Juror-AI has introduced an open-source GitHub Action designed to automate pull request reviews using multiple frontier AI models. The system allows different models to analyze code changes simultaneously, with reports on the same defect consolidated into a single entry. Each review generates a receipt detailing its findings.
The Juror GitHub Action integrates into existing workflows by adding a YAML file to the `.github/workflows/` directory. It operates on the user's GitHub Actions runner, ensuring that code remains within the user's environment, with only API calls made to the model providers. Setup involves adding provider API keys as GitHub secrets, with support for OpenAI, Anthropic, and other models. Users can configure one or multiple keys to enable single or multi-model reviews.
Upon opening a pull request, Juror posts a comment indicating it is reviewing, then updates it with findings, a merge score, and billing information. The tool is designed for ease of use, with an optional configuration file and sensible defaults. Users can also test the tool on a real pull request from their terminal without committing a workflow file, with options to print results locally or post them to GitHub.
The developers state that using multiple models addresses limitations found in single-model PR review bots, such as blind spots. By leveraging diverse AI perspectives, Juror aims to provide a more thorough and robust review process for code changes.
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Juror-AI has released an open-source GitHub Action that allows multiple frontier models to review pull requests in parallel. This tool aims to provide comprehensive code review by combining insights from different AI models, running directly on the user's GitHub Actions runner.