Gito
An AI-powered GitHub code review tool that uses LLMs to detect high-confidence, high-impact issues—such as security vulnerabilities, bugs, and maintainability concerns.
Stars: 165
Gito is a lightweight and user-friendly tool for managing and organizing your GitHub repositories. It provides a simple and intuitive interface for users to easily view, clone, and manage their repositories. With Gito, you can quickly access important information about your repositories, such as commit history, branches, and pull requests. The tool also allows you to perform common Git operations, such as pushing changes and creating new branches, directly from the interface. Gito is designed to streamline your GitHub workflow and make repository management more efficient and convenient.
README:
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
- Configuration
- Guides & Reference
- 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 ✨
| Platform | Status |
|---|---|
| GitHub | ✅ Supported |
| GitLab | 🧪 Supported (Beta) |
| Bitbucket | 🛠️ Planned |
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.
| Provider / Runtime | Status |
|---|---|
| OpenAI API | ✅ Supported |
| Anthropic API | ✅ Supported |
| Google API | ✅ Supported |
Local LLM ServicesOllama, LM Studio, vLLM, etc.
|
✅ Supported |
Embedded InferencePyTorch / Transformers
|
✅ Supported |
Any OpenAI-compatible APIMistral, xAI, Azure, Amazon Bedrock, OpenRouter, Fireworks, etc.
|
✅ Supported |
| Tool | Status | Documentation |
|---|---|---|
| Jira | ✅ Supported | Atlassian Jira Integration ↗ |
| Linear | ✅ Supported | Linear Integration ↗ |
🚀 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 addLLM_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.
Prerequisites:
Step 1: Install gito.bot using pip.
pip install gito.botTroubleshooting:
pip may also be available via CLI aspip3depending 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 setupTroubleshooting:
On some systems,gitocommand may not become available immediately after installation.
Try restarting your terminal or runningpython -m gitoinstead.
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 reviewNote: 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 [email protected]:owner/repo.git <FEATURE_BRANCH>..<MAIN_BRANCH>Use interactive help for details:
gito remote --helpGito 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 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=20For 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
- 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 installNote: If
makeis 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 csRun tests:
pytestLooking for a specific feature or having trouble?
Contributions are welcome! ❤️
See CONTRIBUTING.md for details.
Licensed under the MIT License.
© 2025–2026 Vitalii Stepanenko
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