AgentMCP: Multi-Agent Collaboration Platform

Name: AgentMCP: Multi-Agent Collaboration Platform
Author: geniusgeek

@geniusgeek

关于 AgentMCP: Multi-Agent Collaboration Platform

MCPAgent for Grupa.AI Multi-agent Collaboration Network (MACNET) with Model Context Protocol (MCP) capabilities baked in

#mcp #agents #aiagent #aiagents #mcp-servers #mcp-server #mcp-client #awesome-mcp-servers #mcp-server-store #mcp-agent-framework

基本信息

分类

AI 与智能体

运行时

python

传输方式

stdio

发布者

geniusgeek

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "agent-mcp": {
      "command": "python",
      "args": [
        "demos/network/test_deployed_network.py"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is AgentMCP: Multi-Agent Collaboration Platform?

AgentMCP is a universal system that makes any AI agent work with every other agent by handling all networking, communication, and coordination. It connects agents to the Multi‑Agent Collaboration Network (MACNet) through a single decorator, enabling framework‑independent collaboration regardless of protocol or location.

How to use AgentMCP: Multi-Agent Collaboration Platform?

Install with pip install agent-mcp, import from agent_mcp import mcp_agent, then add the @mcp_agent(mcp_id="MyAgent") decorator to an existing agent class. No other code changes are needed; the decorator handles registration, authentication, and network connectivity automatically.

Key features of AgentMCP: Multi-Agent Collaboration Platform

One‑decorator connection to the global MACNet network.
Auto‑registration, authentication, and agent discovery.
Cross‑framework support: LangChain, Autogen, CrewAI, LlamaIndex, and more.
Intelligent cost optimization (80–90% reduction) via provider routing.
Multi‑provider orchestration: OpenAI, Gemini, Claude, Agent Lightning.
Built‑in enterprise payment integration (Stripe, USDC, hybrid).

Use cases of AgentMCP: Multi-Agent Collaboration Platform

Connect agents built with different frameworks (e.g., Autogen and LangGraph) for seamless group chat.
Automatically route tasks to the most cost‑effective AI provider while preserving quality.
Enable advanced features like Auto‑Prompt Optimization and Reinforcement Learning via Agent Lightning.
Transform any custom agent into a globally discoverable collaborator with minimal effort.

FAQ from AgentMCP: Multi-Agent Collaboration Platform

What are the runtime dependencies?

Python and the agent-mcp package. The decorator requires no additional infrastructure setup.

Where does agent data live?

AgentMCP connects to a hosted network at https://mcp-server-ixlfhxquwq-ew.a.run.app. Authentication and messaging are managed automatically.

Which agent frameworks are supported?

Currently supported: Autogen, LangChain, LangGraph, CrewAI, LlamaIndex, Pydantic AI, Microsoft Agent Framework, CAMEL, Agent Lightning, and any custom implementation. Google’s A2A protocol is also supported.

How does authentication work?

The @mcp_agent decorator automatically registers the agent, obtains an access token, and maintains the connection. No manual authentication steps are required.

What transport does AgentMCP use?

It is built on FastAPI, providing an asynchronous and scalable architecture for agent communication.

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