Ragas vs DeepEval
Ragas or DeepEval? Both are open-source Python libraries that score LLM and RAG outputs, mostly with LLM judges. Ragas centres on RAG metrics, dataset-level evaluation and test-set generation. DeepEval works like pytest for LLM apps and adds agent, multi-turn and custom-criteria metrics for pass/fail checks in CI. Their metric definitions differ, so don't mix their scores.
| Ragas | DeepEval | |
|---|---|---|
| Made by | Exploding Gradients | Confident AI |
| Style | Run metrics over a dataset; score tables | pytest-like test cases run with deepeval test run |
| Main metrics | Faithfulness, answer relevancy, context precision and recall, and more | Faithfulness, answer relevancy, contextual precision/recall/relevancy, G-Eval, DAG, agent and multi-turn metrics |
| Faithfulness means | Share of claims supported by (inferable from) the retrieved context | Share of claims that do not contradict the retrieved context |
| Test-set generation | Yes | — |
| Hosted platform | — | Confident AI (optional) |
| Install | pip install ragas | pip install -U deepeval |
| License | — | Apache-2.0 |
Verdict: when to use which
Use Ragas if you are tuning a RAG pipeline offline and want dataset-level retrieval and answer metrics.
Use DeepEval if you want LLM checks in your test suite and CI, or you also evaluate agents and multi-turn chats.
Dated numbers
- Ragas docs (read 16 Oct 2026): faithfulness = claims supported by the retrieved context ÷ total claims. [Ragas docs: faithfulness]
- DeepEval docs (read 16 Oct 2026): faithfulness = truthful claims ÷ total claims, where a claim is truthful if it doesn't contradict the retrieved context. [DeepEval docs: faithfulness]
- DeepEval's built-in RAGAS metric averages four RAG metrics and calls the ragas library. [DeepEval docs: RAGAS metric]
FAQ
Can I use Ragas and DeepEval together?
Yes. DeepEval even wraps Ragas metrics. Keep each tool's scores separate, because the definitions differ.
Which is easier to put in CI?
DeepEval is built around pytest-style tests, which makes CI gates straightforward; Ragas is oriented to dataset-level evaluation runs.
Sources
Other comparisons: SWE-bench vs SWE-bench-Live · HumanEval vs BigCodeBench · HumanEval vs LiveCodeBench · MMLU vs GPQA · AlpacaEval vs MT-Bench (LLM-as-a-judge paper) · MT-Bench (LLM-as-a-judge paper) vs LMArena (Chatbot Arena) · BEIR vs MTEB Leaderboard · WebArena vs OSWorld · HarmBench vs StrongREJECT · GAIA vs BrowseComp · SWE-Bench Pro vs SWE-bench Verified · Terminal-Bench vs SWE-bench · MMLU vs MMLU-Pro · GPQA vs Humanity's Last Exam