Key takeaways
- Multi-agent architecture lets you run specialized agents (coder, writer, researcher) that hand off work to each other
- Team collaboration via chain execution and fan-out — agents work in parallel
- Live TUI dashboard visualizes agent team conversations and chains in real-time
FAQ
What is TinyClaw?
A multi-agent AI assistant where teams of specialized agents collaborate via chain execution and fan-out in isolated workspaces.
How much does TinyClaw cost?
Free and open source (MIT). You pay for Claude or OpenAI API costs.
Who competes with TinyClaw?
Antfarm (multi-agent workflows), OpenClaw (single-agent), NanoBot (Python multi-platform).
Executive Summary
TinyClaw is a multi-agent AI assistant where specialized agents collaborate on tasks. Run a coder, writer, and researcher simultaneously — they hand off work to teammates via chain execution and fan-out. Each agent operates in an isolated workspace with its own conversation history.
| Attribute | Value |
|---|---|
| Language | Shell / TypeScript |
| License | MIT |
| GitHub Stars | 2.3K ★ |
| Status | Experimental |
Key Capabilities
| Capability | Description |
|---|---|
| Multi-agent | Run multiple isolated agents with specialized roles |
| Team collaboration | Chain execution and fan-out between agents |
| Multi-channel | Discord, WhatsApp, Telegram |
| Team visualization | Live TUI dashboard for monitoring agent chains |
| Parallel processing | Agents process messages concurrently |
| Sender pairing | Access control for who can message your agents |
How It Works
Message an agent with @agent_id prefix:
@coder fix the authentication bug
@writer document the API endpoints
@researcher find papers on transformers
Agents can hand off work to teammates, execute tasks in parallel, and maintain separate conversation contexts.
Strengths
- True multi-agent — Multiple specialized agents, not just one assistant
- Team dynamics — Agents collaborate, delegate, and hand off work
- Visual monitoring — Live TUI shows agent chains in action
- Isolated workspaces — Each agent has own directory and context
- Multi-provider — Anthropic Claude and OpenAI Codex
Cautions
- Experimental — Status badge warns this is early-stage
- Complexity — Multi-agent adds cognitive overhead vs single assistant
- Higher API costs — Multiple agents = multiple API calls
- Limited channels — Only Discord, WhatsApp, Telegram currently
Bottom Line
TinyClaw is for power users who want multiple specialized agents working together. The team collaboration model (chain execution, fan-out) is genuinely different from single-agent assistants. Worth exploring if you have complex workflows that benefit from role specialization.
Recommended for: Users with multi-step workflows that benefit from agent specialization and collaboration.
Not recommended for: Simple personal assistant use cases where one agent suffices.
Research by Ry Walker Research • methodology
Sources