add main.py

main.py

@@ -1,64 +1,53 @@
-#!/usr/bin/env python3
-"""Deep agent that searches the web using Perplexity and writes results to a file.
-
-Usage:
-    python main.py "search query"
-
-The agent will:
-1. Query Perplexity via LangChain's Perplexity wrapper.
-2. Store the answer in a virtual file `output.txt`.
-3. Write the virtual file to the real filesystem.
-"""
-
-import argparse
 import os
-from pathlib import Path
+import 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 FilesystemBackend, LocalShellBackend, CompositeBackend
 
-from langchain_community.utilities import Perplexity
+# LLM setup
-from langchain_core.prompts import ChatPromptTemplate
+llm = ChatOpenAI(
-from langchain_core.output_parsers import StrOutputParser
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+    temperature=0.0,
+)
 
-# Configure Perplexity API key via environment variable
+# Backend: virtual FS + real shell
-# Users should set PERPLEXITY_API_KEY in their environment
+backend = CompositeBackend([
-
+    LocalShellBackend(workspace_dir="./workspace"),
-PERPLEXITY_API_KEY = os.getenv("PERPLEXITY_API_KEY")
+    FilesystemBackend(),
-if not PERPLEXITY_API_KEY:
-    raise RuntimeError("PERPLEXITY_API_KEY environment variable not set")
-
-# Create a simple chain: prompt -> Perplexity -> output parser
-prompt = ChatPromptTemplate.from_messages([
-    ("system", "You are a helpful assistant that answers questions based on web search."),
-    ("human", "{question}")
 ])
 
-llm = Perplexity(api_key=PERPLEXITY_API_KEY, temperature=0.7)
+# Web search tool using duckduckgo-search
-chain = prompt | llm | StrOutputParser()
+@tool
+def web_search(query: str) -> str:
+    """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 e:
+        return f"Search error: {e}"
 
+# Create deep agent
+agent = create_deep_agent(
+    llm=llm,
+    tools=[web_search],
+    backend=backend,
+    system_prompt="You are a helpful research agent.",
+)
 
-def search_and_write(query: str, output_path: Path) -> None:
+async def main():
-    """Search the web for *query* and write the answer to *output_path*.
+    # Example query
-
+    query = "Python async programming"
-    The function creates a virtual file in memory and then writes it to disk.
+    result = await agent.ainvoke(
-    """
+        {"messages": [HumanMessage(content=f"Search for {query}")]},
-    print(f"Searching for: {query}")
+        {"configurable": {"thread_id": "session-1"}},
-    answer = chain.invoke({"question": query})
+    )
-    # Virtual file content
+    print(result["messages"][-1].content)
-    virtual_file_content = f"Query: {query}\n\nAnswer:\n{answer}\n"
-    # Write to real filesystem
-    output_path.write_text(virtual_file_content, encoding="utf-8")
-    print(f"Result written to {output_path.resolve()}")
-
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Deep agent search tool")
+    asyncio.run(main())
-    parser.add_argument("query", type=str, help="Search query")
-    parser.add_argument(
-        "--output",
-        type=str,
-        default="output.txt",
-        help="Path to write the result file (default: output.txt)",
-    )
-    args = parser.parse_args()
-
-    output_path = Path(args.output)
-    search_and_write(args.query, output_path)