Cheese Brain

DuckDB-powered knowledge management system for fast retrieval across 22+ entity types (projects, contacts, tools, workflows, decisions, etc.). Use when you need to recall context about past projects, look up configuration details, find tool documentation, retrieve contact information, search workflows/procedures, or query any tracked knowledge. Supports sub-millisecond keyword search and BM25 full-text search with relevance ranking.

Fast, persistent knowledge base for AI agents and humans. Store and retrieve entities (projects, contacts, tools, workflows, decisions) with sub-millisecond search.

Installation

Cheese Brain is a Python package that requires installation before use:

bash

# Clone the repository
git clone https://github.com/mhugo22/cheese-brain.git
cd cheese-brain

# Create virtual environment and install
python3 -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows
pip install -e .

Verify installation:

bash

cheese-brain stats

If this shows statistics (entity counts, database size), the installation is successful.

When to Use This Skill

Use Cheese Brain when you need to:

• Recall project context - "What's the email monitor project?" → instant project details with repo, path, schedule
• Look up tools - "Where's the backup script?" → tool location, usage, related workflows
• Find contact info - "Scout calendar feed?" → contact with calendar URL, location, timezone
• Search workflows - "How do I restore config?" → step-by-step workflow + related tools
• Retrieve decisions - "Why did we choose DuckDB?" → past decision with rationale + date
• Query infrastructure - "What's the Telegram channel ID?" → integration details with tokens, config

Key advantage: Persistent memory across sessions. You don't "remember" things — you query Cheese Brain and get instant context.

Common Usage Patterns

Quick Search (Most Common)

bash

# Keyword search (fast, loose matching)
cheese-brain search "email monitor"
cheese-brain search "backup config"
cheese-brain search "calendar feed"

# Full-text search (BM25 relevance ranking)
cheese-brain fts "email monitoring"
cheese-brain fts "backup config" --category tool

Output: Table with title, category, tags + full entity details for matches.

Get Specific Entity

When search returns multiple results, get the exact one by ID:

bash

cheese-brain get <entity-id>

Output: Full entity details including JSON data field (paths, URLs, schedules, etc.).

Add New Entity

When you learn something new worth persisting:

bash

cheese-brain add \
  --title "New Project Name" \
  --category project \
  --tags "tag1,tag2,tag3" \
  --data '{"repo": "https://github.com/...", "status": "active"}'

Categories: project, tool, workflow, contact, decision, bookmark, infrastructure, habit, idea, etc.

Data field: Freeform JSON for entity-specific details (paths, URLs, schedules, credentials, etc.).

Update Entity

bash

cheese-brain update <entity-id> --title "New Title" --tags "new,tags"
cheese-brain update <entity-id> --data '{"status": "shipped", "deployed": "2026-02-17"}'

List & Browse

bash

cheese-brain list                     # All entities (most recent first)
cheese-brain list --category project  # Filter by category
cheese-brain list --tag shipped       # Filter by tag
cheese-brain list --limit 10          # Limit results

Stats & Tags

bash

cheese-brain stats                    # Database statistics
cheese-brain tags                     # Tag usage analysis

Query Examples by Use Case

"What's the email monitor project?"

bash

cheese-brain search "email monitor"
# Returns: Email Monitor project with repo, path, cron schedule, run command

"How do I backup the config?"

bash

cheese-brain search "backup config"
# Returns: 5 entities (backup script, restore script, workflow, recovery guide, gateway)

"Calendar feed URL?"

bash

cheese-brain search "calendar feed"
# Returns: Contact entity with ICS feed URL + location details

"What projects are shipped?"

bash

cheese-brain list --category project --tag shipped
# Returns: SketchySkills, Gabby Gmail, Cheese Brain, etc.

"Find anything about monitoring"

bash

cheese-brain fts "monitoring"
# Returns: BM25-ranked results (Gabby Gmail, news monitor, cost monitor, etc.)

Advanced Features

Full-Text Search (FTS)

For relevance-ranked results when you have many entities:

bash

# First-time setup (one-time only)
cheese-brain create-fts-index

# Search with relevance ranking
cheese-brain fts "backup automation"
cheese-brain fts "email calendar" --category tool
cheese-brain fts "security logging" --limit 10

When to use FTS vs keyword search:

• FTS: Large knowledge base (>100 entities), multi-word queries, need best matches first
• Keyword: Quick lookups, exact matches, small knowledge base

Export & Backup

bash

# JSON export (human-readable)
cheese-brain export backup.json

# Parquet export (2-9x smaller, columnar format)
cheese-brain export backup.parquet --format parquet

# Restore from backup
cheese-brain restore-backup backup.json  # Auto-detects format

Note: Automated daily backups may already be configured via OpenClaw cron (check ~/.cheese-brain/backups/).

Data Model

Each entity has:

• id (UUID) - Unique identifier
• title (string) - Entity name
• category (string) - Entity type (project, tool, contact, etc.)
• tags (array) - Searchable tags
• data (JSON) - Freeform structured data (paths, URLs, schedules, etc.)
• created_at / updated_at / deleted_at (timestamps)

Example entity:

json

{
  "id": "550e8400-e29b-41d4-a716-446655440000",
  "title": "Email Monitor Project",
  "category": "project",
  "tags": ["automation", "nodejs", "gmail", "calendar", "telegram", "shipped"],
  "data": {
    "repo": "https://github.com/username/email-monitor",
    "path": "/path/to/workspace/email-monitor/",
    "schedule": "7am, 1pm, 5pm, 9pm CST",
    "run_command": "node process.js",
    "telegram_channel": "-100XXXXXXXXXX"
  },
  "created_at": "2026-02-17T06:55:00Z",
  "updated_at": "2026-02-17T08:30:00Z"
}

Performance

• Search: <1ms keyword search, ~5ms FTS
• Database size: ~0.3 MB per entity (varies with data field)
• Scales to: 100k+ entities (constant-time FTS, linear keyword scan)

Troubleshooting

Command not found:

• Ensure virtual environment is activated: source /path/to/cheese-brain/venv/bin/activate
• Or use full path: /path/to/cheese-brain/venv/bin/cheese-brain

Database locked:

• Close other Cheese Brain processes
• Check ~/.cheese-brain/ for stale lock files

Slow queries:

• Create FTS index if not already done: cheese-brain create-fts-index
• Check database size: cheese-brain stats
• Consider archiving old entities (soft delete with --deleted flag)

Documentation

• Full documentation: https://github.com/mhugo22/cheese-brain
• Backup/recovery guide: BACKUP_RECOVERY.md in repo
• FTS guide: FTS.md in repo
• Performance analysis: PERFORMANCE_ANALYSIS.md in repo
• Security: SECURITY.md in repo

Security Features

• File permissions: Database/backups auto-secured (0600 owner-only)
• Sensitive redaction: api_key, token, password auto-hidden (use --reveal to show)
• Encrypted exports: cheese-brain export --encrypt for password-protected backups
• Data validation: Max 1MB per entity, max 10 nesting levels, SQL injection protection

Best practice: Don't store secrets in plain text. Use password managers (1Password, Bitwarden) and reference them:

json

{"api_key_location": "1Password: OpenAI API", "notes": "Retrieve from vault"}

Tips for Effective Use

1. Tag consistently - Use lowercase, hyphenated tags (e.g., email-monitoring, not Email Monitoring)
2. Use data field - Store structured info (paths, URLs, schedules) in JSON data field for easy retrieval
3. Search first, then get - Use search to find candidates, then get <id> for full details
4. FTS for discovery - Use full-text search when you're not sure what you're looking for
5. Update frequently - Keep entity status current (active → shipped, urls, paths)
6. Soft delete - Don't hard delete; use --deleted flag to mark inactive (recoverable)