DeepEval

DeepEval is an open-source Python (and TypeScript) framework for evaluating and unit-testing LLM applications, with 50+ research-backed metrics covering RAG, agents, conversations, and safety, and a pytest-style workflow that fits CI/CD. The framework is Apache-2.0 and free, while Confident AI is the managed cloud that adds dashboards, dataset versioning, production evaluations, and team features.

DeepEval brings a familiar testing workflow to LLM evaluation: you write test cases and assert against metrics much like pytest, so evaluation can live in your CI/CD pipeline. It ships with a large library of research-backed metrics, over 50, covering RAG (faithfulness, answer relevancy, contextual precision/recall), agents, single- and multi-turn conversations, and safety/red-teaming, and supports LLM-as-a-judge scoring with custom metrics. This makes regression testing of prompts and models practical rather than ad hoc. The framework is Apache-2.0 licensed and free to use for any purpose. Confident AI, the company behind DeepEval, offers a managed cloud that layers dashboards, dataset versioning, online/production evaluations, human annotation, real-time alerts, and team features on top of the open-source framework. Paid plans have historically started around $19-20/month per user for team features, with a free tier and custom enterprise pricing, giving the standard open-source-core plus paid-cloud structure. DeepEval is a strong fit for engineering teams that want evaluation to behave like software testing and to gate deployments on quality metrics. As with other eval tools, results depend on how judge models and metrics are configured, and the category is competitive, so the pytest-style ergonomics and breadth of metrics are its main differentiators.