add main.py

main.py

@@ -1,16 +1,9 @@
-"""Deep agent: web search + virtual files → real filesystem export."""
-import os
-import asyncio
-from pathlib import Path
-
-from dotenv import load_dotenv
+import os, asyncio
 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 LocalShellBackend, CompositeBackend
-
-load_dotenv()
+from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
 
 llm = ChatOpenAI(
     model="openai/gpt-oss-20b:free",
@@ -19,63 +12,35 @@ llm = ChatOpenAI(
     temperature=0.0,
 )
 
-WORKSPACE = Path("./workspace")
-REAL_OUT = Path("./output")
-
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
 
 @tool
 def web_search(query: str) -> str:
-    """Search the web for information and return snippets."""
+    """Search the web for information.""
     try:
         from duckduckgo_search import DDGS
         with DDGS() as ddgs:
             results = list(ddgs.text(query, max_results=5))
         return "\n".join(f"{r['title']}: {r['body']}" for r in results)
-    except Exception as exc:
-        return f"Search error: {exc}"
-
-
-backend = CompositeBackend(
-    default=LocalShellBackend(
-        root_dir=str(WORKSPACE),
-        virtual_mode=True,
-        inherit_env=True,
-    ),
-)
+    except Exception as e:
+        return f"Search error: {e}"
 
 agent = create_deep_agent(
-    model=llm,
+    llm=llm,
     tools=[web_search],
     backend=backend,
-    system_prompt=(
-        "You are a research agent. "
-        "Use web_search to find information, then create virtual files with write_file. "
-        "When done, export all virtual files to real filesystem by calling execute with "
-        "a shell command that copies /virtual/ contents to the output directory."
-    ),
+    system_prompt="You are a helpful research agent.",
 )
 
-
-async def main(query: str = "Python LangChain agent best practices 2024") -> None:
-    WORKSPACE.mkdir(parents=True, exist_ok=True)
-    REAL_OUT.mkdir(parents=True, exist_ok=True)
-
-    config = {"configurable": {"thread_id": "research-session-1"}}
+async def main():
     result = await agent.ainvoke(
-        {"messages": [HumanMessage(
-            content=(
-                f"Search the web for: {query}\n"
-                "Save your findings to results.txt as a virtual file.\n"
-                "Then export the virtual file results.txt to the real filesystem "
-                f"at {REAL_OUT}/results.txt"
-            )
-        )]},
-        config,
+        {"messages": [HumanMessage(content="Search for Python best practices and save to results.txt")]},
+        {"configurable": {"thread_id": "session-1"}},
     )
     print(result["messages"][-1].content)
 
-
 if __name__ == "__main__":
-    import sys
-    q = " ".join(sys.argv[1:]) if len(sys.argv) > 1 else "Python LangChain agent best practices 2024"
-    asyncio.run(main(q))
+    asyncio.run(main())