Gito

✨ 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 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

🎯 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 ✨

🌐 Supported Platforms & Integrations

🧩 Git Platforms

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.

🤖 LLM Providers / Runtimes

🗂️ Issue Trackers

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

🔒 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@v7
      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~=4.4
    - name: Run AI code analysis
      env:
        LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
        LLM_API_TYPE: openai
        MODEL: "gpt-5.5"
        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@v7
      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.

Alternatively, install Gito locally and run gito deploy from your repository root. The deployment wizard will guide you through setting up AI-powered code reviews and automatically generate or update the required workflow files. GitHub Actions and GitLab CI are both supported. For GitLab, see the GitLab Setup Guide, or refer to the GitLab workflow templates for manual configuration.

2. Running Code Analysis Locally

2.1 Install Gito locally

Note: If you use uvx, you can skip this step.
When using commands like uvx gito.bot setup, uvx gito.bot review, uvx will install everything required on demand.

Option 1: Install gito.bot using pip.

Prerequisites:

• Python 3.11 / 3.12 / 3.13
• Git

Run the following command to install the latest stable release from PyPI:

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>

Option 2: 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

2.2 Configure LLM connection

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

Alternatively, if you have uvx installed, you can run the setup command via uvx:

uvx gito.bot setup

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

2.3 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 git@github.com: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