lgtmaybe

lgtmaybe reviews pull and merge requests on GitHub, GitLab, and Gitea. You can also review a local Git diff before pushing. Choose a hosted model, ollama, or another OpenAI-compatible endpoint. Reviews on a code host post inline comments and one summary; local reviews print findings in your terminal.

It reads the diff and a little surrounding code, but only comments on changed lines. It never checks out or runs the change. Generated files and binaries are skipped, secrets are redacted, and all author-supplied text is treated as untrusted.

It checks for:

• Correctness and security — logic errors, missed awaits, injection, auth mistakes, and leaked secrets.
• Code health — performance problems, needless complexity, deprecations, and risky dependencies.
• Tests and documentation — missing or weak tests, stale comments, and undocumented APIs.
• Intent — whether the change does what its title, description, and commits say it does.
• Ponytail — code you don't need, standard-library opportunities, and simpler ways to get the job done.

Findings are graded from info to critical and land on the exact changed line. A clean change just gets a 👍 LGTM!.

Slash commands can refresh a review (/review, /improve), answer a question (/ask), post a structured description (/describe), or post a change overview (/diagram). The overview describes high-impact areas and includes a flowchart; it adds a sequence diagram when the change alters a runtime flow. Run lgtmaybe diagram to print a text version locally.

flowchart LR
    web["Storefront<br/>React<br/>places orders"]
    api["Order API<br/>Python<br/>creates orders (changed)"]
    db["Order database<br/>PostgreSQL<br/>stores orders"]
    queue["Order events<br/>SQS<br/>buffers events (new)"]
    worker["Notification worker<br/>Python<br/>sends confirmations (new)"]
    email["Email provider<br/>delivers messages"]
    web -->|calls| api
    api -->|stores in| db
    api -->|publishes to| queue
    queue -->|delivers to| worker
    worker -->|sends via| email

Start here

• Tutorial — Getting started: your first review with ollama, locally and free.
• How-to — task recipes: choose a review model, run locally, Bedrock OIDC, Vertex WIF, Azure OpenAI, GitHub Action, GitLab, Gitea.
• Reference — Configuration: every config field and schema.
• Explanation — What gets reviewed, Architecture, Auth model, Data & privacy.

Providers

Provider	Auth
openai	OPENAI_API_KEY
anthropic	ANTHROPIC_API_KEY
openrouter	OPENROUTER_API_KEY
zai	ZAI_API_KEY — GLM / Zhipu AI; optional --api-base for the China / coding-plan endpoint
bedrock	Ambient AWS creds — GitHub OIDC, no static key
vertex	Ambient GCP creds — Workload Identity Federation, no key
azure	Ambient Azure AD creds — GitHub OIDC, no static key (or AZURE_API_KEY) + endpoint
ollama	None — local only, zero cost
openai-compatible	--api-base to any OpenAI /v1 endpoint; key optional (placeholder for keyless local servers)

Where it posts

The model provider and the code host are independent — any provider above works on any host below.

Host	How it runs	Token	Guide
GitHub	GitHub Action	GITHUB_TOKEN	GitHub Action
GitLab	GitLab CI job	GITLAB_TOKEN	Review on GitLab
Gitea	Gitea Actions	GITEA_TOKEN	Review on Gitea
None	lgtmaybe review locally	—	Install the CLI

The same review runs on all three hosts. Their APIs and workflows differ in thread resolution, incremental reviews, and keyless cloud auth. See Architecture for the details.

For AI agents

The site root publishes a curated llms.txt index of these docs, plus a whole-corpus llms-full.txt, for LLM crawlers and coding agents.