Updated LangGraph reflection agent with max_rounds logic.: update main.py

main.py

@@ -1,86 +1,105 @@
-"""LangGraph reflection agent example.
-
-The agent writes a short answer, critiques it, and rewrites if needed.
 """
+LangGraph Reflection Agent
 
-from typing import TypedDict, Dict
+Usage:
+    python main.py "Your question"
+"""
 import sys
+from typing import TypedDict, Dict
 
-from langgraph.graph import StateGraph, END
-from langgraph.prebuilt import create_react_agent
-from langchain_openai import ChatOpenAI
-
-# Define state
+# State definition
 class ReflectState(TypedDict):
     question: str
     draft: str
     critique: str
-    verdict: str  # "ok" | "needs_revision"
+    verdict: str  # 'ok' or 'needs_revision'
     round: int
     max_rounds: int
 
-# LLM
-llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
-
-# Draft node
-async def draft_answer(state: ReflectState) -> Dict:
-    prompt = f"Write a short answer (5–10 sentences) to the following question: {state['question']}"
-    response = await llm.ainvoke(prompt)
-    state['draft'] = response.content
+# Node functions
+async def draft_answer(state: Dict) -> Dict:
+    from langchain_openai import ChatOpenAI
+    llm = ChatOpenAI(model="gpt-3.5-turbo")
+    prompt = f"Write a concise answer (5–10 sentences) to the following question:\n\n{state['question']}"
+    response = await llm.invoke(prompt)
+    state["draft"] = response.content
     return state
 
-# Critique node
-async def reflect(state: ReflectState) -> Dict:
+async def reflect(state: Dict) -> Dict:
+    from langchain_openai import ChatOpenAI
+    llm = ChatOpenAI(model="gpt-3.5-turbo")
     prompt = (
-        f"You are a critic. Evaluate the following draft answer for completeness, specificity, and lack of filler.\n"
+        f"You are a critic evaluating the draft answer for completeness, specificity, and lack of filler.\n"
         f"Draft: {state['draft']}\n"
-        f"Give a verdict: 'ok' or 'needs_revision'.\n"
-        f"If needs_revision, provide 2–3 points of critique."
+        "Provide verdict (ok or needs_revision) and 2–3 bullet points of critique."
     )
-    response = await llm.ainvoke(prompt)
-    # Simple parsing: first line verdict, rest critique
-    lines = response.content.strip().splitlines()
-    verdict = lines[0].strip().lower()
-    critique = "\n".join(lines[1:]).strip()
-    state['verdict'] = verdict
-    state['critique'] = critique
+    response = await llm.invoke(prompt)
+    # Simple parsing
+    text = response.content.strip()
+    if "needs_revision" in text.lower():
+        state["verdict"] = "needs_revision"
+    else:
+        state["verdict"] = "ok"
+    state["critique"] = text
     return state
 
-# Rewrite node
-async def rewrite(state: ReflectState) -> Dict:
+async def rewrite(state: Dict) -> Dict:
+    from langchain_openai import ChatOpenAI
+    llm = ChatOpenAI(model="gpt-3.5-turbo")
     prompt = (
-        f"Rewrite the draft answer taking into account the following critique: {state['critique']}\n"
-        f"Original draft: {state['draft']}"
+        f"Rewrite the draft answer incorporating the following critique:\n"
+        f"Critique: {state['critique']}\n"
+        "Provide a revised concise answer (5–10 sentences)."
     )
-    response = await llm.ainvoke(prompt)
-    state['draft'] = response.content
-    state['round'] += 1
+    response = await llm.invoke(prompt)
+    state["draft"] = response.content
+    state["round"] += 1
     return state
 
 # Build graph
-builder = StateGraph(ReflectState)
-builder.add_node("draft_answer", draft_answer)
-builder.add_node("reflect", reflect)
-builder.add_node("rewrite", rewrite)
+from langgraph.graph import StateGraph
 
-builder.set_entry_point("draft_answer")
-builder.add_edge("draft_answer", "reflect")
-builder.add_conditional_edges(
+graph_builder = StateGraph(ReflectState)
+
+graph_builder.add_node("draft_answer", draft_answer)
+
+graph_builder.add_node("reflect", reflect)
+
+graph_builder.add_node("rewrite", rewrite)
+
+# Connections
+start_edge = "draft_answer"
+end_edge = None  # will be set in condition
+
+def should_end(state: Dict) -> str:
+    if state["verdict"] == "ok":
+        return "END"
+    if state["round"] >= state.get("max_rounds", 2):
+        return "END"
+    return "rewrite"
+
+# Add edges with condition
+from langgraph.graph import END
+
+graph_builder.set_entry_point(start_edge)
+
+graph_builder.add_conditional_edges(
     "reflect",
-    lambda state: (
-        "END"
-        if state["verdict"] == "ok"
-        or state.get("round", 0) >= state.get("max_rounds", 2)
-        else "rewrite"
-    ),
+    should_end,
+    {
+        "rewrite": "rewrite",
+        "END": END,
+    },
 )
-builder.add_edge("rewrite", "reflect")
+# rewrite -> reflect
 
-graph = builder.compile()
+graph_builder.add_edge("rewrite", "reflect")
+
+graph = graph_builder.compile()
 
 if __name__ == "__main__":
     if len(sys.argv) < 2:
-        print("Usage: python main.py <question>")
+        print("Usage: python main.py \"Your question\"")
         sys.exit(1)
     question = sys.argv[1]
     initial_state: ReflectState = {
@@ -92,6 +111,7 @@ if __name__ == "__main__":
         "max_rounds": 2,
     }
     result = graph.invoke(initial_state)
-    print("\nFinal answer:\n", result["draft"])
-    print("\nCritique:\n", result["critique"])
-    print("\nVerdict:\n", result["verdict"])
+    print("\n--- Final Answer ---")
+    print(result["draft"])
+    print("\n--- Critique ---")
+    print(result["critique"])