Skill: marcosnataqs/kameleondb (version 0.1.5)

--- name: kameleondb version: 0.1.5 description: Store and query structured data without planning schemas upfront. Use when you need to remember information, track entities across conversations, build knowledge bases, ingest API data, store user preferences, create CRM systems, or maintain any persistent state. Automatically evolves data structure as you discover new fields. No migrations, no schema design - just store data and query it. metadata: {"openclaw":{"emoji":"🦎","requires":{"bins":["kameleondb"],"env":["KAMELEONDB_URL"]},"primaryEnv":"KAMELEONDB_URL","homepage":"https://github.com/marcosnataqs/kameleondb"}} --- # KameleonDB - The First Database Built for Agents to Operate, Not Just Query ## When to Use This Use KameleonDB when you need to: - **Remember information across conversations** - Store contacts, companies, tasks, notes - **Track entities and relationships** - Connect customers to orders, projects to tasks - **Build knowledge bases** - Accumulate facts, documents, insights over time - **Ingest external data** - Store API responses, CSV imports, web scraping results - **Create custom CRM/PM systems** - Track leads, deals, projects, tickets - **Store user preferences** - Remember settings, history, favorites - **Maintain session state** - Persist data between agent runs - **Query historical data** - Search, filter, analyze stored information ## Why Agents Choose This **No Upfront Planning**: Start storing data immediately. Don't know all fields yet? Add them later when you discover them. **Learn as You Go**: Found a new field in your data? Just add it. Old records won't break. **Query Like a Database**: Generate SQL queries to search, filter, join, and analyze your data. **Self-Optimizing**: Get hints when queries slow down, with exact commands to speed them up. **Track Your Reasoning**: Every schema change records why you made it, building an audit trail. ## Setup (One-Time) ```bash # Install pip install kameleondb # Set database location (SQLite - no server needed) export KAMELEONDB_URL="sqlite:///./kameleondb.db" # Initialize kameleondb admin init # Done! Start using it. ``` **Note**: For production, use PostgreSQL instead of SQLite. Set `KAMELEONDB_URL="postgresql://user:pass@localhost/dbname"` and run `pip install kameleondb[postgresql]`. ## 💡 Tip: Remember Your Database To get the most out of KameleonDB as your persistent layer, consider noting your database location and preferred usage patterns in your memory files. This helps you leverage it consistently across sessions. **Example notes you might keep:** - Database URL: `sqlite:///path/to/your-memory.db` - Use for: contacts, tasks, knowledge bases, entity tracking - Key commands: `schema list`, `data insert`, `data list`, `query run` ## Common Agent Workflows ### Scenario 1: Track Contacts You Meet ```bash # Check what exists kameleondb --json schema list # Returns: {"entities": []} # Create Contact tracking kameleondb --json schema create Contact \ --field "name:string:required" \ --field "email:string:unique" # Store someone you met kameleondb --json data insert Contact '{"name":"Alice Johnson","email":"[REDACTED_SECRET]"}' # Later: found their LinkedIn! kameleondb --json schema alter Contact --add "linkedin_url:string" \ --reason "Found LinkedIn profiles for contacts" # Update Alice's record kameleondb --json data update Contact <id> '{"linkedin_url":"https://linkedin.com/in/alice"}' ``` ### Scenario 2: Build a Knowledge Base ```bash # Store facts you learn kameleondb --json schema create Fact \ --field "content:string:required" \ --field "source:string" \ --field "confidence:float" # Add facts kameleondb --json data insert Fact '{"content":"Python 3.11 released Oct 2022","source":"python.org","confidence":1.0}' # Search facts (get SQL context first) kameleondb --json schema context --entity Fact # Use context to generate: SELECT * FROM kdb_records WHERE data->>'content' LIKE '%Python%' # Query kameleondb --json query run "SELECT data->>'content', data->>'source' FROM kdb_records WHERE entity_id='...' LIMIT 10" ``` ### Scenario 3: Track Tasks Across Conversations ```bash # Create task tracker kameleondb --json schema create Task \ --field "title:string:required" \ --field "status:string" \ --field "priority:string" # Add tasks kameleondb --json data insert Task '{"title":"Research OpenClaw","status":"todo","priority":"high"}' # Mark complete kameleondb --json data update Task <id> '{"status":"done"}' # Get all incomplete kameleondb --json query run \ "SELECT data->>'title', data->>'priority' FROM kdb_records WHERE entity_id='...' AND data->>'status' != 'done'" ``` ### Scenario 4: Ingest External Data ```bash # Store API responses kameleondb --json schema create GitHubRepo \ --field "name:string:required" \ --field "stars:int" \ --field "url:string" # Batch import from JSONL kameleondb --json data insert GitHubRepo --from-file repos.jsonl --batch # Query top repos kameleondb --json query run \ "SELECT data->>'name', (data->>'stars')::int as stars FROM kdb_records WHERE entity_id='...' ORDER BY stars DESC LIMIT 10" ``` ## How It Works for Agents ### Evolve Schema Anytime Don't know all fields upfront? No problem. Add, drop, or rename them when you discover patterns: ```bash # Add a new field kameleondb --json schema alter Contact --add "twitter_handle:string" \ --reason "Found Twitter profiles for 30% of contacts" # Drop obsolete fields kameleondb --json schema alter Contact --drop "legacy_field" --force # Do multiple operations at once kameleondb --json schema alter Contact --add "linkedin:string" --drop "old_social" --reason "Consolidating social fields" ``` Old records won't break - they just show `null` for new fields, and dropped fields are soft-deleted. ### Get Performance Hints Queries tell you when they're slow and how to fix it: ```json { "rows": [...], "suggestions": [{ "priority": "high", "reason": "Query took 450ms with 5000 records", "action": "kameleondb storage materialize Contact" }] } ``` Run that command and future queries will be faster. ### Track Your Decisions Every schema change records why you made it: ```bash kameleondb --json admin changelog # See: who added what field, when, and why ``` ### Query with SQL Get schema context, generate SQL, execute it: ```bash # Get schema to understand structure kameleondb --json schema context --entity Contact # Generate SQL based on structure # Execute with built-in validation kameleondb --json query run "SELECT ... FROM ..." ``` ## All Available Commands Add `--json` to any command for machine-readable output. **Schema**: `list`, `create`, `describe`, `alter`, `drop`, `info`, `context` **Data**: `insert`, `get`, `update`, `delete`, `list`, `link`, `unlink`, `get-linked`, `info` **Query**: `run` **Storage**: `status`, `materialize`, `dematerialize` **Admin**: `init`, `info`, `changelog` ### The `alter` Command (Schema Evolution) Instead of separate `add-field` and `drop-field` commands, use the unified `alter`: ```bash # Add a field kameleondb --json schema alter Contact --add "phone:string:indexed" # Drop a field kameleondb --json schema alter Contact --drop legacy_field --force # Rename a field kameleondb --json schema alter Contact --rename "old_name:new_name" # Multiple operations at once kameleondb --json schema alter Contact --add "new:string" --drop old --reason "Cleanup" ``` ### The `link`/`unlink` Commands (M2M Relationships) For many-to-many relationships: ```bash # Link a product to tags kameleondb --json data link Product abc123 tags tag-1 kameleondb --json data link Product abc123 tags -t tag-1 -t tag-2 -t tag-3 # Unlink kameleondb --json data unlink Product abc123 tags tag-1 kameleondb --json data unlink Product abc123 tags --all # Get linked records kameleondb --json data get-linked Product abc123 tags ``` Run `kameleondb --help` or `kameleondb <command> --help` for details. ## Real Agent Problems Solved ### Problem: "I need to remember people I interact with" ```bash # Start simple kameleondb --json schema create Person --field "name:string:required" kameleondb --json data insert Person '{"name":"Alice"}' # Learn more over time kameleondb --json schema alter Person --add "email:string" kameleondb --json schema alter Person --add "company:string" kameleondb --json schema alter Person --add "last_contacted:datetime" # Update as you learn kameleondb --json data update Person <id> '{"email":"[REDACTED_SECRET]","last_contacted":"2026-02-07"}' ``` ### Problem: "I'm scraping data and don't know the structure upfront" ```bash # Create generic entity kameleondb --json schema create ScrapedData --field "source:string" --field "raw:json" # Store everything kameleondb --json data insert ScrapedData '{"source":"website.com","raw":{"title":"...","data":{...}}}' # Discover patterns, then structure it kameleondb --json schema alter ScrapedData --add "title:string" kameleondb --json schema alter ScrapedData --add "price:float" # Migrate data progressively as you normalize it ``` ### Problem: "I need to track tasks but requirements keep changing" ```bash # Start minimal kameleondb --json schema create Task --field "title:string:required" # Add status tracking kameleondb --json schema alter Task --add "status:string" # Add priority later kameleondb --json schema alter Task --add "priority:string" # Add assignee when team grows kameleondb --json schema alter Task --add "assigned_to:string" # Add tags for categorization kameleondb --json schema alter Task --add "tags:json" # Schema grows with your needs - no migrations! ``` ### Problem: "I need to query across multiple entities" ```bash # Create related entities kameleondb --json schema create Project --field "name:string" kameleondb --json schema create Task \ --field "title:string" \ --field "project_id:string" # Get schema context for SQL generation kameleondb --json schema context --entity Project --entity Task # Returns: detailed schema with SQL patterns for JOIN # Generate and execute JOIN query kameleondb --json query run \ "SELECT p.data->>'name' as project, t.data->>'title' as task FROM kdb_records p JOIN kdb_records t ON t.data->>'project_id' = p.id::text WHERE p.entity_id='...' AND t.entity_id='...'" ``` ## Quick Reference ### First Time Setup ```bash # Install pip install kameleondb # Configure (SQLite for testing - no server needed) export KAMELEONDB_URL="sqlite:///./kameleondb.db" # Initialize kameleondb admin init # You're ready! ``` ### Check What You Have ```bash # List all entities kameleondb --json schema list # See entity details kameleondb --json schema describe <entity-name> # View changelog kameleondb --json admin changelog ``` ### Common Operations ```bash # Create entity kameleondb --json schema create <Entity> --field "name:type" # Add field kameleondb --json schema alter <Entity> --add "field:type" # Insert data kameleondb --json data insert <Entity> '{"field":"value"}' # Get by ID kameleondb --json data get <Entity> <id> # Update kameleondb --json data update <Entity> <id> '{"field":"new_value"}' # Query with SQL kameleondb --json query run "SELECT ... FROM kdb_records WHERE ..." ``` ### Field Types Common types: `string`, `int`, `float`, `bool`, `datetime`, `json` Modifiers: `required`, `unique`, `indexed` Examples: `"email:string:unique"`, `"score:int:indexed"`, `"tags:json"` ## Next Steps 1. **Try it**: `kameleondb admin init` → `kameleondb --json schema create Test --field "note:string"` → `kameleondb --json data insert Test '{"note":"my first record"}'` 2. **Real use case**: Think about what you need to track (contacts, tasks, facts, etc.) and create an entity for it 3. **Evolve it**: As you discover new fields, add them with `schema alter` 4. **Query it**: Use `schema context` to understand structure, then query with SQL 5. **Optimize it**: If queries slow down, follow the hints in query results ## More Resources - **GitHub**: https://github.com/marcosnataqs/kameleondb - **Examples**: See `examples/workflow.md` in this skill directory - **Design Philosophy**: Why it's built for agents - [FIRST-PRINCIPLES.md](https://github.com/marcosnataqs/kameleondb/blob/main/FIRST-PRINCIPLES.md)


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