Nayjest/Gito: An AI-powered GitHub code review tool that uses LL...

Code Coverage

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.

📋 Table of Contents

Why Gito?
Perfect For
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

✨ Why Gito?

[⚡] 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 GitHub Actions and posts results as PR comments
[🎛️] Infinitely Flexible: Adapt to any project's standards—configure review rules, severity levels, and focus areas, build custom workflows

🎯 Perfect For

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 ✨

🔒 Security & Privacy

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.

🚀 Quickstart

1. Review Pull Requests via GitHub Actions

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~=3.6
    - 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.

2. Running Code Analysis Locally

Initial Local Setup

Prerequisites:

Python 3.11 / 3.12 / 3.13
Git

Option A: Install via pip (recommended)

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.

Option B: Windows Standalone Installer

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.

Perform your first AI code review locally

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 [email protected]:owner/repo.git <FEATURE_BRANCH>..<MAIN_BRANCH>

Use interactive help for details:

gito remote --help

🔧 Configuration

Gito uses a two-layer configuration model:

Scope	Location	Purpose
Environment	`~/.gito/.env` or OS environment variables	LLM provider, model, API keys, concurrency
Project	`<repo>/.gito/config.toml`	Review behavior, prompts, templates, integrations

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 Configuration

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)

Example

# ~/.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.

CI/CD Environments

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

Project Configuration

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.

Configuration Inheritance Model

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.

Common Customizations

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.

Example

# .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.

📚 Guides & Reference

For more detailed information, check out these articles:

Or browse all documentation in the /documentation directory.

🚧 Known Limitations

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.

💻 Development Setup

Cloning the repository:

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

Install dependencies:

make install

Format code and check style:

make black
make cs

Run tests:

pytest

🤝 Contributing

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

📝 License

Licensed under the MIT License.

Gito
Gito copied to clipboard

Metadata

📋 Table of Contents

✨ Why Gito?

🎯 Perfect For

🔒 Security & Privacy

🚀 Quickstart

1. Review Pull Requests via GitHub Actions

2. Running Code Analysis Locally

Initial Local Setup

Option A: Install via pip (recommended)

Option B: Windows Standalone Installer

Perform your first AI code review locally

🔧 Configuration

Environment Configuration

Example

CI/CD Environments

Project Configuration

Configuration Inheritance Model

Common Customizations

Example

📚 Guides & Reference

🚧 Known Limitations

💻 Development Setup

🤝 Contributing

📝 License

← Metadata

Owner

Metadata

Gito Gito copied to clipboard

Metadata

📋 Table of Contents

✨ Why Gito?

🎯 Perfect For

🔒 Security & Privacy

🚀 Quickstart

1. Review Pull Requests via GitHub Actions

2. Running Code Analysis Locally

Initial Local Setup

Option A: Install via pip (recommended)

Option B: Windows Standalone Installer

Perform your first AI code review locally

🔧 Configuration

Environment Configuration

Example

CI/CD Environments

Project Configuration

Configuration Inheritance Model

Common Customizations

Example

📚 Guides & Reference

🚧 Known Limitations

💻 Development Setup

🤝 Contributing

📝 License

← Metadata

Owner

Metadata

Gito
Gito copied to clipboard