DeepEval is an open-source, Python-based evaluation framework for assessing LLM performance. It can be used to evaluate retrieval-augmented generation (RAG) and other applications built with frameworks such as LlamaIndex or LangChain, as well as to baseline and benchmark models. DeepEval goes beyond word-matching scores, assessing accuracy, relevance and consistency to provide more reliable evaluation in real-world scenarios. It includes metrics such as hallucination detection, answer relevancy and hyperparameter optimization and supports GEval for creating custom, use case–specific metrics. Our teams are using DeepEval to fine-tune agentic outputs using the LLM as a judge technique. It integrates with pytest and CI/CD pipelines, making it easy to adopt and valuable for continuous evaluation. For teams building LLM-based applications in regulated environments, Inspect AI, developed by the UK AI Safety Institute, offers an alternative with stronger focus on auditing and compliance.
DeepEval is an open-source python-based evaluation framework, for evaluating LLM performance. You can use it to evaluate retrieval-augmented generation (RAG) and other kinds of apps built with popular frameworks like LlamaIndex or LangChain, as well as to baseline and benchmark when you're comparing different models for your needs. DeepEval provides a comprehensive suite of metrics and features to assess LLM performance, including hallucination detection, answer relevancy and hyperparameter optimization. It offers integration with pytest and, along with its assertions, you can easily integrate the test suite in a continuous integration (CI) pipeline. If you're working with LLMs, consider trying DeepEval to improve your testing process and ensure the reliability of your applications.
