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promptfoo

by promptfoo

Open-source CLI and library for LLM prompt testing, evaluation, and red-teaming with multi-provider comparison.

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AIMenta verdict
Recommended
5/5

"Open-source LLM testing and red-teaming — APAC AI teams use promptfoo to evaluate APAC LLM prompts with automated test suites, compare APAC model outputs across providers (OpenAI, Anthropic, Llama), and run adversarial APAC red-teaming to find prompt injection vulnerabilities."

Features
6
Use cases
1
Watch outs
3
What it does

Key features

  • Automated prompt test suites with assertion-based quality checks
  • Multi-provider model comparison (OpenAI, Anthropic, Llama, Mistral, local)
  • Red-teaming and adversarial testing for prompt injection and safety
  • CI/CD integration for prompt regression testing
  • Shareable HTML evaluation reports with side-by-side model comparison
  • YAML/JSON configuration for reproducible evaluation runs
When to reach for it

Best for

  • APAC AI engineering teams building LLM applications who need systematic prompt quality assurance, model selection evaluation, and adversarial safety testing before production deployment.
Don't get burned

Limitations to know

  • ! Primarily CLI-focused with limited GUI
  • ! Red-teaming effectiveness depends on attack library coverage
  • ! Requires APAC engineering familiarity for advanced configuration
Context

About promptfoo

promptfoo is an open-source developer tool for testing and evaluating large language model (LLM) prompts and configurations. APAC AI engineering teams use promptfoo to build automated test suites for LLM applications — defining expected output formats, factual assertions, and quality thresholds — and run these tests in CI/CD pipelines to catch prompt regressions before they reach production.

promptfoo supports multi-provider evaluation, allowing APAC teams to compare outputs from OpenAI GPT-4, Anthropic Claude, Meta Llama, Mistral, and local models in side-by-side reports. This makes promptfoo particularly valuable for APAC model selection and migration decisions — evaluating whether switching from GPT-4 to a cheaper model maintains acceptable quality across the team's actual prompt library.

The tool includes a red-teaming mode for adversarial testing: automatically generating prompt injection attempts, jailbreaks, and harmful content tests to identify safety vulnerabilities in APAC LLM applications before public deployment. promptfoo's eval reports are shareable HTML or JSON, enabling APAC teams to document model selection rationale and prompt change justifications for enterprise governance processes.

Beyond this tool

Where this category meets practice depth.

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