zcyynl

zcyynl / claw-multi-agent

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OpenClaw multi-agent orchestration skill — run AI agents in parallel, like a team.

15
0
100% credibility
Found Mar 04, 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

An OpenClaw skill that enables teams of AI agents to work in parallel across different roles for faster research, analysis, and report generation.

How It Works

1
🔍 Discover the AI Team Skill

You hear about a handy add-on for your OpenClaw AI helper that lets it build teams of smart assistants to tackle big tasks together.

2
📥 Add It to Your AI Helper

With one simple command, you install the multi-agent skill into OpenClaw, and it's ready to use without any extra setup.

3
💬 Chat with Your AI

You simply tell your AI something like 'Use a team to research LangChain, CrewAI, and AutoGen' or 'Deep dive into this topic and make a report'.

4
🎯 AI Assembles the Team Automatically

Your AI smartly picks the best teamwork style—whether searching together, analyzing in parallel, or both—and sends everyone off at once.

5
⚡ Team Works Side by Side

The assistants collaborate fast: some hunt for info, others analyze or write, all finishing quicker than one alone.

📊 Get Your Polished Results

You receive a clear, combined report or analysis with summaries, comparisons, or multiple drafts—saving tons of time and effort.

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AI-Generated Review

What is claw-multi-agent?

claw-multi-agent is a Python skill for OpenClaw that runs AI agents in parallel, like a team tackling complex tasks from research to report writing. It fixes single-agent issues—limited views, bloating context, slow serial runs—by spawning independent sessions for roles like researchers searching or analysts reasoning, saving tokens and cutting times by 50-78%. Install via npx clawhub for zero-config use in OpenClaw, with natural commands like "multi-agent survey LangChain and CrewAI."

Why is it gaining traction?

Unlike native OpenClaw's serial agent spawning, it delivers true multi-agent orchestration with parallel execution, auto-routing based on task keywords, and modes like commander for web searches or pipeline for lightweight multi-model analysis. The hook is effortless speed and efficiency: say "parallel research three frameworks" and get aggregated results without manual orchestration. Token savings from isolated sessions make it a quick win over heavier frameworks like LangChain or AutoGen.

Who should use this?

OpenClaw users orchestrating agent teams for parallel surveys, framework comparisons, or multi-angle reports—think AI devs prototyping swarms or product managers needing fast pros/cons breakdowns. Suited for tasks like "depth analysis of Claw vs Copilot agents" where diverse perspectives beat solo runs.

Verdict

Early at 14 stars and 1.0% credibility score, with solid docs but unproven scale—test in toy OpenClaw setups before production. Worth a spin for parallel agent fans; skip if not already in the Claw ecosystem.

(178 words)

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