LLM Code Review is an AI-powered GitHub Action that automatically reviews pull requests and labels issues using large language models hosted on Groq and the RoBERTa model. This tool helps open source maintainers manage their repositories more efficiently by providing automated code reviews and issue labeling.
Follow these steps to set up LLM Code Review in your repository:
GROQ_API_KEY
and paste your Groq API key as the value..github/workflows
directory if it doesn't already exist.llm-code-review.yml
.name: AI Code Review on: pull_request: types: [opened, synchronize, reopened] issues: types: [opened, reopened] jobs: repofix: runs-on: ubuntu-latest steps: - name: Run RepoFixAI uses: Manav916/llm-code-review@main with: groq_api_key: ${{ secrets.GROQ_API_KEY }} groq_model: 'llama3-70b-8192' github_token: ${{ secrets.GITHUB_TOKEN }} # exclude_extensions: 'txt' repo_owner: ${{ github.repository_owner }} repo_name: ${{ github.event.repository.name }} event_number: ${{ github.event.number || github.event.issue.number }} # when listening for both pull requests and issues event_name: ${{ github.event_name }}
With these steps completed, LLM Code Review will automatically run on new pull requests and issues in your repository.
https://github.com/user-attachments/assets/9ff68224-3f7e-472f-adc7-4ee99f68e3df
https://github.com/user-attachments/assets/6a9ed60f-a7b8-48d0-a1c6-ecf49e6226c7
You can customize the behavior of LLM Code Review by modifying the workflow file:
groq_model
: Specify the Groq model to use for code review.exclude_extensions
: Uncomment and modify to exclude specific file types from review.There are no datasets linked
There are no datasets linked