lgtmaybe

https://lgtmaybe.coles.codes/
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lgtmaybe is an AI-powered code review tool that analyzes pull request diffs and provides inline...
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### Install azure extra, sign in, set endpoint, and run review

Source: https://lgtmaybe.coles.codes/how-to/review-with-azure

Keyless local setup: install the azure extra, sign in with az login (or use a managed identity), set AZURE_API_BASE, and run lgtmaybe review with --provider azure and --model my-gpt-4o-deployment. DefaultAzureCredential finds the az login session or managed identity; no static key is stored.

```bash
pip install 'lgtmaybe[azure]'
az login

export AZURE_API_BASE="https://<resource>.openai.azure.com"

lgtmaybe review \
  --provider azure \
  --model my-gpt-4o-deployment

```

--------------------------------

### Full .lgtmaybe.yml example

Source: https://lgtmaybe.coles.codes/how-to/configure-lgtmaybe-yml

Complete example configuration file with provider, model, severity, path filters, token limits, and categories.

```yaml
provider: openai
model: gpt-5.5
min_severity: low
include_paths:
  - "src/**"
  - "lib/**"
exclude_paths:
  - "**/__pycache__/**"
  - "**/*.min.js"
max_files: 30
max_input_tokens: 80000
categories:
  - security
  - correctness
  - tests

```

--------------------------------

### vLLM with max-model-len and max-concurrency

Source: https://lgtmaybe.coles.codes/how-to/use-a-custom-openai-compatible-endpoint

Start vLLM with a larger max-model-len and run lgtmaybe with a higher max-concurrency. The commands show the recommended setup for latency-sensitive reviews, with the server allocating KV blocks dynamically.

```bash
vllm serve <model> --port 8000 --max-model-len 32768

```

```bash
lgtmaybe review --provider openai-compatible --model <model> \
  --api-base http://localhost:8000/v1 --max-concurrency 6

```

--------------------------------

### Install lgtmaybe with cloud provider extras

Source: https://lgtmaybe.coles.codes/how-to/install-the-cli

Install the CLI with specific cloud SDKs required for keyless authentication.

```bash
pip install 'lgtmaybe[bedrock]'   # or [vertex] / [azure]
```

--------------------------------

### Install lgtmaybe on Windows via WinGet

Source: https://lgtmaybe.coles.codes/how-to/install-the-cli

Install the portable executable for Windows x86_64 using WinGet.

```bash
winget install --id MattJColes.lgtmaybe --exact
```

--------------------------------

### Run lgtmaybe review with OpenRouter and qwen/qwen3.8-max

Source: https://lgtmaybe.coles.codes/how-to/choose-a-review-model

Runs a review with the OpenRouter provider and the qwen/qwen3.8-max model. Swapping in another hosted model only changes the model ID; the provider setup stays the same. See Review with OpenRouter for authentication and GitHub Action examples.

```bash
lgtmaybe review --provider openrouter --model qwen/qwen3.8-max

```

--------------------------------

### Example review output

Source: https://lgtmaybe.coles.codes/tutorial/getting-started

Example output from the review command, showing a finding and summary line.

```text
src/app.py:2  [MEDIUM] Import order
  sys should be sorted before os

1 finding · provider ollama · model qwen3.6:27b

```

--------------------------------

### Install lgtmaybe on macOS via Homebrew

Source: https://lgtmaybe.coles.codes/how-to/install-the-cli

Install the CLI from the project's Homebrew tap.

```bash
brew tap MattJColes/tap
brew trust MattJColes/tap
brew install lgtmaybe
```

--------------------------------

### Install lgtmaybe with Vertex extra and run review

Source: https://lgtmaybe.coles.codes/how-to/review-with-vertex-wif

Install the 'vertex' extra for lgtmaybe, set the required environment variables, and run a review with the Vertex provider. The command uses the gemini-3-pro model and does not accept a static API key.

```bash
pip install 'lgtmaybe[vertex]'

export VERTEXAI_PROJECT=my-project
export VERTEXAI_LOCATION=us-central1

lgtmaybe review \
  --provider vertex \
  --model gemini-3-pro
```

--------------------------------

### llama.cpp local server: start server and review with lgtmaybe

Source: https://lgtmaybe.coles.codes/how-to/use-a-custom-openai-compatible-endpoint

For local servers like llama.cpp, no API key is needed. Start the server, then run lgtmaybe with the local base URL. lgtmaybe uses a placeholder key because the OpenAI client requires one.

```bash
llama-server -m ./model.gguf --port 8000        # serves the OpenAI API at /v1

```

```bash
lgtmaybe review \
  --provider openai-compatible \
  --model local-model \
  --api-base http://localhost:8000/v1

```

--------------------------------

### Install lgtmaybe via pip

Source: https://lgtmaybe.coles.codes/how-to/install-the-cli

Install the CLI on any OS with Python 3.11+.

```bash
pip install lgtmaybe
```

--------------------------------

### Start ollama and pull a model

Source: https://lgtmaybe.coles.codes/tutorial/getting-started

Starts the local ollama server and pulls the qwen3.6:27b model. Leave `ollama serve` running in a separate terminal.

```bash
ollama serve          # starts the local server on http://localhost:11434
ollama pull qwen3.6:27b    # or any model you prefer

```

--------------------------------

### Install lgtmaybe with Bedrock extra and run review

Source: https://lgtmaybe.coles.codes/how-to/review-with-bedrock-oidc

Install the 'bedrock' extra via pip, then run lgtmaybe review with the Bedrock provider and a model ID. Requires Python 3.11+ and ambient AWS credentials. The Action image already bundles boto3.

```bash
pip install 'lgtmaybe[bedrock]'

lgtmaybe review \
  --provider bedrock \
  --model us.anthropic.claude-sonnet-4-6

```

--------------------------------

### Verify lgtmaybe installation

Source: https://lgtmaybe.coles.codes/how-to/install-the-cli

Verify the installation by checking the command list.

```bash
lgtmaybe --help
```

--------------------------------

### Install static analysis tools

Source: https://lgtmaybe.coles.codes/how-to/reduce-review-cost

Installs the lgtmaybe static-analysis extra, which includes ruff, bandit, mypy, and semgrep with local rules. These tools run over changed files and feed findings to lenses as hints.

```bash
pip install 'lgtmaybe[static-analysis]'
```

--------------------------------

### Use --fallback-model CLI flag

Source: https://lgtmaybe.coles.codes/how-to/configure-lgtmaybe-yml

Command-line example to set the fallback model for a single review run.

```bash
lgtmaybe review --fallback-model qwen/qwen3.8-max

```

--------------------------------

### Configure fallback_model with openrouter

Source: https://lgtmaybe.coles.codes/how-to/configure-lgtmaybe-yml

Example configuration using openrouter provider with a fallback model for reviews.

```yaml
provider: openrouter
model: google/gemini-3.7-flash
fallback_model: qwen/qwen3.8-max

```

--------------------------------

### Save agent instructions to review.txt

Source: https://lgtmaybe.coles.codes/how-to/fix-findings-with-an-ai-agent

Redirect the --format agent output to review.txt so an AI agent can read and apply the corrections. Uses the same ollama provider and model as the previous example.

```bash
lgtmaybe review --provider ollama --model qwen3.6:27b \
  --api-base http://localhost:11434 --format agent > review.txt

```

--------------------------------

### Complete OpenAI workflow with github_identity: lgtmaybe

Source: https://lgtmaybe.coles.codes/how-to/post-as-a-github-app

Use this workflow to post as lgtmaybe[bot] with the public App. Requires installing the App, adding id-token: write, and setting github_identity: lgtmaybe. The job fails with setup guidance if id-token: write is missing, the App is not installed, the workflow is not trusted, or the identity service is unavailable.

```yaml
name: lgtmaybe

on:
  pull_request_target:
  issue_comment:
    types: [created]

permissions:
  contents: read
  id-token: write

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v7 # base repository only
      - uses: MattJColes/lgtmaybe@v2
        with:
          github_identity: lgtmaybe
          provider: openai
          model: gpt-5.5
          api_key: ${{ secrets.OPENAI_API_KEY }}

```

--------------------------------

### vLLM server startup and review command

Source: https://lgtmaybe.coles.codes/how-to/use-a-custom-openai-compatible-endpoint

Start a vLLM server on port 8000 and then run lgtmaybe against it. The server command uses the meta-llama/Llama-3.1-8B-Instruct model; the review command specifies the same model and the local API base.

```bash
vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000

```

```bash
lgtmaybe review \
  --provider openai-compatible \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --api-base http://localhost:8000/v1

```

--------------------------------

### Get findings as JSON

Source: https://lgtmaybe.coles.codes/how-to/run-locally-with-ollama

Add --json to get a machine-readable array of findings that can be piped into other tooling. The CLI prints a readable listing by default and never posts anywhere.

```bash
lgtmaybe review \
  --provider ollama \
  --model qwen3.6:27b \
  --api-base http://localhost:11434 \
  --json
```

--------------------------------

### directory_rules configuration with path-scoped instructions

Source: https://lgtmaybe.coles.codes/how-to/scope-review-instructions-to-a-directory

Example configuration for a code review tool (likely `code-review` or similar) that defines directory rules. It shows how to scope instructions to specific paths using globs, with separate rules for payments/billing, tests, and Terraform files. The rules are independent and applied in the order written.

```yaml
provider: bedrock
model: bedrock/anthropic.claude-sonnet-4-5-20250929-v1:0

directory_rules:
  - paths: ["payments/**", "billing/**"]
    instructions: |
      This is money-handling code. Treat any rounding, currency-conversion or
      retry change as high severity, and flag any write path that is not
      idempotent under a duplicated webhook.

  - paths: ["tests/**"]
    instructions: |
      Test code. Do not flag duplication, long functions, or missing
      docstrings here — explicit and repetitive is the house style for tests.
      Do still flag assertions that can never fail.

  - paths: ["infra/**/*.tf"]
    instructions: |
      Terraform. Flag any IAM policy with a `*` action or resource, and any
      storage bucket or database that becomes publicly reachable.

```

--------------------------------

### Configure provider, model, and fallback

Source: https://lgtmaybe.coles.codes/how-to/reduce-review-cost

Sets provider to openrouter, primary model to google/gemini-3.7-flash, and fallback to qwen/qwen3.8-max. Both models use the same provider and credentials. A failed call still spends the primary model's attempts before adding the second model.

```yaml
provider: openrouter
model: google/gemini-3.7-flash
fallback_model: qwen/qwen3.8-max
```

--------------------------------

### Check lgtmaybe version

Source: https://lgtmaybe.coles.codes/how-to/install-the-cli

Check the installed version of the CLI.

```bash
lgtmaybe --version
```

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### Set provider to anthropic

Source: https://lgtmaybe.coles.codes/how-to/configure-lgtmaybe-yml

Example showing how to set the provider field to anthropic.

```yaml
provider: anthropic

```

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### Example models summary line

Source: https://lgtmaybe.coles.codes/reference/profile

A summary line displayed under the table when multiple models are used during a run.

```text
models: luna (7 calls), sonnet (1 call)
```
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