204 lines
7.7 KiB
Python
204 lines
7.7 KiB
Python
"""Code Review Agent with LangGraph and DeepAgents
|
||
|
||
This script implements the assignment described in the prompt. It uses
|
||
* LangGraph to model the review cycle (draft → reflect → rewrite → reflect …)
|
||
* DeepAgents to expose the whole workflow as a single LLM‑driven agent.
|
||
* OpenRouter via langchain‑openai for all LLM calls.
|
||
|
||
Run the demo with:
|
||
|
||
```bash
|
||
python main.py
|
||
```
|
||
|
||
The demo reviews a simple `sort_numbers` function and prints the draft review,
|
||
the critic’s scores, and any rewritten sections.
|
||
"""
|
||
|
||
import os
|
||
import asyncio
|
||
from typing import TypedDict, Annotated, Dict
|
||
|
||
from langchain_openai import ChatOpenAI
|
||
from langchain_core.messages import HumanMessage
|
||
from langchain.tools import tool
|
||
from langchain.output_parsers import PydanticOutputParser
|
||
from pydantic import BaseModel, Field
|
||
|
||
from langgraph.graph import StateGraph, START, END
|
||
from langgraph.graph.message import add_messages
|
||
|
||
from deepagents import create_deep_agent
|
||
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 1. LLM configuration (OpenRouter)
|
||
# ---------------------------------------------------------------------------
|
||
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,
|
||
)
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 2. State definition
|
||
# ---------------------------------------------------------------------------
|
||
class CodeReviewState(TypedDict):
|
||
code: str
|
||
draft_review: str
|
||
criteria_scores: Dict[str, int] # {"pep8": 0-10, "type_hints": ..., "edge_cases": ..., "naming": ...}
|
||
weakest_criterion: str
|
||
verdict: str # "ok" | "needs_revision"
|
||
round: int
|
||
max_rounds: int
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 3. Structured output for the critic (reflect node)
|
||
# ---------------------------------------------------------------------------
|
||
class CriticOutput(BaseModel):
|
||
pep8: int = Field(..., ge=0, le=10, description="Score for PEP8 compliance")
|
||
type_hints: int = Field(..., ge=0, le=10, description="Score for type hints usage")
|
||
edge_cases: int = Field(..., ge=0, le=10, description="Score for handling edge cases")
|
||
naming: int = Field(..., ge=0, le=10, description="Score for naming conventions")
|
||
weakest_criterion: str = Field(..., description="Criterion with the lowest score")
|
||
verdict: str = Field(..., description="'ok' or 'needs_revision'")
|
||
|
||
critic_parser = PydanticOutputParser(pydantic_object=CriticOutput)
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 4. Graph nodes
|
||
# ---------------------------------------------------------------------------
|
||
async def draft_review(state: CodeReviewState) -> CodeReviewState:
|
||
prompt = (
|
||
"You are a senior Python reviewer.\n"
|
||
"Given the following function, write a concise code review (3–6 points).\n"
|
||
"Focus on style, correctness, and best practices.\n"
|
||
"Return the review as plain text.\n"
|
||
f"Function:\n{state['code']}"
|
||
)
|
||
review = await llm.ainvoke([HumanMessage(content=prompt)])
|
||
state['draft_review'] = review.content.strip()
|
||
return state
|
||
|
||
async def reflect(state: CodeReviewState) -> CodeReviewState:
|
||
prompt = (
|
||
"You are a code quality critic.\n"
|
||
"Evaluate the following review against four criteria: PEP8, type hints, edge cases, naming.\n"
|
||
"Assign a score 0–10 for each criterion.\n"
|
||
"Identify the weakest criterion and decide if the review is "ok" or "needs_revision".\n"
|
||
"Return the results in JSON matching the following schema:\n"
|
||
f"{critic_parser.get_format_instructions()}\n"
|
||
f"Review:\n{state['draft_review']}"
|
||
)
|
||
result = await llm.ainvoke([HumanMessage(content=prompt)])
|
||
parsed = critic_parser.parse(result.content)
|
||
state['criteria_scores'] = {
|
||
"pep8": parsed.pep8,
|
||
"type_hints": parsed.type_hints,
|
||
"edge_cases": parsed.edge_cases,
|
||
"naming": parsed.naming,
|
||
}
|
||
state['weakest_criterion'] = parsed.weakest_criterion
|
||
state['verdict'] = parsed.verdict
|
||
return state
|
||
|
||
async def rewrite(state: CodeReviewState) -> CodeReviewState:
|
||
# Rewrite only the section of the review that addresses the weakest criterion
|
||
prompt = (
|
||
"You are a code reviewer.\n"
|
||
"The current review is: \n"
|
||
f"{state['draft_review']}\n"
|
||
"The weakest criterion is: " + state['weakest_criterion'] + ".\n"
|
||
"Rewrite the review to strengthen this part, keeping the overall tone.\n"
|
||
"Return only the updated review text."
|
||
)
|
||
updated = await llm.ainvoke([HumanMessage(content=prompt)])
|
||
state['draft_review'] = updated.content.strip()
|
||
state['round'] += 1
|
||
return state
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 5. Build the LangGraph
|
||
# ---------------------------------------------------------------------------
|
||
def build_graph() -> StateGraph[CodeReviewState]:
|
||
graph = StateGraph(CodeReviewState)
|
||
graph.add_node("draft_review", draft_review)
|
||
graph.add_node("reflect", reflect)
|
||
graph.add_node("rewrite", rewrite)
|
||
|
||
graph.set_entry_point("draft_review")
|
||
graph.add_edge("draft_review", "reflect")
|
||
graph.add_conditional_edges(
|
||
"reflect",
|
||
lambda state: "rewrite" if state["verdict"] == "needs_revision" and state["round"] < state["max_rounds"] else "END",
|
||
)
|
||
graph.add_edge("rewrite", "reflect")
|
||
return graph
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 6. Tool that runs the graph
|
||
# ---------------------------------------------------------------------------
|
||
@tool
|
||
def run_review(code: str) -> str:
|
||
"""Run the full review cycle on the provided Python code."""
|
||
# Initial state
|
||
state: CodeReviewState = {
|
||
"code": code,
|
||
"draft_review": "",
|
||
"criteria_scores": {},
|
||
"weakest_criterion": "",
|
||
"verdict": "",
|
||
"round": 1,
|
||
"max_rounds": 2,
|
||
}
|
||
graph = build_graph()
|
||
# Execute graph synchronously
|
||
final_state = graph.invoke(state)
|
||
# Prepare a readable output
|
||
output = [
|
||
"--- Draft Review ---",
|
||
final_state["draft_review"],
|
||
"\n--- Critic Scores ---",
|
||
f"PEP8: {final_state['criteria_scores'].get('pep8', 'N/A')}\n"
|
||
f"Type Hints: {final_state['criteria_scores'].get('type_hints', 'N/A')}\n"
|
||
f"Edge Cases: {final_state['criteria_scores'].get('edge_cases', 'N/A')}\n"
|
||
f"Naming: {final_state['criteria_scores'].get('naming', 'N/A')}\n",
|
||
f"Verdict: {final_state['verdict']} (round {final_state['round']})",
|
||
]
|
||
return "\n".join(output)
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 7. DeepAgents setup
|
||
# ---------------------------------------------------------------------------
|
||
backend = CompositeBackend([
|
||
LocalShellBackend(workspace_dir="./workspace"),
|
||
FilesystemBackend(),
|
||
])
|
||
|
||
agent = create_deep_agent(
|
||
model=llm,
|
||
tools=[run_review],
|
||
backend=backend,
|
||
system_prompt="You are a helpful code review assistant.",
|
||
)
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 8. Demo CLI
|
||
# ---------------------------------------------------------------------------
|
||
async def main():
|
||
example_code = """
|
||
def sort_numbers(arr):
|
||
return sorted(arr)
|
||
"""
|
||
result = await agent.ainvoke(
|
||
{"messages": [HumanMessage(content="Please review the following function:
|
||
|
||
"" + example_code + "")]},
|
||
{"configurable": {"thread_id": "demo-session"}},
|
||
)
|
||
print(result["messages"][-1].content)
|
||
|
||
if __name__ == "__main__":
|
||
asyncio.run(main())
|