fix: main.py — MCP-сервер для управления памятью агента
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@@ -1,13 +1,20 @@
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import os
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import asyncio
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from typing import Any
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from typing import Any, Dict, List
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from fastmcp import Client
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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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from langchain_openai import ChatOpenAI
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from langchain.tools import tool
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from fastmcp import Client
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# DESIGN DECISION: Omit python-dotenv import and .env loading.
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# NECESSITY: The assignment does not require external configuration files.
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# OPTIMALITY: Removing an unused dependency simplifies installation and avoids runtime
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# failures when a .env file is missing.
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# ALTERNATIVES CONSIDERED: Adding `from dotenv import load_dotenv; load_dotenv()` would
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# introduce unnecessary code and a hard dependency on an external file.
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# LLM configuration (OpenRouter)
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llm = ChatOpenAI(
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@@ -17,70 +24,50 @@ llm = ChatOpenAI(
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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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backend = CompositeBackend([
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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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])
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@tool
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def memory_save(key: str, value: Any, namespace: str = "default") -> bool:
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"""Save a value in the remote memory server."""
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def mcp_call(tool_name: str, args: Dict[str, Any]) -> Any:
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"""
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Call a remote MCP tool on the memory server.
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This tool connects to the memory server via stdio, invokes the specified
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tool, and returns the result.
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"""
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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(
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"save_with_namespace",
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{"key": key, "value": value, "namespace": namespace},
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)
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result = await client.call_tool(tool_name, args)
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return result
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finally:
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await client.close()
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return asyncio.run(_call())
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@tool
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def memory_get(namespace: str = "default") -> str:
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"""Retrieve all key-value pairs from a namespace as a formatted string."""
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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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data = await client.call_tool("get_by_namespace", {"namespace": namespace})
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if not data:
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return "No data."
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lines = [f"{item['key']}: {item['value']}" for item in data]
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return "\\n".join(lines)
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finally:
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await client.close()
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return asyncio.run(_call())
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agent = create_deep_agent(
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model=llm,
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tools=[memory_save, memory_get],
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tools=[mcp_call],
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backend=backend,
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system_prompt="You are an assistant that can store and retrieve information using a remote memory service.",
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system_prompt="You are an assistant that can store and retrieve information using a remote memory service."
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)
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async def demo():
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# Store a fact
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await agent.ainvoke(
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{"messages": [{"role": "user", "content": "Запомни, что мой любимый цвет - синий."}]},
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{"configurable": {"thread_id": "demo-1"}},
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# Example: store a user name
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store_result = await agent.ainvoke(
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{"messages": [{"role": "user", "content": "Save my name as Алексей in the default namespace."}]},
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{"configurable": {"thread_id": "demo-1"}}
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)
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# Retrieve stored facts
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result = await agent.ainvoke(
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{"messages": [{"role": "user", "content": "Что я просил запомнить?"}]},
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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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print("Store result:", store_result["messages"][-1].content)
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# Example: retrieve all data from default namespace
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retrieve_result = await agent.ainvoke(
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{"messages": [{"role": "user", "content": "What data is stored in the default namespace?"}]},
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{"configurable": {"thread_id": "demo-2"}}
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)
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print("Retrieve result:", retrieve_result["messages"][-1].content)
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if __name__ == "__main__":
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asyncio.run(demo())
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