Promptfoo vs DeepEval
Both are free, open-source ways to test your LLM app, and both are good. The split is about how you like to work. Promptfoo is config-and-CLI first: you describe your tests in a YAML file and run them from the terminal or CI. DeepEval feels like writing unit tests: you author them in Python, the way you already test the rest of your code.
| Dimension | Promptfoo | DeepEval |
|---|---|---|
| What it is | A config-driven eval runner + red-teaming tool | A pytest-style eval library for Python |
| License | Open source | Open source |
| You write tests as | YAML config (and JS/TS if you want) | Python test functions |
| Feels like | A CLI you point at prompts and models | Writing pytest cases for your model |
| Metrics | Built-in asserts + custom + model-graded | A large library of ready metrics (incl. RAG) |
| Red-teaming | Yes, a real strength | Some, via safety metrics |
| CI integration | Very strong, built for it | Strong, runs anywhere pytest runs |
| Best for | Teams who want a gate fast, in any stack | Python teams who live in pytest |
You want an eval gate running in CI this week, you are not necessarily a Python shop, or you need built-in red-teaming and prompt-injection scanning. Promptfoo gets you from zero to a passing gate with a config file and one command.
Your codebase is Python and your team already writes pytest. DeepEval lets your LLM tests live right next to your normal tests, with a deep library of metrics you can drop in, so evals stop feeling like a separate thing.
You will not regret either one, so choose by muscle memory. If your team reaches for pytest without thinking, use DeepEval. If you want the fastest path to a gate that also does red-teaming, or you work across languages, use Promptfoo. The mistake is spending a week choosing. Pick one, write ten real test cases, and wire it into CI. That single step beats the perfect framework you never set up.