add agent.py

agent.py

@@ -0,0 +1,120 @@
+"""
+LangGraph agent that builds a research brief.
+
+The graph follows the specification from the assignment:
+* Outline node – generates 4‑5 bullet points for the topic.
+* Research step node – for each outline item performs one web search via Tavily and creates a short note.
+* Synthesize node – combines all notes into a coherent brief.
+"""
+
+import os
+from typing import TypedDict, List, Optional
+
+from langgraph.graph import StateGraph, START, END
+from langchain_openai import ChatOpenAI
+from langchain_tavily.tools import TavilySearchResults
+from langchain_core.messages import HumanMessage
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# ---------- State definition --------------------------------------------
+class BriefState(TypedDict):
+    topic: str
+    outline: List[str] | None
+    step_index: int
+    notes: List[str]
+    final_brief: Optional[str]
+
+# ---------- 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=0.5,
+)
+
+search_tool = TavilySearchResults(max_results=3, tavily_api_key=os.getenv("TAVILY_API_KEY"))
+
+# ---------- Node functions ----------------------------------------------
+async def outline(state: BriefState) -> BriefState:
+    """Generate an outline of 4‑5 research points for the topic."""
+    prompt = (
+        f"You are a research assistant.\n"
+        f"Topic: {state['topic']}\n"
+        f"Provide 4–5 concise bullet points that could serve as sections of a short research brief."
+    )
+    response = await llm.ainvoke([HumanMessage(content=prompt)])
+    text = response.content.strip()
+    # split by newlines or bullets
+    lines = [l.strip("- ") for l in text.splitlines() if l.strip()]
+    state["outline"] = lines[:5]  # ensure max 5
+    state["step_index"] = 0
+    state["notes"] = []
+    return state
+
+async def research_step(state: BriefState) -> BriefState:
+    """For the current outline item perform a web search and create a short note."""
+    idx = state["step_index"]
+    if state["outline"] is None or idx >= len(state["outline"]):
+        return state
+    point = state["outline"][idx]
+    # Search via Tavily tool
+    search_query = f"{point} topic"
+    results = await search_tool.ainvoke(search_query)
+    # Build a short note (5‑8 sentences) summarizing the first result
+    if results:
+        snippet = results[0].snippet or ""
+        note = f"**{point}:** {snippet[:200]}..."
+    else:
+        note = f"**{point}:** No relevant information found."
+    state["notes"].append(note)
+    state["step_index"] += 1
+    return state
+
+async def synthesize(state: BriefState) -> BriefState:
+    """Combine all notes into a coherent brief with headings."""
+    if not state.get("notes"):
+        state["final_brief"] = "No research was conducted."
+        return state
+    sections = [f"### {note.split(':')[0][2:]}\n{note.split(':',1)[1].strip()}" for note in state["notes"]]
+    brief = "\n\n".join(sections)
+    state["final_brief"] = brief
+    return state
+
+# ---------- Graph construction -------------------------------------------
+builder = StateGraph(BriefState)
+builder.add_node("outline", outline)
+builder.add_node("research_step", research_step)
+builder.add_node("synthesize", synthesize)
+
+builder.set_entry_point("outline")
+builder.add_edge("outline", "research_step")
+# loop until all steps processed
+builder.add_conditional_edges(
+    "research_step",
+    lambda state: "synthesize" if state["step_index"] >= len(state.get("outline", [])) else "research_step",
+)
+builder.set_finish_point("synthesize")
+
+BriefGraph = builder.compile()
+
+# ---------- Demo runner -----------------------------------------------
+async def run_demo(topic: str) -> None:
+    from langgraph.checkpoint.memory import MemorySaver
+    memory = MemorySaver()
+    state = {"topic": topic, "outline": None, "step_index": 0, "notes": [], "final_brief": None}
+    result = await BriefGraph.ainvoke(state, config={"configurable": {"thread_id": "demo"}}, checkpointer=memory)
+    print("\n=== Outline ===")
+    for i, p in enumerate(result["outline"]):
+        print(f"{i+1}. {p}")
+    print("\n=== Notes ===")
+    for n in result["notes"]:
+        print(n)
+    print("\n=== Final Brief ===")
+    print(result["final_brief"])
+
+if __name__ == "__main__":
+    import asyncio
+    default_topic = "Как студенту безопасно подключать MCP к LangChain"
+    asyncio.run(run_demo(default_topic))