Deepeval

Open-Source LLM Evaluation Framework

Deepeval is an open-source evaluation framework for testing the output quality, relevance, and factual accuracy of LLM applications. It provides a comprehensive suite of evaluation metrics — from answer relevance and faithfulness to hallucination detection and contextual recall — enabling developers to run automated, quantitative tests on their AI systems the same way unit tests work for traditional software.

Pytest-Style LLM Testing

Deepeval integrates with the familiar pytest testing framework, allowing developers to write LLM evaluation tests using familiar patterns. Test cases define an input, the actual LLM output, and optionally an expected output or retrieval context. Deepeval then runs a battery of metric evaluations and provides pass/fail results with detailed explanations, enabling LLM testing to integrate into existing CI/CD pipelines.

• 20+ built-in evaluation metrics for LLM outputs
• Pytest integration for familiar test-driven development
• RAG-specific metrics: contextual relevance, faithfulness, recall
• Hallucination and bias detection metrics
• CI/CD integration for continuous quality testing

For AI Engineers

Deepeval is used by AI engineers who want to apply software engineering discipline to LLM development. Rather than evaluating model outputs subjectively, Deepeval enables objective, automated quality measurement that can catch regressions before they reach production users.