Gito is an open-source AI code reviewer that works with any language model provider. It detects issues in GitHub pull requests or local codebase changes—instantly, reliably, and without vendor lock-in.

Get consistent, thorough code reviews in seconds—no waiting for human availability.

• Why Gito?
• Perfect For
• Supported Platforms & Integrations
• Security & Privacy
• Quickstart
  • 1. Review Pull Requests via GitHub Actions
  • 2. Running Code Analysis Locally
• Configuration
• Guides & Reference
  • Command Line Reference ↗
  • Configuration Cookbook ↗
  • GitHub Setup Guide ↗
  • Integrations
    • Linear Integration ↗
    • Atlassian Jira Integration ↗
  • Troubleshooting ↗
  • Documentation generation with Gito ↗
• Known Limitations
• Development Setup
• Contributing
• License

• [⚡] Lightning Fast: Get detailed code reviews in seconds, not days—powered by parallelized LLM processing
• [🔧] Vendor Agnostic: Works with any language model provider (OpenAI, Anthropic, Google, local models, etc.)
• [🔒] Private & Secure: Your code goes directly to your chosen LLM inference provider or local model—no intermediary servers
• [🌐] Universal: Supports all major programming languages and frameworks
• [🔍] Comprehensive Analysis: Detect issues across security, performance, maintainability, best practices, and much more
• [📈] Consistent Quality: Never tired, never biased—consistent review quality every time
• [🚀] Easy Integration: Automatically reviews pull requests via CI/CD workflows (GitHub Actions, etc), posts results as PR comments, and reacts to maintainer comments
• [🎛️] Infinitely Flexible: Adapt to any project's standards—configure review rules, severity levels, and focus areas, build custom workflows

• Solo developers who want expert-level code review without the wait
• Teams looking to catch issues before human review
• Open source projects maintaining high code quality at scale
• CI/CD pipelines requiring automated quality gates

✨ See code review in action ✨

ℹ️ Gito ships ready-to-use CI/CD workflows for these platforms, with full support for triggering actions via PR comments, automatic review posting, and PR lifecycle integration.

Not on this list? Gito works anywhere—via custom CI/CD pipelines or directly from the CLI.

🚀 More platforms and integrations are coming — Gito is built to grow with your stack.

Gito keeps your source code private by design: it is designed as a stateless, client-side tool with a strict zero-retention policy.

• No middleman: Source code is transmitted directly from your environment (CI/CD runner or local machine) to your explicitly configured LLM provider. If you use a local model, your code never leaves your network. We never see your code.
• No data collection: Your code isn't stored, logged, or retained by Gito.
• Fully auditable: 100% open source. Verify every line yourself.

Create a .github/workflows/gito-code-review.yml file 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 } # 'write' for leaving the summary comment
    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 analysis
      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 --token ${{ secrets.GITHUB_TOKEN }}
    - uses: actions/upload-artifact@v6
      with:
        name: ai-code-review-results
        path: |
          code-review-report.md
          code-review-report.json

⚠️ Make sure to add LLM_API_KEY to your repository's GitHub secrets.

💪 Done!
PRs to your repository will now receive AI code reviews automatically. ✨
See GitHub Setup Guide for more details.

Prerequisites:

• Python 3.11 / 3.12 / 3.13
• Git

Step 1: Install gito.bot using pip.

pip install gito.bot

Troubleshooting:
pip may also be available via CLI as pip3 depending on your Python installation.

To install from repository source / specific branch:

pip install git+https://github.com/Nayjest/Gito.git@<branch-or-tag>

Download the latest Windows installer from Releases.

The installer includes:

• Standalone executable (no Python required)
• Automatic PATH configuration
• Start Menu shortcuts
• Easy uninstallation

Step 2: Perform initial setup

The following command will perform one-time setup using an interactive wizard. You will be prompted to enter LLM configuration details (API type, API key, etc). Configuration will be saved to ~/.gito/.env.

gito setup

Troubleshooting:
On some systems, gito command may not become available immediately after installation.
Try restarting your terminal or running python -m gito instead.

Step 1: Navigate to your repository root directory.
Step 2: Switch to the branch you want to review.
Step 3: Run the following command:

gito review

Note: This will analyze the current branch against the repository main branch by default.
Files that are not staged for commit will be ignored.
See gito --help for more options.

Reviewing remote repository

gito remote git@github.com:owner/repo.git <FEATURE_BRANCH>..<MAIN_BRANCH>

Use interactive help for details:

gito remote --help

Gito uses a two-layer configuration model:

Note: Environment configuration defines external resources and credentials — it's machine-specific and never committed to version control. Project configuration defines review behavior and can be shared across your team.

Environment settings control LLM inference, API Keys and apply system-wide.

Gito uses ai-microcore for vendor-agnostic LLM access. All settings are configured via OS environment variables or .env files.

Default location: ~/.gito/.env
(Created automatically via gito setup)

# ~/.gito/.env
LLM_API_TYPE=openai
LLM_API_KEY=sk-...
LLM_API_BASE=https://api.openai.com/v1/
MODEL=gpt-5.2
MAX_CONCURRENT_TASKS=20

For all supported options, see the ai-microcore configuration guide.

In CI workflows, configure LLM settings via workflow environment variables. Use your platform's secrets management (GitHub Secrets, GitLab CI Variables) for API keys.

Gito supports per-repository customization through a .gito/config.toml file placed at the root of your project. This allows you to tailor code review behavior to your specific codebase, coding standards, and workflow requirements.

Project settings follow a layered override model:

Bundled Defaults (config.toml) → Project Config (<your-repo>/.gito/config.toml)

Any values defined in your project's .gito/config.toml are merged on top of the built-in defaults. You only need to specify the settings you want to change—everything else falls back to sensible defaults.

• Review prompts — Tailor AI instructions, review criteria, and quality thresholds
• Output templates — Customize report format for GitHub comments and CLI
• Post-processing — Python snippets to filter or transform detected issues
• Bot behavior — Mention triggers, retries, comment handling
• Pipeline integrations — Jira, Linear, etc.

Explore the bundled config.toml for the complete list of available options.

# .gito/config.toml
mention_triggers = ["gito", "/check"]
collapse_previous_code_review_comments = true

# Files to provide as context
aux_files = [
    'documentation/command_line_reference.md'
]

exclude_files = [
    'poetry.lock',
]

[prompt_vars]
# Custom instructions injected into the system prompts
awards = ""  # Disable awards
requirements = """
- All public functions must have docstrings.
"""

For detailed guidance, see the 📖 Configuration Cookbook.

For more detailed information, check out these articles:

• Command Line Reference
• Configuration Cookbook
• GitHub Setup Guide
• Integrations
  • Linear Integration
  • Atlassian Jira Integration
• Documentation generation with Gito
• Troubleshooting

Or browse all documentation in the /documentation directory.

Gito cannot modify files inside .github/workflows when reacting to GitHub PR comments (e.g., "Gito fix issue 2").
This is a GitHub security restriction that prevents workflows from modifying other workflow files using the default GITHUB_TOKEN.

While using a Personal Access Token (PAT) with the workflow scope would bypass this limitation, it is not recommended as a workaround. PATs have broader permissions, longer lifespans, and are tied to individual user accounts, making them less secure than the default GITHUB_TOKEN for CI/CD pipelines.

Clone the repository and navigate to it:

git clone https://github.com/Nayjest/Gito.git
cd Gito

Install dependencies:

make install

Note: If make is not available on your system, you can run the underlying command directly:
pip install -e ".[dev]"
See the Makefile for all available commands.

Format code and check style:

make black
make cs

Run tests:

pytest

Looking for a specific feature or having trouble?
Contributions are welcome! ❤️
See CONTRIBUTING.md for details.

Licensed under the MIT License.

© 2025–2026 Vitalii Stepanenko