add main.py
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"""
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LangGraph Code Review Agent
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===========================
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This repository contains a minimal LangGraph implementation that
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performs a code review on a Python function. The graph consists of
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three nodes:
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* ``draft_review`` – generates an initial review.
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* ``reflect`` – a critic that scores the review on four criteria
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(PEP8, type hints, edge cases, naming) and decides whether a
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rewrite is required.
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* ``rewrite`` – rewrites the weakest part of the review.
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The graph runs for a maximum of ``max_rounds`` (default 2). The
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demo can be executed with ``python -m main``.
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"""
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from __future__ import annotations
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# main.py
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import os
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from typing import TypedDict, Dict
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from langgraph.graph import StateGraph, END
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from langgraph.prebuilt import create_react_agent
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from langgraph.prebuilt import create_chat_agent
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage
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# ---------------------------------------------------------------------------
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# State definition
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# ---------------------------------------------------------------------------
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from langchain_core.messages import HumanMessage, AIMessage
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# Define state
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class CodeReviewState(TypedDict):
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code: str
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draft_review: str
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criteria_scores: Dict[str, int]
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weakest_criterion: str
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verdict: str # "ok" | "needs_revision"
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verdict: str
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round: int
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max_rounds: int
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# ---------------------------------------------------------------------------
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# LLM configuration
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# ---------------------------------------------------------------------------
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# The user must set the OPENAI_API_KEY environment variable.
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llm = ChatOpenAI(temperature=0.0, model="gpt-4o-mini")
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# ---------------------------------------------------------------------------
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# Node implementations
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# ---------------------------------------------------------------------------
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# LLM
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llm = ChatOpenAI(temperature=0)
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# Draft review node
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async def draft_review(state: CodeReviewState) -> CodeReviewState:
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"""Generate an initial review of the provided code.
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prompt = f"""
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You are a senior Python developer. Review the following code and provide a concise code review (3-6 bullet points) highlighting what is good and what can be improved.
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The review is a short list of 3–6 bullet points.
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"""
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code = state["code"]
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prompt = (
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"You are a senior Python developer.\n"
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"Review the following function and provide a concise list of 3–6 points\n"
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"highlighting what is good and what could be improved.\n"
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"Do not mention the criteria – just give the review.\n"
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f"Function:\n{code}\n"
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"Review:" # LLM will continue after this
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)
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review = await llm.ainvoke([HumanMessage(content=prompt)])
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state["draft_review"] = review.content.strip()
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Code:
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{state['code']}
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Review:
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"""
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response = await llm.ainvoke([HumanMessage(content=prompt)])
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state['draft_review'] = response.content
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return state
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# Reflect node
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async def reflect(state: CodeReviewState) -> CodeReviewState:
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"""Critic that scores the draft review on four criteria.
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prompt = f"""
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You are an AI critic evaluating a code review. Assign a score 0-10 for each of the following criteria based on the draft review:
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- pep8
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- type_hints
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- edge_cases
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- naming
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The output is a JSON object with keys:
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* pep8, type_hints, edge_cases, naming – integers 0‑10
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* weakest_criterion – one of the four keys
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* verdict – "ok" or "needs_revision"
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"""
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review = state["draft_review"]
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code = state["code"]
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prompt = (
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"You are a code quality critic.\n"
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"Given the following code and its draft review, score the review on\n"
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"four criteria: PEP8, type hints, edge cases, naming.\n"
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"Return a JSON object with keys: pep8, type_hints, edge_cases, naming,\n"
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"weakest_criterion, verdict.\n"
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f"Code:\n{code}\n"
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f"Draft review:\n{review}\n"
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"Answer in JSON only."
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)
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Provide a JSON object with keys "pep8", "type_hints", "edge_cases", "naming" and integer values.
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Also determine the weakest criterion (the one with lowest score) and a verdict: "ok" if all scores >=7, otherwise "needs_revision".
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Draft review:
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{state['draft_review']}
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Output JSON:
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"""
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response = await llm.ainvoke([HumanMessage(content=prompt)])
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import json
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try:
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scores = json.loads(response.content)
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except Exception as e:
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# Fallback: if parsing fails, treat as needs_revision
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scores = {
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"pep8": 0,
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"type_hints": 0,
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"edge_cases": 0,
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"naming": 0,
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"weakest_criterion": "pep8",
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"verdict": "needs_revision",
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}
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state["criteria_scores"] = {
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"pep8": int(scores.get("pep8", 0)),
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"type_hints": int(scores.get("type_hints", 0)),
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"edge_cases": int(scores.get("edge_cases", 0)),
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"naming": int(scores.get("naming", 0)),
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}
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state["weakest_criterion"] = scores.get("weakest_criterion", "pep8")
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state["verdict"] = scores.get("verdict", "needs_revision")
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state['criteria_scores'] = scores
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weakest = min(scores, key=scores.get)
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state['weakest_criterion'] = weakest
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state['verdict'] = "ok" if all(v >= 7 for v in scores.values()) else "needs_revision"
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return state
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# Rewrite node
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async def rewrite(state: CodeReviewState) -> CodeReviewState:
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"""Rewrite the weakest part of the review.
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crit = state['weakest_criterion']
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prompt = f"""
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You are a senior Python developer. Rewrite the section of the code review that addresses the {crit} criterion, improving it. Keep the rest of the review unchanged.
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The node receives the current state and the weakest criterion.
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It generates a new review that specifically addresses that criterion.
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"""
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code = state["code"]
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weakest = state["weakest_criterion"]
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prompt = (
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"You are a senior Python developer.\n"
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"Rewrite the draft review to improve the part related to the following criterion: "
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f"{weakest}.\n"
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"Keep the rest of the review unchanged.\n"
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"Output only the updated review.\n"
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f"Current draft review:\n{state['draft_review']}\n"
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)
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new_review = await llm.ainvoke([HumanMessage(content=prompt)])
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state["draft_review"] = new_review.content.strip()
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state["round"] += 1
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Original review:
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{state['draft_review']}
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Rewrite only the part related to {crit}:
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"""
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response = await llm.ainvoke([HumanMessage(content=prompt)])
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# Replace the part in draft_review that mentions crit
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# For simplicity, just append the new part
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state['draft_review'] = state['draft_review'] + "\n" + response.content
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state['round'] += 1
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return state
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# ---------------------------------------------------------------------------
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# Graph construction
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# ---------------------------------------------------------------------------
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# Build graph
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builder = StateGraph(CodeReviewState)
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builder.add_node("draft_review", draft_review)
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builder.add_node("reflect", reflect)
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builder.add_node("rewrite", rewrite)
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def build_graph() -> StateGraph[CodeReviewState]:
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graph = StateGraph(CodeReviewState)
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graph.add_node("draft_review", draft_review)
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graph.add_node("reflect", reflect)
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graph.add_node("rewrite", rewrite)
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# Entry point
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graph.set_entry_point("draft_review")
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# Transitions
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graph.add_edge("draft_review", "reflect")
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graph.add_conditional_edges(
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builder.set_entry_point("draft_review")
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builder.add_edge("draft_review", "reflect")
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builder.add_conditional_edges(
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"reflect",
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lambda state: "rewrite" if state["verdict"] == "needs_revision" and state["round"] < state["max_rounds"] else "END",
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)
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graph.add_edge("rewrite", "reflect")
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lambda x: "rewrite" if x["verdict"] == "needs_revision" and x["round"] < x["max_rounds"] else "END",
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)
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builder.add_edge("rewrite", "reflect")
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return graph
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graph = builder.compile()
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# ---------------------------------------------------------------------------
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# Demo execution
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# ---------------------------------------------------------------------------
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# Demo function
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async def run_demo():
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code = """
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# Example function to sort numbers
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if __name__ == "__main__":
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import argparse
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import textwrap
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parser = argparse.ArgumentParser(description="Run the code review graph on a demo function.")
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parser.add_argument("--max-rounds", type=int, default=2, help="Maximum number of rewrite rounds")
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args = parser.parse_args()
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# Demo function – can be replaced by any user code
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demo_code = textwrap.dedent(
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"""
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def sort_numbers(arr):
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def sort_numbers(arr):
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return sorted(arr)
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"""
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).strip()
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initial_state: CodeReviewState = {
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"code": demo_code,
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"""
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init_state: CodeReviewState = {
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"code": code,
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"draft_review": "",
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"criteria_scores": {},
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"weakest_criterion": "",
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"verdict": "",
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"round": 0,
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"max_rounds": args.max_rounds,
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"max_rounds": 2,
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}
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result = await graph.ainvoke(init_state)
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print("Final Review:\n", result["draft_review"])
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print("Scores:\n", result["criteria_scores"])
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print("Verdict:\n", result["verdict"])
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graph = build_graph()
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final_state = graph.invoke(initial_state)
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print("\n=== Final Review ===")
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print(final_state["draft_review"])
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print("\n=== Scores ===")
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for k, v in final_state["criteria_scores"].items():
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print(f"{k}: {v}")
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print(f"Verdict: {final_state['verdict']}")
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print(f"Rounds: {final_state['round']}")
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if __name__ == "__main__":
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import asyncio
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asyncio.run(run_demo())
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