ArtemisAI

ArtemisAI / SWE-Squad

Public

Autonomous Software Engineering Agents — self-healing, self-diagnosing development team powered by Claude Code and A2A protocol

github.comArtemisAISWE-Squad a2a-protocol agentic-ai ai-agents automation autonomous-agents
10
2
100% credibility
Found Mar 19, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

SWE Squad provides a coordinated team of AI agents that monitor software projects for errors, investigate causes, apply fixes safely, and ensure stability before new work.

How It Works

1
🔍 Discover SWE Squad

You learn about a helpful team of AI watchers that spot problems in your code project and fix them automatically while you rest.

2
📥 Get the files

Download the simple setup files to your computer.

3
🔗 Connect your project

Point it to your code project so it knows what to watch and keep safe.

4
📱 Add notifications

Link a chat app to get friendly updates on fixes and alerts.

5
▶️ Start the magic

Flip the switch and let the AI team begin scanning and healing your code.

6
🛡️ See it in action

Watch as it finds issues, figures them out, tests fixes, and keeps everything stable.

✅ Rest easy

Your project runs smoothly with fewer bugs, and you're always in the loop.

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Star Growth

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AI-Generated Review

What is SWE-Squad?

SWE-Squad deploys a team of autonomous software agents that monitor production logs, detect errors, triage issues, investigate root causes with Claude Code, and implement fixes on isolated git branches—reverting automatically if tests fail. Built in Python with Supabase for shared storage and A2A protocol for agent coordination, it acts like an autonomous software engineer handling bugs overnight, complete with semantic memory for learning from past fixes and CLI tools for status checks. Developers get self-healing repos without constant manual firefighting.

Why is it gaining traction?

Unlike solo autonomous coders or GitHub Copilot wrappers, SWE-Squad runs a full dev squad with role specialization, smart model routing (Haiku for scans, Opus for crises), and a "Ralph Wiggum" stability gate that blocks features until bugs clear. Zero-cost replays of cached fixes and Telegram alerts make it hands-off yet observable, standing out in the autonomous software agents space for production reliability over raw code gen.

Who should use this?

DevOps leads tired of pager alerts at 3AM, indie hackers scaling solo with no night shifts, or teams experimenting with autonomous software development like A2A-powered squads. Ideal for Python repos with pytest suites and GitHub integration, especially if you're evaluating autonomous software testing or self-diagnosing systems.

Verdict

Early alpha with just 10 stars and 1.0% credibility score—docs are solid but lacks broad testing and web dashboard. Worth forking for autonomous software engineer experiments if you have Claude access; otherwise, star and watch for maturity.

(187 words)

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