add main.py

main.py

@@ -0,0 +1,125 @@
+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 ----------
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+# ---------- State ----------
+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()
+    state["round"] = 0
+    state["max_rounds"] = state.get("max_rounds", 2)
+    return state
+
+async def reflect(state: ReflectState) -> ReflectState:
+    prompt = (
+        "You are a critic. Evaluate the following draft answer for completeness, specificity, and lack of filler.\n"
+        f"Draft: {state['draft']}\n"
+        "Return a JSON object with fields: verdict (ok or needs_revision) and critique (2–3 bullet points)."
+    )
+    response = await llm.ainvoke([HumanMessage(content=prompt)])
+    # Simple extraction of JSON
+    import json, re
+    try:
+        data = json.loads(re.search(r"\{.*\}", response.content, re.S).group(0))
+    except Exception:
+        data = {"verdict": "needs_revision", "critique": "Could not parse critique."}
+    state["critique"] = data.get("critique", "")
+    state["verdict"] = data.get("verdict", "needs_revision")
+    return state
+
+async def rewrite(state: ReflectState) -> ReflectState:
+    prompt = (
+        "Rewrite the draft answer incorporating the following critique. Keep the answer concise (5–10 sentences).\n"
+        f"Critique: {state['critique']}\n"
+        f"Original draft: {state['draft']}"
+    )
+    response = await llm.ainvoke([HumanMessage(content=prompt)])
+    state["draft"] = response.content.strip()
+    state["round"] += 1
+    return state
+
+# ---------- Graph ----------
+graph = StateGraph(ReflectState)
+graph.add_node("draft_answer", draft_answer)
+graph.add_node("reflect", reflect)
+graph.add_node("rewrite", rewrite)
+
+# Entry point
+graph.set_entry_point("draft_answer")
+
+# Transitions
+graph.add_edge("draft_answer", "reflect")
+# If ok -> END
+graph.add_conditional_edges(
+    "reflect",
+    lambda state: "rewrite" if state["verdict"] == "needs_revision" and state["round"] < state["max_rounds"] else "END",
+)
+# rewrite -> reflect
+graph.add_edge("rewrite", "reflect")
+
+app = graph.compile()
+
+# ---------- DeepAgent wrapper ----------
+@tool
+def run_graph(question: str) -> str:
+    """Run the reflection graph for a given question."""
+    state: ReflectState = {
+        "question": question,
+        "draft": "",
+        "critique": "",
+        "verdict": "",
+        "round": 0,
+        "max_rounds": 2,
+    }
+    result = app.invoke(state)
+    return result["draft"]
+
+agent = create_deep_agent(
+    model=llm,
+    tools=[run_graph],
+    backend=backend,
+    system_prompt="You are a helpful agent that can answer questions and self‑critique using the provided tool.",
+)
+
+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("Final answer:\n", result["messages"][-1].content)
+
+if __name__ == "__main__":
+    asyncio.run(main())