DeepEval vs Ragas: choosing an LLM eval library
Ragas if the unit you are evaluating is a retrieval pipeline and the question is whether retrieval works. DeepEval if it is an agent and you want evals to feel like unit tests. Both stop at the dataset you assembled; production traffic, non-engineer access and trace linkage are where a platform starts.[1][3]
What is the difference between DeepEval and Ragas?
Ragas is focused on RAG pipeline evaluation, built around a small set of reference-free metrics that score retrieval and generation quality without ground-truth labels. DeepEval is broader: a general LLM and agent testing framework with a pytest-style workflow and a much larger metric library. Both run inside your test suite rather than as a hosted platform.
At a glance
| Decision factor | DeepEval | Ragas |
|---|---|---|
| Primary scope | General LLM and agent evaluation | RAG pipeline evaluation |
| Workflow | pytest-style assertions in a test suite | Evaluate a pipeline over a dataset |
| Metric library | Large and broad: RAG, agents, multi-turn, safety | Small and focused: the reference-free RAG family |
| Ground truth | Varies by metric | Core metrics are reference-free (no labels needed) |
| Behind it | Confident AI (commercial platform alongside the OSS library) | Originated from a 2023 paper on reference-free RAG evaluation |
| Runs in CI | Yes, the pytest integration makes this natural | Yes |
| Licence | Check the repo's LICENSE file | Check the repo's LICENSE file |
About the table
Checked on 16 September 2026. Both projects move quickly, so treat anything load-bearing as worth re-reading in their docs. Deliberately no metric names or counts appear in the table: both projects add metrics frequently and a list published today will be wrong within a release or two. Read each project's metrics docs directly; that is the only version that is current.
One practical note that is easy to miss: DeepEval can run Ragas metrics through its own interface, so 'which library' is not always an either/or. If you want Ragas's RAG metrics but prefer DeepEval's testing ergonomics, you may not have to choose.
The real decision
Not 'which library is better'. It is what is the unit you are evaluating?
A retrieval pipeline. Your failures look like: the answer was not supported by the retrieved context, or the right chunk was never retrieved. You want metrics that separate retrieval quality from generation quality, because conflating them means fixing the wrong half. Ragas is built around exactly that split.
An agent. Your failures look like: wrong tool, wrong argument, unnecessary loop, gave up too early. Retrieval metrics do not describe any of that. You want assertions over behaviour and trajectory. DeepEval's testing-framework shape fits better.
Both, which is common: agents that retrieve. Plenty of teams use a RAG-specific library for the retrieval leg and something broader for agent behaviour. That is a reasonable architecture, not a failure to choose, and both are open source so the cost of running both is mostly conceptual overhead.
What libraries are good at
Genuinely good, and worth saying plainly on a vendor's site: they live in your repo, run in CI, cost nothing, version with your code, and make regression gating trivial. For a team that wants evals to fail a build, a library is the shortest path there. Many teams never need more than this, and that is a legitimate end state. See What Is an Eval Harness? for the scaffolding either library slots into.
Where libraries stop
Three limits show up at roughly the same point, usually when an agent reaches production.
Production traffic. A library runs against a dataset you assembled. It does not sample live traffic, and the failures that matter most are the ones your dataset never contained.
Non-engineer access. Library output is CI logs. When a support lead or PM needs to see why a run failed, someone has to translate, and that person becomes a bottleneck.
Trace linkage. A failing score tells you a case failed. Connecting that to the full trajectory, which step, what arguments, what was retrieved, usually means separate instrumentation. See agent tracing.
None of these is a flaw in the libraries. They are a different product category. The natural end state for a lot of teams is a library gating CI and a platform watching production, which is why 'DeepEval vs Ragas' and 'library vs platform' are separate questions that get tangled together.
Where Prefactor fits
We sell the layer this section describes. Read it with that in mind.
Prefactor is an agent evaluation platform rather than a library: the same eval definitions run as offline release gates and online against sampled production traffic, trajectories are captured and scored at step level, and a failing score links to the step that caused it. The honest framing is that it addresses the three limits above, not that it replaces a library you are happy with. If your evals run in CI and nothing hurts, stay there.
The same evals scored against sampled live sessions, not only the dataset you assembled.
A UI where a support lead or PM can inspect a failing run without an engineer translating CI logs.
A failing score linked to the step, the arguments and the retrieved context that caused it.
Which one fits
Pick DeepEval if
- You are testing a multi-step agent and want evals to feel like unit tests.
- You want one library spanning RAG, agents, multi-turn and safety.
- Your evals live in pytest and CI.
- You want Ragas's RAG metrics inside DeepEval's workflow.
Pick Ragas if
- Your system is a RAG pipeline and the question is whether retrieval works.
- You want reference-free metrics with no labelled ground truth.
- You need retrieval quality separated from generation quality.
- A small, focused metric set is what you want, not a limit.
How we reviewed this comparison
Reviewed against public product and documentation pages on September 16, 2026. If a vendor has changed a feature, product name, or positioning since then, send a correction and we will update it. Numbered source links in the page body point to the ordered sources below.
Sources reviewed
Prefactor contextMethodology
- Reviewed public product, documentation, and launch material visible at the time of writing.
- Mapped each page to the primary buyer, control layer, and runtime capabilities each vendor describes publicly.
- Prefer direct product and documentation pages over analyst summaries or reseller material.
What's the difference between DeepEval and Ragas?
Which is better for evaluating RAG?
Which is better for evaluating AI agents?
Can I use DeepEval and Ragas together?
When do I need more than an eval library?
Related
When the library is not enough
Prefactor runs the same eval definitions as CI gates and against production traffic, with step-level trajectory scoring and failing scores linked to the step that caused them. Book a demo to see it on your agents.
Unified performance platform for agents, authentication, and risk management