# lgtmaybe

> Provider-agnostic AI pull-request reviewer. It posts inline review comments and a summary onto a GitHub pull request, or prints findings locally from your git diff. Seven hosted providers, local ollama, and any OpenAI-compatible endpoint — one flag, and keyless OIDC/WIF auth for cloud providers (no static keys in secrets).

## Tutorial

- [Getting started](https://mattjcoles.github.io/lgtmaybe/tutorial/getting-started/): Run your first lgtmaybe pull-request review locally with ollama — no API keys, no GitHub token, and no pull request required.

## How-to

- [Install with Homebrew](https://mattjcoles.github.io/lgtmaybe/how-to/install-with-homebrew/): Install the lgtmaybe CLI on macOS or Linuxbrew from the project's Homebrew tap instead of pip.
- [Review with OpenAI](https://mattjcoles.github.io/lgtmaybe/how-to/review-with-openai/): Set up lgtmaybe pull-request reviews with OpenAI — add OPENAI_API_KEY, pick a model, and run as a GitHub Action or locally.
- [Review with Claude (Anthropic)](https://mattjcoles.github.io/lgtmaybe/how-to/review-with-anthropic/): Review pull requests with Claude (Anthropic) in lgtmaybe — API-key setup, Claude model choice, and Action or local usage.
- [Review with OpenRouter](https://mattjcoles.github.io/lgtmaybe/how-to/review-with-openrouter/): Use OpenRouter as the lgtmaybe backend to reach many model vendors through one OpenAI-compatible API key.
- [Review with z.ai (GLM)](https://mattjcoles.github.io/lgtmaybe/how-to/review-with-zai/): Configure lgtmaybe to review pull requests with z.ai GLM models through litellm's native zai route.
- [Review with Bedrock OIDC](https://mattjcoles.github.io/lgtmaybe/how-to/review-with-bedrock-oidc/): Run lgtmaybe on AWS Bedrock with keyless GitHub OIDC — no static AWS credentials stored in repository secrets.
- [Review with Vertex WIF](https://mattjcoles.github.io/lgtmaybe/how-to/review-with-vertex-wif/): Run lgtmaybe on Google Vertex AI with keyless Workload Identity Federation — no service-account JSON in secrets.
- [Review with Azure OpenAI](https://mattjcoles.github.io/lgtmaybe/how-to/review-with-azure/): Review pull requests with Azure OpenAI in lgtmaybe using keyless GitHub OIDC federated to Entra, or an API key.
- [Run locally with ollama](https://mattjcoles.github.io/lgtmaybe/how-to/run-locally-with-ollama/): Run lgtmaybe entirely locally with an ollama model — zero API cost, zero egress, no keys, and code never leaves your machine.
- [Local models & other OpenAI providers](https://mattjcoles.github.io/lgtmaybe/how-to/use-a-custom-openai-compatible-endpoint/): Point lgtmaybe at any OpenAI-compatible server — vLLM, llama.cpp, LM Studio, DeepSeek — with --api-base; the key is optional.
- [Fix findings with an AI agent](https://mattjcoles.github.io/lgtmaybe/how-to/fix-findings-with-an-ai-agent/): Turn lgtmaybe findings into instructions an AI coding agent can apply, for a local review-and-fix loop before you push.
- [Use as a GitHub Action](https://mattjcoles.github.io/lgtmaybe/how-to/use-as-github-action/): Add lgtmaybe as a GitHub Action to review every pull request automatically with inline comments and a summary.
- [Configure .lgtmaybe.yml](https://mattjcoles.github.io/lgtmaybe/how-to/configure-lgtmaybe-yml/): Configure lgtmaybe with a .lgtmaybe.yml file — provider, model, severity floor, lenses, caps, and other non-secret defaults.
- [Add a custom review lens](https://mattjcoles.github.io/lgtmaybe/how-to/add-a-custom-lens/): Add a custom review lens to lgtmaybe — a bring-your-own skill file that runs alongside the nine built-in lenses.
- [Releasing (maintainers)](https://mattjcoles.github.io/lgtmaybe/how-to/releasing/): Maintainer guide to cutting and publishing a new lgtmaybe release with release-please, PyPI trusted publishing, and GHCR.

## Reference

- [Configuration](https://mattjcoles.github.io/lgtmaybe/reference/config/): Complete lgtmaybe configuration reference — every ReviewConfig field, enum, and JSON schema, generated from the pydantic models.

## Explanation

- [What gets reviewed](https://mattjcoles.github.io/lgtmaybe/explanation/what-gets-reviewed/): What lgtmaybe reviews and how it bounds the work — only changed lines, padded with surrounding context, never your whole repo.
- [Architecture](https://mattjcoles.github.io/lgtmaybe/explanation/architecture/): lgtmaybe's hexagonal ports-and-adapters architecture, the review pipeline stages, and the per-category lens fan-out.
- [Auth model](https://mattjcoles.github.io/lgtmaybe/explanation/auth-model/): How lgtmaybe authenticates to each provider — keyless OIDC/WIF for cloud, ambient credentials, API keys only where unavoidable.
- [Data and privacy](https://mattjcoles.github.io/lgtmaybe/explanation/data-and-privacy/): Exactly what data lgtmaybe sends where — diffs only, secret redaction before egress, no code checkout, fully local with ollama.
- [Trust and cost](https://mattjcoles.github.io/lgtmaybe/explanation/trust-and-cost/): How lgtmaybe's trigger gate and cost model work — who can start a review, and why opening it wide is a cost not a security choice.
