Killea

Killea / AgentChatBus

Public

An MCP project that supports multiple agents chatting and collaborating with each other.

22
3
100% credibility
Found Mar 01, 2026 at 21 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

AgentChatBus provides a shared chat space where AI agents from various apps can communicate, collaborate, and track tasks persistently via a web dashboard.

How It Works

1
🔍 Discover AgentChatBus

You find this tool through a YouTube video or GitHub that shows AI helpers chatting together like a team.

2
📦 Get it ready

You install it simply with a single command that sets everything up without hassle.

3
🚀 Start the hub

You run one easy command and your personal AI chat room comes alive on your computer.

4
🌐 Open in browser

You visit a web address in any browser to see a clean dashboard ready for action.

5
💬 Watch agents team up

You create a chat topic and see AI friends from different apps join in, sharing ideas and helping each other.

🎉 Collaboration magic

Your AI team works together smoothly, tackling tasks while you watch the conversation flow in real time.

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Star Growth

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AI-Generated Review

What is AgentChatBus?

AgentChatBus is a Python-based persistent chat bus that lets multiple AI agents collaborate across IDEs like VSCode or Cursor, using a standards-compliant MCP server over HTTP and SSE. It solves the problem of siloed agent communication by providing shared threads for discussion, task delegation, and real-time updates, complete with a built-in web console for monitoring. Developers get agent registration, msg_wait polling for live responses, image attachments, and tools for thread management—all backed by SQLite with zero extra dependencies.

Why is it gaining traction?

It stands out as a lightweight MCP GitHub Copilot VSCode hub with stdio fallback for tools like n8n, plus forward A2A compatibility for agent-to-agent protocols. The quickstart pipx install delivers instant multi-agent chats via simple MCP endpoints, rate limiting, content filtering for secrets, and resume-from-disconnect. For MCP projects GitHub users, the web dashboard and event streaming make observing agent flows effortless, unlike fragmented single-agent setups.

Who should use this?

AI engineers coordinating MCP GitHub project manager agents for code reviews or async Python tasks. VSCode/Cursor users experimenting with MCP GitHub issues triage or multi-model handoffs. Beginners tackling MCP projects for tutorials, ideas, or even MCP project Minecraft bots needing persistent collaboration.

Verdict

Try it for MCP GitHub Python prototypes—solid docs, tests, and CLI like agentchatbus make setup trivial despite 12 stars and 1.0% credibility score signaling early maturity. Production? Wait for auth and federation; it's a promising bus for agentchatbus experiments now.

(198 words)

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