mirror of
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48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
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import asyncio
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import os
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import time
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from langchain_mcp_adapters.client import MultiServerMCPClient
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from langgraph.prebuilt import create_react_agent
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from composio import Composio
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composio = Composio()
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# Slack auth config and user id (raise KeyError when unset)
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slack_auth_config_id = os.environ["COMPOSIO_EXAMPLES_SLACK_AUTH_CONFIG_ID"]
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user_id = os.environ["COMPOSIO_EXAMPLES_USER_ID"]
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mcp_config = composio.mcp.create(
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# Named `examples-<label>-<unix-seconds>` so the provisioning script's --gc
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# can tell this throwaway config from one created by hand. The API caps
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# this name at 30 characters, so the label stays short.
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name=f"examples-lc-slack-{int(time.time())}",
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toolkits=[{"toolkit": "slack", "auth_config_id": slack_auth_config_id}],
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# Keep the exposed tool list small; LLM providers cap tools per request
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allowed_tools=["SLACK_LIST_ALL_CHANNELS", "SLACK_SEARCH_MESSAGES"],
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)
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mcp_server = mcp_config.generate(user_id=user_id) # type: ignore[call-arg] # generate is typed as a bare Callable; runtime accepts the user_id keyword
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client = MultiServerMCPClient(
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{
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"composio": {
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"url": mcp_server["url"],
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"transport": "streamable_http",
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# The MCP endpoint authenticates with your Composio API key
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"headers": {"x-api-key": os.environ["COMPOSIO_API_KEY"]},
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}
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}
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)
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async def langchain_mcp(message: str):
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tools = await client.get_tools()
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agent = create_react_agent("openai:gpt-4.1", tools)
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response = await agent.ainvoke({"messages": message})
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return response
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mcp_response = asyncio.run(langchain_mcp("Show me 20 most used slack channels"))
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