AI-powered GitHub code review tool that detects high-impact issues using LLMs.
Gito is worth checking the docs before setup with trust notes worth reviewing. Check agent compatibility and use-case fit before adding it to your workflow.
gh repo view Nayjest/Gito --webOpen the official repository or website.
Check the README for package manager, auth, and platform requirements.
Try it in a small test task inside your agent workflow.
Repository setup guidance
Looks usable, but maintenance, license, or security notes deserve a closer look.
Last commit was about 29 days ago.
420 GitHub stars indicate community interest.
69 open issues signal maintenance load.
A CLI tool to create and hide GitHub comments using the GitHub REST API.
Local GitHub Actions runner for AI agents — test CI/CD workflows locally before pushing.
AI-powered GitHub automation for issue triage, PR review, labeling, and security scanning.
Gito is a tool that automatically reviews code changes in GitHub pull requests or local code. It uses AI to find bugs, security problems, and code quality issues. You can use it with many different AI providers, and it works fast.
Gito is an open-source AI code reviewer that integrates with GitHub and other platforms. It analyzes pull requests and local code changes to detect security vulnerabilities, bugs, and maintainability issues. It is vendor-agnostic, supporting any LLM provider (OpenAI, Anthropic, Google, local models), ensuring privacy by sending code directly to the chosen provider. Gito offers comprehensive analysis across security, performance, and best practices, with parallelized processing for speed. It is designed for developers and teams seeking consistent, automated code review without human delay. The tool includes configuration options, integrations with Linear and Jira, and supports all major programming languages.
AI-powered GitHub code review tool that detects high-impact issues using LLMs.
Software developers, DevOps engineers, Tech leads and code reviewers
The setup section provides repository-level starting guidance, not a guarantee of an independently verified installation. Check the official README and release notes.
Code is sent to the configured LLM provider; ensure provider trust and data handling policies. AI reviews may miss context or produce false positives; human review still recommended.
Similar or complementary options to evaluate include github-comment, Agent-CI, aptu.
Aug 20, 2026
MIT license detected.
2 security/trust notes recorded.
Setup difficulty is 3/5.
420
Stars
40
Forks
69
Issues
MIT
License