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alexcpn/code-review-agent

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Agentic AI Code Review in Pure Python - No Agentic Framework

Agentic code review pipeline that plans, calls tools, and produces structured findings without any heavyweight framework. Implemented in plain Python using uv, FastAPI, and a small footprint wrapper library nmagents. Deep code context comes from a Tree-Sitter-backed Model Context Protocol (MCP) server.

What this repo demonstrates

  • —End-to-end AI review loop in a few hundred lines of Python (code_review_agent.py)
  • —Tool-augmented LLM via Tree-Sitter AST introspection from an MCP server
  • —Deterministic step planning/execution with JSON repair and YAML logs
  • —Works with OpenAI or any OpenAI-compatible endpoint (ollam,vllm)
  • —Ships as a FastAPI service, CLI helper, and Docker image

Demo https://alexcpn-code-review-agent.hf.space/

Using Tools https://alexcpn-treesitter-mcp.hf.space/mcp/

How it works

  • —Fetch the PR diff, ask the LLM for a per-file review plan, then execute each step.
  • —MCP server (codereview_mcp_server) exposes AST tools (definitions, call-sites, docstrings) using Tree-Sitter.
  • —Minimal orchestration comes from nmagents Command pattern: plan → optional tool calls → critique/patch suggestions → YAML logs.

Models are effective with very detailed prompts instead of one-liners. Illustration prompt is prompts/code_review_prompts.txt with context populated at place holders.

Results are good if a task can be broken into steps and each step executed in place. This keeps the context tight.

Models which gives good result are GPT 4.1 Nano, GPT 5 Nano.

Also this will run with any OpenAI API comptatible model; Like ollam (with Microsoft phi3.5 model) and vllm (with Google gemma model) wtih a laptop GPU.

Note that these small models are really not that good with complex tasks like this.

Core flow (excerpt from review_orchestrator.py)

python
file_diffs = git_utils.get_pr_diff_url(repo_url, pr_number)
response = call_llm_command.execute(context)                # plan steps
response_data, _ = parse_json_response_with_repair(...)     # repair/parse plan

tools = step.get("tools", [])
if tools:
    tool_outputs = await execute_step_tools(step, ast_tool_call_command)

step_context = load_prompt(diff_or_code_block=diff, tool_outputs=step.get("tool_results", ""))
step_response = call_llm_command.execute(step_context)      # execute each step

Prerequisites

  • —Python 3.10+
  • —uv installed
  • —.env with OPENAI_API_KEY=...
  • —Running MCP server with AST tools (e.g., codereview_mcp_server) reachable at CODE_AST_MCP_SERVER_URL

Setup

Start the Code Review MCP server

bash
git clone https://github.com/alexcpn/codereview_mcp_server.git
cd codereview_mcp_server
uv run python http_server.py  # serves MCP at http://127.0.0.1:7860/mcp/

Running Locally with Ray (Pure Ray)

This is the simplest way to run the agent without Kubernetes complexity.

Start Ray

Start a local Ray cluster instance:

bash
uv run ray start --head
# if there is problem with start up, kill old process
ray stop --force

Note: This starts Ray on your local machine. You can view the dashboard at http://127.0.0.1:8265

Run Redis with persistent storage:

docker run -d \
  -p 6380:6379 \
  --name redis-review \
  -v $(pwd)/redis-data:/data \
  redis \
  redis-server --appendonly yes

To delete older jobs

redis-cli --scan --pattern "review:*" | xargs redis-cli del

Run the Agentic AI Webserver

Note - see the .env (copy) file and create a .env file with the same variables but correct values

OPENAI_API_KEY=xxx
REDIS_PORT=6380
AST_MCP_SERVER_URL=http://127.0.0.1:7860/mcp/
RAY_ADDRESS="auto"
uv run web_server.py

This will start the web server on http://0.0.0.0:8000/

You will get a UI to trigger the review and see triggered reviews and steps

webpage

Deploying to Hugging Face Spaces

This repository includes a configuration to deploy directly to Hugging Face Spaces (Docker SDK).

  1. 1.Create a New Space:
  2. 2.Go to Hugging Face Spaces.
  3. 3.Create a new Space.
  4. 4.Select Docker as the SDK.
  1. 1.Upload Files:
  2. 2.Upload the contents of this repository to your Space.
  3. 3.Important: You must tell Hugging Face to use Dockerfile.hf instead of the default Dockerfile.
  4. 4.You can do this by renaming Dockerfile.hf to Dockerfile in the Space, or by configuring the Space settings if supported.
  5. 5.Recommendation: Rename Dockerfile to Dockerfile.local and Dockerfile.hf to Dockerfile before pushing to the Space.
  1. 1.Set Secrets:
  2. 2.In your Space settings, go to Settings > Variables and secrets.
  3. 3.Add a new secret: OPENAI_API_KEY with your API key.
  1. 1.Run:
  2. 2.The Space will build and start.
  3. 3.Once running, you will see the web interface.

References