An AI-powered code review tool that leverages Large Language Models (LLMs) to identify critical issues in code. It focuses on detecting high-confidence, high-impact problems such as security vulnerabilities, bugs, and maintainability concerns.

Gito is an open-source AI code reviewer designed to work with any language model provider, ensuring flexibility and avoiding vendor lock-in. It can analyze code directly within GitHub pull requests or local codebase changes, providing instant and reliable feedback.

Key features include:

• Lightning Fast: Delivers detailed code reviews in seconds using parallelized LLM processing.
• Vendor Agnostic: Compatible with various LLM providers like OpenAI, Anthropic, Google, and local models.
• Private & Secure: Code is transmitted directly to the chosen LLM provider or local model, with no intermediary servers, ensuring privacy.
• Universal Support: Works with all major programming languages and frameworks.
• Comprehensive Analysis: Detects issues related to security, performance, maintainability, and best practices.
• Consistent Quality: Provides reliable and unbiased review quality consistently.
• Easy Integration: Seamlessly integrates with CI/CD workflows (e.g., GitHub Actions) to automate reviews, post results as PR comments, and respond to maintainer feedback.
• Infinitely Flexible: Highly customizable to adapt to project-specific standards, allowing configuration of review rules, severity levels, and focus areas.

Gito is ideal for solo developers seeking expert-level code reviews, teams aiming to catch issues early, open-source projects maintaining high code quality, and CI/CD pipelines requiring automated quality gates.

It supports integration with platforms like GitHub, GitLab (Beta), and plans for Bitbucket. LLM providers include OpenAI-compatible APIs, Anthropic, Google, and local LLM services. Issue trackers like Jira and Linear are also supported.

The tool emphasizes security and privacy, operating as a stateless, client-side tool with a zero-retention policy. Code is never seen or stored by Gito.

Quickstart guides are available for setting up reviews via GitHub Actions or running code analysis locally. Configuration can be managed through environment variables and project-specific TOML files, allowing for deep customization of prompts, output formats, and bot behavior.