5月28日 06:28
How does MCP integrate with other AI frameworks (like LangChain, LlamaIndex)?
MCP can be integrated with various AI frameworks and tools to extend its functionality and application scenarios. Here are detailed integration methods and best practices:
Integration Architecture Design
MCP integration should consider following aspects:
- Framework Compatibility: Ensure compatibility with target frameworks
- Performance Impact: Minimize impact on system performance
- Functional Completeness: Maintain completeness of MCP and framework functionality
- Error Handling: Properly handle errors during integration
- Configuration Management: Unified management of integration configuration
1. Integration with LangChain
from langchain.agents import AgentExecutor, create_openai_tools_agent from langchain_openai import ChatOpenAI from langchain.tools import Tool from mcp.server import Server class MCPLangChainIntegration: def __init__(self, mcp_server: Server): self.mcp_server = mcp_server self.langchain_tools = [] async def convert_mcp_to_langchain_tools(self) -> list: """Convert MCP tools to LangChain tools""" mcp_tools = await self.mcp_server.list_tools() langchain_tools = [] for tool_info in mcp_tools: tool = Tool( name=tool_info["name"], description=tool_info["description"], func=self._create_tool_wrapper(tool_info["name"]) ) langchain_tools.append(tool) return langchain_tools def _create_tool_wrapper(self, tool_name: str): """Create tool wrapper""" async def wrapper(**kwargs): result = await self.mcp_server.call_tool( tool_name, kwargs ) return result return wrapper async def create_langchain_agent(self): """Create LangChain Agent""" # Convert MCP tools tools = await self.convert_mcp_to_langchain_tools() # Create LLM llm = ChatOpenAI( model="gpt-4", temperature=0 ) # Create Agent agent = create_openai_tools_agent(llm, tools) # Create AgentExecutor agent_executor = AgentExecutor( agent=agent, tools=tools, verbose=True ) return agent_executor async def run_agent(self, query: str): """Run Agent""" agent_executor = await self.create_langchain_agent() result = await agent_executor.ainvoke({ "input": query }) return result["output"]
2. Integration with LlamaIndex
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader from llama_index.core.tools import QueryEngineTool, ToolMetadata from llama_index.core.agent import ReActAgent from mcp.server import Server class MCPLlamaIndexIntegration: def __init__(self, mcp_server: Server): self.mcp_server = mcp_server async def create_mcp_query_engine(self, tool_name: str): """Create MCP query engine""" async def query_engine_fn(query: str) -> str: result = await self.mcp_server.call_tool( tool_name, {"query": query} ) return result return query_engine_fn async def create_llamaindex_tools(self) -> list: """Create LlamaIndex tools""" mcp_tools = await self.mcp_server.list_tools() tools = [] for tool_info in mcp_tools: query_engine = await self.create_mcp_query_engine( tool_info["name"] ) tool = QueryEngineTool( query_engine=query_engine, metadata=ToolMetadata( name=tool_info["name"], description=tool_info["description"] ) ) tools.append(tool) return tools async def create_react_agent(self): """Create ReAct Agent""" tools = await self.create_llamaindex_tools() agent = ReActAgent.from_tools( tools=tools, verbose=True ) return agent async def query_with_agent(self, query: str): """Query with Agent""" agent = await self.create_react_agent() response = agent.query(query) return response.response
3. Integration with AutoGPT
from autogpt.agent.agent import Agent from autogpt.config import Config from autogpt.models.command import Command from mcp.server import Server class MCPAutoGPTIntegration: def __init__(self, mcp_server: Server): self.mcp_server = mcp_server self.command_registry = {} async def register_mcp_commands(self): """Register MCP commands""" mcp_tools = await self.mcp_server.list_tools() for tool_info in mcp_tools: command = Command( name=tool_info["name"], description=tool_info["description"], function=self._create_command_function(tool_info["name"]) ) self.command_registry[tool_info["name"]] = command def _create_command_function(self, tool_name: str): """Create command function""" async def command_function(**kwargs): result = await self.mcp_server.call_tool( tool_name, kwargs ) return result return command_function async def create_autogpt_agent(self, config: Config): """Create AutoGPT Agent""" # Register MCP commands await self.register_mcp_commands() # Create Agent agent = Agent( ai_name="MCP-Agent", ai_role="Assistant", commands=self.command_registry, config=config ) return agent async def run_autogpt_task(self, task: str): """Run AutoGPT task""" config = Config() agent = await self.create_autogpt_agent(config) result = await agent.run(task) return result
4. Integration with FastAPI
from fastapi import FastAPI, HTTPException, Depends from fastapi.middleware.cors import CORSMiddleware from mcp.server import Server from pydantic import BaseModel class MCPFastAPIIntegration: def __init__(self, mcp_server: Server): self.mcp_server = mcp_server self.app = FastAPI(title="MCP API") self._setup_middleware() self._setup_routes() def _setup_middleware(self): """Setup middleware""" self.app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) def _setup_routes(self): """Setup routes""" @self.app.get("/tools") async def list_tools(): """List all tools""" tools = await self.mcp_server.list_tools() return {"tools": tools} @self.app.post("/tools/{tool_name}/call") async def call_tool(tool_name: str, params: dict): """Call tool""" try: result = await self.mcp_server.call_tool( tool_name, params ) return {"result": result} except Exception as e: raise HTTPException(status_code=500, detail=str(e)) @self.app.get("/resources") async def list_resources(): """List all resources""" resources = await self.mcp_server.list_resources() return {"resources": resources} @self.app.get("/resources/{uri}") async def read_resource(uri: str): """Read resource""" try: content = await self.mcp_server.read_resource(uri) return {"content": content} except Exception as e: raise HTTPException(status_code=500, detail=str(e)) @self.app.get("/prompts") async def list_prompts(): """List all prompts""" prompts = await self.mcp_server.list_prompts() return {"prompts": prompts} @self.app.get("/health") async def health_check(): """Health check""" return {"status": "healthy"} def get_app(self) -> FastAPI: """Get FastAPI application""" return self.app
5. Integration with WebSocket
import asyncio import json from fastapi import WebSocket from mcp.server import Server class MCPWebSocketIntegration: def __init__(self, mcp_server: Server): self.mcp_server = mcp_server self.active_connections = [] async def handle_websocket(self, websocket: WebSocket): """Handle WebSocket connection""" await websocket.accept() self.active_connections.append(websocket) try: while True: # Receive message data = await websocket.receive_text() message = json.loads(data) # Handle message response = await self._handle_message(message) # Send response await websocket.send_text(json.dumps(response)) except Exception as e: print(f"WebSocket error: {e}") finally: self.active_connections.remove(websocket) async def _handle_message(self, message: dict) -> dict: """Handle message""" message_type = message.get("type") if message_type == "list_tools": tools = await self.mcp_server.list_tools() return {"type": "tools_list", "data": tools} elif message_type == "call_tool": result = await self.mcp_server.call_tool( message["tool_name"], message.get("params", {}) ) return {"type": "tool_result", "data": result} elif message_type == "list_resources": resources = await self.mcp_server.list_resources() return {"type": "resources_list", "data": resources} else: return {"type": "error", "message": "Unknown message type"} async def broadcast_message(self, message: dict): """Broadcast message to all connections""" message_text = json.dumps(message) for connection in self.active_connections: try: await connection.send_text(message_text) except Exception as e: print(f"Failed to send message: {e}")
6. Integration with GraphQL
import strawberry from strawberry.types import Info from mcp.server import Server @strawberry.type class MCPTool: name: str description: str @strawberry.type class MCPResource: uri: str name: str description: str @strawberry.type class Query: @strawberry.field async def tools(self, info: Info) -> list[MCPTool]: """Get all tools""" mcp_server = info.context["mcp_server"] tools = await mcp_server.list_tools() return [ MCPTool( name=tool["name"], description=tool["description"] ) for tool in tools ] @strawberry.field async def resources(self, info: Info) -> list[MCPResource]: """Get all resources""" mcp_server = info.context["mcp_server"] resources = await mcp_server.list_resources() return [ MCPResource( uri=resource["uri"], name=resource["name"], description=resource["description"] ) for resource in resources ] @strawberry.type class Mutation: @strawberry.mutation async def call_tool( self, tool_name: str, params: dict, info: Info ) -> str: """Call tool""" mcp_server = info.context["mcp_server"] result = await mcp_server.call_tool(tool_name, params) return str(result) class MCPGraphQLIntegration: def __init__(self, mcp_server: Server): self.mcp_server = mcp_server self.schema = strawberry.Schema( query=Query, mutation=Mutation ) async def execute_query(self, query: str, variables: dict = None): """Execute GraphQL query""" context = {"mcp_server": self.mcp_server} result = await self.schema.execute( query, variable_values=variables, context_value=context ) if result.errors: return { "errors": [str(error) for error in result.errors] } return {"data": result.data}
7. Integration with gRPC
import grpc from concurrent import futures import mcp_pb2 import mcp_pb2_grpc from mcp.server import Server class MCPServicer(mcp_pb2_grpc.MCPServicer): def __init__(self, mcp_server: Server): self.mcp_server = mcp_server async def ListTools( self, request: mcp_pb2.ListToolsRequest, context: grpc.ServicerContext ) -> mcp_pb2.ListToolsResponse: """List tools""" tools = await self.mcp_server.list_tools() tool_protos = [ mcp_pb2.Tool( name=tool["name"], description=tool["description"] ) for tool in tools ] return mcp_pb2.ListToolsResponse(tools=tool_protos) async def CallTool( self, request: mcp_pb2.CallToolRequest, context: grpc.ServicerContext ) -> mcp_pb2.CallToolResponse: """Call tool""" params = dict(request.params) result = await self.mcp_server.call_tool( request.tool_name, params ) return mcp_pb2.CallToolResponse(result=str(result)) class MCPGRPCIntegration: def __init__(self, mcp_server: Server, port: int = 50051): self.mcp_server = mcp_server self.port = port self.server = None async def start_server(self): """Start gRPC server""" self.server = grpc.aio.server( futures.ThreadPoolExecutor(max_workers=10) ) mcp_pb2_grpc.add_MCPServicer_to_server( MCPServicer(self.mcp_server), self.server ) self.server.add_insecure_port(f'[::]:{self.port}') await self.server.start() print(f"gRPC server started on port {self.port}") async def stop_server(self): """Stop gRPC server""" if self.server: await self.server.stop(0) print("gRPC server stopped")
Best Practices:
- Async Processing: Use async programming to avoid blocking
- Error Handling: Properly handle errors during integration
- Performance Optimization: Cache frequently called results
- Logging: Log all integration operations
- Test Coverage: Write integration tests to ensure functionality
- Complete Documentation: Provide clear integration documentation
Through integration with other AI frameworks and tools, you can extend MCP's functionality and application scenarios.