A CLI tool that automatically generates git commit messages using AI by analyzing staged changes.
AI Commits is a command-line tool that automatically generates descriptive git commit messages using AI models. It analyzes staged code changes and produces context-aware commit messages, eliminating the manual effort of writing commit descriptions while maintaining project history quality.
Developers and teams who use git for version control and want to automate and improve their commit message workflow, particularly those working on projects with frequent commits.
It saves time, reduces commit message inconsistency, and supports multiple AI providers and commit formats while integrating seamlessly with existing git workflows through both CLI and git hook modes.
A CLI that writes your git commit messages for you with AI
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Supports OpenAI, TogetherAI, Groq, xAI, OpenRouter, Ollama, LM Studio, and custom endpoints, allowing users to choose based on cost, privacy, or performance needs, as detailed in the setup section.
Offers both CLI and git hook modes, including a prepare-commit-msg hook, enabling automatic commit message generation within standard git workflows without disrupting developer habits.
Generates commit messages in plain, conventional, gitmoji, or subject+body styles, adapting to project standards like Conventional Commits, with easy configuration via the --type flag.
Provides interactive setup, config file support, and environment variables, simplifying initial and ongoing configuration, including model selection and API key management.
Generating commit messages, especially multiple options with the --generate flag, consumes tokens and incurs costs with paid AI providers, which can add up over time, as warned in the README.
Sending code diffs to external AI services poses privacy risks for sensitive projects, and while local providers like Ollama exist, they require additional setup and may not match cloud performance.
AI-generated messages might not always capture the full context or intent of changes, requiring manual review and editing, which could reduce the time-saving benefits in complex scenarios.