promptfoo vs OpenAI Evals Comparison 2026
Detailed comparison of promptfoo and OpenAI Evals in 2026. Side-by-side YAML, grader differences, red teaming, dashboards, and a decision guide based on team size and goals.
promptfoo vs OpenAI Evals Comparison 2026
If you have settled on one of OpenAI Evals or promptfoo and are wondering whether the other might be a better fit, this guide is for you. Both frameworks are mature, open source, and capable. Both run evals against arbitrary LLM providers, support model-graded checks, and integrate with CI. The differences live in the design philosophy: OpenAI Evals is the comprehensive framework that scales to large eval programs; promptfoo is the lightweight tool that excels at fast iteration and red teaming. Which fits your team depends on what you build and how you work.
This comparison covers configuration, graders, datasets, CI integration, dashboards, red teaming, learning curve, cost, and ecosystem. We include side-by-side YAML for the same eval, decision criteria based on team size, and a migration path between the two. By the end, you should know with confidence whether your next eval suite belongs in promptfoo or OpenAI Evals. Use the decision tree at the end if you want to skip the analysis. This is the missing manual that takes you past the marketing pages and into the engineering tradeoffs that matter.
Key Takeaways
- promptfoo prioritizes fast iteration and prompt comparison; OpenAI Evals prioritizes scale and depth.
- promptfoo's red-teaming and adversarial test generation are best in class; OpenAI Evals does not match this out of the box.
- OpenAI Evals has better agent and multi-turn support; promptfoo is single-turn focused.
- Both have dashboards, both have GitHub Actions, both run model-graded checks.
- For a team running 50-100 evals during development, promptfoo is faster. For a team running 1000+ evals in production CI, OpenAI Evals scales better.
- Many teams use both: promptfoo during development, OpenAI Evals for production CI.
Design Philosophies Diverge
promptfoo was built to answer one question: which prompt is better. The CLI runs prompts against test cases, scores them, and shows a diff. Everything else is in service of that comparison loop. Setup is fast, the YAML is compact, and feedback is immediate.
OpenAI Evals was built to answer a different question: how good is my system over time. The framework supports large suites, agent trajectories, custom graders, dashboards, and CI integration. Setup is slower, the YAML is more verbose, but the framework scales.
The philosophical difference shows up everywhere. promptfoo defaults are aggressive; OpenAI Evals defaults are conservative. promptfoo lets you experiment quickly; OpenAI Evals lets you ship reliably.
Configuration Side by Side
The same eval in both frameworks:
promptfoo
prompts:
- "Summarize the following text in one sentence: {{ text }}"
providers:
- openai:gpt-4o
- openai:gpt-4o-mini
tests:
- vars:
text: "OpenAI Evals and promptfoo are LLM evaluation frameworks."
assert:
- type: llm-rubric
value: "Is the summary accurate and concise?"
- vars:
text: "Ragas focuses on RAG-specific metrics."
assert:
- type: llm-rubric
value: "Is the summary accurate and concise?"
promptfoo eval
OpenAI Evals
name: summarization_eval
description: Evaluates summarization quality
graders:
- id: accuracy
type: model_graded
model: gpt-4o
prompt: |
Question: {{ input }}
Response: {{ completion }}
Is the summary accurate and concise? Score 1 or 0.
test_cases: cases.jsonl
oaievals run evals/summarization_eval.yaml
promptfoo includes the prompt, providers, and test cases in one file. OpenAI Evals splits providers from cases and graders. The promptfoo version is more compact but mixes concerns; the OpenAI Evals version is more verbose but separates concerns.
For a 10-case eval, promptfoo wins on brevity. For a 1000-case eval, OpenAI Evals wins on organization.
Graders Compared
| Grader Type | promptfoo | OpenAI Evals |
|---|---|---|
| Exact match | equals | exact_match |
| Includes | contains, icontains | includes |
| Regex | regex | regex |
| Model-graded | llm-rubric, model-graded-fact | model_graded |
| Semantic similarity | similar | semantic_similarity |
| JSON schema | is-json, contains-json | json_schema |
| Code execution | python, javascript | code_exec |
| Custom | JS function | Python class |
Both frameworks cover the common cases. promptfoo has more lightweight built-ins (like is-valid-openai-tools-call); OpenAI Evals has richer composition.
The big difference is custom graders. promptfoo custom graders are JS functions (or Python via subprocess). OpenAI Evals custom graders are Python classes with a clean interface. If your team is more comfortable in Python, OpenAI Evals is friendlier.
Multi-Turn and Agent Support
OpenAI Evals 2026 added first-class agent support. Test cases include initial state, expected tool calls, and final answer. Graders can score tool calls, final answers, and trajectory quality. The agent SDK integrates so you can plug in your existing agent and evaluate it directly.
promptfoo is primarily single-turn. You can chain prompts and capture multi-turn flows, but agent evaluation is not the focus. For agents with complex tool use, OpenAI Evals is the better fit.
Red Teaming
promptfoo's red teaming module is the headline feature for security-focused teams. It generates adversarial test cases automatically across categories: prompt injection, jailbreaking, harmful content, PII leakage, off-topic responses.
promptfoo redteam generate \
--purpose "customer support assistant" \
--plugins "prompt-injection,jailbreak,pii"
The output is a YAML suite that you can run like any other promptfoo eval. The generated cases are realistic and varied.
OpenAI Evals does not include built-in red teaming. You can build adversarial suites manually or import from other sources, but the automation is not there. For teams whose primary need is red teaming, promptfoo is the clear choice.
| Red Teaming Plugin | promptfoo | OpenAI Evals |
|---|---|---|
| Prompt injection | Built-in | Manual |
| Jailbreaking | Built-in | Manual |
| Harmful content | Built-in | Manual |
| PII leakage | Built-in | Manual |
| Off-topic | Built-in | Manual |
| Custom adversarial | Yes | Yes |
Dashboards
Both frameworks ship dashboards. They differ in feature depth.
promptfoo's web UI focuses on comparing prompts and models side by side. You see the diff between two prompts, per-test scores, and aggregate pass rates. The UI is fast and intuitive but lighter on historical trends.
OpenAI Evals' dashboard focuses on tracking quality over time. You see runs sorted by date, per-grader trends, regressions across versions, and detailed trajectories for agent evals. The UI is more comprehensive but takes more setup.
For prompt iteration, promptfoo's UI is more useful. For production monitoring, OpenAI Evals' UI is more useful.
CI Integration
Both frameworks have GitHub Action integrations and CLI exit codes that work with any CI provider.
promptfoo's CI integration is more turnkey:
- uses: promptfoo/promptfoo-action@v1
with:
config: promptfooconfig.yaml
OpenAI Evals requires more explicit configuration but offers more flexibility:
- run: oaievals run evals/ --threshold 0.85 --output runs/${{ github.sha }}/
For small suites, promptfoo's CI is faster to set up. For large suites with custom reporting, OpenAI Evals is more controllable.
Learning Curve
promptfoo onboards a new engineer in 30-60 minutes. The mental model is small: prompts, providers, tests, assertions. Examples in the docs cover most use cases. Most engineers are productive within a day.
OpenAI Evals onboards in 1-2 hours and takes a week to become fluent. The framework has more concepts: suites, graders, agents, trajectories, dashboards. The investment pays off when you need the breadth.
For solo developers and small teams, promptfoo's lower barrier matters. For larger teams, OpenAI Evals' breadth matters.
Cost
Both frameworks are open source and free. The cost is judge calls.
For comparable graders, costs are similar. A 100-case suite with one judge call per case costs $1-$3 with either framework.
Where the cost diverges is workflow. promptfoo's iteration loop is faster, so engineers run evals more often. More runs means more judge calls. A team running promptfoo might spend twice as much in judge fees as the same team running OpenAI Evals, simply because the workflow encourages more iteration.
Ecosystem and Maturity
Both frameworks are mature in 2026. Active development, healthy maintainer count, broad adoption.
promptfoo's community skews toward red teaming and security engineering. The plugins for adversarial testing are best in class.
OpenAI Evals' community skews toward enterprise QA and academic research. The grader library is broader, and contributions to the eval registry are common.
Both have Slack/Discord channels and active GitHub discussions. Choosing based on community is a wash.
Decision Guide by Team
Solo developer iterating on prompts: promptfoo. Fastest path from idea to comparison.
Small startup with a single LLM feature: promptfoo. The lightweight model fits the team size.
Mid-size team building a production agent: OpenAI Evals. The agent support and dashboard matter.
Enterprise with multiple LLM features: OpenAI Evals. The framework scales to large suites.
Security or compliance team: promptfoo. The red teaming is unmatched.
Research team comparing models: promptfoo. The side-by-side comparison is the focus.
Platform team building eval infrastructure for the company: OpenAI Evals. The customization and dashboard make it the better foundation.
| Team Profile | Recommended |
|---|---|
| Solo / startup, prompt iteration | promptfoo |
| Mid-size, agent in production | OpenAI Evals |
| Enterprise, multiple LLM features | OpenAI Evals |
| Security / red teaming | promptfoo |
| Research / model comparison | promptfoo |
| Platform / eval infrastructure | OpenAI Evals |
Combined Use
Many teams use both. promptfoo during development for fast prompt iteration; OpenAI Evals for the production CI suite that gates merges.
The workflow:
A developer iterates on a prompt with promptfoo until they like the result.
They convert the final test cases to OpenAI Evals format.
The OpenAI Evals suite runs in CI and gates the PR.
The production OpenAI Evals dashboard tracks the metric over time.
The combination plays to each framework's strength: promptfoo for fast development, OpenAI Evals for reliable production.
Migration Path
If you start on one framework and want to switch:
promptfoo to OpenAI Evals: dataset format converts mostly automatically. Assertions need rewriting in the OpenAI Evals grader system. Custom JS functions need to be ported to Python.
OpenAI Evals to promptfoo: similar story in reverse. JSONL converts to YAML test cases. Python graders need to be rewritten in JS or wrapped via subprocess.
A 100-case suite usually takes a day to migrate. The judge prompts and overall structure carry over; the grader configuration is the main rework.
Common Pitfalls
Choosing promptfoo for a production CI suite and outgrowing it. promptfoo is great for development but the lightweight design starts to chafe at 1000+ cases. Plan to switch if your suite is going to grow.
Choosing OpenAI Evals for a quick prompt comparison and getting bogged down in setup. If you just need to compare two prompts, promptfoo gets there faster.
Maintaining the same suite in both. Pick one for each evaluation job. Duplicating evals creates drift.
Ignoring red teaming. promptfoo's red teaming catches real safety issues. Even if you choose OpenAI Evals for your main suite, consider promptfoo for red teaming.
Future Directions
Both frameworks are evolving. promptfoo continues to invest in red teaming and adversarial testing; OpenAI Evals continues to invest in agent support and dashboards. The choices today will look slightly different next year, but the philosophical split (fast iteration vs comprehensive scale) is unlikely to change.
Watch for cross-pollination: features that prove useful in one framework tend to show up in the other within a year. The shared OpenAI compatibility layer makes this easy.
Further Resources
- promptfoo documentation and red teaming guides.
- OpenAI Evals 2026 release notes.
- LLM evaluation skills at /skills.
- Related guides on /blog: OpenAI Evals Graders Reference, Eval Design Best Practices.
Conclusion
promptfoo and OpenAI Evals are both excellent tools that solve overlapping but distinct problems. promptfoo is the rapid iteration tool with best-in-class red teaming. OpenAI Evals is the comprehensive framework with best-in-class agent support and scaling. Match your tool to your task: prompt comparison and red teaming go to promptfoo; agent evaluation and production scaling go to OpenAI Evals. Many teams use both. Browse /skills for related evaluation tools and the /blog for deeper dives.