Ragas vs DeepEval: Choosing an LLM Evaluation Framework
Ragas and DeepEval are the two most-adopted open-source LLM evaluation frameworks. Both give you programmatic scoring for LLM outputs, but they optimise for different workflows: Ragas is built around RAG-specific metrics with data-first ergonomics; DeepEval is a pytest-style framework built around unit-test ergonomics with a broad metric catalogue.
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What we're comparing
RAG-focused evaluation framework built around dataset-oriented metrics (faithfulness, context precision, context recall, answer relevance).
Pytest-style LLM evaluation framework with 14+ metrics including G-Eval, hallucination, bias, toxicity, and RAG-specific metrics.
Side-by-side
| Attribute | Ragas | DeepEval |
|---|---|---|
| Primary optimisation | RAG pipeline evaluation with dataset-first API | Unit-test-style LLM evaluation with pytest integration |
| Core metrics | Faithfulness, context precision & recall, answer relevance, noise sensitivity | G-Eval, hallucination, bias, toxicity, faithfulness, contextual precision/recall, answer relevancy |
| LLM-as-judge | Yes; judge prompts configurable per metric | Yes; G-Eval lets you author custom judge criteria in natural language |
| CI integration | Programmatic; runs via scripts, no built-in test runner | Native pytest plugin — `pytest test_llm.py` gates PRs directly |
| Dataset format | HuggingFace Datasets; opinionated (question, contexts, answer, ground_truth) | Flexible; test-case objects composed inline in test files |
| Non-RAG use cases | Supported but the API assumes retrieval context; awkward for pure generation | First-class — designed for any LLM output including summarisation, code, agents |
| Hosted dashboard | Yes (Ragas App, paid tier); OSS runs local | Yes (Confident AI, paid); OSS runs local |
| License | Apache 2.0 | Apache 2.0 |
| Best-fit team | Data-science / ML teams building RAG systems with notebook workflows | Engineering teams wiring LLM eval into CI alongside code tests |
When to use which
- Use Ragas
Your primary workload is RAG, your team lives in notebooks and pandas, and you want RAG-specific metrics without reinventing the wheel.
- Use DeepEval
You want LLM eval to feel like unit tests — one command in CI, pass/fail per PR, works across QA, RAG, summarisation, code, and agents in the same framework.
FAQs
Can I use both?
Yes, and some teams do — Ragas for the RAG-pipeline test suite (data-scientist owned), DeepEval for the application-level tests in CI (engineering owned). Just budget the extra maintenance cost of two eval configs.
Which uses less compute?
Roughly equivalent per metric — both rely on LLM-as-judge calls that dominate cost. The bigger lever is batching and caching, not framework choice.
Do either evaluate agent trajectories?
DeepEval has explicit agent-tool-call metrics; Ragas focuses on the RAG pipeline. For deep agent trajectory eval, look at Inspect AI or a custom harness.
What about Promptfoo?
Promptfoo optimises for prompt/model A/B testing and red-teaming rather than framework-of-record evaluation. See our Promptfoo vs OpenAI Evals comparison.