PromptFoo – Streamlining LLM Application Development And Security Testing

An innovative tool designed to revolutionize the testing, evaluation, and security of LLM applications. This versatile tool supports a test-driven development approach, allowing developers to optimize prompts, models, and APIs efficiently.

Whether you’re using CLI, integrating into CI/CD, or seeking robust security through automated red teaming, promptfoo offers a comprehensive solution to enhance the reliability and security of your LLM apps.

promptfoo is a tool for testing, 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 instead of trial-and-error.

npx promptfoo@latest init

Why Choose Promptfoo?

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.

Workflow

Start by establishing a handful of test cases – core use cases and failure cases that you want to ensure your prompt can handle.

As you explore modifications to the prompt, use promptfoo eval to rate all outputs. This ensures the prompt is actually improving overall.

As you collect more examples and establish a user feedback loop, continue to build the pool of test cases.

Usage – Evals

To get started, run this command:

npx promptfoo@latest init

This will create a promptfooconfig.yaml placeholder in your current directory.

After editing the prompts and variables to your liking, run the eval command to kick off an evaluation:

npx promptfoo@latest eval

For more information click here.