promptfoo: test your LLM app locally

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

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promptfoo produces matrix views that let you quickly evaluate outputs across many prompts and inputs:

It works on the command line too:

It also produces high-level vulnerability and risk reports:

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

Usage - red teaming/pentesting

Run this command:

npx promptfoo@latest redteam init

This will ask you questions about what types of vulnerabilities you want to find and walk you through running your first scan.

Configuration

The YAML configuration format runs each prompt through a series of example inputs (aka "test case") and checks if they meet requirements (aka "assert").

See the Configuration docs for a detailed guide.

prompts:
  - file://prompt1.txt
  - file://prompt2.txt
providers:
  - openai:gpt-4o-mini
  - ollama:llama3.1:70b
tests:
  - description: 'Test translation to French'
    vars:
      language: French
      input: Hello world
    assert:
      - type: contains-json
      - type: javascript
        value: output.length < 100

  - description: 'Test translation to German'
    vars:
      language: German
      input: How's it going?
    assert:
      - type: llm-rubric
        value: does not describe self as an AI, model, or chatbot
      - type: similar
        value: was geht
        threshold: 0.6 # cosine similarity

Supported assertion types

See Test assertions for full details.

Deterministic eval metrics

Model-assisted eval metrics

Every test type can be negated by prepending not-. For example, not-equals or not-regex.

Tests from spreadsheet

Some people prefer to configure their LLM tests in a CSV. In that case, the config is pretty simple:

prompts:
  - file://prompts.txt
providers:
  - openai:gpt-4o-mini
tests: file://tests.csv

See example CSV.

Command-line

If you're looking to customize your usage, you have a wide set of parameters at your disposal.