add: main.py

main.py

@@ -0,0 +1,120 @@
+import os
+import asyncio
+from typing import TypedDict, Annotated
+
+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 langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+
+# ---------- 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 for deepagents ----------
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+# ---------- State definition ----------
+class ReflectState(TypedDict):
+    question: str
+    draft: str
+    critique: str
+    verdict: str  # ok | needs_revision
+    round: int
+    max_rounds: int
+
+# ---------- Nodes ----------
+async def draft_answer(state: ReflectState) -> ReflectState:
+    prompt = f"Write a concise answer (5–10 sentences) to the following question: {state['question']}"
+    response = await llm.ainvoke([HumanMessage(content=prompt)])
+    state['draft'] = response.content.strip()
+    return state
+
+async def reflect(state: ReflectState) -> ReflectState:
+    prompt = (
+        f"You are a critic. Evaluate the following draft answer for completeness, specificity, and lack of filler.\n\nDraft: {state['draft']}\n\nProvide a verdict (ok or needs_revision) and 2–3 bullet points of critique."
+    )
+    response = await llm.ainvoke([HumanMessage(content=prompt)])
+    text = response.content.strip()
+    # Simple parsing: first line verdict, rest critique
+    lines = text.splitlines()
+    verdict_line = lines[0].lower()
+    verdict = "ok" if "ok" in verdict_line else "needs_revision"
+    critique = "\n".join(lines[1:]).strip()
+    state['verdict'] = verdict
+    state['critique'] = critique
+    return state
+
+async def rewrite(state: ReflectState) -> ReflectState:
+    prompt = (
+        f"Rewrite the draft answer taking into account the following critique: {state['critique']}\n\nOriginal draft: {state['draft']}"
+    )
+    response = await llm.ainvoke([HumanMessage(content=prompt)])
+    state['draft'] = response.content.strip()
+    state['round'] += 1
+    return state
+
+# ---------- Graph ----------
+async def run_graph(question: str, max_rounds: int = 2) -> str:
+    graph = StateGraph(ReflectState)
+    graph.add_node("draft_answer", draft_answer)
+    graph.add_node("reflect", reflect)
+    graph.add_node("rewrite", rewrite)
+
+    graph.set_entry_point("draft_answer")
+    graph.add_edge("draft_answer", "reflect")
+    graph.add_conditional_edges(
+        "reflect",
+        lambda x: "rewrite" if x["verdict"] == "needs_revision" and x["round"] < x["max_rounds"] else "END",
+    )
+    graph.add_edge("rewrite", "reflect")
+
+    graph.add_edge("END", END)
+
+    app = graph.compile()
+    initial_state: ReflectState = {
+        "question": question,
+        "draft": "",
+        "critique": "",
+        "verdict": "",
+        "round": 0,
+        "max_rounds": max_rounds,
+    }
+    final_state = await app.ainvoke(initial_state)
+    return final_state["draft"]
+
+# ---------- DeepAgent tool ----------
+@tool
+async def answer_question(query: str) -> str:
+    """Generate a refined answer using self‑reflection graph."""
+    return await run_graph(query)
+
+# ---------- DeepAgent ----------
+agent = create_deep_agent(
+    model=llm,
+    tools=[answer_question],
+    backend=backend,
+    system_prompt="You are an AI assistant that answers questions. Use the provided tool to generate answers.",
+)
+
+# ---------- CLI ----------
+async def main():
+    question = "Объясни студенту разницу между tool и resource в MCP"
+    result = await agent.ainvoke(
+        {"messages": [HumanMessage(content=question)]},
+        {"configurable": {"thread_id": "session-1"}},
+    )
+    print("\nFinal answer:\n", result["messages"][-1].content)
+
+if __name__ == "__main__":
+    asyncio.run(main())