My agent

backend/examples/cli_research.py

@@ -1,43 +1,44 @@
 import argparse
-from langchain_core.messages import HumanMessage
+import asyncio
+import os
+from dotenv import load_dotenv
 from agent.graph import graph
 
+load_dotenv()
 
-def main() -> None:
-    """Run the research agent from the command line."""
-    parser = argparse.ArgumentParser(description="Run the LangGraph research agent")
-    parser.add_argument("question", help="Research question")
+async def main():
+    # Setup command line arguments
+    parser = argparse.ArgumentParser(description="Local Research CLI")
+    parser.add_argument("topic", help="The topic to research")
     parser.add_argument(
-        "--initial-queries",
-        type=int,
-        default=3,
-        help="Number of initial search queries",
-    )
-    parser.add_argument(
-        "--max-loops",
-        type=int,
-        default=2,
-        help="Maximum number of research loops",
-    )
-    parser.add_argument(
-        "--reasoning-model",
-        default="gemini-2.5-pro-preview-05-06",
-        help="Model for the final answer",
+        "--dir", 
+        required=True, 
+        help="Path to the local directory containing .md files for research"
     )
     args = parser.parse_args()
 
-    state = {
-        "messages": [HumanMessage(content=args.question)],
-        "initial_search_query_count": args.initial_queries,
-        "max_research_loops": args.max_loops,
-        "reasoning_model": args.reasoning_model,
+    # Configure the graph with the local search directory
+    config = {
+        "configurable": {
+            "search_dir": args.dir,
+            "max_research_loops": 2 # Efficient looping for documentation analysis
+        }
     }
 
-    result = graph.invoke(state)
-    messages = result.get("messages", [])
-    if messages:
-        print(messages[-1].content)
+    # Initialize the research with the user's topic
+    inputs = {"messages": [("user", args.topic)]}
+
+    print(f"Starting research in: {args.dir}")
+    print("-" * 30)
 
+    # Stream the results from the graph
+    async for event in graph.astream(inputs, config=config, stream_mode="values"):
+        message = event.get("messages")
+        if message:
+            if isinstance(message, list):
+                print(message[-1].content)
+            else:
+                print(message.content)
 
 if __name__ == "__main__":
-    main()
+    asyncio.run(main())

backend/pyproject.toml

@@ -11,13 +11,12 @@ requires-python = ">=3.11,<4.0"
 dependencies = [
     "langgraph>=0.2.6",
     "langchain>=0.3.19",
-    "langchain-google-genai",
+    "langchain-groq",
     "python-dotenv>=1.0.1",
     "langgraph-sdk>=0.1.57",
     "langgraph-cli",
     "langgraph-api",
     "fastapi",
-    "google-genai",
 ]