add: main.py

main.py

@@ -0,0 +1,128 @@
+import os
+import asyncio
+from typing import TypedDict, Annotated, List
+
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage
+from langchain.tools import tool
+from langchain_tavily import TavilySearchResults
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+from deepagents import create_deep_agent
+from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
+
+# ----------------- LLM -----------------
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+    temperature=0.0,
+)
+
+# ----------------- Backend -----------------
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+# ----------------- Tavily Tool -----------------
+search_tool = TavilySearchResults(max_results=3)
+
+# ----------------- State -----------------
+class BriefState(TypedDict):
+    topic: str
+    outline: List[str] | None
+    step_index: int
+    notes: List[str]
+    final_brief: str | None
+
+# ----------------- Nodes -----------------
+async def outline_node(state: BriefState) -> BriefState:
+    prompt = f"""Create a concise outline of 4–5 research steps for the topic: {state['topic']}. Return a JSON array of strings."""
+    response = await llm.ainvoke([HumanMessage(content=prompt)])
+    # Parse JSON array
+    import json
+    try:
+        outline = json.loads(response.content)
+        if not isinstance(outline, list):
+            raise ValueError
+    except Exception:
+        outline = ["Step 1: Define scope", "Step 2: Search", "Step 3: Analyze", "Step 4: Summarize"]
+    state.update(outline=outline, step_index=0, notes=[], final_brief=None)
+    return state
+
+async def research_step_node(state: BriefState) -> BriefState:
+    step = state['outline'][state['step_index']]
+    # Perform web search via Tavily
+    results = await search_tool.ainvoke(step)
+    # Summarize results into 5–8 sentences
+    summary_prompt = f"""Summarize the following search results into 5–8 concise sentences for the research step: {step}.
+
+Results:
+{results}"""
+    summary = await llm.ainvoke([HumanMessage(content=summary_prompt)])
+    state['notes'].append(f"{step}\n{summary.content}")
+    state['step_index'] += 1
+    return state
+
+async def synthesize_node(state: BriefState) -> BriefState:
+    # Combine notes into a coherent brief with headings
+    heading_prompt = """Combine the following notes into a ½–1 page research brief. Use the step titles as headings and write in a clear, academic style.
+
+Notes:
+""" + "\n\n".join(state['notes'])
+    brief = await llm.ainvoke([HumanMessage(content=heading_prompt)])
+    state['final_brief'] = brief.content
+    return state
+
+# ----------------- Graph -----------------
+graph = StateGraph(BriefState)
+graph.add_node("outline", outline_node)
+graph.add_node("research_step", research_step_node)
+graph.add_node("synthesize", synthesize_node)
+
+# Entry point
+graph.set_entry_point("outline")
+
+# Conditional edges
+graph.add_conditional_edges(
+    "outline",
+    lambda _: "research_step",
+)
+
+graph.add_conditional_edges(
+    "research_step",
+    lambda state: "synthesize" if state['step_index'] >= len(state['outline']) else "research_step",
+)
+
+graph.add_edge("synthesize", END)
+
+app = graph.compile()
+
+# ----------------- DeepAgent -----------------
+agent = create_deep_agent(
+    model=llm,
+    tools=[search_tool],
+    backend=backend,
+    system_prompt="You are a research assistant that builds a brief based on a topic.",
+)
+
+# ----------------- Main -----------------
+async def main():
+    topic = os.getenv("DEFAULT_TOPIC", "Как студенту безопасно подключать MCP к LangChain")
+    # Run graph to get outline and notes
+    state = await app.ainvoke({"topic": topic, "outline": None, "step_index": 0, "notes": [], "final_brief": None})
+    # Print outline
+    print("\n=== Outline ===")
+    for i, step in enumerate(state['outline'], 1):
+        print(f"{i}. {step}")
+    # Print each research step note
+    for i, note in enumerate(state['notes'], 1):
+        print(f"\n[Step {i}] {note.splitlines()[0]}")
+        print(note.splitlines()[1])
+    # Print final brief
+    print("\n=== Final Brief ===")
+    print(state['final_brief'])
+
+if __name__ == "__main__":
+    asyncio.run(main())