**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?](#-why-gito) - [Perfect For](#-perfect-for) - [Supported Platforms & Integrations](#-supported-platforms--integrations) - [Security & Privacy](#-security--privacy) - [Quickstart](#-quickstart) - [1. Review Pull Requests via GitHub Actions](#1-review-pull-requests-via-github-actions) - [2. Running Code Analysis Locally](#2-running-code-analysis-locally) - [Configuration](#-configuration) - [Guides & Reference](#-guides--reference) - [Command Line Reference](https://github.com/Nayjest/Gito/blob/main/documentation/command_line_reference.md) ↗ - [Configuration Cookbook](https://github.com/Nayjest/Gito/blob/main/documentation/config_cookbook.md) ↗ - [GitHub Setup Guide](https://github.com/Nayjest/Gito/blob/main/documentation/github_setup.md) ↗ - [GitLab Setup Guide](https://github.com/Nayjest/Gito/blob/main/documentation/gitlab_setup.md) ↗ - Integrations - [Linear Integration](https://github.com/Nayjest/Gito/blob/main/documentation/linear_integration.md) ↗ - [Atlassian Jira Integration](https://github.com/Nayjest/Gito/blob/main/documentation/jira_integration.md) ↗ - [Troubleshooting](https://github.com/Nayjest/Gito/blob/main/documentation/troubleshooting.md) ↗ - [Documentation generation with Gito](https://github.com/Nayjest/Gito/blob/main/documentation/documentation_generation.md) ↗ - [Known Limitations](#-known-limitations) - [Development Setup](#-development-setup) - [Contributing](#-contributing) - [License](#-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 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](https://github.com/Nayjest/Gito/pull/99) ✨ ## 🌐 Supported Platforms & Integrations ### 🧩 Git Platforms | Platform | Status | |-------------|----------------------| | GitHub | ✅ Supported | | GitLab | 🧪 Supported (Beta) | | Bitbucket | 🛠️ Planned | | Local / CLI | ✅ Supported | > 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 | Provider / Runtime | Status | |-------------------------------------------------------------------------------------------------|---------------| | OpenAI-compatible APIs
`Mistral, xAI, Azure, Amazon Bedrock, OpenRouter, Fireworks, and many more` | ✅ Supported | | Anthropic API | ✅ Supported | | Google API | ✅ Supported | | Local LLM Services
`Ollama, vLLM, llama.cpp, SGLang, LM Studio, etc.` | ✅ Supported | | Embedded Inference
using `PyTorch / Transformers` or custom python inference function | ✅ Supported | | Working on top of CLI-based LLM Tools / Coding Agent CLIs
`Claude Code, Gemini CLI, etc.` | ✅ Supported | ### 🗂️ Issue Trackers | Tool | Status | Documentation | |--------|---------------|---------------| | Jira | ✅ Supported | [Atlassian Jira Integration](https://github.com/Nayjest/Gito/blob/main/documentation/jira_integration.md) ↗ | | Linear | ✅ Supported | [Linear Integration](https://github.com/Nayjest/Gito/blob/main/documentation/linear_integration.md) ↗ | > 🚀 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: ```yaml 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](https://github.com/Nayjest/Gito/blob/main/documentation/github_setup.md) 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](https://github.com/Nayjest/Gito/blob/main/documentation/gitlab_setup.md), or refer to the [GitLab workflow templates](https://github.com/Nayjest/Gito/tree/main/gito/tpl/workflows/gitlab) for manual configuration.

### 2. Running Code Analysis Locally #### 2.1 Install Gito locally > **Note:** If you use [uvx](https://docs.astral.sh/uv/guides/tools/), 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](https://github.com/Nayjest/Gito) using [pip](https://en.wikipedia.org/wiki/Pip_(package_manager)). **Prerequisites:** - [Python](https://www.python.org/downloads/) 3.11 / 3.12 / 3.13 - [Git](https://git-scm.com) Run the following command to install the latest stable release from PyPI: ```bash 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: ```bash pip install git+https://github.com/Nayjest/Gito.git@ ``` **Option 2:** Windows Standalone Installer Download the latest Windows installer from [Releases](https://github.com/Nayjest/Gito/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`. ```bash gito setup ``` Alternatively, if you have `uvx` installed, you can run the setup command via `uvx`: ```bash 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: ```bash 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** ```bash gito remote git@github.com:owner/repo.git .. ``` Use interactive help for details: ```bash 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** | `/.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](https://github.com/Nayjest/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 ```bash # ~/.gito/.env LLM_API_TYPE=openai LLM_API_KEY=sk-... LLM_API_BASE=https://api.openai.com/v1/ MODEL=gpt-5.5 MAX_CONCURRENT_TASKS=20 ``` For all supported options, see the [ai-microcore configuration guide](https://github.com/Nayjest/ai-microcore?tab=readme-ov-file#%EF%B8%8F-configuring). #### 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](https://github.com/Nayjest/Gito/blob/main/gito/config.toml)) → **Project Config** (`/.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](https://github.com/Nayjest/Gito/blob/main/gito/config.toml) for the complete list of available options. #### Example ```toml # .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](https://github.com/Nayjest/Gito/blob/main/documentation/config_cookbook.md). ## 📚 Guides & Reference For more detailed information, check out these articles: - [Command Line Reference](https://github.com/Nayjest/Gito/blob/main/documentation/command_line_reference.md) - [Configuration Cookbook](https://github.com/Nayjest/Gito/blob/main/documentation/config_cookbook.md) - [GitHub Setup Guide](https://github.com/Nayjest/Gito/blob/main/documentation/github_setup.md) - [GitLab Setup Guide](https://github.com/Nayjest/Gito/blob/main/documentation/gitlab_setup.md) - Integrations - [Linear Integration](https://github.com/Nayjest/Gito/blob/main/documentation/linear_integration.md) - [Atlassian Jira Integration](https://github.com/Nayjest/Gito/blob/main/documentation/jira_integration.md) - [Documentation generation with Gito](https://github.com/Nayjest/Gito/blob/main/documentation/documentation_generation.md) - [Troubleshooting](https://github.com/Nayjest/Gito/blob/main/documentation/troubleshooting.md) Or browse all documentation in the [`/documentation`](https://github.com/Nayjest/Gito/tree/main/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 Clone the repository and navigate to it: ```bash git clone https://github.com/Nayjest/Gito.git cd Gito ```

Install dependencies: ```bash 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](https://github.com/Nayjest/Gito/blob/main/Makefile) for all available commands. Format code and check style: ```bash make black make cs ``` Run tests: ```bash pytest ```

## 🤝 Contributing **Looking for a specific feature or having trouble?** Contributions are welcome! ❤️ See [CONTRIBUTING.md](https://github.com/Nayjest/Gito/blob/main/CONTRIBUTING.md) for details. ## 📝 License Licensed under the [MIT License](https://github.com/Nayjest/Gito/blob/main/LICENSE). © 2025–2026 [Vitalii Stepanenko](mailto:mail@vitaliy.in)