DeepEval brings the rigor of unit testing to LLM applications using familiar pytest-like syntax. Developers write test cases with assert patterns they already know, making LLM testing feel like regular software testing.
14+ built-in metrics cover faithfulness, hallucination detection, answer relevancy, bias, toxicity, contextual precision, and more. Each metric uses LLM-as-judge scoring that can run with any model provider, including local models for cost efficiency.
Synthetic dataset generation creates test cases automatically from documents or existing examples, reducing the manual effort of building comprehensive test suites. Regression testing tracks metric scores over time to catch quality degradation.
Tests run locally via CLI with CI/CD integration for automated quality gates. The companion platform Confident AI provides a web dashboard for visualizing test results, tracking trends, and collaborating on evaluation datasets.