add: main.py — MCP-сервер для управления памятью агента

main.py

@@ -0,0 +1,74 @@
+import os
+import asyncio
+from typing import Any, Dict
+
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage
+from langchain.tools import tool
+from deepagents import create_deep_agent
+from deepagents.backends import CompositeBackend, LocalShellBackend, FilesystemBackend
+
+# LLM via OpenRouter
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+    temperature=0.0,
+)
+
+backend = CompositeBackend(
+    [
+        LocalShellBackend(workspace_dir="./workspace"),
+        FilesystemBackend(),
+    ]
+)
+
+# Tool that forwards calls to the MCP memory server
+@tool
+def memory_action(action: str, params: Dict[str, Any]) -> str:
+    """
+    Perform a memory operation via the MCP server.
+
+    action: one of save_with_namespace, get_by_namespace, list_keys, delete, get, save
+    params: dictionary of parameters required by the chosen action.
+    Returns a JSON string with the server response.
+    """
+    import json
+    import asyncio
+    from fastmcp import Client
+
+    async def _call():
+        client = Client("python memory_server.py")
+        await client.connect()
+        try:
+            result = await client.call_tool(action, params)
+            return json.dumps(result, ensure_ascii=False)
+        finally:
+            await client.close()
+
+    loop = asyncio.get_event_loop()
+    return loop.run_until_complete(_call())
+
+
+agent = create_deep_agent(
+    model=llm,
+    tools=[memory_action],
+    backend=backend,
+    system_prompt="You are an assistant that can store and retrieve data using a remote memory server.",
+)
+
+async def demo():
+    # Example: store a name and then retrieve it
+    query = (
+        "Save the user name 'Алексей' in the default namespace using the memory_action tool. "
+        "Then read back all entries from the default namespace and return them."
+    )
+    result = await agent.ainvoke(
+        {"messages": [HumanMessage(content=query)]},
+        {"configurable": {"thread_id": "demo-1"}},
+    )
+    print(result["messages"][-1].content)
+
+
+if __name__ == "__main__":
+    asyncio.run(demo())