Skip to content

How to Integrate AI Code Review with ChatGPT in GitHub Using GitHub Actions

Published on March 4, 2025

In the fast-paced world of software development, code reviews are a critical step to ensure quality, maintainability, and collaboration. However, manual reviews can be time-consuming and prone to human oversight. Enter AI-powered code review tools! By leveraging ChatGPT and GitHub Actions, you can automate parts of the code review process, providing consistent feedback on pull requests in real-time.

In this post, I’ll walk you through how to integrate an AI code review tool powered by ChatGPT into your GitHub workflow using a specific GitHub Action called Code Review GPT. This setup will automatically analyze your pull requests and provide suggestions, all seamlessly integrated into your GitHub repository.

Why Use AI for Code Review?

AI tools like ChatGPT can:

  • Spot syntax errors, potential bugs, or inefficiencies in your code.
  • Suggest best practices based on the latest standards.
  • Provide feedback in natural language, making it easy for developers to understand.
  • Save time by automating repetitive review tasks, allowing human reviewers to focus on higher-level design and logic.

While AI won’t replace human reviewers entirely, it’s an excellent first pass to catch common issues and improve code quality before peers dive in.

Prerequisites

Before we get started, ensure you have:

  1. A GitHub repository with a main or stage branch (or adjust the branch names as needed).
  2. An OpenAI API key (you can get one from OpenAI’s website).
  3. Admin access to your repository to set up GitHub Actions and secrets.

Step-by-Step Guide

Let’s set up the Code Review GPT GitHub Action to review your pull requests automatically.

1. Create the GitHub Workflow File

First, you’ll need to define a GitHub Actions workflow. In your repository, create a file at .github/workflows/code-review.yml and add the following configuration:

name: Code Review GPT

on:
  pull_request:
    branches: ['main', 'stage']
    types: [opened, reopened, synchronize]

jobs:
  run_code_review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0

      - name: Code Review GPT
        uses: mattzcarey/code-review-gpt@v0.8.0
        with:
          GITHUB_TOKEN: ${{ github.token }}
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
          MODEL: "gpt-4o"
          REVIEW_LANGUAGE: "English"

  • Trigger: The workflow runs whenever a pull request is opened, reopened, or synchronized (i.e., updated with new commits) targeting the main or stage branches.
  • Job: A single job, run_code_review, runs on an ubuntu-latest virtual machine.
  • Steps:
  • actions/checkout@v4: Checks out your repository code so the Action can access it. fetch-depth: 0 ensures the full history is available for diff analysis.
  • mattzcarey/code-review-gpt@v0.8.0: Runs the Code Review GPT Action, powered by ChatGPT, to analyze your code.

2. Add Your OpenAI API Key as a Secret

The Action requires an OpenAI API key to communicate with ChatGPT. To keep it secure:

  1. Go to your GitHub repository.
  2. Click Settings > Secrets and variables > Actions.
  3. Click New repository secret.
  4. Name it OPENAI_API_KEY and paste your OpenAI API key as the value.
  5. Save it.

The GITHUB_TOKEN is automatically provided by GitHub, so you don’t need to configure it manually.

3. Customize the Action (Optional)

The workflow uses these inputs:

  • MODEL: "gpt-4o": Specifies the ChatGPT model (e.g., gpt-4o). You can change this to other models like gpt-3.5-turbo based on your OpenAI subscription.
  • REVIEW_LANGUAGE: "English": Sets the language for feedback. Adjust this if you prefer another language supported by the Action.

Check the Code Review GPT documentation for additional configuration options, like excluding certain files or tweaking the review scope.

4. Test It Out

  1. Create a new pull request in your repository (e.g., from a feature branch to main).
  2. Watch the Actions tab in your GitHub repo. You’ll see the “Code Review GPT” workflow running.
  3. Once complete, the Action will post comments directly on your pull request with suggestions or feedback generated by ChatGPT.

For example, if your code has an inefficient loop or a missing error handler, ChatGPT might comment:

AI Code Review:

Line 78: Potential security issue—consider validating user input before execution.

Line 23: Consider using a const instead of let for better immutability.

Line 45: This function could be optimized to reduce redundant loops.

5. Iterate and Refine

After testing, tweak the workflow as needed:

  • Adjust the branches in the on section to match your project’s structure.
  • Fine-tune the model or language settings for better results.
  • Combine this with human reviews for a hybrid approach.

Benefits of This Setup

  • Speed: Instant feedback as soon as a PR is opened or updated.
  • Consistency: AI applies the same standards across all reviews.
  • Scalability: Works for small teams or large projects without extra effort.
  • Learning Tool: Junior developers can learn from AI suggestions.

Limitations to Keep in Mind

  • Context: ChatGPT might miss project-specific conventions or complex business logic that human reviewers would catch.
  • Cost: OpenAI API usage isn’t free—monitor your usage to avoid surprises.
  • False Positives: AI suggestions aren’t always perfect, so human oversight is still valuable.

Conclusion

Integrating ChatGPT into your GitHub workflow with the Code Review GPT Action is a game-changer for automating code reviews. With just a few lines of YAML and an API key, you can bring AI-powered insights to your pull requests, saving time and improving code quality.

Post Views: 113
Published inAutomationci/cd