deepeval vs Ragas
Pytest for LLM apps: 40+ research-backed metrics — G-Eval, RAG suite, agent task completion, hallucination — as unit tests you run in CI, judged by any LLM including local ones. — versus — Evaluation toolkit for your RAG and agent pipelines — faithfulness, relevance, and more.
The two default pip installs for LLM evaluation: Ragas is RAG-centric (faithfulness, relevance); DeepEval covers the same RAG suite plus agents, chatbots, safety metrics and pytest-style CI integration. Broader tool vs sharper tool.
| deepeval | Ragas | |
|---|---|---|
| Stars | 18k | 16k |
| Forks | 1.9k | 1.7k |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | today | 6 months ago |
| Topics | evals | evals, rag |
| Curated connections | 7 | 8 |
deepeval — the curator's take
The most complete general-purpose eval framework: pytest ergonomics ('assert_test' in CI), metrics with explanations grounded in the research they implement, component-level tracing for agents, plus benchmark harnesses (MMLU, HumanEval) when you need them. If you're evaluating LLM apps in Python and don't know where to start, start here. NOT vendor-neutral: the free framework feeds Confident AI's platform for reports and regression tracking — usable without it, but the gravity is real. And LLM-as-judge metrics inherit judge bias: treat scores as regression signals, not absolute truth. RAG-only shops may prefer Ragas' tighter focus.
Ragas — the curator's take
Evaluation toolkit for your RAG and agent pipelines — faithfulness, relevance, and more.