LLM Evals Comparison: OpenAI Evals vs promptfoo vs Ragas 2026
Compare the three leading LLM evaluation frameworks in 2026. Feature matrix, code samples, pricing, and a decision guide for choosing OpenAI Evals, promptfoo, or Ragas.
LLM Evals Comparison: OpenAI Evals vs promptfoo vs Ragas 2026
Choosing an LLM evaluation framework is a high-leverage decision. The framework you pick shapes how your team thinks about quality, how fast new evals get written, and how reliably your evals catch regressions. The three most widely adopted choices in 2026 are OpenAI Evals, promptfoo, and Ragas. Each has a different design philosophy, a different sweet spot, and a different cost profile. The wrong choice creates years of friction; the right choice compounds into a competitive advantage.
This guide compares the three frameworks across every dimension that matters: dataset format, grader system, framework integration, production support, dashboards, agent evaluation, cost, and ecosystem. We include side-by-side code samples for the same eval written three ways, a detailed feature matrix, decision guidance based on team type, and a migration path between frameworks. By the end, you should be able to make an informed choice for your team and know exactly how to get started. Use the decision tree at the bottom if you want to skip the analysis and jump to a recommendation.
Key Takeaways
- OpenAI Evals is the most flexible and agent-friendly framework, with rich graders and a dashboard. Open source and free.
- promptfoo is the fastest to set up and best for prompt iteration and red teaming. CLI-first, lightweight, very low onboarding cost.
- Ragas is the most opinionated framework for RAG-specific evaluation. Specialized metrics, deep integration with LangChain and LlamaIndex.
- For agents, choose OpenAI Evals. For prompt iteration and red teaming, choose promptfoo. For RAG, choose Ragas.
- All three can run side by side; many teams use multiple frameworks for different evaluation jobs.
- Cost is mostly judge-driven for all three; the framework choice has minor cost impact.
Framework Philosophies
OpenAI Evals is the comprehensive framework. It supports agents, multi-turn conversations, tool calls, custom graders, and a dashboard. Test cases live in YAML; graders are configured per suite. The framework requires more setup than alternatives but pays off as your eval program scales.
promptfoo is the rapid iteration framework. The core use case is comparing prompts and models side by side. CLI-first, with web UI as a follow-on. Test cases live in YAML; graders are a small set of common types plus model-graded. Lightweight enough to learn in 20 minutes.
Ragas is the RAG-specific framework. The core use case is measuring retrieval and generation quality in RAG pipelines. Eight built-in metrics tied to RAG concepts (faithfulness, context recall, etc.). Best when your application is RAG; less useful for non-RAG LLM workflows.
| Framework | Sweet Spot |
|---|---|
| OpenAI Evals | Agents, comprehensive suites, production CI |
| promptfoo | Prompt comparison, red teaming, fast iteration |
| Ragas | RAG quality measurement, retrieval evaluation |
Feature Matrix
| Feature | OpenAI Evals | promptfoo | Ragas |
|---|---|---|---|
| Open source | Yes | Yes | Yes |
| Dataset format | YAML + JSONL | YAML | Python/HF Dataset |
| Built-in graders | 11 | 12 | 8 metrics |
| Custom graders | Python | JS/Python | Python |
| Model-graded | Yes | Yes | Yes (default) |
| Agent traces | Yes | Partial | No |
| Tool-call grading | Yes | No | No |
| RAG metrics | Custom | Custom | Built-in |
| Web dashboard | Yes | Yes | No |
| CI integration | CLI + GH Action | CLI + GH Action | Pytest |
| Red teaming | Limited | Built-in | No |
| Cost | Free + judge | Free + judge | Free + judge |
| Learning curve | Medium | Low | Low (if RAG) |
| Maturity 2026 | High | High | High |
| Best community | Largest | Active | Growing |
Code Comparison: Same Eval Three Ways
Consider a simple eval: given a question and a retrieved context, the model should produce an answer that is faithful to the context. Here is the same eval implemented in all three frameworks.
OpenAI Evals
name: faithfulness_check
type: completion
graders:
- id: faithful
type: model_graded
model: gpt-4o
prompt: |
Is the answer faithful to the context?
Question: {{ input }}
Context: {{ context }}
Answer: {{ completion }}
Score 1 if every claim in the answer appears in the context.
test_cases: cases.jsonl
oaievals run evals/faithfulness_check.yaml
promptfoo
prompts:
- file://prompts/answer.txt
providers:
- openai:gpt-4o
tests:
- vars:
question: "What is the limit?"
context: "Limits are 100 MB."
assert:
- type: llm-rubric
provider: openai:gpt-4o
value: "Is the response faithful to the context?"
promptfoo eval
Ragas
from datasets import Dataset
from ragas import evaluate
from ragas.metrics import faithfulness
dataset = Dataset.from_dict({
"question": ["What is the limit?"],
"contexts": [["Limits are 100 MB."]],
"answer": ["The limit is 100 MB."],
})
result = evaluate(dataset, metrics=[faithfulness])
print(result)
The three implementations look similar but trade off differently. OpenAI Evals is the most verbose but the most extensible. promptfoo is the most compact for the common case but less powerful for unusual graders. Ragas is the most opinionated and requires almost no grader configuration because the metric is built-in.
Where Each Framework Wins
OpenAI Evals wins when:
You build agents with tool calls. The framework has first-class agent support.
You need custom graders. The Python extension point is rich and well-documented.
You want a dashboard for cross-team visibility. The dashboard is mature and easy to deploy.
Your eval suite has dozens of distinct cases that need different graders.
promptfoo wins when:
You iterate on prompts and want to compare them side by side. The CLI shows diffs and pass rates per prompt.
You red team. promptfoo has built-in adversarial test generation.
You want to onboard a new engineer in under an hour. The mental model is simple.
You evaluate many small prompts rather than one large system.
Ragas wins when:
You build RAG. The eight built-in metrics cover everything you need to measure RAG quality.
You use LangChain or LlamaIndex. Integration is one or two lines of code.
You want production-tested metrics that match academic literature. Ragas papers cover the metric foundations.
You want minimal config; the defaults are good for most RAG cases.
Production Use Cases
| Use Case | Recommended |
|---|---|
| Customer support agent quality | OpenAI Evals |
| Comparing GPT-4o vs Claude on prompts | promptfoo |
| RAG faithfulness monitoring | Ragas |
| Prompt red teaming | promptfoo |
| Multi-turn conversation quality | OpenAI Evals |
| Retrieval recall measurement | Ragas |
| Pre-launch checklist for a new model | promptfoo |
| Continuous regression detection | OpenAI Evals |
Many teams use two or three together. promptfoo for fast prompt iteration during development, then OpenAI Evals or Ragas for the production CI suite. The frameworks coexist well because they have different sweet spots.
Setup and Learning Curve
OpenAI Evals takes 1-2 hours to set up and a few days to feel comfortable. Most of the learning is around the YAML format and grader configuration. Once you understand those, productivity is high.
promptfoo takes 20-30 minutes to set up and an hour to feel comfortable. The CLI-first design and inline YAML keep the cognitive load low.
Ragas takes 30 minutes if you already know LangChain or LlamaIndex. The metrics are well-named and the API is small. The learning is mostly about interpreting the metric scores.
| Framework | Setup time | Time to first useful eval |
|---|---|---|
| OpenAI Evals | 1-2 hours | 1 day |
| promptfoo | 20-30 minutes | 1-2 hours |
| Ragas | 30 minutes | 2-4 hours |
For teams just starting with LLM evals, promptfoo is the lowest-friction entry point. For teams committing to a long-term eval program, OpenAI Evals is the most scalable.
Migration Paths
Moving between frameworks is feasible but not free.
OpenAI Evals to promptfoo: dataset conversion is straightforward. Graders need rewriting because the type systems differ. Custom Python graders may need rewriting in JS.
promptfoo to OpenAI Evals: similar story. Datasets convert. Graders need rewriting in the new format.
Ragas to anything else: the dataset format converts to standard JSONL. The metrics need to be replicated as custom graders, which is straightforward but requires understanding the metric formulas.
Most teams that switch do so because they outgrew the original framework. promptfoo to OpenAI Evals is common when an eval program scales. Ragas tends to be sticky because the metrics are specialized.
Cost Analysis
Framework cost is almost zero. The cost driver is the judge model.
For a 100-case suite with one judge call per case at GPT-4o pricing, each run costs $1 to $3. For a 1000-case suite, $10 to $30 per run. Running 20 times per day across 5 PRs and 1 nightly is $400 to $1200 per month. For a small team, this is real money but small compared to engineering salary.
| Suite Size | Cost per Run (GPT-4o judge) | Monthly Cost (20 runs/day) |
|---|---|---|
| 100 cases | $1-$3 | $60-$180 |
| 500 cases | $5-$15 | $300-$900 |
| 1000 cases | $10-$30 | $600-$1800 |
| 5000 cases | $50-$150 | $3000-$9000 |
Strategies to reduce cost: use cheaper judges for low-stakes graders, cache results, run smaller suites at PR time, and run the full suite less frequently. All three frameworks support these patterns.
Ecosystem and Community
OpenAI Evals has the largest community and the most contributed evaluations. The framework is widely used in academic research and enterprise teams. Stack Overflow and Discord support is active.
promptfoo has a smaller but very engaged community. The maintainers respond quickly on GitHub. Adoption is growing especially in security-focused teams that value the red-teaming features.
Ragas has a focused community of RAG practitioners. Academic papers reference Ragas metrics. LangChain and LlamaIndex documentation includes Ragas examples.
For long-term bets, all three have strong sustainability signals. None looks at risk of abandonment.
Decision Guide
Use this decision tree to pick a framework:
If your primary system is RAG, choose Ragas.
If your primary work is comparing prompts and models, choose promptfoo.
If you build agents with tool calls, choose OpenAI Evals.
If you need custom graders and a dashboard, choose OpenAI Evals.
If you red team, choose promptfoo.
If you want the lowest setup cost, choose promptfoo.
If you want a single framework for everything, choose OpenAI Evals.
If two of these apply, use two frameworks. There is no penalty for using promptfoo for prompt comparison and Ragas for RAG metrics in the same project.
Common Mistakes When Choosing
Picking based on marketing instead of fit. The website is not the framework. Use the side-by-side code comparison above.
Underestimating learning curve. Teams that try to use OpenAI Evals as a quick prompt comparison tool find it heavy; teams that try to use promptfoo for agent eval find it limited.
Switching after a year. Once your suite has 500 test cases, switching costs are real. Choose carefully up front.
Mixing without intent. Using all three in one project without a clear division of responsibilities creates confusion. Decide which framework owns which evaluation.
Combined Patterns
Some teams use all three frameworks deliberately.
promptfoo for the prompt iteration phase. Developers compare prompt versions side by side and commit when they like the result.
OpenAI Evals for the production CI suite. Every PR runs the suite and blocks regressions.
Ragas for production monitoring of the RAG pipeline. The metrics expose retrieval and generation issues separately.
This combination works because each framework owns a phase of the lifecycle. The cost is mental overhead; the benefit is using the right tool for each job.
Further Resources
- OpenAI Evals 2026 documentation and source.
- promptfoo documentation and examples.
- Ragas documentation and source.
- Browse LLM evaluation skills at /skills.
- Deeper guides on /blog for each framework.
Conclusion
OpenAI Evals, promptfoo, and Ragas each have a clear sweet spot. OpenAI Evals for agents and comprehensive suites. promptfoo for prompt iteration and red teaming. Ragas for RAG quality. Most teams need only one; some teams benefit from using multiple. The right choice depends on what you build, how you work, and where you are in your eval program lifecycle. Use the decision guide to pick, then commit and iterate. Browse /skills for related evaluation tools and the /blog for deeper dives.