promptfoo: The Open-Source Tool for LLM Evaluation and Red Teaming

by ddos · October 8, 2025

promptfoo is an open-source CLI and library for evaluating and red-teaming LLM apps.

With promptfoo, you can:

Build reliable prompts, models, and RAGs with benchmarks specific to your use-case
Secure your apps with automated red teaming and pentesting
Speed up evaluations with caching, concurrency, and live reloading
Score outputs automatically by defining metrics
Use as a CLI, library, or in CI/CD
Use OpenAI, Anthropic, Azure, Google, HuggingFace, open-source models like Llama, or integrate custom API providers for any LLM API

The goal: test-driven LLM development, not trial-and-error.

Promptfoo also produces high-level vulnerability and risk reports:

There are many different ways to evaluate prompts. Here are some reasons to consider promptfoo:

Developer friendly: promptfoo is fast, with quality-of-life features like live reloads and caching.
Battle-tested: Originally built for LLM apps serving over 10 million users in production. Our tooling is flexible and can be adapted to many setups.
Simple, declarative test cases: Define evals without writing code or working with heavy notebooks.
Language agnostic: Use Python, Javascript, or any other language.
Share & collaborate: Built-in share functionality & web viewer for working with teammates.
Open-source: LLM evals are a commodity and should be served by 100% open-source projects with no strings attached.
Private: This software runs completely locally. The evals run on your machine and talk directly with the LLM.

Test-driven prompt engineering is much more effective than trial-and-error.

Serious LLM development requires a systematic approach to prompt engineering. Promptfoo streamlines the process of evaluating and improving language model performance.

Define test cases: Identify core use cases and failure modes. Prepare a set of prompts and test cases that represent these scenarios.
Configure evaluation: Set up your evaluation by specifying prompts, test cases, and API providers.
Run evaluation: Use the command-line tool or library to execute the evaluation and record model outputs for each prompt.
Analyze results: Set up automatic requirements, or review results in a structured format/web UI. Use these results to select the best model and prompt for your use case.
Feedback loop: As you gather more examples and user feedback, continue to expand your test cases.

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