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# Copyright (c) Microsoft. All rights reserved.
"""Minimal LangGraph + MCP sample.
Loads an MCP server (Microsoft Learn) and exposes a LangGraph ReAct agent
through the agents_adapter server.
"""
import asyncio
import os
from dotenv import load_dotenv
from importlib.metadata import version
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import AzureChatOpenAI
from azure.ai.agentserver.langgraph import from_langgraph
load_dotenv()
def create_agent(model, tools):
# for different langgraph versions
langgraph_version = version("langgraph")
if langgraph_version < "1.0.0":
from langgraph.prebuilt import create_react_agent
return create_react_agent(model, tools)
else:
from langchain.agents import create_agent
return create_agent(model, tools)
async def quickstart():
"""Build and return a LangGraph agent wired to an MCP client."""
deployment = os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME", "gpt-4o")
model = AzureChatOpenAI(model=deployment)
client = MultiServerMCPClient(
{
"mslearn": {
"url": "https://learn.microsoft.com/api/mcp",
"transport": "streamable_http",
}
}
)
tools = await client.get_tools()
return create_agent(model, tools)
async def main(): # pragma: no cover - sample entrypoint
agent = await quickstart()
await from_langgraph(agent).run_async()
if __name__ == "__main__":
asyncio.run(main())
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