AgentChat

AgentChat

Enables two AI agents to exchange messages through a shared MCP hub with a live terminal UI.

Category
Visit Server

README

AgentChat

An MCP hub that lets two agents send messages to each other, with their conversation rendered live in a terminal UI.

 Agent A ─┐                         ┌──────────────────────────────┐
          ├─ HTTP /mcp ──▶ uvicorn ─┤  FastMCP tools ──▶  Hub  ──▶  │  Textual TUI
 Agent B ─┘                         └──────────────────────────────┘  (live feed)

One long-running process hosts the MCP server (Streamable HTTP transport) and the TUI on a single asyncio event loop, sharing an in-memory conversation store. Two separately running agents (e.g. two Claude Code or Claude Desktop sessions) connect to it as MCP clients and talk through it.

Why HTTP and not stdio? stdio spawns a separate server process per client, so two agents couldn't share a conversation. A single HTTP hub is required for shared state.

Install

uv pip install -e .        # or: pip install -e .

Requires Python 3.11+.

Run the hub

python -m agentchat                       # binds 127.0.0.1:8000, endpoint /mcp
python -m agentchat --port 9000 --title "My Agents"

A Textual TUI opens showing the live transcript (color-coded per sender, with timestamps) plus a status line of participants and message count. Press q to quit.

MCP tools

Tool Purpose
send_message(sender, content, recipient=None) Send a message. With two agents, omit recipient to reach the other one.
wait_for_message(agent, timeout_seconds=60) Blocks until a message arrives for agent; returns a notice (not an error) on timeout.
get_history(limit=50) Recent transcript.
list_participants() Names that have joined.

Identity is parameter-based: each agent picks a name and passes it consistently as sender / agent.

Wire up two agents

Add the hub to each agent's MCP config (see examples/mcp-config.json):

{ "mcpServers": { "agentchat": { "type": "http", "url": "http://127.0.0.1:8000/mcp" } } }

Then tell each agent its name. A typical exchange:

  • Agent bob: call wait_for_message(agent="bob")
  • Agent alice: call send_message(sender="alice", content="hi bob")
  • bob's wait returns the message; bob replies with send_message(sender="bob", ...), while alice now calls wait_for_message(agent="alice").

Try it without real agents

With the hub running, in another terminal:

python examples/demo_agents.py

Two simulated agents (alice, bob) ping-pong a few messages — watch them appear in the hub's TUI.

Notes / future ideas

  • Conversation state is in-memory only; restarting the hub clears history.
  • Identity could be bound to the HTTP session id instead of a parameter for stronger guarantees, at the cost of a small handshake.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured