Free AI Pull Request Reviews: A 20-Minute Setup

Friday, 6:47 PM. You push a branch, open a PR, and get back to your coffee. Monday morning, your reviewer comments: "What if data is null?" You know that feeling. The one where you wish a second pair of eyes existed, one that never sleeps and never gets annoyed.

That second pair of eyes is now free. MonkeyCode, an open-source project, ships with free model access and a free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach. You can wire it into your workflow in about twenty minutes. Here's the exact path.

What we're building

A GitHub Action that runs on every pull request. It calls the MonkeyCode API, sends your diff, and posts a review comment with potential issues. No more "did you handle null?" from humans. The machine handles the obvious stuff first.

Step 1: Get your access

Head to the MonkeyCode repo and read the README. The setup changes, so trust the README over this article. You'll need an API key or a CLI login. For this guide, I'll assume you have a key.

Step 2: The review script

Create a file called review.py. It reads the diff from stdin, sends it to the API, and prints the review.

#!/usr/bin/env python3
import json
import os
import sys
import urllib.request

API_URL = os.environ.get("MONKEYCODE_API_URL", "https://api.monkeycode.example/v1/review")
API_KEY = os.environ.get("MONKEYCODE_API_KEY")

def read_diff():
    return sys.stdin.read()

def send_review(diff):
    payload = json.dumps({
        "diff": diff,
        "language": "python",
        "instructions": "Review this diff for bugs, edge cases, and style issues. Be concise."
    }).encode()
    req = urllib.request.Request(API_URL, data=payload, headers={
        "Content-Type": "application/json",
        "Authorization": f"Bearer {API_KEY}"
    })
    with urllib.request.urlopen(req) as resp:
        return json.loads(resp.read())

def main():
    diff = read_diff()
    if not diff.strip():
        print("No diff to review.")
        return
    result = send_review(diff)
    print(result.get("review", "No review returned."))

if __name__ == "__main__":
    main()

Note: The API endpoint and request format are placeholders. Check the current docs for the real shape. The principle stays: send a diff, get a review.

Step 3: Wire it into GitHub Actions

Create .github/workflows/review.yml:

name: AI Review
on:
  pull_request:
    types: [opened, synchronize]

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0
      - name: Get diff
        run: git diff origin/${{ github.event.pull_request.base.ref }}...HEAD > /tmp/diff.txt
      - name: Run review
        env:
          MONKEYCODE_API_KEY: ${{ secrets.MONKEYCODE_API_KEY }}
        run: |
          python review.py < /tmp/diff.txt > /tmp/review.txt
      - name: Comment on PR
        uses: actions/github-script@v7
        with:
          script: |
            const fs = require('fs');
            const body = fs.readFileSync('/tmp/review.txt', 'utf8');
            if (body.trim()) {
              await github.rest.issues.createComment({
                owner: context.repo.owner,
                repo: context.repo.repo,
                issue_number: context.issue.number,
                body: `## AI Review\n\n${body}`
              });
            }

Now every PR gets a comment from your new reviewer. It's not perfect, but it catches the "you forgot null" class of bugs before a human has to.

Step 4: Make it yours

The real power is in the instructions. Change the prompt to match your team's standards. For example:

• "Flag any TODO or FIXME comments."
• "Check that all new functions have docstrings."
• "Warn if exception handling is too broad."

The free server has limits. It's shared capacity, so expect occasional latency. It's not for confidential code. And the model isn't a senior engineer. It's a fast, tireless junior who reads every line.

Who should skip this

If your codebase is proprietary or regulated, don't send it to a free server. If your PRs are huge, the diff will exceed context limits. If you need deterministic behavior, a free tier won't give you that. For everyone else, this is a twenty-minute investment that pays off in fewer "did you handle null?" comments.

The point

Free AI access isn't just about saving money. It's about removing the friction between you and a second opinion. The setup above is a starting point. Once you see it work, you'll think of a dozen more ways to use it.

Try it on your next PR. Your future self will thank you.