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https://github.com/russellgn/ai-codelint

Use LLMs to lint code.
https://github.com/russellgn/ai-codelint

ai async-openai code-quality-analyzer generative-ai ide-extension language-server-protocol linter lsp openrouter rust-lang tower-lsp typescript vscode

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Use LLMs to lint code.

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# AI CodeLint

Use LLMs to lint code.

AI CodeLint uses LLMs to identify logic bugs that survive compilation and traditional linting.

## Install (Recommended)

macOS/Linux:

```sh
curl -fsSL https://raw.githubusercontent.com/RussellGN/AI-CodeLint/main/scripts/install.sh | sh
```

Windows (PowerShell):

```powershell
irm https://raw.githubusercontent.com/RussellGN/AI-CodeLint/main/scripts/install.ps1 | iex
```

After install:

```sh
ai-codelint --version
```

## Development Status

AI CodeLint is in heavy development and is not production-ready yet.

- Features and APIs can change quickly.
- Breaking changes are expected while core behavior is refined.
- Current releases should be treated as pre-release testing builds.

## Interface

AI CodeLint consists of one standalone binary: `ai-codelint`.

That binary currently exposes two modes of operation via the command line:

- CLI mode: run lint checks from the terminal, like conventional linters.
- LSP server mode: run as a language server so IDE clients can request diagnostics.

The VS Code extension is now live on the Visual Studio Marketplace:

- [AI CodeLint for VS Code](https://marketplace.visualstudio.com/items?itemName=russell-gn.ai-codelint)

Other IDE extensions are incoming.

## Inference

AI CodeLint intentionally uses [OpenRouter](https://openrouter.ai/) so inference-provider and model selection is quick and easy to change and experiment with.

- **You will need an [OpenRouter api key](https://openrouter.ai/keys) to use AI CodeLint**.
- At the time of writing, signing up is free, with free models available for use with limits.
- Run `ai-codelint --configure` to set up api key and other optional runtime defaults.

Model quality note:

- Best results so far come from frontier models.
- Lesser models can produce highly variable lint quality and consistency.
- Recommended model: `anthropic/claude-sonnet-4.6`

## Examples

This repository includes intentionally buggy code samples under [`lint samples/`](https://github.com/RussellGN/AI-CodeLint/tree/main/lint%20samples/).

These samples cover:

- syntax/semantics mistakes (should be ignored by linter),
- easy rule-based linter catches,
- subtle logic bugs,
- edge-case behavior and review-evasive patterns.

They are useful for evaluation, and comparing model performance.

Resources:

- Official Docs (still in development): [russellgn.github.io/AI-CodeLint](https://russellgn.github.io/AI-CodeLint)