Richchen-maker

Richchen-maker / openclaw-multi-agent-team

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🐊 OpenClaw Multi-Agent Team Framework — Build your own AI team in minutes. Flywheel architecture with Blackboard coordination. E-commerce team as first example.

github.comopenclawopenclaw
20
4
85% credibility
Found Feb 28, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A framework for building and running collaborative teams of AI agents that handle complex workflows like e-commerce research, data collection, and security tasks by sharing work through a central notice board.

How It Works

1
🏭 Discover the AI Team Factory

You find a simple way to create teams of smart helpers that work together like a company on any task you need.

2
🔧 Set Up Your Workshop

Follow a quick guide to prepare everything so your teams can start working right away.

3
🛒 Pick a Ready Team

Choose an example team, like shopping experts or data gatherers, to get started fast.

4
💬 Give Your First Task

Just tell the team what to do, like 'analyze bluetooth earphones for selling', and they jump into action.

5
🤝 Watch Them Collaborate

The leader assigns jobs, specialists handle their parts, and they share notes to keep everything smooth.

6
🔄 Teams Help Each Other

If one team hits a snag, like missing info, it calls in another team automatically to fix it.

📊 Get Your Complete Report

In minutes, you receive a detailed summary with confident decisions, all done without lifting a finger.

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AI-Generated Review

What is openclaw-multi-agent-team?

OpenClaw Multi-Agent Team is a Python framework for building multi-agent teams in minutes using OpenClaw runtime, flywheel architecture, and blackboard coordination. It solves the chaos of solo agents by letting you define roles, workflows, and tools for collaborative teams that run like a company—e-commerce as the first example. Trigger a task via CLI or chat, and agents decompose, dispatch, and iterate autonomously.

Why is it gaining traction?

Its blackboard system decouples agents via shared files, enabling reliable multi-agent coordination without message passing headaches. Developers hook on the event bus for cross-team triggers, like e-commerce handing off data gaps to collection agents. Ready Python examples across e-commerce, data, and security show instant value, with customization via simple templates.

Who should use this?

AI engineers prototyping agent swarms for e-commerce scouting, competitive pricing, or product launches. Data teams needing autonomous pipelines that self-heal via events. Security researchers building evasion teams, but stick to defensive use cases.

Verdict

Worth forking for multi-agent experiments if you're on OpenClaw—strong docs and examples punch above 15 stars. 0.85% credibility flags early maturity; test thoroughly before production.

(178 words)

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