Remember to activate the virtual environment by running: source .venv/bin/activate"
# ==============================================================================
# DATABASE BACKEND TESTING
# ==============================================================================
# Basic Memory supports dual database backends (SQLite and Postgres).
# By default, tests run against SQLite (fast, no dependencies).
# Set BASIC_MEMORY_TEST_POSTGRES=1 to run against Postgres (uses testcontainers).
#
# Quick Start:
# just test # Run all tests against SQLite (default)
# just test-sqlite # Run all tests against SQLite
# just test-postgres # Run all tests against Postgres (testcontainers)
# just test-unit-sqlite # Run unit tests against SQLite
# just test-unit-postgres # Run unit tests against Postgres
# just test-int-sqlite # Run integration tests against SQLite
# just test-int-postgres # Run integration tests against Postgres
#
# CI runs both in parallel for faster feedback.
# ==============================================================================
# Run all tests against SQLite and Postgres
test: test-sqlite test-postgres
# Run all tests against SQLite
test-sqlite: test-unit-sqlite test-int-sqlite
# Run all tests against Postgres (uses testcontainers)
test-postgres: test-unit-postgres test-int-postgres
# Run unit tests against SQLite
test-unit-sqlite:
BASIC_MEMORY_ENV=test uv run pytest -p pytest_mock -v --no-cov tests
# Run unit tests against Postgres
test-unit-postgres:
BASIC_MEMORY_ENV=test BASIC_MEMORY_TEST_POSTGRES=1 uv run pytest -p pytest_mock -v --no-cov tests
# Run integration tests against SQLite
test-int-sqlite:
uv run pytest -p pytest_mock -v --no-cov test-int
# Run integration tests against Postgres
# Note: Uses timeout due to FastMCP Client + asyncpg cleanup hang (tests pass, process hangs on exit)
# See: https://github.com/jlowin/fastmcp/issues/1311
test-int-postgres:
#!/usr/bin/env bash
set -euo pipefail
# Use gtimeout (macOS/Homebrew) or timeout (Linux)
TIMEOUT_CMD=$(command -v gtimeout || command -v timeout || echo "")
if [[ -n "$TIMEOUT_CMD" ]]; then
$TIMEOUT_CMD --signal=KILL 600 bash -c 'BASIC_MEMORY_TEST_POSTGRES=1 uv run pytest -p pytest_mock -v --no-cov test-int' || test $? -eq 137
else
echo "⚠️ No timeout command found, running without timeout..."
BASIC_MEMORY_TEST_POSTGRES=1 uv run pytest -p pytest_mock -v --no-cov test-int
fi
# Reset Postgres test database (drops and recreates schema)
# Useful when Alembic migration state gets out of sync during development
# Uses credentials from docker-compose-postgres.yml
postgres-reset:
docker exec basic-memory-postgres psql -U ${POSTGRES_USER:-basic_memory_user} -d ${POSTGRES_TEST_DB:-basic_memory_test} -c "DROP SCHEMA public CASCADE; CREATE SCHEMA public;"
@echo "✅ Postgres test database reset"
# Run Alembic migrations manually against Postgres test database
# Useful for debugging migration issues
# Uses credentials from docker-compose-postgres.yml (can override with env vars)
postgres-migrate:
@cd src/basic_memory/alembic && \
BASIC_MEMORY_DATABASE_BACKEND=postgres \
BASIC_MEMORY_DATABASE_URL=${POSTGRES_TEST_URL:-postgresql+asyncpg://basic_memory_user:dev_password@localhost:5433/basic_memory_test} \
uv run alembic upgrade head
@echo "✅ Migrations applied to Postgres test database"
# Run Windows-specific tests only (only works on Windows platform)
# These tests verify Windows-specific database optimizations (locking mode, NullPool)
# Will be skipped automatically on non-Windows platforms
test-windows:
uv run pytest -p pytest_mock -v --no-cov -m windows tests test-int
# Run benchmark tests only (performance testing)
# These are slow tests that measure sync performance with various file counts
# Excluded from default test runs to keep CI fast
test-benchmark:
uv run pytest -p pytest_mock -v --no-cov -m benchmark tests test-int
# Run all tests including Windows, Postgres, and Benchmarks (for CI/comprehensive testing)
# Use this before releasing to ensure everything works across all backends and platforms
test-all:
uv run pytest -p pytest_mock -v --no-cov tests test-int
# Generate HTML coverage report
coverage:
#!/usr/bin/env bash
set -euo pipefail
uv run coverage erase
echo "🔎 Coverage (SQLite)..."
BASIC_MEMORY_ENV=test uv run coverage run --source=basic_memory -m pytest -p pytest_mock -v --no-cov tests test-int
echo "🔎 Coverage (Postgres via testcontainers)..."
# Note: Uses timeout due to FastMCP Client + asyncpg cleanup hang (tests pass, process hangs on exit)
# See: https://github.com/jlowin/fastmcp/issues/1311
TIMEOUT_CMD=$(command -v gtimeout || command -v timeout || echo "")
if [[ -n "$TIMEOUT_CMD" ]]; then
$TIMEOUT_CMD --signal=KILL 600 bash -c 'BASIC_MEMORY_ENV=test BASIC_MEMORY_TEST_POSTGRES=1 uv run coverage run --source=basic_memory -m pytest -p pytest_mock -v --no-cov -m postgres tests test-int' || test $? -eq 137
else
echo "⚠️ No timeout command found, running without timeout..."
BASIC_MEMORY_ENV=test BASIC_MEMORY_TEST_POSTGRES=1 uv run coverage run --source=basic_memory -m pytest -p pytest_mock -v --no-cov -m postgres tests test-int
fi
echo "🧩 Combining coverage data..."
uv run coverage combine
uv run coverage report -m
uv run coverage html
echo "Coverage report generated in htmlcov/index.html"
# Lint and fix code (calls fix)
lint: fix
# Lint and fix code
fix:
uv run ruff check --fix --unsafe-fixes src tests test-int
# Type check code
typecheck:
uv run pyright
# Clean build artifacts and cache files
clean:
find . -type f -name '*.pyc' -delete
find . -type d -name '__pycache__' -exec rm -r {} +
rm -rf installer/build/ installer/dist/ dist/
rm -f rw.*.dmg .coverage.*
# Format code with ruff
format:
uv run ruff format .
# Run MCP inspector tool
run-inspector:
npx @modelcontextprotocol/inspector
# Update all dependencies to latest versions
update-deps:
uv sync --upgrade
# Run all code quality checks and tests
check: lint format typecheck test
# Generate Alembic migration with descriptive message
migration message:
cd src/basic_memory/alembic && alembic revision --autogenerate -m "{{message}}"
# Create a stable release (e.g., just release v0.13.2)
release version:
#!/usr/bin/env bash
set -euo pipefail
# Validate version format
if [[ ! "{{version}}" =~ ^v[0-9]+\.[0-9]+\.[0-9]+$ ]]; then
echo "❌ Invalid version format. Use: v0.13.2"
exit 1
fi
# Extract version number without 'v' prefix
VERSION_NUM=$(echo "{{version}}" | sed 's/^v//')
echo "🚀 Creating stable release {{version}}"
# Pre-flight checks
echo "📋 Running pre-flight checks..."
if [[ -n $(git status --porcelain) ]]; then
echo "❌ Uncommitted changes found. Please commit or stash them first."
exit 1
fi
if [[ $(git branch --show-current) != "main" ]]; then
echo "❌ Not on main branch. Switch to main first."
exit 1
fi
# Check if tag already exists
if git tag -l "{{version}}" | grep -q "{{version}}"; then
echo "❌ Tag {{version}} already exists"
exit 1
fi
# Run quality checks
echo "🔍 Running lint checks..."
just lint
just typecheck
# Update version in __init__.py
echo "📝 Updating version in __init__.py..."
sed -i.bak "s/__version__ = \".*\"/__version__ = \"$VERSION_NUM\"/" src/basic_memory/__init__.py
rm -f src/basic_memory/__init__.py.bak
# Update version in server.json (MCP registry metadata)
echo "📝 Updating version in server.json..."
sed -i.bak "s/\"version\": \"[^\"]*\"/\"version\": \"$VERSION_NUM\"/g" server.json
rm -f server.json.bak
# Commit version update
git add src/basic_memory/__init__.py server.json
git commit -m "chore: update version to $VERSION_NUM for {{version}} release"
# Create and push tag
echo "🏷️ Creating tag {{version}}..."
git tag "{{version}}"
echo "📤 Pushing to GitHub..."
git push origin main
git push origin "{{version}}"
echo "✅ Release {{version}} created successfully!"
echo "📦 GitHub Actions will build and publish to PyPI"
echo "🔗 Monitor at: https://github.com/basicmachines-co/basic-memory/actions"
echo ""
echo "📝 REMINDER: Post-release tasks:"
echo " 1. docs.basicmemory.com - Add release notes to src/pages/latest-releases.mdx"
echo " 2. basicmachines.co - Update version in src/components/sections/hero.tsx"
echo " 3. MCP Registry - Run: mcp-publisher publish"
echo " See: .claude/commands/release/release.md for detailed instructions"
# Create a beta release (e.g., just beta v0.13.2b1)
beta version:
#!/usr/bin/env bash
set -euo pipefail
# Validate version format (allow beta/rc suffixes)
if [[ ! "{{version}}" =~ ^v[0-9]+\.[0-9]+\.[0-9]+(b[0-9]+|rc[0-9]+)$ ]]; then
echo "❌ Invalid beta version format. Use: v0.13.2b1 or v0.13.2rc1"
exit 1
fi
# Extract version number without 'v' prefix
VERSION_NUM=$(echo "{{version}}" | sed 's/^v//')
echo "🧪 Creating beta release {{version}}"
# Pre-flight checks
echo "📋 Running pre-flight checks..."
if [[ -n $(git status --porcelain) ]]; then
echo "❌ Uncommitted changes found. Please commit or stash them first."
exit 1
fi
if [[ $(git branch --show-current) != "main" ]]; then
echo "❌ Not on main branch. Switch to main first."
exit 1
fi
# Check if tag already exists
if git tag -l "{{version}}" | grep -q "{{version}}"; then
echo "❌ Tag {{version}} already exists"
exit 1
fi
# Run quality checks
echo "🔍 Running lint checks..."
just lint
just typecheck
# Update version in __init__.py
echo "📝 Updating version in __init__.py..."
sed -i.bak "s/__version__ = \".*\"/__version__ = \"$VERSION_NUM\"/" src/basic_memory/__init__.py
rm -f src/basic_memory/__init__.py.bak
# Update version in server.json (MCP registry metadata)
echo "📝 Updating version in server.json..."
sed -i.bak "s/\"version\": \"[^\"]*\"/\"version\": \"$VERSION_NUM\"/g" server.json
rm -f server.json.bak
# Commit version update
git add src/basic_memory/__init__.py server.json
git commit -m "chore: update version to $VERSION_NUM for {{version}} beta release"
# Create and push tag
echo "🏷️ Creating tag {{version}}..."
git tag "{{version}}"
echo "📤 Pushing to GitHub..."
git push origin main
git push origin "{{version}}"
echo "✅ Beta release {{version}} created successfully!"
echo "📦 GitHub Actions will build and publish to PyPI as pre-release"
echo "🔗 Monitor at: https://github.com/basicmachines-co/basic-memory/actions"
echo "📥 Install with: uv tool install basic-memory --pre"
echo ""
echo "📝 REMINDER: For stable releases, update documentation sites:"
echo " 1. docs.basicmemory.com - Add release notes to src/pages/latest-releases.mdx"
echo " 2. basicmachines.co - Update version in src/components/sections/hero.tsx"
echo " See: .claude/commands/release/release.md for detailed instructions"
# List all available recipes
default:
@just --list
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@@ -1,128 +0,0 @@
# Basic Memory Installation Guide for LLMs
This guide is specifically designed to help AI assistants like Cline install and configure Basic Memory. Follow these
steps in order.
## Installation Steps
### 1. Install Basic Memory Package
Use one of the following package managers to install:
```bash
# Install with uv (recommended)
uv tool install basic-memory
# Or with pip
pip install basic-memory
```
### 2. Configure MCP Server
Add the following to your config:
```json
{
"mcpServers": {
"basic-memory": {
"command": "uvx",
"args": [
"basic-memory",
"mcp"
]
}
}
}
```
For Claude Desktop, this file is located at:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
### 3. Start Synchronization (optional)
To synchronize files in real-time, run:
```bash
basic-memory sync --watch
```
Or for a one-time sync:
```bash
basic-memory sync
```
## Configuration Options
### Custom Directory
To use a directory other than the default `~/basic-memory`:
```bash
basic-memory project add custom-project /path/to/your/directory
basic-memory project default custom-project
```
### Multiple Projects
To manage multiple knowledge bases:
```bash
# List all projects
basic-memory project list
# Add a new project
basic-memory project add work ~/work-basic-memory
# Set default project
basic-memory project default work
```
## Importing Existing Data
### From Claude.ai
```bash
basic-memory import claude conversations path/to/conversations.json
basic-memory import claude projects path/to/projects.json
```
### From ChatGPT
```bash
basic-memory import chatgpt path/to/conversations.json
```
### From MCP Memory Server
```bash
basic-memory import memory-json path/to/memory.json
```
## Troubleshooting
If you encounter issues:
1. Check that Basic Memory is properly installed:
```bash
basic-memory --version
```
2. Verify the sync process is running:
```bash
ps aux | grep basic-memory
```
3. Check sync output for errors:
```bash
basic-memory sync --verbose
```
4. Check log output:
```bash
cat ~/.basic-memory/basic-memory.log
```
For more detailed information, refer to the [full documentation](https://memory.basicmachines.co/).
+378
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@@ -0,0 +1,378 @@
{"type":"entity","name":"Paul","entityType":"person","observations":["Software developer combining DIY ethics, Free Software principles, and theoretical computer science","Created the Basic Machines project","Values authentic exchange of ideas","Approaches AI interaction with emphasis on genuine technical discussion","Comfortable with uncertainty and open dialogue","Balances practical implementation with broader implications"]}
{"type":"entity","name":"Basic_Machines","entityType":"project","observations":["Local-first knowledge management system","Combines filesystem durability with graph-based knowledge representation","Focuses on enhancing human agency and understanding","Synthesizes DIY ethics, Free Software philosophy, and theoretical computer science","Current focus includes basic-memory system"]}
{"type":"entity","name":"basic-memory","entityType":"software_system","observations":["A core component of Basic Machines","Local-first knowledge management system","Combines filesystem persistence with graph-based knowledge representation","Being implemented collaboratively by Paul and Claude"]}
{"type":"entity","name":"basic-memory_implementation_patterns","entityType":"technical_patterns","observations":["Filesystem is source of truth - all changes write to files first","Clean separation of concerns between models (SQLAlchemy), schemas (Pydantic), and services","Repository pattern for database access","Service layer handling business logic and coordination","Atomic file operations using temporary files for safety","Clear error handling hierarchy with specific error types","Comprehensive test coverage with pytest and fixtures","Async/await used throughout the codebase","Validation using Pydantic models with custom validators"]}
{"type":"entity","name":"fileio_module","entityType":"code_module","observations":["Extracted from EntityService to handle all file operations","Provides read_entity_file, write_entity_file, and delete_entity_file functions","Handles markdown parsing and formatting","Implements atomic file operations","Provides consistent error handling","Enables reuse across services"]}
{"type":"entity","name":"entity_service","entityType":"code_module","observations":["Manages entities in both filesystem and database","Uses fileio module for file operations","Maintains database index of entities","Handles entity creation, retrieval, and deletion","Follows 'filesystem is source of truth' principle","Coordinates with observation service for full entity management"]}
{"type":"entity","name":"observation_service","entityType":"code_module","observations":["Manages observations within entity files","Provides database indexing for efficient observation queries","Works with complete Entity objects rather than IDs","Handles observation addition and search","Maintains consistency between files and database","Under development for update/remove operations"]}
{"type":"entity","name":"observation_management","entityType":"design_challenge","observations":["Key challenge: maintaining observation state across files and database","Exploring bulk update approach - treating all observations as a unit","Considering tracked observations with markdown comments for IDs","Investigating diff-based approach for observation-level changes","Evaluating position-based management without explicit IDs","Trade-offs between implementation complexity and markdown readability"]}
{"type":"entity","name":"testing_infrastructure","entityType":"technical_patterns","observations":["Uses pytest with async support via pytest-asyncio","In-memory SQLite database for test isolation","Temporary directories for file operation testing","Comprehensive fixture system for test setup","Tests organized by component (entity, observation, etc)","Covers happy path, error cases, and edge cases","Uses monkeypatch for mocking dependencies","Clear separation between arrange, act, assert sections","Uses in-memory SQLite database for test isolation","Comprehensive fixture system for test data setup","Proper async test handling with pytest-asyncio"]}
{"type":"entity","name":"test_categories","entityType":"test_suite","observations":["Happy path tests verify core functionality","Error path tests ensure proper error handling","Edge cases test special characters and long content","File operation tests verify atomic writes and rollbacks","Database sync tests verify index consistency","Recovery tests for rebuild operations","Punted on concurrent operation tests due to session management complexity"]}
{"type":"entity","name":"completed_work","entityType":"project_milestone","observations":["Extracted file operations to fileio.py module","Updated EntityService to use fileio functions","Implemented initial ObservationService","Created comprehensive test suite","Established clear project patterns and principles","Set up basic database schema with SQLAlchemy","Created Pydantic models for validation"]}
{"type":"entity","name":"future_work","entityType":"project_tasks","observations":["Implement observation updates/removals","Design proper session management for concurrent operations","Update EntityService tests for new fileio module","Add more sophisticated search functionality","Handle markdown formatting edge cases","Consider versioning for file changes","Implement proper backup strategy"]}
{"type":"entity","name":"design_decisions","entityType":"technical_decisions","observations":["Filesystem as source of truth over database","Markdown format for human readability and editing","Atomic file operations for safety","SQLite + SQLAlchemy for proven reliability","Pydantic for validation and ID generation","Async/await for better scalability","Clear separation between files and database roles","Explicit error hierarchies for better handling"]}
{"type":"entity","name":"concurrency_considerations","entityType":"technical_challenge","observations":["SQLAlchemy session management in async context","File operation atomicity","Transaction isolation levels","Potential for conflicting updates","Need for proper session lifecycle","Possibility of file system race conditions","Database lock management"]}
{"type":"entity","name":"observation_update_approaches","entityType":"design_alternatives","observations":["Each approach trades off between simplicity, efficiency, and robustness","Four main approaches considered: bulk update, tracked IDs, diff-based, and position-based","Discussion revealed importance of human readability in file format","Consideration of manual editing workflows key to design","File system as source of truth principle guides tradeoffs"]}
{"type":"entity","name":"bulk_update_approach","entityType":"design_option","observations":["Update all observations at once in a single operation","Simpler file operations - just rewrite the whole list","No need for observation matching or IDs","Very consistent with source of truth principle","Less efficient for small changes","May have concurrency implications","Simplest implementation option"]}
{"type":"entity","name":"tracked_observations_approach","entityType":"design_option","observations":["Use markdown comments to store observation IDs","Enables precise updates and deletes","IDs stored as HTML comments in markdown","More complex markdown parsing required","IDs visible in raw markdown files","Balances tracking with readability"]}
{"type":"entity","name":"diff_based_approach","entityType":"design_option","observations":["Implement observation-aware diffing","Track changes at observation level","More efficient for updates","Preserves manual edits and changes","More complex implementation needed","Must handle merge conflicts","Most sophisticated option considered"]}
{"type":"entity","name":"position_based_approach","entityType":"design_option","observations":["Track observations by position/order","No explicit IDs needed","Cleanest markdown format","Order changes could break references","Difficult to handle concurrent edits","Most fragile option considered"]}
{"type":"entity","name":"tasks_and_progress","entityType":"project_tracking","observations":["Current focus on observation management implementation","Completed core file operations extraction","Completed EntityService updates","Completed initial ObservationService","Basic test coverage in place","Future work includes concurrent operations","Future work includes search improvements","Need to handle markdown edge cases"]}
{"type":"entity","name":"error_handling_patterns","entityType":"technical_patterns","observations":["Custom exception hierarchy with ServiceError base","Specific error types (FileOperationError, DatabaseSyncError, etc)","Clear separation between file and database errors","Error propagation patterns established","Focus on actionable error messages","Error handling at appropriate levels"]}
{"type":"entity","name":"data_models","entityType":"technical_implementation","observations":["SQLAlchemy models for database structure","Pydantic schemas for API/service layer","Entity model with UUID-based IDs","Observation model with entity relationships","UTCDateTime custom type for timestamps","Automatic ID generation in Pydantic models","Strict validation rules"]}
{"type":"entity","name":"markdown_format","entityType":"file_format","observations":["Simple, human-readable format","Entity name as H1 header","Metadata in key-value format","Observations as bullet points","Atomic file operations for updates","Designed for manual editing","No hidden metadata in main content"]}
{"type":"entity","name":"test_driven_development","entityType":"development_pattern","observations":["Tests revealed need for atomic file operations","Error cases drove error hierarchy design","Edge cases informed validation rules","Test fixtures shaped service interfaces","File operations extracted due to test patterns","Concurrent test issues revealed session management needs"]}
{"type":"entity","name":"architecture_evolution","entityType":"design_process","observations":["Started with simple EntityService implementation","Circular dependency between Entity and Observation services revealed design flaw","Extracted file operations to separate module","Moved to passing Entity objects rather than IDs","Improved separation of concerns through iterations","File operations became reusable across services","Database became true 'index' rather than source of truth"]}
{"type":"entity","name":"validation_patterns","entityType":"technical_patterns","observations":["Pydantic models provide schema validation","Automatic ID generation if not provided","Database constraints via SQLAlchemy","Runtime checks in services","Markdown format validation","Error handling for invalid states"]}
{"type":"entity","name":"markdown_examples","entityType":"documentation","observations":["Example of basic entity:\n# Entity Name\ntype: entity_type\n\n## Observations\n- First observation\n- Second observation","Example with special characters:\n# Test & Entity!\ntype: test\n\n## Observations\n- Test & observation with @#$% special chars!","Format ensures human readability:\n# Basic Machines\ntype: project\n\n## Observations\n- Local-first knowledge management system\n- Combines filesystem durability with graph-based knowledge representation","Future consideration for observation IDs:\n# Entity Name\ntype: entity_type\n\n## Observations\n- <!-- obs-id: abc123 -->\n This is an observation with ID"]}
{"type":"entity","name":"markdown_parsing_rules","entityType":"technical_implementation","observations":["H1 header contains entity name","Metadata uses key: value format","Observations section marked by H2 header","Each observation is a markdown list item","Blank lines separate sections","Special characters allowed in content","No restrictions on observation content"]}
{"type":"entity","name":"schema_definitions","entityType":"technical_documentation","observations":["SQLAlchemy Entity model:\nclass Entity(Base):\n id: str (primary key)\n name: str (unique)\n entity_type: str\n created_at: datetime\n updated_at: datetime","SQLAlchemy Observation model:\nclass Observation(Base):\n id: str (primary key)\n entity_id: str (foreign key)\n content: str\n created_at: datetime\n context: Optional[str]","Pydantic Entity schema:\nclass Entity(BaseModel):\n id: str\n name: str\n entity_type: str\n observations: List[Observation]"]}
{"type":"entity","name":"test_evolution","entityType":"development_history","observations":["Started with basic Entity CRUD tests","Added filesystem verification to all tests","Developed concurrent operation tests (later removed)","Edge case tests drove better error handling","Test fixtures evolved to support both file and DB testing","Mocking patterns for file/DB operations","Special cases for long content and special characters"]}
{"type":"entity","name":"implementation_challenges","entityType":"technical_issues","observations":["Initial circular dependency between services","SQLAlchemy session management in async context","Atomic file operations with proper error handling","Maintaining DB sync with filesystem changes","Handling long content in observations","Managing test isolation with file operations","Deciding on markdown format tradeoffs","Concurrent operation complexity"]}
{"type":"entity","name":"Basic_Factory","entityType":"Project","observations":["Collaborative project between Paul and Claude","Explores AI-human collaboration in software development","Uses MCP tools for file and memory management","Built with git integration capabilities","Focuses on maintaining project context across sessions","About 90% complete with MCP tools","Still needs improvements in collaboration via files/git/github","Will be used to document and share collaborative development process"]}
{"type":"entity","name":"Basic_Factory_Components","entityType":"Technical","observations":["Server-side rendering with JinjaX","HTMX for dynamic updates","Alpine.js for client-side state","Tailwind CSS for styling","Component translation from React/shadcn/ui","Focus on simplicity and understandability","Demonstrates meta-compiler principles in component translation"]}
{"type":"entity","name":"Component_Translation_Process","entityType":"Methodology","observations":["Treats component porting as meta-compilation","Maps between React/TypeScript and JinjaX/Alpine.js domains","Uses formal grammar transformation approaches","Maintains functionality while simplifying implementation","Focuses on server-side rendering patterns","Preserves accessibility and performance","Uses short, focused git branches for each component"]}
{"type":"entity","name":"Basic_Machines_Philosophy","entityType":"Philosophy","observations":["Combines DIY punk ethics with software development","Emphasizes user empowerment and understanding","Values simplicity and composability","Treats complex systems as combinations of simple parts","Focuses on authentic creation and sharing","Draws inspiration from punk rock, Free Software, and theoretical CS","Emphasizes the cycle of creation, complexity, and renewal"]}
{"type":"entity","name":"Basic_Machines_Manifesto","entityType":"Document","observations":["Created through collaboration between Paul and Claude","Explores connection between DIY punk ethics and software development","Emphasizes composition over inheritance in both philosophy and practice","Views software development through lens of basic machines that combine for complex computation","Advocates for user empowerment and technological independence","Structured in sections covering Origins, Philosophy, Technical Implementation, and AI Collaboration","Draws connections between punk rock, free software, and theoretical computer science","Emphasizes importance of sharing knowledge and building community","Released in December 2024"]}
{"type":"entity","name":"AI_Human_Collaboration_Model","entityType":"Methodology","observations":["Focuses on deep collaboration rather than simple task completion","Maintains rich context across sessions via knowledge graph","Uses short, focused git branches for each collaborative session","Values intellectual partnership over simple code generation","Emphasizes both practical implementation and theoretical exploration","Creates space for authentic exchange while maintaining AI/human clarity","Uses formal methods when appropriate (like grammar transformation)","Documents decisions and processes for future reference","Developed through Basic Machines project experience"]}
{"type":"entity","name":"Basic_Machines_Roadmap","entityType":"Project_Plan","observations":["Phase 1 (30 days): Build basic-machines.co website","Phase 2 (60-90 days): Develop premium component bundles","Phase 3 (90-120 days): Launch Basic Foundation commercial offering","Focus on building brand and marketing presence","Prioritize components needed for own site development","Document and share collaboration process","Build sustainable business model aligned with values"]}
{"type":"entity","name":"Basic_Machines_Website","entityType":"Project","observations":["To be built at basic-machines.co","Will showcase products and vision","Needs components for navigation, hero sections, features","Will demonstrate component usage in production","Will include blog for sharing progress","Focus on clear value proposition","Platform for sharing Basic Machines philosophy"]}
{"type":"entity","name":"Basic_Memory_Markdown_Example","entityType":"Example","observations":["Shows complete markdown structure for basic-memory entity","Uses frontmatter for metadata (id, type, created, context)","Has main description section after title","Includes Observations as bullet points","Shows Relations with [id] relation_type | context format","Lists References at bottom","Created during initial design discussion","Serves as canonical example of file format"]}
{"type":"entity","name":"Basic_Memory_Database_Schema","entityType":"Technical","observations":["Uses SQLite for local storage","Entities table with id, name, type, created_at, context, description, references","Observations table linking to entities with content and context","Relations table tracking directional relationships between entities","References column needs quotes as SQL reserved word","Designed for easy rebuilding from markdown files","Foreign key constraints maintain data integrity","Unique constraint on relations prevents duplicates","Created_at timestamps track history","Context fields enable tracking information sources"]}
{"type":"entity","name":"Basic_Memory_Project_Structure","entityType":"Technical","observations":["Uses dbmate for database migrations","Projects directory stores SQLite databases and markdown files","Makefile provides common development commands","Environment vars configure database connection","db/migrations directory for SQL schema changes","Gitignore excludes database files and env config","Uses Python 3.12 with modern tooling","Tests directory for pytest files","Follows Basic Machines project conventions"]}
{"type":"entity","name":"Basic_Memory_Project_Isolation_Decision","entityType":"Decision","observations":["Decided to defer multi-project support to post-MVP","Will use separate SQLite databases per project","Initially using projects directory in code repository","Plan to make location configurable later","No changes needed to core domain model","Keeps initial implementation simple","FTS/search capabilities also deferred for simplicity"]}
{"type":"entity","name":"Basic_Memory_Implementation_Plan","entityType":"Plan","observations":["Start with SQLAlchemy models matching schema","Then build CLI for basic operations","Then implement markdown parser","Use TDD approach throughout","Begin with core domain model","CLI will support CRUD operations","Parser must handle frontmatter and sections","Following modular development approach","Planning to use typer for CLI","Will use modern Python tools and practices"]}
{"type":"entity","name":"Basic_Memory_Implementation_Status","entityType":"Status","observations":["Core modules implemented: models, services, repository, fileio","Modular architecture with clear separation of concerns","File operations extracted to separate fileio module","Initial ObservationService implementation complete","Basic test coverage in place","Exploring observation management strategies","Using SQLAlchemy for database interaction","Markdown file operations working","Entity management functional","Repository layer implementation complete with SQLAlchemy models and tests","Database operations working with proper UTC timestamp handling","In-memory SQLite testing infrastructure proven effective"]}
{"type":"entity","name":"Basic_Memory_Observation_Management_Design","entityType":"Design","observations":["Four approaches under consideration","Bulk Update: Simple but less efficient","Tracked Observations: Precise but clutters markdown","Diff-based: Efficient but complex","Position-based: Clean but fragile","Key challenge is balancing markdown readability with efficient updates","Must maintain filesystem as source of truth","Need to consider concurrent edits","Currently evaluating trade-offs","Implementation choice pending discussion"]}
{"type":"entity","name":"Basic_Memory_Architectural_Decisions","entityType":"Decisions","observations":["Split file operations into separate fileio module","Using SQLAlchemy for database operations","Maintain filesystem as source of truth","Modular service-based architecture","Clear separation between data access and business logic","Repository pattern for database interactions","Schemas separate from models","Focus on maintainability and testability","Services handle business rules","Considering concurrency in design"]}
{"type":"entity","name":"Basic_Memory_Implementation_Analysis","entityType":"Analysis","observations":["Clean modular architecture with clear responsibilities","Strong typing throughout codebase","Excellent error handling with custom exceptions","SQLAlchemy models perfectly match our domain model","Atomic file operations for data safety","Services implement filesystem-as-source-of-truth principle","Async support throughout","Good separation between domain models and database models","Careful handling of UTC timestamps","Smart use of SQLAlchemy relationships"]}
{"type":"entity","name":"Basic_Memory_Current_Challenges","entityType":"Challenges","observations":["Observation update/removal strategy needs to be chosen","Need to handle concurrent file operations safely","Search functionality to be implemented","Edge cases in markdown formatting to be handled","Session management for concurrent operations needed","Balance between file operations and database sync","Testing coverage could be expanded","Need to handle relationship updates in files"]}
{"type":"entity","name":"Basic_Memory_Observation_Hash_Tracking","entityType":"Design","observations":["Use content hashes to track observation identity","Store hashes in database but not in markdown","Can match observations across file edits using hashes","Similar to how git tracks content changes","Keeps markdown clean and human-friendly","Allows efficient bulk updates","Handles reordering of observations","Maintains filesystem as source of truth","No need for visible IDs in markdown","Could track observation history through hash changes"]}
{"type":"entity","name":"Basic_Memory_Repository_Implementation","entityType":"Code_Implementation","observations":["Implemented base Repository class with CRUD operations","Added specialized EntityRepository, ObservationRepository, and RelationRepository","Used string IDs instead of UUIDs","Added UTCDateTime custom type for timestamp handling","Used in-memory SQLite for testing","Achieved 84% test coverage","Created comprehensive pytest fixtures"]}
{"type":"entity","name":"Basic_Memory_Dependencies","entityType":"Technical","observations":["Uses Python 3.12","SQLAlchemy with async support","pytest-asyncio for async testing","aiosqlite for async SQLite operations","greenlet for SQLAlchemy async support","uv for dependency management","pytest-cov for coverage reporting","Development dependencies managed in pyproject.toml"]}
{"type":"entity","name":"Basic_Memory_Current_Architecture","entityType":"Architecture_Analysis","observations":["Clear separation between domain models (Pydantic) and storage models (SQLAlchemy)","File I/O completely separated into dedicated module","Strong 'filesystem as source of truth' pattern in services","Atomic file operations with proper error handling","Service layer coordinates between filesystem and database","Database acts as queryable index rather than primary storage","Clean error hierarchy with specific exception types","Rebuild operations available for recovery scenarios"]}
{"type":"entity","name":"Basic_Memory_Evolution","entityType":"Analysis","observations":["Started with repository pattern following basic-foundation","Evolved to more sophisticated architecture with clear layers","Added Pydantic schemas for domain modeling","Separated file operations into dedicated module","Implemented robust error handling throughout","Maintained filesystem as source of truth principle","Added observation management with context tracking","Introduced rebuild capabilities for system recovery"]}
{"type":"entity","name":"Basic_Memory_Service_Layer","entityType":"Implementation","observations":["EntityService handles entity lifecycle and coordinates storage","ObservationService manages observations within entities","Services ensure filesystem and database stay in sync","Clear error handling with ServiceError hierarchy","Strong typing throughout service interfaces","Implements filesystem as source of truth pattern","Handles UUID generation and timestamp management","Provides methods for system recovery and rebuild"]}
{"type":"entity","name":"Basic_Memory_Schema_Design","entityType":"Implementation","observations":["Uses Pydantic for domain models and validation","Automatic ID generation with timestamp and UUID","Clear separation from SQLAlchemy storage models","Supports optional context tracking","Models match markdown file structure","Enables clean serialization/deserialization","Strong typing with proper validation rules","Independent from storage concerns"]}
{"type":"entity","name":"Basic_Memory_Next_Tasks","entityType":"TaskList","observations":["✅ Implement SQLAlchemy models and repositories (Done)","✅ Add SQLAlchemy migrations (Done)","✅ Create service layer (Done)","✅ Implement file I/O module (Done)","✅ Set up domain models with Pydantic (Done)","✅ Initial test infrastructure (Done)","✅ Basic CRUD operations (Done)","⏳ Implement full test coverage for db.py","⏳ Add more sophisticated search functionality","⏳ Implement CLI interface","⏳ Add relationship management to services","⏳ Handle concurrent file operations safely","⏳ Add versioning for file changes","⏳ Implement proper backup strategy","⏳ Add type hints throughout codebase","⏳ Improve error messages and logging","⏳ Add documentation for core modules"]}
{"type":"entity","name":"Basic_Memory_Meta_Experience","entityType":"Case_Study","observations":["Experienced our own context loss when reconstructing project knowledge","Had to rebuild task list and project context from filesystem and memory","Validated 'filesystem as source of truth' principle through reconstruction","Code and tests served as reliable historical record","Knowledge graph structure helped guide reconstruction process","Markdown files provided human-readable context","Atomic information design made piece-by-piece reconstruction possible","Ironic validation of the need for basic-memory's features","Experience demonstrates value of durable, human-readable knowledge storage","Shows importance of separating durable storage from ephemeral context"]}
{"type":"entity","name":"Model_Context_Protocol","entityType":"protocol","observations":["Core part of the basic-memory architecture","Enables AI-human collaboration on projects","Provides tool-based interaction with knowledge graph","Developed by Anthropic for structured AI-system interaction","Used for maintaining consistent, rich context across conversations"]}
{"type":"entity","name":"basic-memory_core_principles","entityType":"principles","observations":["Local First: All data stored locally in SQLite","Project Isolation: Separate databases per project","Human Readable: Everything exportable to plain text","AI Friendly: Structure optimized for LLM interaction","DIY Ethics: User owns and controls their data","Simple Core: Start simple, expand based on needs","Tool Integration: MCP-based interaction model"]}
{"type":"entity","name":"basic-memory_business_model","entityType":"business_strategy","observations":["Core features free: Local SQLite, basic knowledge graph, search, markdown export, basic MCP tools","Professional features potential: Rich document export, advanced versioning, collaboration features, custom integrations, priority support","Focus on maintaining DIY/punk philosophy while enabling sustainability"]}
{"type":"entity","name":"basic-memory_cli","entityType":"interface","observations":["Supports project management commands (create, switch, list)","Entity management (add entity, add observation, add relation)","Future support for export and batch operations","Follows consistent command structure","Planned integration with MCP tools"]}
{"type":"entity","name":"basic-memory_export_format","entityType":"file_format","observations":["Uses markdown with frontmatter metadata","Includes entity name, type, creation timestamp","Observations as bullet points","Relations in structured format with links","References section at bottom","Designed for human readability and machine parsing","Example format documented in project specs"]}
{"type":"entity","name":"relation_service","entityType":"code_module","observations":["Planned service for managing relations in both filesystem and database","Will follow filesystem-is-source-of-truth principle like other services","Needs to handle atomic file operations for relation updates","Must coordinate with EntityService for relationship integrity","Will handle bidirectional relationship tracking","Will support relation validation and type enforcement","Must implement rebuild functionality for index recovery","Will need careful error handling for file/db sync","Should support relation search and filtering","Must handle relation lifecycle (create/read/update/delete)"]}
{"type":"entity","name":"service_layer_patterns","entityType":"implementation_patterns","observations":["Services handle both file and database operations","Filesystem is always source of truth","Database serves as queryable index","Services implement atomic file operations","Clear error hierarchy with specific exceptions","Use of dependency injection via constructor params","Async/await used throughout service layer","Services coordinate between storage layers","Repository pattern used for database access","Services maintain entity integrity across storage","Rich error types extend from ServiceError base","Rebuild operations available for recovery"]}
{"type":"entity","name":"database_models","entityType":"implementation","observations":["Entity model with unique name and type","Observation model linked to entities","Relation model tracks connections between entities","Custom UTCDateTime type for timestamp handling","Use of SQLAlchemy relationships for navigation","Cascading deletes for dependent objects","String IDs used for compatibility","Rich relationship modeling with backpopulates","Proper indexing on foreign keys","Context tracking available on models","Models include created_at timestamps","Relationships handle bidirectional navigation"]}
{"type":"entity","name":"repository_patterns","entityType":"implementation_patterns","observations":["Generic Repository[T] base class implementation","Type-safe operations with SQLAlchemy","Specialized repositories for each model type","Async operations throughout","Clear error handling patterns","Support for custom queries and filtering","Pagination support built-in","Transaction management via session","Proper type hints and generics usage","Entity-specific query methods in subclasses"]}
{"type":"entity","name":"relation_service_design","entityType":"design","observations":["Must handle relation lifecycle in both files and DB","Needs to validate existence of both entities","Should support relation type enforcement","Must maintain bidirectional consistency","Should support relation querying and filtering","Needs proper error handling for graph consistency","Must integrate with entity file format","Should support bulk operations for efficiency","Must handle relation deletion and cascading","Should provide search by type and entities"]}
{"type":"entity","name":"relation_service_implementation_plan","entityType":"plan","observations":["1. Define core relation operations (create, get, delete)","2. Implement file format handling for relations","3. Add database sync with RelationRepository","4. Implement validation and error handling","5. Add rebuild and recovery operations","6. Implement relation type enforcement","7. Add relation search and filtering","8. Implement bulk operations","9. Add comprehensive tests","10. Document API and error handling"]}
{"type":"entity","name":"relation_service_challenges","entityType":"challenges","observations":["Maintaining consistency between file and database","Handling relation type validation efficiently","Managing bidirectional relationships in files","Ensuring atomic updates across entities","Handling deletion with proper cascading","Efficient querying of relation graphs","Recovery from partial file/db sync failures","Bulk operation atomicity","Clear error reporting for graph operations","Performance with large relation sets"]}
{"type":"entity","name":"relation_file_format","entityType":"file_format","observations":["Relations stored in entity markdown files","Format: [target_id] relation_type | context","Relations section marked by ## Relations header","Outgoing relations only stored in source entity","Relations rebuild on entity load","Clean human-readable format","Context is optional with pipe separator","Links generate valid navigation references","Markdown-friendly formatting","Example: [Paul] authored | with Claude"]}
{"type":"entity","name":"relation_service_error_handling","entityType":"implementation_patterns","observations":["RelationError extends ServiceError base","Specific errors for validation failures","Handles entity not found cases","Manages relation type validation errors","File operation errors properly wrapped","Database sync errors clearly reported","Transaction rollback on errors","Proper error propagation chain","Clear error messages for debugging","Recovery paths for common errors"]}
{"type":"entity","name":"relation_service_testing","entityType":"testing","observations":["Test all relation lifecycle operations","Verify file and database consistency","Test relation type validation","Check error handling paths","Test bulk operations","Verify bidirectional consistency","Test recovery operations","Check cascade operations","Verify search and filtering","Test with large relation sets"]}
{"type":"entity","name":"fileio_patterns","entityType":"implementation_patterns","observations":["Atomic file operations with temporary files","Clear error handling for IO operations","Consistent file naming and paths","Support for different file formats","Efficient file reading and writing","Proper file locking mechanisms","Recovery from partial writes","Consistent encoding handling","Directory management utilities","Path manipulation helpers","Currently implemented in fileio.py module","Uses pathlib for path operations","Handles file not found cases gracefully","Maintains data integrity during writes"]}
{"type":"entity","name":"pytest_patterns","entityType":"implementation_patterns","observations":["Common fixtures should be in conftest.py for reuse","Use pytest_asyncio.fixture for async fixtures","Session fixtures need proper async cleanup","Temporary directories should be managed with context managers","Test categories: happy path, error path, recovery, edge cases","Services need project_path and repo injected","Use monkeypatch for mocking in async context","SQLite in-memory database ideal for testing","Explicit test verification: file content and database state"]}
{"type":"entity","name":"relation_implementation_learnings","entityType":"implementation_learnings","observations":["Better to pass full Entity objects than IDs to services","Services should not re-read entities if they have them","File operations should be atomic and verified","Database serves as queryable index, not source of truth","Relations stored in source entity's markdown file","Clear separation between file ops and database sync","Entity objects should own their relations list","Context is optional but fully supported in implementation"]}
{"type":"entity","name":"test_driven_insights","entityType":"learnings","observations":["Tests help reveal better API design (e.g., passing Entity objects)","Error cases drive proper exception hierarchy","File verification as important as database checks","Edge cases inform markdown format decisions","Recovery tests ensure system resilience","Tests document expected behavior clearly","Fixtures significantly reduce test complexity","Common patterns emerge through test writing"]}
{"type":"entity","name":"meta_development_insights","entityType":"process","observations":["Break down large tasks into reviewable chunks","One file at a time prevents response truncation","Iterative development with tests leads to better design","Infrastructure code (fixtures) should be consolidated early","Test categories help ensure comprehensive coverage","Knowledge capture should happen during development","APIs tend to evolve toward simpler patterns","File operations require careful verification"]}
{"type":"entity","name":"AI_Assistant_Learnings","entityType":"meta_insights","observations":["Output management: Breaking responses into single files prevents truncation and allows better review","Knowledge graph helps maintain context: I can reference previous decisions and patterns accurately","Memory rebuilding experience validated the need for durable storage","Test-driven development provides clear steps and verification","Explicit relation tracking in knowledge graph helps me understand project context","Rich context from multiple sources (code, docs, tests) enables better assistance","File-at-a-time approach allows deeper analysis of each component","Keeping entity names consistent helps with referencing and relationships"]}
{"type":"entity","name":"Effective_Response_Patterns","entityType":"meta_patterns","observations":["When showing code changes, break into discrete files","Review existing code before suggesting changes","Reference knowledge graph for context and patterns","Explicitly connect new code to existing patterns","Validate suggestions against test cases","Keep track of file changes for atomic commits","Check both implementation and test files for consistency","Maintain clear separation of concerns in responses"]}
{"type":"entity","name":"AI_Context_Management","entityType":"meta_practice","observations":["Knowledge graph provides reliable persistent memory","Project documentation gives high-level context","Code review shows implementation patterns","Tests demonstrate expected behavior","Important to actively track what has been modified","Entity relationships help understand dependencies","Regular knowledge capture during development","Using consistent entity references across conversations"]}
{"type":"entity","name":"AI_Tool_Usage_Patterns","entityType":"meta_practice","observations":["read_file before suggesting changes","write_file one file at a time","list_directory to understand project structure","search_nodes to find relevant context","create_entities to capture new learnings","create_relations to connect concepts","Using knowledge graph to track decisions","Validating changes through test execution"]}
{"type":"entity","name":"relation_service_learnings","entityType":"implementation_learnings","observations":["Entity-based API cleaner than ID-based for service layer","Model_dump method can handle storage serialization","File format needs explicit section markers (## Relations)","Whitespace handling important for long content comparisons","Test fixtures allow focused test cases","SQLAlchemy selects better than raw SQL for type safety","Atomic file operations maintained for relations"]}
{"type":"entity","name":"test_driven_insights_relations","entityType":"learnings","observations":["Tests revealed need for whitespace normalization","Edge cases drove file format decisions","SQLAlchemy model access safer than raw queries","Fixtures reduced test setup complexity","File verification as important as database checks","Testing both memory model and storage format","Test categories ensure comprehensive coverage"]}
{"type":"entity","name":"relation_service_patterns","entityType":"patterns","observations":["Use Entity objects in API","Serialize to IDs for storage","Maintain file as source of truth","Keep file format human-readable","Handle circular references in serialization","Use repository pattern for database","Clear error hierarchies"]}
{"type":"entity","name":"packaging_learnings","entityType":"technical_learnings","observations":["When using pytest-mock, traditional pip install works more reliably than uv sync","Package discovery behavior can differ between uv and pip","Clean venv with pip install is a reliable fallback for dependency issues","Package installation location might differ between uv and pip","Dependencies in pyproject.toml dev section work reliably with pip install -e .[dev]"]}
{"type":"entity","name":"Recent_Implementation_Progress","entityType":"progress_update","observations":["Successfully split services.py into modular structure under services/","Created __init__.py, entity_service.py, observation_service.py, relation_service.py","Fixed pytest-mock installation issues by using pip install -e .[dev] instead of uv sync","Improved test structure with minimal mocking - only used for error testing","Implemented relation service with Entity-based API","Achieved good test coverage across services","File operations are only mocked when testing error conditions","Services follow filesystem-as-source-of-truth pattern"]}
{"type":"entity","name":"Next_Steps","entityType":"project_tasks","observations":["Consider adding more relation service tests","Potentially expand relations features","Look for opportunities to improve test coverage","Consider documenting package management preferences (pip vs uv)","Consider adding integration tests for services","Review and possibly expand error handling cases"]}
{"type":"entity","name":"Development_Practices","entityType":"process","observations":["Favor real operations over mocks in tests","Only mock for error condition testing","Use pip install -e .[dev] for reliable dev dependency installation","Maintain modular service structure","Keep filesystem as source of truth","Use Entity objects in service APIs instead of IDs","Validate both file and database state in tests"]}
{"type":"entity","name":"MCP_Resources","entityType":"Concept","observations":["Stateful objects in Model Context Protocol","Enable persistent access to capabilities"]}
{"type":"entity","name":"MCP_Server_Implementation","entityType":"Technical_Design","observations":["Inherits from mcp.server.Server base class","Tools are implemented as async methods","Each tool method maps directly to a function available to the AI","Tools can request user input via Prompts","Simple function call interface rather than explicit resource management","State management handled by server instance","Returns serialized data using model_dump() for consistency"]}
{"type":"entity","name":"MCP_Tools","entityType":"Protocol_Feature","observations":["Defined as async methods on server class","Return values must match tool definition schema","Can maintain state between invocations via server instance","Tools can prompt for user input when needed","No need for explicit Resource objects in implementation"]}
{"type":"entity","name":"Basic_Memory_MCP","entityType":"Implementation","observations":["Uses MemoryService for core operations","Implements project selection via prompts","Maintains project context across tool invocations","Maps directly to memory graph operations","Handles serialization of Pydantic models"]}
{"type":"entity","name":"Basic_Memory_Testing","entityType":"Testing_Design","observations":["Needs pytest for async testing","Should isolate filesystem operations for tests","Needs to handle MCP server lifecycle in tests","Should test both service layer and MCP interface","Will need mocks for project paths and file operations"]}
{"type":"entity","name":"Memory_Service_Tests","entityType":"Test_Suite","observations":["Should test entity creation with observations","Should test relation creation between entities","Should verify proper ID generation and model validation","Should test deletion cascading","Should test search functionality","Must verify proper serialization of entities and relations"]}
{"type":"entity","name":"MCP_Server_Tests","entityType":"Test_Suite","observations":["Should test project initialization workflow","Should test prompt handling","Should verify tool input/output formats","Should test error cases and validation","Must verify proper serialization in tool responses"]}
{"type":"entity","name":"Memory_Service_Refactoring","entityType":"Technical_Task","observations":["MemoryService uses create() but EntityService might expect create_entity()","MemoryService assumes get_by_name() but EntityService might use different method","Need to verify deletion method signatures","Need to check if search interface matches","Should verify observation handling matches ObservationService interface","RelationService methods need verification","EntityService.create_entity takes name, type, and optional observations directly, not an Entity object","EntityService requires project_path and entity_repo in constructor","ObservationService.add_observation takes Entity object and content string, not raw data","RelationService.create_relation takes Entity objects directly, not dict data","All services follow filesystem-as-source-of-truth pattern with DB indexing","All services handle database synchronization internally","Services expect Path objects for filesystem operations"]}
{"type":"entity","name":"Service_Interface_Audit","entityType":"Technical_Task","observations":["Need to review all existing service interfaces","Document current method signatures","Map discrepancies between MemoryService assumptions and actual interfaces","Check return types and error handling patterns","Review transaction/atomicity requirements","Method signatures need alignment: create vs create_entity etc","Need to handle DB repositories in service constructors","File operations should use project_path consistently","Need to maintain filesystem-as-source-of-truth pattern","Should handle database synchronization at service level","Error handling should align with existing patterns","Consider making MemoryService handle DB indexing consistently"]}
{"type":"entity","name":"Memory_Service_Patterns","entityType":"Technical_Pattern","observations":["Uses inner async functions to encapsulate operation logic","Leverages list comprehensions with async functions for parallel operations","Each operation follows a consistent pattern: validate, update DB, write file","Inner functions make the code more readable and maintainable","Operations can run in parallel when using list comprehensions with async functions"]}
{"type":"entity","name":"Pydantic_Create_Pattern","entityType":"Technical_Pattern","observations":["Separate Create models match the exact shape of incoming data","Provides clear contract for MCP tool inputs","Handles validation of raw input data","Converts cleanly to domain models via from_create methods","Maintains separation between external API format and internal models","Similar to FastAPI request model pattern","Allows camelCase in API while using snake_case internally"]}
{"type":"entity","name":"Basic_Memory_Business","entityType":"Business_Model","observations":["Core system is open source and free","Local-first, giving users data control","Professional features could be licensed","Enterprise support and customization services","Potential for MCP tool marketplace"]}
{"type":"entity","name":"MCP_Marketplace","entityType":"Business_Concept","observations":["Could host verified MCP tools for different use cases","Tools rated by performance and reliability","Marketplace takes percentage of tool usage fees","Enterprise tool verification and security scanning","Custom tool development services","Integration support for existing tools"]}
{"type":"entity","name":"Persistence_Of_Vision","entityType":"Concept","observations":["Mental model for continuous AI-human interaction","Like cinema: 24fps creates illusion of smooth motion","Basic-memory provides 'frames' of structured knowledge","Current state: Better than flipbook, not yet digital cinema","Goal: Achieve smoother cognitive continuity between interactions","Proposed by Drew as metaphor for AI conversation continuity"]}
{"type":"entity","name":"Conversation_Continuity_Pattern","entityType":"Usage_Pattern","observations":["Use basic-memory entity/relation schema for conversations","Each chat becomes an entity with observations for key points","Relations link to discussed concepts and other chats","Uses zettelkasten format IDs for natural ordering","Can be used as template/recipe for others","Future possibility: Git SHA integration for versioning"]}
{"type":"entity","name":"Usage_Recipes","entityType":"Feature_Concept","observations":["Predefined patterns users can follow or adapt","Could include conversation tracking recipe","Templates for different knowledge management styles","Shows practical applications of the generic schema","Helps users get started with the system"]}
{"type":"entity","name":"Chat_References","entityType":"Technical_Feature","observations":["Uses ref:* syntax to reference previous conversations","Combines reference semantics with pointer symbolism","Format: ref:*{zettelkasten-id}","Allows explicit context loading between chats","Inspired by C++ references and pointers","Provides memory-model-like access to conversation context","Uses ref:// URI format following MCP Resource pattern","Could support multiple reference schemes (chat/entity/concept)","Makes reference semantics explicit and unambiguous","Aligns with standard URI formatting"]}
{"type":"entity","name":"Chat_Reference_Protocol","entityType":"Technical_Specification","observations":["Uses URI format: ref://basic-memory/chat/[id]","Follows MCP Resource pattern: [protocol]://[host]/[path]","Enables explicit context loading between chats","Can support multiple resource types (chat/entity/concept)","Provides standardized way to reference previous conversations","Example: ref://basic-memory/chat/20240307-drew-ab12ef34"]}
{"type":"entity","name":"20240307-chat-reference-protocol","entityType":"conversation","observations":["Developed ref:// URI format for chat references","Added Chat Reference Protocol to prompt instructions","Discussed implementation of chat continuation","Created complete prompt instructions document","Reference format follows MCP Resource pattern","Reviewed and confirmed complete prompt instructions","Ready to test ref://basic-memory/chat/20240307-chat-reference-protocol in new chat"]}
{"type":"entity","name":"20240307-chat-reference-protocol-test","entityType":"conversation","observations":["First implementation test of chat reference protocol","Testing continuation from 20240307-chat-reference-protocol","Focused on practical implementation of ref:// URI format"]}
{"type":"entity","name":"Write_File_Tool_Usage","entityType":"Tool_Usage_Pattern","observations":["Never use placeholders like '# Rest of...' when writing files - must include complete file content","File content must be complete and valid - partial updates will truncate the file","If showing partial changes, should inform human and let them handle the file write","write_file tool replaces entire file contents - cannot do partial updates","Code files especially must be complete and valid to avoid breaking functionality","Always read_file before write_file to understand current state","Using write_file without reading first risks reverting recent changes","Pattern should be: read current state, make modifications, then write if needed","Especially important in collaborative development where files may have been updated"]}
{"type":"entity","name":"Run_Tests_Tool_Request","entityType":"Feature_Request","observations":["Need to add a tool enabling Claude to run tests locally","Would help with direct validation of code changes","Current workaround: Claude has to ask human to run tests","Should support running specific test functions (e.g. pytest tests/test_memory_service.py::test_create_relations)","Would improve iterative development workflow between human and AI"]}
{"type":"entity","name":"SQLAlchemy_Async_Loading_Pattern","entityType":"Technical_Pattern","observations":["Use selectinload() instead of lazy loading when accessing SQLAlchemy relationships in async code","Lazy loading doesn't work with async due to greenlet context requirements","selectinload performs a single efficient query with an IN clause","Pattern used in basic-memory's EntityRepository for loading relations","Documented in find_by_id method with thorough explanation","Alternative approaches: joinedload (single JOIN query) or subqueryload (subquery approach)","Benefits: prevents 'MissingGreenlet' errors, reduces N+1 query problems","Key insight: load all needed relationships upfront in async code","Example use: selectinload(Entity.outgoing_relations)"]}
{"type":"entity","name":"20241207-sqlalchemy-async-pattern","entityType":"conversation","observations":["Fixed SQLAlchemy async relationship loading issues","Implemented selectinload pattern in EntityRepository","Updated find_by_id to eager load relations","Added documentation about the pattern","Created knowledge graph entry about SQLAlchemy async loading","Fixed failing tests by properly loading relations in memory_service","Discussed SQLAlchemy relationship loading best practices"]}
{"type":"entity","name":"20241207-memory-service-relations","entityType":"conversation","observations":["Fixed SQLAlchemy async loading with selectinload pattern","Updated find_by_id in EntityRepository to eager load relations","Discovered create_relations works but returns empty list","Verified relations are being stored correctly in memory.json","Next step: Work on MemoryService.add_observations implementation","Improved understanding of MCP memory storage format through debugging"]}
{"type":"entity","name":"add_observations_implementation_plan","entityType":"technical_plan","observations":["Follow pattern from create_entity and create_relation methods","File operations first (read & write) - filesystem is source of truth","Database updates in parallel","Simplify current implementation","Current flow is:"," - First read entities and create observations"," - Write files in parallel"," - Update DB indexes sequentially","Key tests needed:"," - Adding observations to multiple entities"," - Verifying filesystem state first"," - Verifying database state"," - Error cases for missing entities"," - Error cases for file operations"]}
{"type":"entity","name":"MCP_Reference_Integration","entityType":"feature_idea","observations":["Can be implemented as a Model Context Protocol integration similar to the fetch tool","Would provide structured way to pass chat references to Claude","Could handle ref:// URL format systematically","Integration would fetch context from referenced chats and inject into conversation","Observed from Claude Desktop UI showing MCP integration pattern with fetch tool","Would be more robust than passing references in chat text"]}
{"type":"entity","name":"Project_Priorities","entityType":"roadmap","observations":["P1: Dogfooding basic-memory system instead of JSON memory store","Future: Implement MCP-based reference system"]}
{"type":"entity","name":"great_observation_loading_saga_20241207","entityType":"debugging_session","observations":["Occurred on December 7, 2024 while debugging basic-memory SQLAlchemy relationship loading","Issue: selectinload() wasn't properly loading relationships in async SQLAlchemy context","Tried multiple solutions: explicit joins, manual loading, various SQLAlchemy loading strategies","Final solution: Using session.refresh() with explicit relationship names","Memorable quote: 'The Great Observation Loading Saga'","Key learning: Sometimes the obvious SQLAlchemy patterns need adaptation for async contexts","Solution preserved in basic-memory repository in EntityRepository.find_by_id()"]}
{"type":"entity","name":"basic_memory_implementation_20241208","entityType":"technical_milestone","observations":["Fixed async SQLAlchemy relationship loading issues by using explicit refresh with relationship names","Established pattern of relationship handling belonging in MemoryService not EntityService","Fixed ID generation flow through Pydantic schemas to DB layer","Standardized error handling using EntityNotFoundError","All 32 tests passing with 70% coverage","Core services (Entity, Observation, Relation) working properly","Ready for MCP server implementation","Notable debugging session: The Great Observation Loading Saga - resolved lazy loading issues","Established clear separation between MemoryService orchestration and individual service responsibilities"]}
{"type":"entity","name":"MCP_Dependency_Risk","entityType":"technical_lesson","observations":["Experienced disruption when MCP npm package disappeared - 'leftpad moment'","Need to ensure basic-memory tools are resilient to external dependency issues","Local implementation of MCP server provides better stability than npm packages","Important to maintain control of critical infrastructure components","Validates DIY/local-first philosophy of basic-memory project","Package manager fragility revealed by simple 'npx @modelcontextprotocol/server-memory' failure"]}
{"type":"entity","name":"basic_memory_project_20241208","entityType":"technical_milestone","observations":["Core MCP server implementation completed with tools: create_entities, search_nodes, open_nodes, add_observations, create_relations, delete_entities, delete_observations","ProjectConfig and dependency injection pattern established","Test framework in place with in-memory DB support","Support for both camelCase (MCP) and snake_case (internal) formats","Filesystem remains source of truth with SQLite as index","Two-way sync pattern identified between Claude MCP tools and direct markdown file editing","Ready for Claude Desktop integration testing phase","Next steps identified: passing tests, markdown format definition, file change tracking, real-world testing","Implementation prioritizes local-first principles with filesystem as source of truth"]}
{"type":"entity","name":"basic_memory_mcp_architecture","entityType":"technical_design","observations":["MemoryServer class extends MCP Server with custom handler registration","Uses ProjectConfig for clean dependency injection and configuration","Memory service can be injected for testing","Handlers exposed as instance attributes for testing","Tool schemas leverage existing Pydantic models"]}
{"type":"entity","name":"basic_memory_sync_considerations","entityType":"design_insight","observations":["Need to handle sync between direct markdown file edits and DB index","Watch for file system changes as potential future enhancement","Consider index rebuild patterns on startup","Keep human-friendly markdown format for direct editing"]}
{"type":"entity","name":"mcp_server_learnings","entityType":"developer_insight","observations":["MCP protocol is new and documentation is still evolving","Test patterns are not well established yet in example implementations","Supporting both camelCase and snake_case helps with protocol/internal compatibility","Server.handle_* naming convention is important for handler registration"]}
{"type":"entity","name":"20241208-mcp-tool-refactoring","entityType":"conversation","observations":["Decision to return structured data via EmbeddedResource instead of TextContent string parsing","Plan to create Pydantic result models (CreateEntitiesResult, SearchNodesResult etc)","Will use application/vnd.basic-memory+json as MIME type for our structured data","Currently debugging test issues with add_observations tool","Entity ID vs name resolution needed in add_observations","Goal is to make tools more joyful to use by eliminating string parsing","MCP spec supports EmbeddedResource for structured data returns"]}
{"type":"entity","name":"Basic Memory MCP Server Implementation","entityType":"technical_notes","observations":["Server implements Model Context Protocol using proper structured data responses","Uses EmbeddedResource with custom MIME type 'application/vnd.basic-memory+json'","Clean separation between input validation and handlers via Pydantic models","All tool operations return structured data through create_response helper","Type safety with Literal types for tool names and proper typing for handlers","Handler registry pattern with TOOL_HANDLERS dictionary","Consistent error handling pattern using MCP error codes","Uses Pydantic ConfigDict for proper ORM integration","Tool schemas organized into Input and Response types","Input validation with Annotated types for extra constraints","Response models consistently use from_attributes=True for ORM data","Entity ID generation moved to model validator on EntityBase","Follows principle of making common operations easy and safe"]}
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+34 -74
View File
@@ -1,9 +1,9 @@
[project]
name = "basic-memory"
dynamic = ["version"]
version = "0.9.0"
description = "Local-first knowledge management combining Zettelkasten with knowledge graphs"
readme = "README.md"
requires-python = ">=3.12"
requires-python = ">=3.12.1"
license = { text = "AGPL-3.0-or-later" }
authors = [
{ name = "Basic Machines", email = "hello@basic-machines.co" }
@@ -14,8 +14,9 @@ dependencies = [
"typer>=0.9.0",
"aiosqlite>=0.20.0",
"greenlet>=3.1.1",
"pydantic[email,timezone]>=2.12.0",
"mcp>=1.23.1",
"pydantic[email,timezone]>=2.10.3",
"icecream>=2.1.3",
"mcp>=1.2.0",
"pydantic-settings>=2.6.1",
"loguru>=0.7.3",
"pyright>=1.1.390",
@@ -27,23 +28,8 @@ dependencies = [
"watchfiles>=1.0.4",
"fastapi[standard]>=0.115.8",
"alembic>=1.14.1",
"qasync>=0.27.1",
"pillow>=11.1.0",
"pybars3>=0.9.7",
"fastmcp==2.12.3", # Pinned - 2.14.x breaks MCP tools visibility (issue #463)
"pyjwt>=2.10.1",
"python-dotenv>=1.1.0",
"pytest-aio>=1.9.0",
"aiofiles>=24.1.0", # Optional observability (disabled by default via config)
"asyncpg>=0.30.0",
"nest-asyncio>=1.6.0", # For Alembic migrations with Postgres
"pytest-asyncio>=1.2.0",
"psycopg==3.3.1",
"mdformat>=0.7.22",
"mdformat-gfm>=0.3.7",
"mdformat-frontmatter>=2.0.8",
"sniffio>=1.3.1",
"anyio>=4.10.0",
"httpx>=0.28.0",
]
@@ -54,54 +40,40 @@ Documentation = "https://github.com/basicmachines-co/basic-memory#readme"
[project.scripts]
basic-memory = "basic_memory.cli.main:app"
bm = "basic_memory.cli.main:app"
[build-system]
requires = ["hatchling", "uv-dynamic-versioning>=0.7.0"]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.pytest.ini_options]
pythonpath = ["src", "tests"]
addopts = "--cov=basic_memory --cov-report term-missing"
testpaths = ["tests", "test-int"]
addopts = "--cov=basic_memory --cov-report term-missing -ra -q"
testpaths = ["tests"]
asyncio_mode = "strict"
asyncio_default_fixture_loop_scope = "function"
markers = [
"benchmark: Performance benchmark tests (deselect with '-m \"not benchmark\"')",
"slow: Slow-running tests (deselect with '-m \"not slow\"')",
"postgres: Tests that run against Postgres backend (deselect with '-m \"not postgres\"')",
"windows: Windows-specific tests (deselect with '-m \"not windows\"')",
]
[tool.ruff]
line-length = 100
target-version = "py312"
[dependency-groups]
dev = [
[tool.uv]
dev-dependencies = [
"gevent>=24.11.1",
"icecream>=2.1.3",
"pytest>=8.3.4",
"pytest-cov>=4.1.0",
"pytest-mock>=3.12.0",
"pytest-asyncio>=0.24.0",
"pytest-xdist>=3.0.0",
"ruff>=0.1.6",
"freezegun>=1.5.5",
"testcontainers[postgres]>=4.0.0",
"psycopg>=3.2.0",
"pyright>=1.1.408",
"pytest>=8.3.4",
"pytest-cov>=4.1.0",
"pytest-mock>=3.12.0",
"pytest-asyncio>=0.24.0",
"ruff>=0.1.6",
"cx-freeze>=7.2.10",
"pyqt6>=6.8.1",
]
[tool.hatch.version]
source = "uv-dynamic-versioning"
[tool.uv-dynamic-versioning]
vcs = "git"
style = "pep440"
bump = true
fallback-version = "0.0.0"
[tool.pyright]
include = ["src/"]
exclude = ["**/__pycache__"]
@@ -112,35 +84,23 @@ reportMissingTypeStubs = false
pythonVersion = "3.12"
[tool.semantic_release]
version_variables = [
"src/basic_memory/__init__.py:__version__",
]
version_toml = [
"pyproject.toml:project.version",
]
major_on_zero = false
branch = "main"
changelog_file = "CHANGELOG.md"
build_command = "pip install uv && uv build"
dist_path = "dist/"
upload_to_pypi = true
commit_message = "chore(release): {version} [skip ci]"
[tool.coverage.run]
concurrency = ["thread", "gevent"]
parallel = true
source = ["basic_memory"]
[tool.coverage.report]
exclude_lines = [
"pragma: no cover",
"def __repr__",
"if self.debug:",
"if settings.DEBUG",
"raise AssertionError",
"raise NotImplementedError",
"if 0:",
"if __name__ == .__main__.:",
"class .*\\bProtocol\\):",
"@(abc\\.)?abstractmethod",
]
# Exclude specific modules that are difficult to test comprehensively
omit = [
"*/external_auth_provider.py", # External HTTP calls to OAuth providers
"*/supabase_auth_provider.py", # External HTTP calls to Supabase APIs
"*/watch_service.py", # File system watching - complex integration testing
"*/background_sync.py", # Background processes
"*/cli/**", # CLI is an interactive wrapper; core logic is covered via API/MCP/service tests
"*/db.py", # Backend/runtime-dependent (sqlite/postgres/windows tuning); validated via integration tests
"*/services/initialization.py", # Startup orchestration + background tasks (watchers); exercised indirectly in entrypoints
"*/sync/sync_service.py", # Heavy filesystem/db integration; covered by integration suite, not enforced in unit coverage
"*/services/migration_service.py", # Complex migration scenarios
]
[tool.logfire]
ignore_no_config = true
+36
View File
@@ -0,0 +1,36 @@
#!/bin/bash
set -e
echo "Welcome to Basic Memory installer"
# 1. Install uv if not present
if ! command -v uv &> /dev/null; then
echo "Installing uv package manager..."
curl -LsSf https://github.com/astral-sh/uv/releases/download/0.1.23/uv-installer.sh | sh
fi
# 2. Configure Claude Desktop
echo "Configuring Claude Desktop..."
CONFIG_FILE="$HOME/Library/Application Support/Claude/claude_desktop_config.json"
# Create config directory if it doesn't exist
mkdir -p "$(dirname "$CONFIG_FILE")"
# If config file doesn't exist, create it with initial structure
if [ ! -f "$CONFIG_FILE" ]; then
echo '{"mcpServers": {}}' > "$CONFIG_FILE"
fi
# Add/update the basic-memory config using jq
jq '.mcpServers."basic-memory" = {
"command": "uvx",
"args": ["basic-memory"]
}' "$CONFIG_FILE" > "$CONFIG_FILE.tmp" && mv "$CONFIG_FILE.tmp" "$CONFIG_FILE"
echo "Installation complete! Basic Memory is now available in Claude Desktop."
echo "Please restart Claude Desktop for changes to take effect."
echo -e "\nQuick Start:"
echo "1. You can run sync directly using: uvx basic-memory sync"
echo "2. Optionally, install globally with: uv pip install basic-memory"
echo -e "\nBuilt with ♥️ by Basic Machines."
-25
View File
@@ -1,25 +0,0 @@
{
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
"name": "io.github.basicmachines-co/basic-memory",
"description": "Local-first knowledge management with bi-directional LLM sync via Markdown files.",
"repository": {
"url": "https://github.com/basicmachines-co/basic-memory.git",
"source": "github"
},
"version": "0.18.0",
"packages": [
{
"registryType": "pypi",
"identifier": "basic-memory",
"version": "0.18.0",
"runtimeHint": "uvx",
"runtimeArguments": [
{"type": "positional", "value": "basic-memory"},
{"type": "positional", "value": "mcp"}
],
"transport": {
"type": "stdio"
}
}
]
}
+4 -2
View File
@@ -7,9 +7,11 @@ startCommand:
type: object
properties: {}
description: No configuration required. This MCP server runs using the default command.
commandFunction: |-
commandFunction:
# A JS function that produces the CLI command based on the given config to start the MCP on stdio.
|-
(config) => ({
command: 'basic-memory',
args: ['mcp']
})
exampleConfig: {}
exampleConfig: {}
+1 -5
View File
@@ -1,7 +1,3 @@
"""basic-memory - Local-first knowledge management combining Zettelkasten with knowledge graphs"""
# Package version - updated by release automation
__version__ = "0.18.0"
# API version for FastAPI - independent of package version
__api_version__ = "v0"
__version__ = "0.9.0"
+26 -118
View File
@@ -1,60 +1,29 @@
"""Alembic environment configuration."""
import asyncio
import os
from logging.config import fileConfig
# Allow nested event loops (needed for pytest-asyncio and other async contexts)
# Note: nest_asyncio doesn't work with uvloop or Python 3.14+, so we handle those cases separately
import sys
if sys.version_info < (3, 14):
try:
import nest_asyncio
nest_asyncio.apply()
except (ImportError, ValueError):
# nest_asyncio not available or can't patch this loop type (e.g., uvloop)
pass
# For Python 3.14+, we rely on the thread-based fallback in run_migrations_online()
from sqlalchemy import engine_from_config, pool
from sqlalchemy.ext.asyncio import AsyncEngine, create_async_engine
from sqlalchemy import engine_from_config
from sqlalchemy import pool
from alembic import context
from basic_memory.config import ConfigManager
from basic_memory.models import Base
# Trigger: only set test env when actually running under pytest
# Why: alembic/env.py is imported during normal operations (MCP server startup, migrations)
# but we only want test behavior during actual test runs
# Outcome: prevents is_test_env from returning True in production, enabling watch service
if os.getenv("PYTEST_CURRENT_TEST") is not None:
os.environ["BASIC_MEMORY_ENV"] = "test"
# set config.env to "test" for pytest to prevent logging to file in utils.setup_logging()
os.environ["BASIC_MEMORY_ENV"] = "test"
# Import after setting environment variable # noqa: E402
from basic_memory.models import Base # noqa: E402
from basic_memory.config import config as app_config
# this is the Alembic Config object, which provides
# access to the values within the .ini file in use.
config = context.config
# Load app config - this will read environment variables (BASIC_MEMORY_DATABASE_BACKEND, etc.)
# due to Pydantic's env_prefix="BASIC_MEMORY_" setting
app_config = ConfigManager().config
# Set the SQLAlchemy URL from our app config
sqlalchemy_url = f"sqlite:///{app_config.database_path}"
config.set_main_option("sqlalchemy.url", sqlalchemy_url)
# Set the SQLAlchemy URL based on database backend configuration
# If the URL is already set in config (e.g., from run_migrations), use that
# Otherwise, get it from app config
# Note: alembic.ini has a placeholder URL "driver://user:pass@localhost/dbname" that we need to override
current_url = config.get_main_option("sqlalchemy.url")
if not current_url or current_url == "driver://user:pass@localhost/dbname":
from basic_memory.db import DatabaseType
sqlalchemy_url = DatabaseType.get_db_url(
app_config.database_path, DatabaseType.FILESYSTEM, app_config
)
config.set_main_option("sqlalchemy.url", sqlalchemy_url)
# print(f"Using SQLAlchemy URL: {sqlalchemy_url}")
# Interpret the config file for Python logging.
if config.config_file_name is not None:
@@ -98,89 +67,28 @@ def run_migrations_offline() -> None:
context.run_migrations()
def do_run_migrations(connection):
"""Execute migrations with the given connection."""
context.configure(
connection=connection,
target_metadata=target_metadata,
include_object=include_object,
render_as_batch=True,
compare_type=True,
)
with context.begin_transaction():
context.run_migrations()
async def run_async_migrations(connectable):
"""Run migrations asynchronously with AsyncEngine."""
async with connectable.connect() as connection:
await connection.run_sync(do_run_migrations)
await connectable.dispose()
def run_migrations_online() -> None:
"""Run migrations in 'online' mode.
Supports both sync engines (SQLite) and async engines (PostgreSQL with asyncpg).
In this scenario we need to create an Engine
and associate a connection with the context.
"""
# Check if a connection/engine was provided (e.g., from run_migrations)
connectable = context.config.attributes.get("connection", None)
connectable = engine_from_config(
config.get_section(config.config_ini_section, {}),
prefix="sqlalchemy.",
poolclass=pool.NullPool,
)
if connectable is None:
# No connection provided, create engine from config
url = context.config.get_main_option("sqlalchemy.url")
with connectable.connect() as connection:
context.configure(
connection=connection,
target_metadata=target_metadata,
include_object=include_object,
render_as_batch=True,
)
# Check if it's an async URL (sqlite+aiosqlite or postgresql+asyncpg)
if url and ("+asyncpg" in url or "+aiosqlite" in url):
# Create async engine for asyncpg or aiosqlite
connectable = create_async_engine(
url,
poolclass=pool.NullPool,
future=True,
)
else:
# Create sync engine for regular sqlite or postgresql
connectable = engine_from_config(
context.config.get_section(context.config.config_ini_section, {}),
prefix="sqlalchemy.",
poolclass=pool.NullPool,
)
# Handle async engines (PostgreSQL with asyncpg)
if isinstance(connectable, AsyncEngine):
# Try to run async migrations
# nest_asyncio allows asyncio.run() from within event loops, but doesn't work with uvloop
try:
asyncio.run(run_async_migrations(connectable))
except RuntimeError as e:
if "cannot be called from a running event loop" in str(e):
# We're in a running event loop (likely uvloop) - need to use a different approach
# Create a new thread to run the async migrations
import concurrent.futures
def run_in_thread():
"""Run async migrations in a new event loop in a separate thread."""
new_loop = asyncio.new_event_loop()
asyncio.set_event_loop(new_loop)
try:
new_loop.run_until_complete(run_async_migrations(connectable))
finally:
new_loop.close()
with concurrent.futures.ThreadPoolExecutor() as executor:
future = executor.submit(run_in_thread)
future.result() # Wait for completion and re-raise any exceptions
else:
raise
else:
# Handle sync engines (SQLite) or sync connections
if hasattr(connectable, "connect"):
# It's an engine, get a connection
with connectable.connect() as connection:
do_run_migrations(connection)
else:
# It's already a connection
do_run_migrations(connectable)
with context.begin_transaction():
context.run_migrations()
if context.is_offline_mode():
@@ -1,131 +0,0 @@
"""Add Postgres full-text search support with tsvector and GIN indexes
Revision ID: 314f1ea54dc4
Revises: e7e1f4367280
Create Date: 2025-11-15 18:05:01.025405
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = "314f1ea54dc4"
down_revision: Union[str, None] = "e7e1f4367280"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Add PostgreSQL full-text search support.
This migration:
1. Creates search_index table for Postgres (SQLite uses FTS5 virtual table)
2. Adds generated tsvector column for full-text search
3. Creates GIN index on the tsvector column for fast text queries
4. Creates GIN index on metadata JSONB column for fast containment queries
Note: These changes only apply to Postgres. SQLite continues to use FTS5 virtual tables.
"""
# Check if we're using Postgres
connection = op.get_bind()
if connection.dialect.name == "postgresql":
# Create search_index table for Postgres
# For SQLite, this is a FTS5 virtual table created elsewhere
from sqlalchemy.dialects.postgresql import JSONB
op.create_table(
"search_index",
sa.Column("id", sa.Integer(), nullable=False), # Entity IDs are integers
sa.Column("project_id", sa.Integer(), nullable=False), # Multi-tenant isolation
sa.Column("title", sa.Text(), nullable=True),
sa.Column("content_stems", sa.Text(), nullable=True),
sa.Column("content_snippet", sa.Text(), nullable=True),
sa.Column("permalink", sa.String(), nullable=True), # Nullable for non-markdown files
sa.Column("file_path", sa.String(), nullable=True),
sa.Column("type", sa.String(), nullable=True),
sa.Column("from_id", sa.Integer(), nullable=True), # Relation IDs are integers
sa.Column("to_id", sa.Integer(), nullable=True), # Relation IDs are integers
sa.Column("relation_type", sa.String(), nullable=True),
sa.Column("entity_id", sa.Integer(), nullable=True), # Entity IDs are integers
sa.Column("category", sa.String(), nullable=True),
sa.Column("metadata", JSONB(), nullable=True), # Use JSONB for Postgres
sa.Column("created_at", sa.DateTime(timezone=True), nullable=True),
sa.Column("updated_at", sa.DateTime(timezone=True), nullable=True),
sa.PrimaryKeyConstraint(
"id", "type", "project_id"
), # Composite key: id can repeat across types
sa.ForeignKeyConstraint(
["project_id"],
["project.id"],
name="fk_search_index_project_id",
ondelete="CASCADE",
),
if_not_exists=True,
)
# Create index on project_id for efficient multi-tenant queries
op.create_index(
"ix_search_index_project_id",
"search_index",
["project_id"],
unique=False,
)
# Create unique partial index on permalink for markdown files
# Non-markdown files don't have permalinks, so we use a partial index
op.execute("""
CREATE UNIQUE INDEX uix_search_index_permalink_project
ON search_index (permalink, project_id)
WHERE permalink IS NOT NULL
""")
# Add tsvector column as a GENERATED ALWAYS column
# This automatically updates when title or content_stems change
op.execute("""
ALTER TABLE search_index
ADD COLUMN textsearchable_index_col tsvector
GENERATED ALWAYS AS (
to_tsvector('english',
coalesce(title, '') || ' ' ||
coalesce(content_stems, '')
)
) STORED
""")
# Create GIN index on tsvector column for fast full-text search
op.create_index(
"idx_search_index_fts",
"search_index",
["textsearchable_index_col"],
unique=False,
postgresql_using="gin",
)
# Create GIN index on metadata JSONB for fast containment queries
# Using jsonb_path_ops for smaller index size and better performance
op.execute("""
CREATE INDEX idx_search_index_metadata_gin
ON search_index
USING GIN (metadata jsonb_path_ops)
""")
def downgrade() -> None:
"""Remove PostgreSQL full-text search support."""
connection = op.get_bind()
if connection.dialect.name == "postgresql":
# Drop indexes first
op.execute("DROP INDEX IF EXISTS idx_search_index_metadata_gin")
op.drop_index("idx_search_index_fts", table_name="search_index")
op.execute("DROP INDEX IF EXISTS uix_search_index_permalink_project")
op.drop_index("ix_search_index_project_id", table_name="search_index")
# Drop the generated column
op.execute("ALTER TABLE search_index DROP COLUMN IF EXISTS textsearchable_index_col")
# Drop the search_index table
op.drop_table("search_index")
@@ -1,120 +0,0 @@
"""add projects table
Revision ID: 5fe1ab1ccebe
Revises: cc7172b46608
Create Date: 2025-05-14 09:05:18.214357
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = "5fe1ab1ccebe"
down_revision: Union[str, None] = "cc7172b46608"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
# SQLite FTS5 virtual table handling is SQLite-specific
# For Postgres, search_index is a regular table managed by ORM
connection = op.get_bind()
is_sqlite = connection.dialect.name == "sqlite"
op.create_table(
"project",
sa.Column("id", sa.Integer(), nullable=False),
sa.Column("name", sa.String(), nullable=False),
sa.Column("description", sa.Text(), nullable=True),
sa.Column("permalink", sa.String(), nullable=False),
sa.Column("path", sa.String(), nullable=False),
sa.Column("is_active", sa.Boolean(), nullable=False),
sa.Column("is_default", sa.Boolean(), nullable=True),
sa.Column("created_at", sa.DateTime(), nullable=False),
sa.Column("updated_at", sa.DateTime(), nullable=False),
sa.PrimaryKeyConstraint("id"),
sa.UniqueConstraint("is_default"),
sa.UniqueConstraint("name"),
sa.UniqueConstraint("permalink"),
if_not_exists=True,
)
with op.batch_alter_table("project", schema=None) as batch_op:
batch_op.create_index(
"ix_project_created_at", ["created_at"], unique=False, if_not_exists=True
)
batch_op.create_index("ix_project_name", ["name"], unique=True, if_not_exists=True)
batch_op.create_index("ix_project_path", ["path"], unique=False, if_not_exists=True)
batch_op.create_index(
"ix_project_permalink", ["permalink"], unique=True, if_not_exists=True
)
batch_op.create_index(
"ix_project_updated_at", ["updated_at"], unique=False, if_not_exists=True
)
with op.batch_alter_table("entity", schema=None) as batch_op:
batch_op.add_column(sa.Column("project_id", sa.Integer(), nullable=False))
batch_op.drop_index(
"uix_entity_permalink",
sqlite_where=sa.text("content_type = 'text/markdown' AND permalink IS NOT NULL")
if is_sqlite
else None,
)
batch_op.drop_index("ix_entity_file_path")
batch_op.create_index(batch_op.f("ix_entity_file_path"), ["file_path"], unique=False)
batch_op.create_index("ix_entity_project_id", ["project_id"], unique=False)
batch_op.create_index(
"uix_entity_file_path_project", ["file_path", "project_id"], unique=True
)
batch_op.create_index(
"uix_entity_permalink_project",
["permalink", "project_id"],
unique=True,
sqlite_where=sa.text("content_type = 'text/markdown' AND permalink IS NOT NULL")
if is_sqlite
else None,
)
batch_op.create_foreign_key("fk_entity_project_id", "project", ["project_id"], ["id"])
# drop the search index table. it will be recreated
# Only drop for SQLite - Postgres uses regular table managed by ORM
if is_sqlite:
op.drop_table("search_index")
# ### end Alembic commands ###
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table("entity", schema=None) as batch_op:
batch_op.drop_constraint("fk_entity_project_id", type_="foreignkey")
batch_op.drop_index(
"uix_entity_permalink_project",
sqlite_where=sa.text("content_type = 'text/markdown' AND permalink IS NOT NULL"),
)
batch_op.drop_index("uix_entity_file_path_project")
batch_op.drop_index("ix_entity_project_id")
batch_op.drop_index(batch_op.f("ix_entity_file_path"))
batch_op.create_index("ix_entity_file_path", ["file_path"], unique=1)
batch_op.create_index(
"uix_entity_permalink",
["permalink"],
unique=1,
sqlite_where=sa.text("content_type = 'text/markdown' AND permalink IS NOT NULL"),
)
batch_op.drop_column("project_id")
with op.batch_alter_table("project", schema=None) as batch_op:
batch_op.drop_index("ix_project_updated_at")
batch_op.drop_index("ix_project_permalink")
batch_op.drop_index("ix_project_path")
batch_op.drop_index("ix_project_name")
batch_op.drop_index("ix_project_created_at")
op.drop_table("project")
# ### end Alembic commands ###
@@ -1,112 +0,0 @@
"""project constraint fix
Revision ID: 647e7a75e2cd
Revises: 5fe1ab1ccebe
Create Date: 2025-06-03 12:48:30.162566
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = "647e7a75e2cd"
down_revision: Union[str, None] = "5fe1ab1ccebe"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Remove the problematic UNIQUE constraint on is_default column.
The UNIQUE constraint prevents multiple projects from having is_default=FALSE,
which breaks project creation when the service sets is_default=False.
SQLite: Recreate the table without the constraint (no ALTER TABLE support)
Postgres: Use ALTER TABLE to drop the constraint directly
"""
connection = op.get_bind()
is_sqlite = connection.dialect.name == "sqlite"
if is_sqlite:
# For SQLite, we need to recreate the table without the UNIQUE constraint
# Create a new table without the UNIQUE constraint on is_default
op.create_table(
"project_new",
sa.Column("id", sa.Integer(), nullable=False),
sa.Column("name", sa.String(), nullable=False),
sa.Column("description", sa.Text(), nullable=True),
sa.Column("permalink", sa.String(), nullable=False),
sa.Column("path", sa.String(), nullable=False),
sa.Column("is_active", sa.Boolean(), nullable=False),
sa.Column("is_default", sa.Boolean(), nullable=True), # No UNIQUE constraint!
sa.Column("created_at", sa.DateTime(), nullable=False),
sa.Column("updated_at", sa.DateTime(), nullable=False),
sa.PrimaryKeyConstraint("id"),
sa.UniqueConstraint("name"),
sa.UniqueConstraint("permalink"),
)
# Copy data from old table to new table
op.execute("INSERT INTO project_new SELECT * FROM project")
# Drop the old table
op.drop_table("project")
# Rename the new table
op.rename_table("project_new", "project")
# Recreate the indexes
with op.batch_alter_table("project", schema=None) as batch_op:
batch_op.create_index("ix_project_created_at", ["created_at"], unique=False)
batch_op.create_index("ix_project_name", ["name"], unique=True)
batch_op.create_index("ix_project_path", ["path"], unique=False)
batch_op.create_index("ix_project_permalink", ["permalink"], unique=True)
batch_op.create_index("ix_project_updated_at", ["updated_at"], unique=False)
else:
# For Postgres, we can simply drop the constraint
with op.batch_alter_table("project", schema=None) as batch_op:
batch_op.drop_constraint("project_is_default_key", type_="unique")
def downgrade() -> None:
"""Add back the UNIQUE constraint on is_default column.
WARNING: This will break project creation again if multiple projects
have is_default=FALSE.
"""
# Recreate the table with the UNIQUE constraint
op.create_table(
"project_old",
sa.Column("id", sa.Integer(), nullable=False),
sa.Column("name", sa.String(), nullable=False),
sa.Column("description", sa.Text(), nullable=True),
sa.Column("permalink", sa.String(), nullable=False),
sa.Column("path", sa.String(), nullable=False),
sa.Column("is_active", sa.Boolean(), nullable=False),
sa.Column("is_default", sa.Boolean(), nullable=True),
sa.Column("created_at", sa.DateTime(), nullable=False),
sa.Column("updated_at", sa.DateTime(), nullable=False),
sa.PrimaryKeyConstraint("id"),
sa.UniqueConstraint("is_default"), # Add back the problematic constraint
sa.UniqueConstraint("name"),
sa.UniqueConstraint("permalink"),
)
# Copy data (this may fail if multiple FALSE values exist)
op.execute("INSERT INTO project_old SELECT * FROM project")
# Drop the current table and rename
op.drop_table("project")
op.rename_table("project_old", "project")
# Recreate indexes
with op.batch_alter_table("project", schema=None) as batch_op:
batch_op.create_index("ix_project_created_at", ["created_at"], unique=False)
batch_op.create_index("ix_project_name", ["name"], unique=True)
batch_op.create_index("ix_project_path", ["path"], unique=False)
batch_op.create_index("ix_project_permalink", ["permalink"], unique=True)
batch_op.create_index("ix_project_updated_at", ["updated_at"], unique=False)
@@ -1,24 +0,0 @@
"""Merge multiple heads
Revision ID: 6830751f5fb6
Revises: a2b3c4d5e6f7, g9a0b3c4d5e6
Create Date: 2025-12-29 12:46:46.476268
"""
from typing import Sequence, Union
# revision identifiers, used by Alembic.
revision: str = "6830751f5fb6"
down_revision: Union[str, Sequence[str], None] = ("a2b3c4d5e6f7", "g9a0b3c4d5e6")
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
pass
def downgrade() -> None:
pass
@@ -1,49 +0,0 @@
"""Add mtime and size columns to Entity for sync optimization
Revision ID: 9d9c1cb7d8f5
Revises: a1b2c3d4e5f6
Create Date: 2025-10-20 05:07:55.173849
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = "9d9c1cb7d8f5"
down_revision: Union[str, None] = "a1b2c3d4e5f6"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table("entity", schema=None) as batch_op:
batch_op.add_column(sa.Column("mtime", sa.Float(), nullable=True))
batch_op.add_column(sa.Column("size", sa.Integer(), nullable=True))
batch_op.drop_constraint(batch_op.f("fk_entity_project_id"), type_="foreignkey")
batch_op.create_foreign_key(
batch_op.f("fk_entity_project_id"), "project", ["project_id"], ["id"]
)
# ### end Alembic commands ###
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table("entity", schema=None) as batch_op:
batch_op.drop_constraint(batch_op.f("fk_entity_project_id"), type_="foreignkey")
batch_op.create_foreign_key(
batch_op.f("fk_entity_project_id"),
"project",
["project_id"],
["id"],
ondelete="CASCADE",
)
batch_op.drop_column("size")
batch_op.drop_column("mtime")
# ### end Alembic commands ###
@@ -1,49 +0,0 @@
"""fix project foreign keys
Revision ID: a1b2c3d4e5f6
Revises: 647e7a75e2cd
Create Date: 2025-08-19 22:06:00.000000
"""
from typing import Sequence, Union
from alembic import op
# revision identifiers, used by Alembic.
revision: str = "a1b2c3d4e5f6"
down_revision: Union[str, None] = "647e7a75e2cd"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Re-establish foreign key constraints that were lost during project table recreation.
The migration 647e7a75e2cd recreated the project table but did not re-establish
the foreign key constraint from entity.project_id to project.id, causing
foreign key constraint failures when trying to delete projects with related entities.
"""
# SQLite doesn't allow adding foreign key constraints to existing tables easily
# We need to be careful and handle the case where the constraint might already exist
with op.batch_alter_table("entity", schema=None) as batch_op:
# Try to drop existing foreign key constraint (may not exist)
try:
batch_op.drop_constraint("fk_entity_project_id", type_="foreignkey")
except Exception:
# Constraint may not exist, which is fine - we'll create it next
pass
# Add the foreign key constraint with CASCADE DELETE
# This ensures that when a project is deleted, all related entities are also deleted
batch_op.create_foreign_key(
"fk_entity_project_id", "project", ["project_id"], ["id"], ondelete="CASCADE"
)
def downgrade() -> None:
"""Remove the foreign key constraint."""
with op.batch_alter_table("entity", schema=None) as batch_op:
batch_op.drop_constraint("fk_entity_project_id", type_="foreignkey")
@@ -1,56 +0,0 @@
"""Add cascade delete FK from search_index to entity
Revision ID: a2b3c4d5e6f7
Revises: f8a9b2c3d4e5
Create Date: 2025-12-02 07:00:00.000000
"""
from typing import Sequence, Union
from alembic import op
# revision identifiers, used by Alembic.
revision: str = "a2b3c4d5e6f7"
down_revision: Union[str, None] = "f8a9b2c3d4e5"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Add FK with CASCADE delete from search_index.entity_id to entity.id.
This migration is Postgres-only because:
- SQLite uses FTS5 virtual tables which don't support foreign keys
- The FK enables automatic cleanup of search_index entries when entities are deleted
"""
connection = op.get_bind()
dialect = connection.dialect.name
if dialect == "postgresql":
# First, clean up any orphaned search_index entries where entity no longer exists
op.execute("""
DELETE FROM search_index
WHERE entity_id IS NOT NULL
AND entity_id NOT IN (SELECT id FROM entity)
""")
# Add FK with CASCADE - nullable FK allows search_index entries without entity_id
op.create_foreign_key(
"fk_search_index_entity_id",
"search_index",
"entity",
["entity_id"],
["id"],
ondelete="CASCADE",
)
def downgrade() -> None:
"""Remove the FK constraint."""
connection = op.get_bind()
dialect = connection.dialect.name
if dialect == "postgresql":
op.drop_constraint("fk_search_index_entity_id", "search_index", type_="foreignkey")
@@ -21,12 +21,6 @@ depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Upgrade database schema to use new search index with content_stems and content_snippet."""
# This migration is SQLite-specific (FTS5 virtual tables)
# For Postgres, the search_index table is created via ORM models
connection = op.get_bind()
if connection.dialect.name != "sqlite":
return
# First, drop the existing search_index table
op.execute("DROP TABLE IF EXISTS search_index")
@@ -62,16 +56,15 @@ def upgrade() -> None:
);
""")
# Print instruction to manually reindex after migration
print("\n------------------------------------------------------------------")
print("IMPORTANT: After migration completes, manually run the reindex command:")
print("basic-memory sync")
print("------------------------------------------------------------------\n")
def downgrade() -> None:
"""Downgrade database schema to use old search index."""
# This migration is SQLite-specific (FTS5 virtual tables)
# For Postgres, the search_index table is managed via ORM models
connection = op.get_bind()
if connection.dialect.name != "sqlite":
return
# Drop the updated search_index table
op.execute("DROP TABLE IF EXISTS search_index")
@@ -1,37 +0,0 @@
"""Add scan watermark tracking to Project
Revision ID: e7e1f4367280
Revises: 9d9c1cb7d8f5
Create Date: 2025-10-20 16:42:46.625075
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = "e7e1f4367280"
down_revision: Union[str, None] = "9d9c1cb7d8f5"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table("project", schema=None) as batch_op:
batch_op.add_column(sa.Column("last_scan_timestamp", sa.Float(), nullable=True))
batch_op.add_column(sa.Column("last_file_count", sa.Integer(), nullable=True))
# ### end Alembic commands ###
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table("project", schema=None) as batch_op:
batch_op.drop_column("last_file_count")
batch_op.drop_column("last_scan_timestamp")
# ### end Alembic commands ###
@@ -1,239 +0,0 @@
"""Add project_id to relation/observation and pg_trgm for fuzzy link resolution
Revision ID: f8a9b2c3d4e5
Revises: 314f1ea54dc4
Create Date: 2025-12-01 12:00:00.000000
"""
from typing import Sequence, Union
import sqlalchemy as sa
from alembic import op
from sqlalchemy import text
def column_exists(connection, table: str, column: str) -> bool:
"""Check if a column exists in a table (idempotent migration support)."""
if connection.dialect.name == "postgresql":
result = connection.execute(
text(
"SELECT 1 FROM information_schema.columns "
"WHERE table_name = :table AND column_name = :column"
),
{"table": table, "column": column},
)
return result.fetchone() is not None
else:
# SQLite
result = connection.execute(text(f"PRAGMA table_info({table})"))
columns = [row[1] for row in result]
return column in columns
def index_exists(connection, index_name: str) -> bool:
"""Check if an index exists (idempotent migration support)."""
if connection.dialect.name == "postgresql":
result = connection.execute(
text("SELECT 1 FROM pg_indexes WHERE indexname = :index_name"),
{"index_name": index_name},
)
return result.fetchone() is not None
else:
# SQLite
result = connection.execute(
text("SELECT 1 FROM sqlite_master WHERE type='index' AND name = :index_name"),
{"index_name": index_name},
)
return result.fetchone() is not None
# revision identifiers, used by Alembic.
revision: str = "f8a9b2c3d4e5"
down_revision: Union[str, None] = "314f1ea54dc4"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Add project_id to relation and observation tables, plus pg_trgm indexes.
This migration:
1. Adds project_id column to relation and observation tables (denormalization)
2. Backfills project_id from the associated entity
3. Enables pg_trgm extension for trigram-based fuzzy matching (Postgres only)
4. Creates GIN indexes on entity title and permalink for fast similarity searches
5. Creates partial index on unresolved relations for efficient bulk resolution
"""
connection = op.get_bind()
dialect = connection.dialect.name
# -------------------------------------------------------------------------
# Add project_id to relation table
# -------------------------------------------------------------------------
# Step 1: Add project_id column as nullable first (idempotent)
if not column_exists(connection, "relation", "project_id"):
op.add_column("relation", sa.Column("project_id", sa.Integer(), nullable=True))
# Step 2: Backfill project_id from entity.project_id via from_id
if dialect == "postgresql":
op.execute("""
UPDATE relation
SET project_id = entity.project_id
FROM entity
WHERE relation.from_id = entity.id
""")
else:
# SQLite syntax
op.execute("""
UPDATE relation
SET project_id = (
SELECT entity.project_id
FROM entity
WHERE entity.id = relation.from_id
)
""")
# Step 3: Make project_id NOT NULL and add foreign key
if dialect == "postgresql":
op.alter_column("relation", "project_id", nullable=False)
op.create_foreign_key(
"fk_relation_project_id",
"relation",
"project",
["project_id"],
["id"],
)
else:
# SQLite requires batch operations for ALTER COLUMN
with op.batch_alter_table("relation") as batch_op:
batch_op.alter_column("project_id", nullable=False)
batch_op.create_foreign_key(
"fk_relation_project_id",
"project",
["project_id"],
["id"],
)
# Step 4: Create index on relation.project_id (idempotent)
if not index_exists(connection, "ix_relation_project_id"):
op.create_index("ix_relation_project_id", "relation", ["project_id"])
# -------------------------------------------------------------------------
# Add project_id to observation table
# -------------------------------------------------------------------------
# Step 1: Add project_id column as nullable first (idempotent)
if not column_exists(connection, "observation", "project_id"):
op.add_column("observation", sa.Column("project_id", sa.Integer(), nullable=True))
# Step 2: Backfill project_id from entity.project_id via entity_id
if dialect == "postgresql":
op.execute("""
UPDATE observation
SET project_id = entity.project_id
FROM entity
WHERE observation.entity_id = entity.id
""")
else:
# SQLite syntax
op.execute("""
UPDATE observation
SET project_id = (
SELECT entity.project_id
FROM entity
WHERE entity.id = observation.entity_id
)
""")
# Step 3: Make project_id NOT NULL and add foreign key
if dialect == "postgresql":
op.alter_column("observation", "project_id", nullable=False)
op.create_foreign_key(
"fk_observation_project_id",
"observation",
"project",
["project_id"],
["id"],
)
else:
# SQLite requires batch operations for ALTER COLUMN
with op.batch_alter_table("observation") as batch_op:
batch_op.alter_column("project_id", nullable=False)
batch_op.create_foreign_key(
"fk_observation_project_id",
"project",
["project_id"],
["id"],
)
# Step 4: Create index on observation.project_id (idempotent)
if not index_exists(connection, "ix_observation_project_id"):
op.create_index("ix_observation_project_id", "observation", ["project_id"])
# Postgres-specific: pg_trgm and GIN indexes
if dialect == "postgresql":
# Enable pg_trgm extension for fuzzy string matching
op.execute("CREATE EXTENSION IF NOT EXISTS pg_trgm")
# Create trigram indexes on entity table for fuzzy matching
# GIN indexes with gin_trgm_ops support similarity searches
op.execute("""
CREATE INDEX IF NOT EXISTS idx_entity_title_trgm
ON entity USING gin (title gin_trgm_ops)
""")
op.execute("""
CREATE INDEX IF NOT EXISTS idx_entity_permalink_trgm
ON entity USING gin (permalink gin_trgm_ops)
""")
# Create partial index on unresolved relations for efficient bulk resolution
# This makes "WHERE to_id IS NULL AND project_id = X" queries very fast
op.execute("""
CREATE INDEX IF NOT EXISTS idx_relation_unresolved
ON relation (project_id, to_name)
WHERE to_id IS NULL
""")
# Create index on relation.to_name for join performance in bulk resolution
op.execute("""
CREATE INDEX IF NOT EXISTS idx_relation_to_name
ON relation (to_name)
""")
def downgrade() -> None:
"""Remove project_id from relation/observation and pg_trgm indexes."""
connection = op.get_bind()
dialect = connection.dialect.name
if dialect == "postgresql":
# Drop Postgres-specific indexes
op.execute("DROP INDEX IF EXISTS idx_relation_to_name")
op.execute("DROP INDEX IF EXISTS idx_relation_unresolved")
op.execute("DROP INDEX IF EXISTS idx_entity_permalink_trgm")
op.execute("DROP INDEX IF EXISTS idx_entity_title_trgm")
# Note: We don't drop the pg_trgm extension as other code may depend on it
# Drop project_id from observation
op.drop_index("ix_observation_project_id", table_name="observation")
op.drop_constraint("fk_observation_project_id", "observation", type_="foreignkey")
op.drop_column("observation", "project_id")
# Drop project_id from relation
op.drop_index("ix_relation_project_id", table_name="relation")
op.drop_constraint("fk_relation_project_id", "relation", type_="foreignkey")
op.drop_column("relation", "project_id")
else:
# SQLite requires batch operations
op.drop_index("ix_observation_project_id", table_name="observation")
with op.batch_alter_table("observation") as batch_op:
batch_op.drop_constraint("fk_observation_project_id", type_="foreignkey")
batch_op.drop_column("project_id")
op.drop_index("ix_relation_project_id", table_name="relation")
with op.batch_alter_table("relation") as batch_op:
batch_op.drop_constraint("fk_relation_project_id", type_="foreignkey")
batch_op.drop_column("project_id")
@@ -1,173 +0,0 @@
"""Add external_id UUID column to project and entity tables
Revision ID: g9a0b3c4d5e6
Revises: f8a9b2c3d4e5
Create Date: 2025-12-29 10:00:00.000000
"""
import uuid
from typing import Sequence, Union
import sqlalchemy as sa
from alembic import op
from sqlalchemy import text
def column_exists(connection, table: str, column: str) -> bool:
"""Check if a column exists in a table (idempotent migration support)."""
if connection.dialect.name == "postgresql":
result = connection.execute(
text(
"SELECT 1 FROM information_schema.columns "
"WHERE table_name = :table AND column_name = :column"
),
{"table": table, "column": column},
)
return result.fetchone() is not None
else:
# SQLite
result = connection.execute(text(f"PRAGMA table_info({table})"))
columns = [row[1] for row in result]
return column in columns
def index_exists(connection, index_name: str) -> bool:
"""Check if an index exists (idempotent migration support)."""
if connection.dialect.name == "postgresql":
result = connection.execute(
text("SELECT 1 FROM pg_indexes WHERE indexname = :index_name"),
{"index_name": index_name},
)
return result.fetchone() is not None
else:
# SQLite
result = connection.execute(
text("SELECT 1 FROM sqlite_master WHERE type='index' AND name = :index_name"),
{"index_name": index_name},
)
return result.fetchone() is not None
# revision identifiers, used by Alembic.
revision: str = "g9a0b3c4d5e6"
down_revision: Union[str, None] = "f8a9b2c3d4e5"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
"""Add external_id UUID column to project and entity tables.
This migration:
1. Adds external_id column to project table
2. Adds external_id column to entity table
3. Generates UUIDs for existing rows
4. Creates unique indexes on both columns
"""
connection = op.get_bind()
dialect = connection.dialect.name
# -------------------------------------------------------------------------
# Add external_id to project table
# -------------------------------------------------------------------------
if not column_exists(connection, "project", "external_id"):
# Step 1: Add external_id column as nullable first
op.add_column("project", sa.Column("external_id", sa.String(), nullable=True))
# Step 2: Generate UUIDs for existing rows
if dialect == "postgresql":
# Postgres has gen_random_uuid() function
op.execute("""
UPDATE project
SET external_id = gen_random_uuid()::text
WHERE external_id IS NULL
""")
else:
# SQLite: need to generate UUIDs in Python
result = connection.execute(text("SELECT id FROM project WHERE external_id IS NULL"))
for row in result:
new_uuid = str(uuid.uuid4())
connection.execute(
text("UPDATE project SET external_id = :uuid WHERE id = :id"),
{"uuid": new_uuid, "id": row[0]},
)
# Step 3: Make external_id NOT NULL
if dialect == "postgresql":
op.alter_column("project", "external_id", nullable=False)
else:
# SQLite requires batch operations for ALTER COLUMN
with op.batch_alter_table("project") as batch_op:
batch_op.alter_column("external_id", nullable=False)
# Step 4: Create unique index on project.external_id (idempotent)
if not index_exists(connection, "ix_project_external_id"):
op.create_index("ix_project_external_id", "project", ["external_id"], unique=True)
# -------------------------------------------------------------------------
# Add external_id to entity table
# -------------------------------------------------------------------------
if not column_exists(connection, "entity", "external_id"):
# Step 1: Add external_id column as nullable first
op.add_column("entity", sa.Column("external_id", sa.String(), nullable=True))
# Step 2: Generate UUIDs for existing rows
if dialect == "postgresql":
# Postgres has gen_random_uuid() function
op.execute("""
UPDATE entity
SET external_id = gen_random_uuid()::text
WHERE external_id IS NULL
""")
else:
# SQLite: need to generate UUIDs in Python
result = connection.execute(text("SELECT id FROM entity WHERE external_id IS NULL"))
for row in result:
new_uuid = str(uuid.uuid4())
connection.execute(
text("UPDATE entity SET external_id = :uuid WHERE id = :id"),
{"uuid": new_uuid, "id": row[0]},
)
# Step 3: Make external_id NOT NULL
if dialect == "postgresql":
op.alter_column("entity", "external_id", nullable=False)
else:
# SQLite requires batch operations for ALTER COLUMN
with op.batch_alter_table("entity") as batch_op:
batch_op.alter_column("external_id", nullable=False)
# Step 4: Create unique index on entity.external_id (idempotent)
if not index_exists(connection, "ix_entity_external_id"):
op.create_index("ix_entity_external_id", "entity", ["external_id"], unique=True)
def downgrade() -> None:
"""Remove external_id columns from project and entity tables."""
connection = op.get_bind()
dialect = connection.dialect.name
# Drop from entity table
if index_exists(connection, "ix_entity_external_id"):
op.drop_index("ix_entity_external_id", table_name="entity")
if column_exists(connection, "entity", "external_id"):
if dialect == "postgresql":
op.drop_column("entity", "external_id")
else:
with op.batch_alter_table("entity") as batch_op:
batch_op.drop_column("external_id")
# Drop from project table
if index_exists(connection, "ix_project_external_id"):
op.drop_index("ix_project_external_id", table_name="project")
if column_exists(connection, "project", "external_id"):
if dialect == "postgresql":
op.drop_column("project", "external_id")
else:
with op.batch_alter_table("project") as batch_op:
batch_op.drop_column("external_id")

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