DeepEval is a lightweight, extensible evaluation framework for large language models (LLMs). It offers a wide range of ready-made metrics (e.g., G-Eval, RAG metrics, hallucination detection) and supports both end-to-end and component-level testing, enabling reproducible benchmarks in local and CI environments.
Key Features
- Rich metrics: G-Eval, RAG-focused metrics (Answer Relevancy, Faithfulness, RAGAS), agentic metrics, and conversational metrics.
- Flexible evaluation: supports dataset/bulk evaluation, pytest integration, and component tracing with decorators.
- Extensible: custom metrics, synthetic dataset generation, CI/CD integration, and integrations with LlamaIndex and Hugging Face.
Use Cases
- Regression testing and benchmarking of LLM-powered products.
- Evaluating RAG retrieval quality and answer faithfulness.
- Assessing agent task completion and tool-calling correctness.
Technical Highlights
- Python-based (requires Python >= 3.9), installable via pip.
- Integrations and examples for popular libraries; supports local NLP models and cloud LLMs.
- Outputs structured evaluation results suitable for analysis and reporting; optional cloud sync with Confident AI platform.