DeepEval vs Ragas

TL;DR: Ragas is well-suited for lightweight experimentation — much like using pandas for quick data analysis. DeepEval takes a broader approach, offering a full evaluation ecosystem designed for production workflows, CI/CD integration, custom metrics, and a path to production through Confident AI, which turns failing production traces into your next eval. The right tool depends on whether you're running ad hoc evaluations or building scalable LLM testing into your LLM stack.

How is DeepEval Different?

1. We're built for developers

DeepEval was created by founders with a mixture of engineering backgrounds from Google and AI research backgrounds from Princeton. What you'll find is DeepEval is much more suited for an engineering workflow, while providing the necessary research in its metrics.

This means:

• Unit-testing in CI/CD pipelines with DeepEval's first-class pytest integration
• Modular, plug-and-play metrics that you can use to build your own evaluation pipeline
• Less bugs and clearer error messages, so you know exactly what is going on
• Extensive customizations with no vendor-locking into any LLM or framework
• Abstracted into clear, extendable classes and methods for better reusability
• Clean, readable code that is essential if you ever need to customize DeepEval for yourself
• Exhaustive ecosystem, meaning you can easily build on top of DeepEval while taking advantage of DeepEval's features

2. We care about your experience, a lot

We care about the usability of DeepEval and wake up everyday thinking about how we can make either the codebase or documentation better to help our users do LLM evaluation better. In fact, everytime someone asks a question in DeepEval's discord, we always try to respond with not just an answer but a relevant link to the documentation that they can read more on. If there is no such relevant link that we can provide users, that means our documentation needs improving.

In terms of the codebase, a recent example is we actually broke away DeepEval's red teaming (safety testing) features into a whole now package, called DeepTeam, which took around a month of work, just so users that primarily need LLM red teaming can work in that repo instead.

3. We have a vibrant community

Whenever we're working, the team is always in the discord community on a voice call. Although we might not be talking all the time (in fact most times on mute), we do this to let users know we're always here whenever they run into a problem.

This means you'll find people are more willing to ask questions with active discussions going on.

4. We ship extremely fast

We always aim to resolve issues in DeepEval's discord in < 3 days. Sometimes, especially if there's too much going on in the company, it takes another week longer, and if you raise an issue on GitHub issues instead, we might miss it, but other than that, we're pretty consistent.

We also take a huge amount of effort to ship the latest features required for the best LLM evaluation in an extremely short amount of time (it took under a week for the entire DAG metric to be built, tested, with documentation written). When we see something that could clearly help our users, we get it done.

5. We offer more features, with less bugs

Our heavy engineering backgrounds allow us to ship more features with less bugs in them. Given that we aim to handle all errors that happen within DeepEval gracefully, your experience when using DeepEval will be a lot better.

There's going to be a few comparison tables in later sections to talk more about the additional features you're going to get with DeepEval.

6. We scale with your evaluation needs

DeepEval and Confident AI are two separate products built by the same team — not the same thing.

• DeepEval is the open-source LLM evaluation framework: metrics, test cases, datasets, synthetic data generation, benchmarks, and CI/CD evals. It runs locally, requires no account, and works fully standalone.
• Confident AI is AI monitoring for LLM apps in production. It runs evals on production traces, clusters the failing ones into failure modes, and turns them into goldens for your next eval.

Ragas metrics, like DeepEval's, only score the test cases someone thought to write. That gap shows up once a RAG pipeline is live: users ask questions nobody put in the dataset, and retrieval breaks in ways no metric was pointed at. Confident AI surfaces those failing production traces before anyone has written an eval for them, routes them to the right annotators, and turns them into goldens you pull back into DeepEval.

Use DeepEval on its own for fast, code-first local evaluation, regression testing, and CI gates. Add Confident AI once the app is in production and you want failing traces to decide what you test next. For enterprise teams, Confident AI is one system for AI quality: evaluation, monitoring, annotation, red teaming, and governance.

DeepEval also pairs with DeepTeam, our open-source red teaming framework.

Comparing DeepEval and Ragas

If DeepEval is so good, why is Ragas so popular? Ragas started off as a research paper that focused on the reference-less evaluation of RAG pipelines in early 2023 and got mentioned by OpenAI during their dev day in November 2023.

But the very research nature of Ragas means that you're not going to get as good a developer experience compared to DeepEval. In fact, we had to re-implement all of Ragas's metrics into our own RAG metrics back in early 2024 because they didn't offer things such as:

• Explanability (reasoning for metric scores)
• Verbose debugging (the thinking process of LLM judges used for evaluation)
• Using any custom LLM-as-a-judge (as required by many organizations)
• Evaluation cost tracking

And our users simply couldn't wait for Ragas to ship it before being able to use it in DeepEval's ecosystem (that's why you see that we have our own RAG metrics, and the RAGASMetric, which just wraps around Ragas' metrics but with less functionality).

For those that argues that Ragas is more trusted because they have a research-paper, that was back in 2023 and the metrics has changed a lot since then.

Metrics

DeepEval and Ragas both specialize in RAG evaluation, however:

• Ragas's metrics has limited support for explanability, verbose log debugging, and error handling, and customizations
• DeepEval's metrics go beyond RAG, with support for agentic workflows, LLM chatbot conversations, all through its plug-and-play metrics.

If you're already in production, Confident AI surfaces the failing production traces your RAG metrics never anticipated and turns them into goldens for your next eval.

Dataset Generation

DeepEval and Ragas both offers in dataset generation, and while Ragas is deeply locked into the Langchain and LlamaIndex ecosystem, meaning you can't easily generate from any documents, and offers limited customizations, DeepEval's synthesizer is 100% customizable within a few lines of code

If you look at the table below, you'll see that DeepEval's synthesizer is very flexible.

Red teaming

We even built a second open-source package dedicated for red teaming within DeepEval's ecosystem, just so you don't have to worry about switching frameworks as you scale to safety testing.

Ragas offers no red teaming at all.

We want users to stay in DeepEval's ecosystem even for LLM red teaming, because this allows us to provide you the same experience you get from DeepEval, even for LLM safety and security testing.

Checkout DeepTeam's documentation, which powers DeepEval's red teaming capabilities, for more detail.

Benchmarks

This was more of a fun project, but when we noticed LLM benchmarks were so get hold of we decided to make DeepEval the first framework to make LLM benchmarks so widely accessible. In the past, benchmarking foundational models were compute-heavy and messy. Now with DeepEval, 10 lines of code is all that is needed.

This is not the entire list (DeepEval has 15 benchmarks and counting), and Ragas offers no benchmarks at all.

Integrations

Both offer integrations, but with a different focus. Ragas' integrations pushes users onto other platforms such as Langsmith and Helicone, while DeepEval is more focused on providing users the means to evaluate their LLM applications no matter whatever stack they are currently using.

You'll notice that Ragas hands you off to third-party platforms such as LangSmith, while DeepEval and Confident AI are built by the same team, so the goldens your failing production traces turn into pull straight back into DeepEval.

Platform

Every DeepEval metric is available on Confident AI, a separate product built by the same team, to run evals on live traces, identify failures in production, and turn them into goldens for your next DeepEval run. Ragas's platform is also called Ragas.

Both have varying degrees of capabilities, and you can draw your own conclusions from the table below.

Confident AI is also self-served, meaning you don't have to talk to us to try it out: sign up for Confident AI.

Conclusion

If there's one thing to remember, we care about your LLM evaluation experience more than anyone else, and apart from anything else this should be more than enough to get started with DeepEval.