add main.py

main.py

@@ -0,0 +1,130 @@
+"""
+LangGraph Research Brief Agent
+=============================
+
+This repository implements a LangGraph agent that generates a short research brief.
+The agent follows the specification from the BroJS assignment:
+
+*   Build an outline of 4‑5 points for a given topic.
+*   For each point perform one web search via Tavily and collect a concise note.
+*   Synthesize all notes into a coherent brief (≈½–1 page).
+*
+The implementation uses the official `langgraph` library, `langchain-openai`
+for LLM calls and `langchain-tavily` for web searching.  The agent is
+exposed through a simple CLI that accepts a topic as an argument.
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+from typing import TypedDict, List
+
+from langgraph.graph import StateGraph, START, END
+from langgraph.checkpoint.memory import MemorySaver
+from langchain_openai import ChatOpenAI
+from langchain_tavily import TavilySearchResults
+from langchain_core.messages import HumanMessage, SystemMessage
+
+# ---------------------------------------------------------------------------
+# 1. State definition
+# ---------------------------------------------------------------------------
+class BriefState(TypedDict):
+    topic: str
+    outline: List[str] | None
+    step_index: int
+    notes: List[str]
+    final_brief: str | None
+
+# ---------------------------------------------------------------------------
+# 2. LLM and tools
+# ---------------------------------------------------------------------------
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://platform.brojs.ru/jrnl-bh/api/inference/v1",
+    api_key=os.getenv("JOURNAL_MCP_PAT"),
+    temperature=0.2,
+)
+
+search_tool = TavilySearchResults(max_results=3, tavily_api_key=os.getenv("TAVILY_API_KEY"))
+
+# ---------------------------------------------------------------------------
+# 3. Nodes
+# ---------------------------------------------------------------------------
+async def outline_node(state: BriefState) -> dict:
+    """Generate a short outline of 4‑5 research points."""
+    system = SystemMessage(
+        content="You are an assistant that creates a concise outline for a research brief.")
+    user = HumanMessage(content=f"Create 4–5 bullet points outlining the main aspects to cover when researching: {state['topic']}")
+    response = await llm.ainvoke([system, user])
+    # Parse bullets
+    bullets = [line.strip("- ").strip() for line in response.content.splitlines() if line.strip().startswith("-")]
+    return {"outline": bullets, "step_index": 0, "notes": []}
+
+async def research_step_node(state: BriefState) -> dict:
+    """For the current outline point perform a web search and collect a short note."""
+    point = state["outline"][state["step_index"]]
+    # Search via Tavily
+    results = await search_tool.ainvoke({"query": point})
+    notes_text = "\n".join([f"{i+1}. {r['title']}: {r['content']}" for i, r in enumerate(results)])
+    new_notes = state["notes"] + [f"**{point}**:\n{notes_text}"]
+    next_index = state["step_index"] + 1
+    return {"notes": new_notes, "step_index": next_index}
+
+async def synthesize_node(state: BriefState) -> dict:
+    """Combine all notes into a single brief."""
+    system = SystemMessage(content="You are an assistant that writes a concise research brief.")
+    user = HumanMessage(
+        content=f"Using the following notes, write a ½–1 page brief on {state['topic']}:\n\n{chr(10).join(state['notes'])}")
+    response = await llm.ainvoke([system, user])
+    return {"final_brief": response.content.strip()}
+
+# ---------------------------------------------------------------------------
+# 4. Graph definition
+# ---------------------------------------------------------------------------
+builder = StateGraph(BriefState)
+builder.add_node("outline", outline_node)
+builder.add_node("research_step", research_step_node)
+builder.add_node("synthesize", synthesize_node)
+
+builder.set_entry_point("outline")
+builder.add_edge("outline", "research_step")
+# Loop until all points processed
+builder.add_conditional_edges(
+    "research_step",
+    lambda state: "synthesize" if state["step_index"] >= len(state["outline"]) else "research_step",
+)
+builder.add_edge("synthesize", END)
+
+graph = builder.compile(checkpointer=MemorySaver())
+
+# ---------------------------------------------------------------------------
+# 5. CLI helper
+# ---------------------------------------------------------------------------
+async def run_brief(topic: str) -> None:
+    state: BriefState = {"topic": topic, "outline": None, "step_index": 0, "notes": [], "final_brief": None}
+    result = await graph.ainvoke(state)
+    print("\n=== Outline ===")
+    for i, point in enumerate(result["outline"]):
+        print(f"{i+1}. {point}")
+    print("\n=== Notes ===")
+    for note in result["notes"]:
+        print(note)
+    print("\n=== Final Brief ===")
+    print(result["final_brief"])
+
+if __name__ == "__main__":
+    if len(sys.argv) < 2:
+        print("Usage: python main.py '<topic>'")
+        sys.exit(1)
+    topic = sys.argv[1]
+    import asyncio
+    asyncio.run(run_brief(topic))
+
+# ---------------------------------------------------------------------------
+# 6. Example usage (for documentation only, not executed by the script)
+# ---------------------------------------------------------------------------
+# Example 1: "How to integrate LangGraph with Tavily"
+# Example 2: "Best practices for building research briefs in AI"
+# Example 3: "Using LangChain and LangGraph for educational projects"
+"""