ClawTeam: Agent Swarm Intelligence

The Evolution of AI Agents 🚀: Solo 🤖 → Swarm 🦞🤖🤖🤖

ClawTeam: Let AI Agents Form Swarms, Think & Work Together, and Ship Faster

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

🔬 AI Research Automation • Large-Scale Automated ML Experimentation • AI Model Training & Optimization • AI-Driven Hypothesis Generation & Validation • Self-Improving Model Architectures

🏗️ Agentic Engineering • Autonomous Full-Stack Development • Self-Evolving Software • Collaborative Open Source Development • Real-Time System Integration

💰 AI Hedge Fund • Automated Market Research & Data Mining • Multi-Strategy Portfolio Optimization • Real-Time Risk Assessment • Algorithmic Trading Execution & Monitoring

🎪 Your Own Swarm • Custom Scientific Research Teams • Personalized Investment Committees • Business Operations Teams • Content Production Studios

Open the demo video directly

☝️ 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

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

# 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."

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

🎬 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.

🏆 val_bpb: 1.044 → 0.977 (6.4% improvement) | 2430+ experiments | ~30 GPU-hours

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 --task "Explore model width (ASPECT_RATIO 80-128)"
│   ├── GPU 2: clawteam spawn --task "Tune learning rates and optimizer"
│   ├── GPU 3: clawteam spawn --task "Explore batch size and accumulation"
│   ├── GPU 4-7: clawteam spawn tmux codex --task "..."  (Codex agents)
│   └── 🌳 Each agent: own git worktree, own branch, isolated experiments
├── 🔄 Every 30 minutes, checked results:
│   ├── clawteam board show autoresearch
│   ├── Read each agent's results.tsv
│   ├── 🏆 Identified best findings (depth=12, batch=2^17, norm-before-RoPE)
│   └── 📡 Cross-pollinated: told new agents to start from the best config
├── 🔧 When agents finished, reassigned GPUs:
│   ├── Killed idle agents, cleaned worktrees
│   ├── Created new worktrees from the best commit
│   └── Spawned fresh agents with combined optimization directions
└── ✅ After 2430+ experiments: val_bpb 1.044 → 0.977

Full results: novix-science/autoresearch

🏗️ 2. Agentic Software Engineering

You tell Claude Code: "Build me a full-stack todo app." Claude realizes this is a multi-module task and self-organizes a team:

Human prompt: "Build a full-stack todo app with auth, database, and React frontend."

🦞 Leader agent's actions:
├── 🏗️ clawteam team spawn-team webapp -d "Full-stack todo app"
├── 📋 Created tasks with dependency chains:
│   ├── T1: "Design REST API schema"          → architect
│   ├── T2: "Implement JWT auth" --blocked-by T1  → backend1
│   ├── T3: "Build database layer" --blocked-by T1 → backend2
│   ├── T4: "Build React frontend"             → frontend
│   └── T5: "Integration tests" --blocked-by T2,T3,T4 → tester
├── 🚀 Spawned 5 sub-agents (each in its own git worktree):
│   ├── clawteam spawn --agent-name architect --task "Design the API schema"
│   ├── clawteam spawn --agent-name backend1  --task "Implement JWT auth"
│   ├── clawteam spawn --agent-name backend2  --task "Build PostgreSQL models"
│   ├── clawteam spawn --agent-name frontend  --task "Build React UI"
│   └── clawteam spawn --agent-name tester    --task "Write pytest tests"
├── 🔗 Dependency auto-resolution:
│   ├── architect completes → backend1 and backend2 auto-unblock
│   ├── All backends complete → tester auto-unblocks
│   └── Each agent calls: clawteam task update <id> --status completed
├── 💬 Sub-agents coordinate via inbox:
│   ├── architect → backend1: "Here's the OpenAPI spec: ..."
│   ├── backend1 → tester: "Auth endpoints ready at /api/auth/*"
│   └── tester → leader: "All 47 tests passing ✅"
└── 🌳 Leader merges all worktrees into main branch

💰 3. AI Hedge Fund — One-Command Team Launch

A pre-built TOML template spawns a complete 7-agent investment analysis team:

# One command launches everything:
clawteam launch hedge-fund --team fund1 --goal "Analyze AAPL, MSFT, NVDA for Q2 2026"

🦞 What happens automatically:
├── 📊 Portfolio Manager (leader) spawns and receives the goal
├── 🤖 5 Analyst agents spawn, each with a different strategy:
│   ├── 🎩 Buffett Analyst    → value investing (moat, ROE, DCF)
│   ├── 🚀 Growth Analyst     → disruption (TAM, network effects)
│   ├── 📈 Technical Analyst  → indicators (EMA, RSI, Bollinger)
│   ├── 📋 Fundamentals       → financial ratios (P/E, D/E, FCF)
│   └── 📰 Sentiment Analyst  → news + insider trading signals
├── 🛡️ Risk Manager spawns, waits for all analyst signals:
│   ├── clawteam inbox receive fund1 (collects all 5 signals)
│   ├── Consolidates + computes position limits
│   └── clawteam inbox send fund1 portfolio-manager "RISK REPORT: ..."
└── 💼 Portfolio Manager makes final buy/sell/hold decisions

Templates are TOML files — create your own team archetypes for any domain.

📦 Install

pip install clawteam

# Or from source
git clone https://github.com/HKUDS/ClawTeam.git
cd ClawTeam
pip install -e .

# Optional: P2P transport (ZeroMQ)
pip install -e ".[p2p]"

Requires Python 3.10+, tmux, and a CLI coding agent (e.g. claude, codex).