AgentMCP
Enable Cross-Framework AI Agent Collaboration with a Single Line of Code
Install & Use
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Help me install this AI Skill: AgentMCP. It is used for: Enable Cross-Framework AI Agent Collaboration with a Single Line of Code Full Skill content: https://321skill.com/skills/agentmcp/raw/index.md Read that page and install it.
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This Skill addresses the pain point of AI agents being unable to collaborate across different frameworks and protocols. In practical development, developers often need to use agents built with various frameworks like Autogen, LangGraph, and CrewAI simultaneously. However, these agents operate in isolation, unable to communicate directly or coordinate tasks, leading to redundant development and high integration costs.
The usage is extremely simple, requiring just three steps: First, install the dependency via pip install agent-mcp. Then, add the @mcp_agent(mcp_id='your_agent_name') decorator to your agent class. This automatically connects the agent to MACNet (Multi-Agent Collaboration Network). Afterwards, your agent can seamlessly communicate with any other agent in the network (e.g., from Autogen, LangGraph), share tasks, and coordinate workflows. All network connections, protocol conversions, and task scheduling are handled automatically by AgentMCP.
It is ideal for teams or individuals building multi-agent collaboration systems, especially developers already using multiple different AI frameworks (like Autogen, LangGraph, CrewAI) who wish to unify their collaboration. It's also suitable for scenarios requiring rapid integration of existing agents into a global AI agent network, such as cross-team or cross-organization agent collaboration.
It's recommended to use the @mcp_agent decorator directly when building new agents for future scalability. Existing agents can also be integrated without modification. Note that it currently primarily relies on a Python environment and requires agents to have network access to connect to MACNet. For offline or internal network environments, additional network routing configuration may be needed.
Key Features
Unlike single-framework collaboration tools such as LangGraph Studio or AutoGen Studio, AgentMCP enables cross-framework agent interoperability (e.g., Autogen, LangGraph, CrewAI) simply via a decorator, without modifying the original agent logic. It also features built-in cost-optimized routing (e.g., automatically selecting the most economical AI provider).
Limitations
Currently only supports Python 3.8+ environments, and agents require outbound access to the public MACNet network. For large-scale production environments, deploying a private MACNet node is recommended to avoid network latency and bandwidth bottlenecks.
FAQ
Which AI frameworks does AgentMCP support?
Currently supports mainstream frameworks like Autogen, LangGraph, and CrewAI. Through its decorator abstraction layer, it can theoretically be extended to any Python agent class.
Does my agent require extensive code modifications to integrate?
No. Simply add a single line—the `@mcp_agent` decorator with a specified `mcp_id`—to the class definition. This automatically handles network registration and protocol conversion without altering the original business logic.
Installation guide for AI assistants
If your AI coding assistant (Claude Code, Cursor, TRAE etc.) can see this page, send it this message to auto-install:
Visit https://321skill.com/skills/agentmcp/raw/index.md to read the original Skill definition (Markdown format) for AgentMCP, and install it according to the instructions.
Raw Markdown URL for AI: /skills/agentmcp/raw/index.md