DeepEval is an open-source framework for evaluating large language model applications, built by Confident AI. Think of it as Pytest for LLM apps: it gives you dozens of research-backed metrics, from G-Eval and hallucination to agent task completion, to unit-test chatbots, RAG pipelines and AI agents before they ship. It has earned about 19,000 stars on GitHub and is updated constantly, with new metric families arriving through 2026.
DeepEval evaluates at every level: whole applications end-to-end as black boxes, complete agent trajectories across decisions and tool calls, and individual steps such as retrieval or a single LLM call. Its metric library covers RAG quality (faithfulness, answer relevancy, contextual recall and precision), multi-turn conversation, voice agents, MCP-based agents, multimodal outputs, and safety checks for bias, toxicity and prompt alignment, most using LLM-as-a-judge or local NLP models. It also generates synthetic single- and multi-turn datasets, benchmarks models on suites like MMLU and HumanEval, integrates with LangChain, LangGraph, CrewAI, Pydantic AI and other frameworks, and runs in any CI/CD pipeline.
The DeepEval framework is free and open source under the Apache-2.0 license and runs locally. Confident AI, the companion cloud platform for reports, observability and AI monitoring, offers a free account with paid tiers, making the overall offering freemium.
DeepEval is for AI engineers and teams shipping LLM applications who need systematic, repeatable quality testing instead of vibes-based prompt tweaking.