139 lines
4.5 KiB
Python
139 lines
4.5 KiB
Python
import asyncio
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import json
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import os
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Optional
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from fnmatch import fnmatch
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from fastmcp import FastMCP, Client
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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 FilesystemBackend, LocalShellBackend, CompositeBackend
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# ------------------- Memory Server -------------------
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class MemoryServer:
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def __init__(self):
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self.mcp = FastMCP("Memory-Server")
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self.storage_path = Path("./memory_data.json")
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def _load_memory(self) -> dict:
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if not self.storage_path.exists():
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return {}
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with open(self.storage_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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def _save_memory(self, data: dict):
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self.storage_path.parent.mkdir(parents=True, exist_ok=True)
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with open(self.storage_path, 'w', encoding='utf-8') as f:
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json.dump(data, f, indent=2, ensure_ascii=False)
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@self.mcp.tool()
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def save(self, key: str, value: Any) -> bool:
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data = self._load_memory()
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data[key] = {
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"value": value,
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"timestamp": datetime.utcnow().isoformat()
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}
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self._save_memory(data)
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return True
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@self.mcp.tool()
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def get(self, key: str) -> Optional[dict]:
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data = self._load_memory()
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if key in data:
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return {"key": key, "value": data[key]["value"], "timestamp": data[key]["timestamp"]}
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return None
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@self.mcp.tool()
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def delete(self, key: str) -> bool:
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data = self._load_memory()
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if key in data:
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del data[key]
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self._save_memory(data)
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return True
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return False
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@self.mcp.tool()
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def list_keys(self, pattern: str = "*") -> list[str]:
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data = self._load_memory()
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return [k for k in data.keys() if fnmatch(k, pattern)]
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@self.mcp.tool()
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def save_with_namespace(self, key: str, value: Any, namespace: str = "default") -> bool:
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full_key = f"{namespace}:{key}"
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return self.save(full_key, value)
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@self.mcp.tool()
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def get_by_namespace(self, namespace: str = "default") -> list[dict]:
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data = self._load_memory()
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result = []
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prefix = f"{namespace}:"
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for k, v in data.items():
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if k.startswith(prefix):
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result.append({"key": k, "value": v["value"], "timestamp": v["timestamp"]})
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return result
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# ------------------- DeepAgent that uses Memory Server -------------------
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# The agent will use a simple tool that calls the memory server via MCP client
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@tool
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def memory_tool(action: str, params: dict) -> str:
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"""Calls the memory server tool specified by action.
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Parameters must include the tool name and its arguments.
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"""
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client = Client("python memory_server.py")
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asyncio.run(client.connect())
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try:
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result = asyncio.run(client.call_tool(action, params))
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return str(result)
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finally:
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asyncio.run(client.close())
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# LLM and backend setup
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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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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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])
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agent = create_deep_agent(
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model=llm,
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tools=[memory_tool],
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backend=backend,
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system_prompt="You are an agent that can store and retrieve data using the memory server.",
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)
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async def run_agent_demo():
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response = await agent.ainvoke(
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{"messages": [HumanMessage(content="Store key 'greeting' with value 'Hello' in default namespace.")]},
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{"configurable": {"thread_id": "demo-1"}},
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)
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print("Agent response:", response["messages"][-1].content)
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# ------------------- Server run -------------------
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if __name__ == "__main__":
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# Start server in background if called directly
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server = MemoryServer()
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# Run server in a separate thread so that the agent demo can also run
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import threading
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server_thread = threading.Thread(target=lambda: server.mcp.run(transport="stdio", show_banner=False, log_level="ERROR"), daemon=True)
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server_thread.start()
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# Give server a moment to start
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asyncio.run(asyncio.sleep(0.5))
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# Run agent demo
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asyncio.run(run_agent_demo())
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# Keep server alive for a short while to allow manual testing
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try:
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while True:
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pass
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except KeyboardInterrupt:
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print("Shutting down.")
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