fix: main.py

main.py

@@ -1,12 +1,14 @@
 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
-from langchain_openai import ChatOpenAI
-from langchain_core.messages import HumanMessage, AIMessage
-from deepagents import create_deep_agent
-from deepagents.backends import CompositeBackend, LocalShellBackend, FilesystemBackend
 
 # ---------- LLM ----------
 llm = ChatOpenAI(
@@ -16,105 +18,116 @@ llm = ChatOpenAI(
     temperature=0.0,
 )
 
-# ---------- State ----------
+# ---------- Backend for deepagents (not used directly in graph but required by create_deep_agent) ----------
-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
-    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)])
-    # Simple parsing: first line verdict, rest critique
-    lines = response.content.strip().splitlines()
-    verdict_line = lines[0].lower()
-    verdict = "ok" if "ok" in verdict_line else "needs_revision"
-    critique = "\n".join(lines[1:]) if len(lines) > 1 else ""
-    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
-    state['round'] += 1
-    return state
-
-# ---------- Graph ----------
-builder = StateGraph(ReflectState)
-builder.add_node("draft_answer", draft_answer)
-builder.add_node("reflect", reflect)
-builder.add_node("rewrite", rewrite)
-
-builder.set_entry_point("draft_answer")
-
-# Transition logic
-def should_rewrite(state: ReflectState) -> str:
-    if state['verdict'] == "ok":
-        return "END"
-    if state['round'] >= state['max_rounds']:
-        return "END"
-    return "rewrite"
-
-builder.add_conditional_edges("reflect", should_rewrite, {
-    "rewrite": "rewrite",
-    "END": "END",
-})
-
-builder.add_edge("rewrite", "reflect")
-
-graph = builder.compile()
-
-# ---------- DeepAgent wrapper ----------
 backend = CompositeBackend([
     LocalShellBackend(workspace_dir="./workspace"),
     FilesystemBackend(),
 ])
 
-agent = create_deep_agent(
+# ---------- State definition ----------
-    model=llm,
+class ReflectState(TypedDict):
-    tools=[],
+    question: str
-    backend=backend,
+    draft: str
-    system_prompt="You are a helper that runs a reflection graph.",
+    critique: str
-)
+    verdict: str  # "ok" | "needs_revision"
+    round: int
+    max_rounds: int
 
-# ---------- CLI ----------
+# ---------- Nodes ----------
-async def run_graph(question: str, max_rounds: int = 2):
+async def draft_answer(state: ReflectState) -> ReflectState:
+    prompt = f"Write a concise answer (5–10 sentences) to the following question:\n\n{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:\n{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. Keep the answer concise (5–10 sentences).\n\nCritique:\n{state['critique']}\n\nOriginal Draft:\n{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")
+
+# Transition logic
+# After draft_answer -> reflect
+graph.add_edge("draft_answer", "reflect")
+# After reflect
+# if ok -> END
+# if needs_revision and round < max_rounds -> rewrite
+# else -> END
+
+def reflect_conditional(state: ReflectState):
+    if state["verdict"] == "ok":
+        return "END"
+    if state["round"] < state["max_rounds"]:
+        return "rewrite"
+    return "END"
+
+graph.add_conditional_edges("reflect", reflect_conditional, {
+    "rewrite": "rewrite",
+    "END": "END",
+})
+
+# After rewrite -> reflect
+graph.add_edge("rewrite", "reflect")
+
+app = graph.compile()
+
+# ---------- DeepAgent wrapper ----------
+# The deepagent will simply forward the human message to the graph and return the final draft.
+@tool
+def run_graph(question: str) -> str:
+    """Run the reflection graph for a given question."""
     initial_state: ReflectState = {
         "question": question,
         "draft": "",
         "critique": "",
         "verdict": "",
         "round": 0,
-        "max_rounds": max_rounds,
+        "max_rounds": 2,
     }
-    result = await graph.ainvoke(initial_state)
+    result = app.invoke(initial_state)
-    return result
+    return result["draft"]
+
+agent = create_deep_agent(
+    model=llm,
+    tools=[run_graph],
+    backend=backend,
+    system_prompt="You are an assistant that can answer questions using a self‑checking process.",
+)
 
 async def main():
-    question = "Объясни студенту разницу между tool и resource в MCP"
+    question = "Объясни студенту разницу между tool и resource в MCP."
-    result = await run_graph(question)
+    response = await agent.ainvoke(
-    print("\n--- Final Draft ---\n")
+        {"messages": [HumanMessage(content=question)]},
-    print(result["draft"])
+        {"configurable": {"thread_id": "session-1"}},
-    print("\n--- Critique ---\n")
+    )
-    print(result["critique"])
+    print("Final answer:\n", response["messages"][-1].content)
-    print("\n--- Verdict ---\n")
-    print(result["verdict"])
 
 if __name__ == "__main__":
     asyncio.run(main())