Promptfoo vs RAGAS
If you are building a RAG system, you will eventually ask which eval tool to wire in. These two answer different questions. Promptfoo is a general eval runner. Point it at prompts, models, or a whole app and gate the results in CI. RAGAS is a Python library of metrics built for retrieval-augmented generation, like faithfulness and context precision.
| Dimension | Promptfoo | RAGAS |
|---|---|---|
| What it is | A general LLM eval runner with red-teaming | A RAG-focused metric library for Python |
| License | Open source | Open source |
| Scope | Any prompt, model, or app | Retrieval and generation pipelines |
| You work in | YAML config and CLI, with JS/TS support | Python scripts and notebooks |
| Metrics | Assertions, model-graded, custom | RAG metrics like faithfulness and context recall |
| Retrieval-aware | Not the default focus | Yes, its whole point |
| CI gating | Built for it | Possible, but you wire it yourself |
| Best for | A test gate across any LLM work | Finding where a RAG pipeline breaks |
You want one tool to test prompts, compare models, gate outputs in CI, and scan for prompt injection across a codebase that is about more than RAG. Promptfoo gets you a passing gate fast from a config file, and it does not care what language your app is written in.
You have a RAG pipeline and you need to know whether retrieval pulls the right chunks and whether the answer stays faithful to them. RAGAS gives you metrics built for exactly that, so you can separate a retrieval problem from a generation problem.
These two are not really competitors, and treating them as either-or is the common mistake. For diagnosing a RAG system, RAGAS metrics tell you something Promptfoo does not: whether the retrieved context was any good. For a durable gate in CI across your whole app, Promptfoo is the better home. In practice I reach for RAGAS to understand why a RAG answer is wrong, then run those checks inside a broader Promptfoo gate. One caveat: many of these metrics are themselves LLM-graded, so read the scores as a strong signal and spot-check them.