146 lines
4.3 KiB
Python
146 lines
4.3 KiB
Python
import os
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import asyncio
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from typing import TypedDict
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage
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from langchain.tools import tool
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from deepagents import create_deep_agent
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from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
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from langgraph.graph import StateGraph, START, END
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# LLM configuration - OpenRouter
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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base_url="https://openrouter.ai/api/v1",
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api_key=os.getenv("OPENAI_API_KEY"),
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temperature=0.0,
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)
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# Backend for deepagents
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backend = CompositeBackend(
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[
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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]
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)
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# ---------- LangGraph definition ----------
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class ReflectState(TypedDict):
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question: str
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draft: str
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critique: str
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verdict: str # ok | needs_revision
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round: int
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max_rounds: int
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def draft_answer(state: ReflectState) -> ReflectState:
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prompt = f"Write a short answer (5-10 sentences) to the following question: {state['question']}"
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msg = HumanMessage(content=prompt)
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response = llm.invoke([msg])
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state["draft"] = response.content
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state["round"] = 0
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return state
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def reflect(state: ReflectState) -> ReflectState:
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prompt = (
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f"You are a critic. Evaluate the following draft answer:\n{state['draft']}\n\n"
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"Provide verdict 'ok' or 'needs_revision' and 2-3 points of critique."
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)
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msg = HumanMessage(content=prompt)
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response = llm.invoke([msg])
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text = response.content.strip()
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verdict = "ok"
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critique = ""
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if "needs_revision" in text.lower():
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verdict = "needs_revision"
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# Extract critique after the word 'Critique:' if present
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lower_text = text.lower()
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if "critique:" in lower_text:
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idx = lower_text.find("critique:")
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critique = text[idx + len("critique:") :].strip()
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else:
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parts = text.split("\n")
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if len(parts) > 1:
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critique = "\n".join(parts[1:]).strip()
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state["verdict"] = verdict
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state["critique"] = critique
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return state
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def rewrite(state: ReflectState) -> ReflectState:
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prompt = (
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f"Rewrite the draft answer to address the following critique:\n{state['critique']}\n\n"
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"Keep the answer short (5-10 sentences)."
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)
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msg = HumanMessage(content=prompt)
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response = llm.invoke([msg])
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state["draft"] = response.content
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state["round"] += 1
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return state
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graph = StateGraph(ReflectState)
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graph.add_node("draft_answer", draft_answer)
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graph.add_node("reflect", reflect)
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graph.add_node("rewrite", rewrite)
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graph.set_entry_point("draft_answer")
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graph.add_edge("draft_answer", "reflect")
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graph.add_conditional_edges(
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"reflect",
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lambda s: (
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"ok"
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if s["verdict"] == "ok"
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else ("rewrite" if s["round"] < s["max_rounds"] else "stop")
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),
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{"ok": END, "rewrite": "rewrite", "stop": END},
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)
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graph.add_edge("rewrite", "reflect")
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compiled_graph = graph.compile()
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def run_reflection(question: str) -> str:
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state: ReflectState = {
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"question": question,
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"draft": "",
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"critique": "",
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"verdict": "",
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"round": 0,
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"max_rounds": 2,
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}
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final_state = compiled_graph.invoke(state)
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output = (
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f"Draft:\n{final_state['draft']}\n\n"
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f"Critique:\n{final_state['critique']}\n\n"
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f"Verdict: {final_state['verdict']}\n\n"
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f"Final answer:\n{final_state['draft']}\n"
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)
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return output
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# ---------- DeepAgents tool ----------
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@tool
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def answer_with_reflection(question: str) -> str:
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"""Generate a short answer with self-reflection and rewrite if needed."""
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return run_reflection(question)
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# ---------- DeepAgents agent ----------
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agent = create_deep_agent(
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model=llm,
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tools=[answer_with_reflection],
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backend=backend,
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system_prompt=(
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"You are a helpful agent that writes short answers and self-reflects. "
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"Use the tool 'answer_with_reflection' to answer questions."
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),
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)
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# ---------- Demo ----------
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async def main():
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question = (
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"Explain to a student the difference between tool and resource in MCP."
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)
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=question)]},
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{"configurable": {"thread_id": "session-1"}},
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)
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print(result["messages"][-1].content)
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if __name__ == "__main__":
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asyncio.run(main()) |