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You can use Evidently together with GitHub Actions to automatically test the outputs of your LLM agent or application - as part of every code push or pull request.

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How the integration work:

  • You define a test dataset of inputs (e.g. test prompts with or without reference answers). You can store it as a file, or save the dataset at Evidently Cloud callable by Dataset ID.
  • Run your LLM system or agent against those inputs inside CI.
  • Evidently automatically evaluates the outputs using the user-specified config (which defines the Evidently descriptors, tests and Report composition), including methods like:
    • LLM judges (e.g., tone, helpfulness, correctness)
    • Custom Python functions
    • Dataset-level metrics like classification quality
  • If any test fails, the CI job fails.
  • You get a detailed test report with pass/fail status and metrics.
Results are stored locally or pushed to Evidently Cloud for deeper review and tracking. The final result is CI-native testing for your LLM behavior - so you can safely tweak prompts, models, or logic without breaking things silently.

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Code example and tutorial

👉 Check the full tutorial and example repo: https://github.com/evidentlyai/evidently-ci-example Action is also available on GitHub Marketplace: https://github.com/marketplace/actions/run-evidently-report
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