74 lines
2.1 KiB
Python
74 lines
2.1 KiB
Python
import os
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import asyncio
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from typing import Any, Dict
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage
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from langchain.tools import tool
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from deepagents import create_deep_agent
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from deepagents.backends import CompositeBackend, LocalShellBackend, FilesystemBackend
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# LLM via OpenRouter
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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base_url="https://openrouter.ai/api/v1",
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api_key=os.getenv("OPENAI_API_KEY"),
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temperature=0.0,
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)
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backend = CompositeBackend(
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[
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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]
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)
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# Tool that forwards calls to the MCP memory server
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@tool
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def memory_action(action: str, params: Dict[str, Any]) -> str:
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"""
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Perform a memory operation via the MCP server.
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action: one of save_with_namespace, get_by_namespace, list_keys, delete, get, save
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params: dictionary of parameters required by the chosen action.
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Returns a JSON string with the server response.
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"""
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import json
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import asyncio
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from fastmcp import Client
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async def _call():
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client = Client("python memory_server.py")
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await client.connect()
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try:
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result = await client.call_tool(action, params)
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return json.dumps(result, ensure_ascii=False)
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finally:
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await client.close()
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loop = asyncio.get_event_loop()
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return loop.run_until_complete(_call())
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agent = create_deep_agent(
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model=llm,
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tools=[memory_action],
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backend=backend,
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system_prompt="You are an assistant that can store and retrieve data using a remote memory server.",
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)
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async def demo():
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# Example: store a name and then retrieve it
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query = (
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"Save the user name 'Алексей' in the default namespace using the memory_action tool. "
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"Then read back all entries from the default namespace and return them."
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)
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=query)]},
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{"configurable": {"thread_id": "demo-1"}},
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)
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print(result["messages"][-1].content)
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if __name__ == "__main__":
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asyncio.run(demo()) |