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GitHub Setup Guide: Integrating Gito with Your Repository

Automate code review for all Pull Requests using AI.
This step-by-step guide shows how to connect Gito to a GitHub repository for continuous, automated PR reviews.

Prerequisites

  • Admin access to your GitHub repository.
  • An API key for your preferred language model provider (e.g., OpenAI, Google Gemini, Anthropic Claude, etc).

1. Add Your LLM API Key as a GitHub Secret

  1. In your GitHub repository, go to Settings → Secrets and variables → Actions.
  2. Click New repository secret.
  3. Enter a name (e.g. LLM_API_KEY), and paste your API key value.
  4. Click Add secret.

Tip: LLM API keys allow the workflow to analyze code changes using an AI model.
If you don't have the necessary permission, ask a repository administrator to add the secret.

You may use a secret manager (such as HashiCorp Vault) to fetch keys at runtime, but for most teams, GitHub Secrets is the simplest approach.

2. Create GitHub workflows

There are two ways to set up Gito for code reviews in your repository:

  • Manually create the workflow file in your repository.
  • Use gito deploy command locally in the context of your repository and commit the generated workflow files.

Note:

  1. This requires the gito CLI tool to be installed locally.
  2. It will also create the workflow for reacting to the GitHub comments (experimental).

Creating workflow file manually

Create a file at .github/workflows/gito-code-review.yml in your repository with the following content:

name: "Gito: AI Code Review"
on:
  pull_request:
    types: [opened, synchronize, reopened]
  workflow_dispatch:
    inputs:
      pr_number:
        description: "Pull Request number"
        required: true
jobs:
  review:
    runs-on: ubuntu-latest
    permissions: { contents: read, pull-requests: write } # required to post review comments
    steps:
      - uses: actions/checkout@v6
        with: { fetch-depth: 0 }
      - name: Set up Python
        uses: actions/setup-python@v6
        with: { python-version: "3.13" }
      - name: Install AI Code Review tool
        run: pip install gito.bot~=4.0
      - name: Run AI code review
        env:
          LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
          LLM_API_TYPE: openai
          MODEL: "gpt-5.2"
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          PR_NUMBER_FROM_WORKFLOW_DISPATCH: ${{ github.event.inputs.pr_number }}
        run: |
          gito --verbose review
          gito github-comment
      - uses: actions/upload-artifact@v6
        with:
          name: gito-code-review-results
          path: |
            code-review-report.md
            code-review-report.json

Notes

  • Set LLM_API_TYPE and MODEL as needed for your chosen LLM provider (see below for links).
  • If you used a different secret name in step 1, update ${{ secrets.LLM_API_KEY }} accordingly.
  • This workflow will:
    • Analyze all pull requests using an LLM,
    • Post a review summary as a PR comment,
    • Upload code review reports as workflow artifacts for you to download if needed.

Example .env setups for other language model providers:

Done! See AI Review Results on Pull Requests

Whenever a PR is opened or updated, you'll see an AI-generated code review comment in the PR discussion.

Tips:

  • To trigger a review for older existing PRs, merge the main branch containing .github/workflows/gito-code-review.yml
  • You may close and reopen the PR to trigger the review again.
  • Download full review artifacts from the corresponding GitHub Actions workflow run.

Customize Review if Needed

  • Create a .gito/config.toml file at your repository root to override default configuration.
  • You can adjust prompts, filtering, report templates, issue criteria, and more.

Troubleshooting

  • Not seeing a PR comment?
    1. On the PR page, click the status icon near the latest commit hash.
    2. Click Details to open the Actions run.
    3. Review logs for any errors (e.g., API key missing, token issues).

Example:

Workflow Diagnostics

Additional Resources

Enjoy fast, LLM-powered pull request reviews and safer merges! 🚀