aicommits

Analyzes git staged changes by extracting the raw diff, chunking it for token limits, and sending it to configurable AI providers (OpenAI, TogetherAI, Groq, Ollama, etc.) via a provider-agnostic abstraction layer. The system constructs context-aware prompts that include the diff payload and optional custom instructions, then parses the AI response into a formatted commit message. This bridges local git operations with remote LLM inference through a structured pipeline.

Integrates with git's prepare-commit-msg hook (installed via 'aicommits hook install') to automatically invoke the AI commit message generator whenever a user runs 'git commit' without providing a message. The hook intercepts the commit workflow at the pre-commit stage, executes the aicommits CLI in headless mode, and writes the generated message directly to the commit message file (.git/COMMIT_EDITMSG), allowing users to review and edit before finalizing.

Allows users to select and configure which specific model to use for each AI provider (e.g., gpt-4, gpt-3.5-turbo for OpenAI; llama2, mistral for Ollama). Model selection is stored in the config file and can be overridden via CLI flags (--model). The system validates that the selected model is available for the chosen provider and passes the model identifier to the provider's API during request construction. Different models have different capabilities, costs, and latencies, giving users control over the quality-speed-cost tradeoff.

Detects when aicommits is running in a non-interactive context (e.g., git hook, CI/CD pipeline, background process) and suppresses interactive prompts, user confirmations, and terminal UI elements. In headless mode, the tool operates entirely via command-line flags and environment variables, writing output to stdout/stderr without expecting user input. This detection is automatic based on terminal availability (isatty checks) and allows the same tool to work in both interactive CLI and automated contexts.

Supports four distinct commit message formats (plain, conventional, gitmoji, subject+body) via a format abstraction layer. Users select their preferred format during setup or override via CLI flags (--type). The system applies format-specific rules to the AI-generated message: conventional commits enforce 'type(scope): description' structure, gitmoji prepends emoji codes, subject+body separates title from detailed description. Format selection is persisted in the config file (~/.aicommits) and applied consistently across all generated messages.

Generates multiple candidate commit messages (via --generate N flag) by making N separate AI API calls with the same diff and prompt, then presents all candidates to the user for interactive selection. Each suggestion is numbered and displayed in the terminal, allowing the user to choose the best option or manually edit. This capability leverages the AI provider's non-determinism (temperature > 0) to produce diverse outputs without requiring multiple model calls to the same provider.

Provides an interactive setup wizard ('aicommits setup') that guides users through selecting an AI provider, entering API credentials, choosing commit message format, and setting optional custom instructions. Configuration is persisted in INI format at ~/.aicommits and can be overridden via CLI flags or environment variables. The system validates credentials by making a test API call to the selected provider before saving, ensuring configuration is functional before use.

Allows users to inject custom instructions into the AI prompt via the --prompt flag or by storing a default prompt in config. These instructions are appended to the system prompt before the diff is sent to the AI, enabling fine-grained control over message tone, style, and content. For example, a user can specify 'Keep messages under 50 characters' or 'Always include the issue number' and the AI will attempt to follow these constraints in its output.

Extends the commit message generation pipeline to generate PR descriptions by analyzing the commit history of a feature branch (commits between base and feature branch). The system aggregates commit messages, diffs, and metadata into a cohesive PR description that summarizes the changes, rationale, and impact. This capability reuses the same AI provider and prompt infrastructure as commit generation but applies it at a higher level of abstraction.

Provides a graphical wrapper around the core aicommits CLI as a VS Code extension (located in vscode-extension/). The extension adds a 'Generate Commit Message' command to the Source Control sidebar, allowing users to generate messages without leaving the editor. The extension communicates with the CLI via subprocess invocation, displays generated messages in a quick-pick menu, and inserts the selected message into the commit message input field. This bridges the gap between CLI-native workflows and IDE-integrated development.

Supports routing all AI provider API requests through an HTTP proxy by accepting proxy configuration via environment variables (HTTP_PROXY, HTTPS_PROXY) or config file settings. The system passes proxy settings to the underlying HTTP client (likely axios or node-fetch) used for API calls, allowing users behind corporate firewalls or VPNs to route requests through proxy servers. This capability is transparent to the user and requires no changes to the core generation logic.

Implements a provider abstraction layer (src/feature/providers/index.ts) that normalizes API calls across 7+ different LLM backends (OpenAI, TogetherAI, Groq, Ollama, LM Studio, xAI, OpenRouter). Each provider has a standardized interface that handles authentication, request formatting, response parsing, and error handling. Users select a provider during setup, and the system automatically routes API calls to the correct provider without requiring code changes. This abstraction decouples the core commit generation logic from provider-specific implementation details.