<h1 align="center"><img src="assets/icon.png" alt="" width="64" style="vertical-align: middle;">  ClawTeam: Agent Swarm Intelligence</h1> <p align="center"> <strong>The Evolution of AI Agents 🚀: Solo 🤖 → Swarm 🦞🤖🤖🤖<br> ClawTeam: Let AI Agents Form Swarms, Think & Work Together, and Ship Faster</strong> </p> <p align="center"> <a href="#-quick-start"><img src="https://img.shields.io/badge/Quick_Start-3_min-blue?style=for-the-badge" alt="Quick Start"></a> <a href="#-use-cases"><img src="https://img.shields.io/badge/Use_Cases-3_Demos-green?style=for-the-badge" alt="Use Cases"></a> <a href="#-features"><img src="https://img.shields.io/badge/Features-12+-purple?style=for-the-badge" alt="Features"></a> <a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-yellow?style=for-the-badge" alt="License"></a> </p> <p align="center"> <img src="https://img.shields.io/badge/python-≥3.10-blue?logo=python&logoColor=white" alt="Python"> <img src="https://img.shields.io/badge/typer-CLI-green" alt="Typer"> <img src="https://img.shields.io/badge/agents-Claude_Code_%7C_Codex_%7C_Any_CLI-blueviolet" alt="Agents"> <img src="https://img.shields.io/badge/transport-File_%7C_ZeroMQ_P2P-orange" alt="Transport"> <a href="https://github.com/HKUDS/.github/blob/main/profile/README.md"><img src="https://img.shields.io/badge/Feishu-Group-E9DBFC?style=flat&logo=feishu&logoColor=white" alt="Feishu"></a> <a href="https://github.com/HKUDS/.github/blob/main/profile/README.md"><img src="https://img.shields.io/badge/WeChat-Group-C5EAB4?style=flat&logo=wechat&logoColor=white" alt="WeChat"></a> </p>

One Command Line: Full Automation. — agents spawn swarms, delegate tasks, and deliver results.

Human provides the goal. The Agent Team orchestrates everything else.

Full compatibility with Claude Code, Codex, OpenClaw, nanobot, Cursor, and any CLI agent. 中文文档 | 한국어

📰 News

2026-03-18 ClawTeam project launched publicly.

2026-03-23 ClawTeam v0.2.0 is released today.

2026-03 The current baseline includes config management, multi-user workflows, Web UI, P2P transport, and team templates.

✨ ClawTeam's Key Features

<table align="center" width="100%"> <tr> <td width="25%" align="center" style="vertical-align: top; padding: 15px;"> <h3>🔬 AI Research Automation</h3> <div align="center"> <img src="https://img.shields.io/badge/AutoResearch-FF6B6B?style=for-the-badge&logo=pytorch&logoColor=white" alt="AutoResearch" /> </div> <img src="assets/scene-autoresearch.png" width="180"> <p align="center"><strong>• Large-Scale Automated ML Experimentation</strong></p> <p align="center"><strong>• AI Model Training & Optimization</strong></p> <p align="center"><strong>• AI-Driven Hypothesis Generation & Validation</strong></p> <p align="center"><strong>• Self-Improving Model Architectures</strong></p> </td> <td width="25%" align="center" style="vertical-align: top; padding: 15px;"> <h3>🏗️ Agentic Engineering</h3> <div align="center"> <img src="https://img.shields.io/badge/Full--Stack_Dev-4ECDC4?style=for-the-badge&logo=git&logoColor=white" alt="Engineering" /> </div> <img src="assets/scene-engineering.png" width="180"> <p align="center"><strong>• Autonomous Full-Stack Development</strong></p> <p align="center"><strong>• Self-Evolving Software</strong></p> <p align="center"><strong>• Collaborative Open Source Development</strong></p> <p align="center"><strong>• Real-Time System Integration</strong></p> </td> <td width="25%" align="center" style="vertical-align: top; padding: 15px;"> <h3>💰 AI Hedge Fund</h3> <div align="center"> <img src="https://img.shields.io/badge/Investment_Analysis-FFD93D?style=for-the-badge&logo=bitcoin&logoColor=black" alt="Hedge Fund" /> </div> <img src="assets/scene-hedgefund.png" width="180"> <p align="center"><strong>• Automated Market Research & Data Mining</strong></p> <p align="center"><strong>• Multi-Strategy Portfolio Optimization</strong></p> <p align="center"><strong>• Real-Time Risk Assessment</strong></p> <p align="center"><strong>• Algorithmic Trading Execution & Monitoring</strong></p> </td> <td width="25%" align="center" style="vertical-align: top; padding: 15px;"> <h3>🎪 Your Own Swarm</h3> <div align="center"> <img src="https://img.shields.io/badge/TOML_Templates-C77DFF?style=for-the-badge&logo=toml&logoColor=white" alt="Templates" /> </div> <img src="assets/scene-template.png" width="180"> <p align="center"><strong>• Custom Scientific Research Teams</strong></p> <p align="center"><strong>• Personalized Investment Committees</strong></p> <p align="center"><strong>• Business Operations Teams</strong></p> <p align="center"><strong>• Content Production Studios</strong></p> </td> </tr> </table>

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https://github.com/user-attachments/assets/fd23be91-5cf4-457c-a77e-bac24b76e58f

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☝️ Intelligent leader agent orchestrates 8 specialized sub-agents across 8 H100 GPUs, autonomously designing experiments and dynamically reallocating resources based on real-time performance.

🧠 The system synthesizes breakthroughs across teams and evolves strategies independently — achieving full research automation without human intervention.

🤔 Why ClawTeam?

Current AI agents are powerful — but they work in isolation. When facing complex tasks, you're stuck manually coordinating multiple agents, juggling context, and stitching together fragmented results.

What if agents could think and work as a team?

ClawTeam unlocks Agent Swarm Intelligence — where AI agents self-organize into collaborative teams, intelligently divide complex work, share insights in real-time, and converge on breakthrough solutions.

• 🚀 Spawns specialized sub-agents — each with dedicated environments and focus areas

• 📋 Designs intelligent task allocation — with smart dependency management

• 💬 Facilitates real-time coordination — seamless inter-agent communication

• 📊 Monitors team performance — tracks progress and identifies bottlenecks

• 🔄 Adapts strategies dynamically — reallocates resources and redirects efforts

✨ The Result?

You set the vision. The swarm executes with collective intelligence.

🎯 Swarm Intelligence in Action

🦞 Agents Spawn Agents

The leader agent calls clawteam spawn to create workers. Each worker gets its own git worktree, tmux window, and identity — automatically.

# The leader agent runs:
clawteam spawn --team my-team \
  --agent-name worker1 \
  --task "Implement auth module"

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🤖 Agents Talk to Agents

Workers check their inbox, update task status, and report results — all through CLI commands that are auto-injected into their prompt.

# A worker agent checks tasks:
clawteam task list my-team --owner me
# Then reports back:
clawteam inbox send my-team leader \
  "Auth done. All tests passing."

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👀 You Just Watch

Monitor the swarm from a tiled tmux view or a Web UI. The leader handles coordination — you intervene only when you want to.

# Watch all agents simultaneously
clawteam board attach my-team
# Or open the web dashboard
clawteam board serve --port 8080

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| | ClawTeam | Other multi-agent frameworks | |---|---------|----------------------------| | 🎯 Who uses it | The AI agents themselves | Humans writing orchestration code | | ⚡ Setup | pip install + one prompt to the leader | Docker, cloud APIs, YAML configs | | 🏗️ Infrastructure | Just a filesystem and tmux | Redis, message queues, databases | | 🤖 Agent support | Any CLI agent (Claude Code, Codex, OpenClaw, custom) | Framework-specific only | | 🌳 Isolation | Git worktrees (real branches, real diffs) | Containers or virtual envs | | 🧠 Intelligence | Swarm self-organizes via CLI commands | Hard-coded orchestration logic |

🎬 Use Cases

🔬 1. Autonomous ML Research — 8 Agents × 8 H100 GPUs

Based on @karpathy's autoresearch.

💫 One Command. Full Automation.

Human input: "Optimize this LLM training setup using 8 GPUs"

The Agent Team handles everything else:

Spawns 8 specialized research agents across H100s
Designs 2000+ autonomous experiments
Achieves breakthrough improvements (val_bpb: 1.044→0.977)
Zero human intervention required

🎯 Pure Research at Scale

Transform months of manual hyperparameter tuning into hours of intelligent automation.

<p align="center"> <img src="assets/autoresearch-progress.png" alt="AutoResearch Progress" width="720"> <br> <em>🏆 val_bpb: 1.044 → 0.977 (6.4% improvement) | 2430+ experiments | ~30 GPU-hours</em> </p>

What agent team did autonomously:

Human prompt: "Use 8 GPUs to optimize train.py. Read program.md for instructions."

🦞 Leader agent's actions:
├── 📖 Read program.md, understand the experiment protocol
├── 🏗️ clawteam team spawn-team autoresearch
├── 🚀 Assigned each GPU a research direction:
│   ├── GPU 0: clawteam spawn --task "Explore model depth (DEPTH 10-16)"
│   ├── GPU 1: clawteam spawn

ClawTeam

Install / Use

README