Promptfoo
CLI and library for testing, evaluating, and red-teaming LLM outputs.
About
Testing LLM applications the way software gets tested is the point of promptfoo, an open-source CLI and library for evaluation and red-teaming released under the MIT license. Developers define test cases declaratively and run side-by-side comparisons of prompts, models, and RAG pipelines across providers including OpenAI, Anthropic, Azure, Bedrock, and local models through Ollama, with automated assertions scoring outputs against expected behavior. Beyond quality evals, it performs adversarial red-teaming and vulnerability scanning that probe applications for jailbreaks, prompt injection, and data leakage, and it integrates into CI/CD so regressions and security findings surface in pull requests. Evaluations run locally, so prompts are not sent to any third-party service, and caching, live reload, and shareable web reports speed up iteration. It installs through npm, Homebrew, or pip and runs with no setup via npx. The project reports adoption by teams whose LLM applications collectively serve millions of end users.
Should you use Promptfoo?
Pick it when
Pick promptfoo when you want to compare prompts and models side by side and fail pull requests on quality regressions or red-team findings, using declarative test configs, local runs, and no Python required.
Look elsewhere when
Skip it if you need production tracing of live traffic, where Langfuse or Phoenix fit, or long multi-turn attack campaigns with persistent results, where PyRIT goes deeper. Python-native component tests suit DeepEval.
Alternatives to Promptfoo
- DeepEval
Python-native Pytest tests with 30+ metrics and component-level checks inside your code; a better fit than config files for Python teams.
- PyRIT
Multi-turn attack strategies like Crescendo and TAP with auditable, resumable memory; no quality evals or prompt comparison.
- Giskard
Automated vulnerability and bias scan that also covers classic ML models; less of a side-by-side prompt comparison tool.
- garak
Scanner-style probing of a model for dozens of weakness classes; tests the model, not your prompts and RAG pipeline.
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Details
- Category
- AI Observability & Evaluation
- Price
- Free
- Platform
- Local/Desktop
- Difficulty
- Easy (2/5)
- License
- MIT
- Added
- Apr 3, 2026
Tags
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