AI code review tool that works with any language model provider. It detects issues in GitHub pull requests or local changes—instantly, reliably, and without vendor lock-in.
Project description
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?
- [⚡] 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.)
- [🌐] 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 ✨
🚀 Quickstart
1. Review Pull Requests via GitHub Actions
Create a .github/workflows/gito-code-review.yml file:
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@v4
with: { fetch-depth: 0 }
- name: Set up Python
uses: actions/setup-python@v5
with: { python-version: "3.13" }
- name: Install AI Code Review tool
run: pip install gito.bot~=3.0
- name: Run AI code analysis
env:
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_TYPE: openai
MODEL: "gpt-4.1"
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@v4
with:
name: ai-code-review-results
path: |
code-review-report.md
code-review-report.json
⚠️ Make sure to add
LLM_API_KEYto 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
Step1: Install gito.bot using pip.
pip install gito.bot
Troubleshooting:
pip may be also available via cli aspip3depending on your Python installation.
Step2: 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,gitocommand may not became available immediately after installation.
Try restarting your terminal or runningpython -m gitoinstead.
Perform your first AI code review locally
Step1: Navigate to your repository root directory.
Step2: Switch to the branch you want to review.
Step3: Run 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.
Seegito --helpfor 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
🔧 Configuration
Change behavior via .gito/config.toml:
- Prompt templates, filtering and post-processing using Python code snippets
- Tagging, severity, and confidence settings
- Custom AI awards for developer brilliance
- Output customization
You can override the default config by placing .gito/config.toml in your repo root.
See default configuration here.
More details can be found in 📖 Configuration Cookbook
📚 Documentation
💻 Development Setup
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.
© 2025 Vitalii Stepanenko
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