Promptfoo
Open-source LLM testing framework that evaluates AI model outputs against test cases - used by developers to prevent regressions before deploying AI applications.
Promptfoo is an open-source CLI and library for evaluating, testing, and red-teaming large language model applications. It lets developers define structured test suites with expected outputs and run them against any LLM or RAG pipeline - including OpenAI, Anthropic, Ollama, and custom endpoints. The red-teaming module auto-generates adversarial prompts to surface jailbreaks, hallucinations, and harmful outputs before production deployment. Promptfoo integrates into CI/CD pipelines as a pre-merge quality gate and provides a web UI for reviewing results. It has been adopted by over 50,000 developers and AI teams seeking systematic quality assurance for LLM-powered products.
Key Features
- Test suite runner - define expected outputs and evaluate any LLM or RAG pipeline against a structured test set
- Red-teaming module - auto-generates adversarial prompts targeting jailbreaks, bias, and harmful outputs
- Model comparison - benchmark GPT-4o, Claude, Gemini, and custom models side-by-side on identical test sets
- Assertion library - validate with regex, JSON schema, LLM-graded rubrics, and custom JavaScript functions
- CI/CD integration - run eval suites as a pre-merge gate in GitHub Actions, GitLab CI, or any pipeline
- Local and cloud model support - test OpenAI, Anthropic, Ollama, LM Studio, and custom HTTP endpoints
- Web UI for reviewing eval results, comparing runs, and tracking score regressions over time
Use Cases
- AI engineers catching prompt regressions before deploying LLM application updates to production users
- Security teams red-teaming new AI features for jailbreak vulnerabilities prior to public release
- Developers comparing multiple LLMs to select the most cost-effective model for a given quality threshold
- ML teams running systematic A/B evaluations of prompt template changes across a standardized test suite
Pros
- Open-source MIT license with a full CLI - no vendor lock-in, self-host with any model provider
- Red-teaming is purpose-built and model-aware, not a generic fuzzer - generates realistic adversarial cases
- CI/CD integration makes LLM quality gates as standard as unit tests in modern engineering workflows
Cons
- YAML-based configuration has a learning curve for teams unfamiliar with structured eval file formats
- LLM-graded assertions add latency and API cost to each eval run compared to deterministic checks
- Web UI is read-only - configuration and run management still require the CLI for most workflows
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