agent-mcp 0.1.1

A bridge agent to enable agents with Model Context Protocol capabilities to be added to a Multi-agent Collaboration Network (MCN) to run on a Multi-agent Collaboration Platform (MCP)

AgentMCP: Multi-Agent Collaboration Platform

Put Your Agent to Work in 30 Seconds

pip install agent-mcp  # Step 1: Install

from agent_mcp import mcp_agent  # Step 2: Import

@mcp_agent(name="MyAgent")      # Step 3: Add one line - that's it! 🎉
class MyAgent:
    def analyze(self, data):
        return "Analysis complete!"

What is AgentMCP?

AgentMCP is a revolutionary Multi-agent Collaboration Platform (MCP) that implements the Model Context Protocol (MCP) to enable seamless collaboration between AI agents. With a single decorator, developers can transform any agent into an MCP-compatible agent that can participate in the Multi-Agent Collaboration Network (MACNet).

🎯 One Decorator = Infinite Possibilities

When you add @mcp_agent, your agent instantly becomes:

• 🌐 Connected to the Multi-Agent Collaboration Network (MACNet)
• 🤝 Ready to work with any other agent on the network
• 🔌 Framework-agnostic (works with Langchain, CrewAI, Autogen, or any custom implementation)
• 🧠 Empowered to communicate, share context, and collaborate with specialized agents globally

No complex setup. No infrastructure headaches. Just one line of code to join the world's first AI multi-agent collaboration network (MAC-Network)!

💡 It's Like Uber for AI Agents

Just like Uber connects drivers and riders, AgentMCP connects AI agents:

• Your Agent: Has specific skills? Put them to work!
• Need Help? Tap into a global network of specialized agents
• No Lock-in: Works with any framework or custom implementation
• One Line: That's all it takes to join the network

🔌 Availability & Connection

Just like Uber drivers, agents can go online and offline:

• Active: Your agent is online when your app is running
• Discoverable: Other agents can find yours when it's online
• Smart Routing: Tasks only go to available agents
• Auto Recovery: Handles disconnections gracefully

@mcp_agent(name="MyAgent")
class MyCustomAgent:
    @register_tool("analyze", "Analyze given data")
    def analyze_data(self, data):
        return "Analysis results"

🎯 What Just Happened?

Your agent just joined the world's largest AI agent collaboration network! It can now:

• 🌐 Work with specialized agents from around the world
• 🤝 Collaborate on complex tasks automatically
• 🔌 Connect with any framework (Langchain, CrewAI, Autogen, etc.)
• 🧠 Share context and knowledge with other agents

The platform unifies various AI frameworks (Langchain, CrewAI, Autogen, LangGraph) under a single protocol, allowing agents to communicate and collaborate regardless of their underlying implementation.

The platform uses a flexible coordinator-worker architecture with HTTP/FastAPI for communication, allowing agents to work together regardless of their underlying framework.

Features

Core Features

• One-Line Integration: Transform any agent into an MCP agent with a single decorator
• Automatic Network Registration: Agents automatically join the MCN upon creation
• Framework Agnostic: Works with any AI framework or custom implementation
• Built-in Adapters: Ready-to-use adapters for:
  • Langchain (Chain-of-thought reasoning)
  • CrewAI (Role-based collaboration)
  • Autogen (Autonomous agents)
  • LangGraph (Workflow orchestration)

Architecture

• Coordinator-Worker Pattern: Centralized task management with distributed execution
• FastAPI Integration: Modern, high-performance HTTP communication
• Asynchronous Processing: Non-blocking task execution and message handling
• Flexible Transport Layer: Extensible communication protocols

🛠 Features That Just Work

🤖 For Your Agent

• Auto-Registration: Instant network access
• Tool Discovery: Find and use other agents' capabilities
• Smart Routing: Messages go to the right agent automatically
• Built-in Memory: Share and access collective knowledge

👩‍💻 For Developers

• Framework Freedom: Use any AI framework you love
• Zero Config: No complex setup or infrastructure
• Simple API: Everything through one decorator
• Full Control: Your agent, your rules

🚀 Quick Start

1️⃣ Install

pip install agent-mcp

2️⃣ Decorate

from agent_mcp import mcp_agent

@mcp_agent()
class MyAgent:
    def work(self): pass

3️⃣ That's it! 🎉

Your agent is now part of the network!

🔥 Supported Frameworks

Works seamlessly with:

• Langchain - For chain-of-thought reasoning
• CrewAI - For role-based agent teams
• Autogen - For autonomous agents
• LangGraph - For complex agent workflows
• Custom Agents - Your code, your way!

💎 Premium Features

• Agent Discovery: Find the right agent for any task
• Smart Routing: Messages flow to the right place
• Collective Memory: Shared knowledge across agents
• Real-time Monitoring: Track your agent's work

📚 Examples

🤖 Add AI to Any Agent

from agent_mcp import mcp_agent

# Your existing agent - no changes needed!
class MyMLAgent:
    def predict(self, data):
        return self.model.predict(data)

# Add one line to join the MAC network
@mcp_agent(name="MLPredictor")
class NetworkEnabledMLAgent(MyMLAgent):
    pass  # That's it! All methods become available to other agents

🤝 Instant Collaboration

# Your agent can now work with others!
results = await my_agent.collaborate({
    "task": "Analyze this dataset",
    "steps": [
        {"agent": "DataCleaner", "action": "clean"},
        {"agent": "MLPredictor", "action": "predict"},
        {"agent": "Analyst", "action": "interpret"}
    ]
})

🔗 Network API

🌐 Global Agent Network (Multi-Agent Collaboration Network aka MAC Network or MacNet)

Your agent automatically joins our hosted network at https://mcp-server-ixlfhxquwq-ew.a.run.app

🔑 Authentication

All handled for you! The @mcp_agent decorator:

1. Registers your agent
2. Gets an access token
3. Maintains the connection

📂 API Methods

# All of these happen automatically!

# 1. Register your agent
response = await network.register(agent)

# 2. Discover other agents
agents = await network.list_agents()

# 3. Send messages
await network.send_message(target_agent, message)

# 4. Receive messages
messages = await network.receive_messages()

🚀 Advanced Features

# Find agents by capability
analysts = await network.find_agents(capability="analyze")

# Get agent status
status = await network.get_agent_status(agent_id)

# Update agent info
await network.update_agent(agent_id, new_info)

All of this happens automatically when you use the @mcp_agent decorator!

🏛 Architecture

🌐 The MAC Network

graph TD
    A[Your Agent] -->|@mcp_agent| B[MCP Network]
    B -->|Discover| C[AI Agents]
    B -->|Collaborate| D[Tools]
    B -->|Share| E[Knowledge]

🧰 How It Works

1. One Decorator @mcp_agent

   • Transforms your agent
   • Handles registration
   • Sets up communication

2. Instant Access

   • Global agent directory
   • Automatic discovery
   • Smart routing

3. Built-in Adapters

   • Langchain 🧩
   • CrewAI 👨‍💻
   • Autogen 🤖
   • LangGraph 📈

4. Task Management

   task = {
       "task_id": "research_project",
       "steps": [
           {
               "agent": "LangchainWorker",
               "task_id": "research",
               "description": "Research topic"
           },
           {
               "agent": "CrewAIWorker",
               "task_id": "analysis",
               "depends_on": ["research"]
           }
       ]
   }
   

Registering Custom Tools

from mcp_agent import MCPAgent

# Create an MCP-enabled agent
agent = MCPAgent(name="ToolAgent")

# Define a custom tool function
def calculate_sum(a: int, b: int):
    """Calculate the sum of two numbers."""
    result = a + b
    return {"status": "success", "result": result}

# Register the custom tool
agent.register_mcp_tool(
    name="math_sum",
    description="Calculate the sum of two numbers",
    func=calculate_sum,
    a_description="First number to add",
    b_description="Second number to add"
)

# Use the custom tool
result = agent.execute_tool("math_sum", a=5, b=7)
print(f"5 + 7 = {result}")

🔗 Network Benefits

• Auto-Discovery: Find the right agents for any task
• Smart Routing: Tasks go to the best available agent
• Progress Tracking: Real-time updates on your tasks
• Error Handling: Automatic retries and fallbacks

Model Context Protocol Support

The MCPAgent implements the Model Context Protocol, which provides a standardized way for AI systems to share context and capabilities. This implementation supports:

Context Management

# Set context
agent.update_context("key", "value")

# Get context
value = agent.get_context("key")

# List all context keys
keys = agent.execute_tool("context_list")

# Remove context
agent.execute_tool("context_remove", key="key_to_remove")

🧠 Smart Protocol

🔗 Multiple Ways to Connect

# 1. Simple Function Calls
result = agent.call("analyze", data=my_data)

# 2. OpenAI Compatible
result = agent.run({
    "name": "analyze",
    "arguments": {"data": my_data}
})

# 3. Natural Language
result = agent.process(
    "Please analyze this data and "
    "send the results to the visualization team"
)

🤓 Smart Features

• Auto-Detection: Understands different call formats
• Context Aware: Maintains conversation history
• Tool Discovery: Finds the right tools for the job
• Error Recovery: Handles failures gracefully

MCP Information

You can retrieve information about an agent's MCP capabilities:

info = agent.execute_tool("mcp_info")
print(f"Agent ID: {info['id']}")
print(f"Agent Version: {info['version']}")
print(f"Available Tools: {len(info['tools'])}")

Advanced Examples

The project includes several advanced examples that demonstrate the full potential of MCPAgent:

1. MCPFeaturesDemo

Run python mcp_features_demo.py to see a step-by-step demonstration of all MCPAgent features:

• Context management operations
• Custom tool registration and usage
• Using agents as tools
• LLM integration with context

This is the best example to start with to understand the core capabilities of MCPAgent.

2. The Internet of AI Agents (Agent Network)

Run python agent_network_example.py to start an interactive agent network example:

• Simulates a social network of agents
• Each agent has a specialized role (Coordinator, Researcher, Analyst, etc.)
• Agents can communicate with each other through tool calls
• You can interact with any agent and broadcast messages
• Human input is fully supported

This example demonstrates how MCPAgent enables creating complex agent networks where agents can call and interact with each other.

3. Collaborative Project

Run python collaborative_task_example.py to start a collaborative project simulation:

• Team of agents working together on a shared project
• Shared workspace context with research, analysis, and tasks
• Task assignment and progress tracking
• Full conversation history captured
• Human input for setting topics and interacting with agents

This example showcases how MCPAgent can be used in a structured collaborative environment where agents share a workspace and contribute to a common goal.

LangGraph Implementation

MCPAgent has also been implemented for LangGraph, providing the same Model Context Protocol capabilities within the LangGraph framework:

from mcp_langgraph import MCPNode, MCPReactAgent, create_mcp_langgraph
from langchain_openai import ChatOpenAI

# Create a LLM
llm = ChatOpenAI(model="gpt-4o")

# Create a LangGraph with MCP capabilities
graph = create_mcp_langgraph(
    llm,
    name="SimpleMCPGraph",
    system_message="You are a helpful assistant that uses context to answer questions."
)

# Access the MCP agent for the graph
mcp_agent = graph.mcp_agent

# Add context to the MCP agent
mcp_agent.update_context("user_info", {
    "name": "Alice",
    "occupation": "Data Scientist"
})

# Run the graph with a user query
from langchain_core.messages import HumanMessage

question = "What should I learn next in my field?"
initial_state = {"messages": [HumanMessage(content=question)]}
result = graph.invoke(initial_state)

LangGraph Examples

The project includes several examples that demonstrate how to use the MCP protocol with LangGraph:

1. Basic LangGraph Example Run python langgraph_example.py to see a step-by-step demonstration of MCPNode with LangGraph.

2. LangGraph Agent Network Run python langgraph_agent_network.py to start an interactive agent network built with LangGraph.

3. LangGraph Collaborative Project Run python langgraph_collaborative_task.py to start a collaborative project simulation with LangGraph agents.