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DeepEval vs Ragas

· 8 min read
Jeffrey Ip

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 integration with Confident AI for team collaboration, reporting, and analysis. 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 classess and methods for better reusability
  • Clean, readable code that is essential if you ever need to customize DeepEval for yourself
  • Exhuastive 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. Alhough 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​

When you use DeepEval, it takes no additional configuration to bring LLM evaluation to your entire organization. Everything is automatically integrated with Confident AI, which is the dashboard/UI for the evaluation results of DeepEval.

This means 0 extra lines of code to:

  • Analyze metric score distributions, averages, and median scores
  • Generate testing reports for you to inspect and debug test cases
  • Download and save testing results as CSV/JSON
  • Share testing reports within your organization and external stakeholders
  • Regression testing to determine whether your LLM app is OK to deploy
  • Experimentation with different models and prompts side-by-side
  • Keep datasets centralized on the cloud

Apart from Confident AI, DeepEval also offers DeepTeam, a new package specific for red teaming, which is for safety testing LLM systems. When you use DeepEval, you won't run into a point where you have to leave its ecosystem because we don't support what you're looking for.

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.

DeepEval also integrates with Confident AI so you can bring these metrics to your organization whenever you're ready.

DeepEval
Ragas
RAG metrics
The popular RAG metrics such as faithfulness
yes
yes
Conversational metrics
Evaluates LLM chatbot conversationals
yes
no
Agentic metrics
Evaluates agentic workflows, tool use
yes
yes
Red teaming metrics
Metrics for LLM safety and security like bias, PII leakage
yes
no
Multi-modal metrics
Metrics involving image generations as well
yes
no
Use case specific metrics
Summarization, JSON correctness, etc.
yes
no
Custom, research-backed metrics
Custom metrics builder should have research-backing
yes
no
Custom, deterministic metrics
Custom, LLM powered decision-based metrics
yes
no
Fully customizable metrics
Use existing metric templates for full customization
yes
no
Explanability
Metric provides reasons for all runs
yes
no
Run using any LLM judge
Not vendor-locked into any framework for LLM providers
yes
no
JSON-confineable
Custom LLM judges can be forced to output valid JSON for metrics
yes
no
Verbose debugging
Debug LLM thinking processes during evaluation
yes
no
Caching
Optionally save metric scores to avoid re-computation
yes
no
Cost tracking
Track LLM judge token usage cost for each metric run
yes
no
Integrates with Confident AI
Custom metrics or not, whether it can be on the cloud
yes
no

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.

DeepEval
Ragas
Generate from documents
Synthesize goldens that are grounded in documents
yes
yes
Generate from ground truth
Synthesize goldens that are grounded in context
yes
no
Generate free form goldens
Synthesize goldens that are not grounded
yes
no
Quality filtering
Remove goldens that do not meet the quality standards
yes
no
Non vendor-lockin
No Langchain, LlamaIndex, etc. required
yes
no
Customize language
Generate in français, español, deutsch, italiano, 日本語, etc.
yes
no
Customize output format
Generate SQL, code, etc. not just simple QA
yes
no
Supports any LLMs
Generate using any LLMs, with JSON confinement
yes
no
Save generations to Confident AI
Not just generate, but bring it to your organization
yes
no

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.

DeepEval
Ragas
Predefined vulnerabilities
Vulnerabilities such as bias, toxicity, misinformation, etc.
yes
no
Attack simulation
Simulate adversarial attacks to expose vulnerabilities
yes
no
Single-turn attack methods
Prompt injection, ROT-13, leetspeak, etc.
yes
no
Multi-turn attack methods
Linear jailbreaking, tree jailbreaking, etc.
yes
no
Data privacy metrics
PII leakage, prompt leakage, etc.
yes
no
Responsible AI metrics
Bias, toxicity, fairness, etc.
yes
no
Unauthorized access metrics
RBAC, SSRF, shell injection, sql injection, etc.
yes
no
Brand image metrics
Misinformation, IP infringement, robustness, etc.
yes
no
Illegal risks metrics
Illegal activity, graphic content, peronsal safety, etc.
yes
no
OWASP Top 10 for LLMs
Follows industry guidelines and standards
yes
no

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.

DeepEval
Ragas
MMLU
Vulnerabilities such as bias, toxicity, misinformation, etc.
yes
no
HellaSwag
Vulnerabilities such as bias, toxicity, misinformation, etc.
yes
no
Big-Bench Hard
Vulnerabilities such as bias, toxicity, misinformation, etc.
yes
no
DROP
Vulnerabilities such as bias, toxicity, misinformation, etc.
yes
no
TruthfulQA
Vulnerabilities such as bias, toxicity, misinformation, etc.
yes
no
HellaSwag
Vulnerabilities such as bias, toxicity, misinformation, etc.
yes
no

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.

DeepEval
Ragas
Pytest
First-class integration with Pytest for testing in CI/CD
yes
no
LangChain & LangGraph
Run evals within the Lang ecosystem, or apps built with it
yes
yes
LlamaIndex
Run evals within the LlamaIndex ecosystem, or apps built with it
yes
yes
Hugging Face
Run evals during fine-tuning/training of models
yes
no
ChromaDB
Run evals on RAG pipelines built on Chroma
yes
no
Weaviate
Run evals on RAG pipelines built on Weaviate
yes
no
Elastic
Run evals on RAG pipelines built on Elastic
yes
no
QDrant
Run evals on RAG pipelines built on Qdrant
yes
no
PGVector
Run evals on RAG pipelines built on PGVector
yes
no
Langsmith
Can be used within the Langsmith platform
yes
yes
Helicone
Can be used within the Helicone platform
yes
yes
Confident AI
Integrated with Confident AI
yes
no

You'll notice that Ragas does not own their platform integrations such as LangSmith, while DeepEval owns Confident AI. This means bringing LLM evaluation to your organization is 10x easier using DeepEval.

Platform​

Both DeepEval and Ragas has their own platforms. DeepEval's platform is called Confident AI, and Ragas's platform is also called Ragas.

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

DeepEval
Ragas
Metric annotation
Annotate the correctness of each metric
yes
yes
Sharable testing reports
Comprehensive reports that can be shared with stakeholders
yes
no
A|B regression testing
Determine any breaking changes before deployment
yes
no
Prompts and models experimentation
Figure out which prompts and models work best
yes
no
Dataset editor
Domain experts can edit datasets on the cloud
yes
no
Dataset revision history & backups
Point in time recovery, edit history, etc.
yes
no
Metric score analysis
Score distributions, mean, median, standard deviation, etc.
yes
no
Metric validation
False positives, false negatives, confusion matrices, etc.
yes
no
Prompt versioning
Edit and manage prompts on the cloud instead of CSV
yes
no
Metrics on the cloud
Run metrics on the platform instead of locally
yes
no
Trigger evals via HTTPs
For users that are using (java/type)script
yes
no
Trigger evals without code
For stakeholders that are non-technical
yes
no
Alerts and notifications
Pings your slack, teams, discord, after each evaluation run.
yes
no
LLM observability & tracing
Monitor LLM interactions in production
yes
no
Online metrics in production
Continuously monitor LLM performance
yes
no
Human feedback collection
Collect feedback from internal team members or end users
yes
no
LLM guardrails
Ultra-low latency guardrails in production
yes
no
LLM red teaming
Managed LLM safety testing and attack curation
yes
no
Self-hosting
On-prem deployment so nothing leaves your data center
yes
no
SSO
Authenticate with your Idp of choice
yes
no
User roles & permissions
Custom roles, permissions, data segregation for different teams
yes
no
Transparent pricing
Pricing should be available on the website
yes
no
HIPAA-ready
For companies in the healthcare indudstry
yes
no
SOCII certification
For companies that need additional security compliance
yes
no

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

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.