promptfoo: test your LLM app locally

promptfoo is a tool for testing and evaluating LLM output quality.

With promptfoo, you can:

• Build reliable prompts, models, and RAGs with benchmarks specific to your use-case
• 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

» View full documentation «

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

It works on the command line too:

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

To get started, run this command:

npx promptfoo@latest init

This will create some placeholders in your current directory: prompts.txt and promptfooconfig.yaml.

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

npx promptfoo@latest eval

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: [prompt1.txt, prompt2.txt]
providers: [openai:gpt-3.5-turbo, ollama:llama2:70b]
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: [prompts.txt]
providers: [openai:gpt-3.5-turbo]
tests: 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.

After running an eval, you may optionally use the view command to open the web viewer:

npx promptfoo view

Examples

Prompt quality

In this example, we evaluate whether adding adjectives to the personality of an assistant bot affects the responses:

npx promptfoo eval -p prompts.txt -r openai:gpt-3.5-turbo -t tests.csv

This command will evaluate the prompts in prompts.txt, substituing the variable values from vars.csv, and output results in your terminal.

You can also output a nice spreadsheet, JSON, YAML, or an HTML file:

Model quality

In the next example, we evaluate the difference between GPT 3 and GPT 4 outputs for a given prompt:

npx promptfoo eval -p prompts.txt -r openai:gpt-3.5-turbo openai:gpt-4 -o output.html

Produces this HTML table:

Usage (node package)

You can also use promptfoo as a library in your project by importing the evaluate function. The function takes the following parameters:

• testSuite: the Javascript equivalent of the promptfooconfig.yaml

  interface EvaluateTestSuite {
    providers: string[]; // Valid provider name (e.g. openai:gpt-3.5-turbo)
    prompts: string[]; // List of prompts
    tests: string | TestCase[]; // Path to a CSV file, or list of test cases
  
    defaultTest?: Omit<TestCase, 'description'>; // Optional: add default vars and assertions on test case
    outputPath?: string | string[]; // Optional: write results to file
  }
  
  interface TestCase {
    // Optional description of what you're testing
    description?: string;
  
    // Key-value pairs to substitute in the prompt
    vars?: Record<string, string | string[] | object>;
  
    // Optional list of automatic checks to run on the LLM output
    assert?: Assertion[];
  
    // Additional configuration settings for the prompt
    options?: PromptConfig & OutputConfig & GradingConfig;
  
    // The required score for this test case.  If not provided, the test case is graded pass/fail.
    threshold?: number;
  }
  
  interface Assertion {
    type: string;
    value?: string;
    threshold?: number; // Required score for pass
    weight?: number; // The weight of this assertion compared to other assertions in the test case. Defaults to 1.
    provider?: ApiProvider; // For assertions that require an LLM provider
  }
  

• options: misc options related to how the tests are run

  interface EvaluateOptions {
    maxConcurrency?: number;
    showProgressBar?: boolean;
    generateSuggestions?: boolean;
  }
  

Example

promptfoo exports an evaluate function that you can use to run prompt evaluations.

import promptfoo from 'promptfoo';

const results = await promptfoo.evaluate({
  prompts: ['Rephrase this in French: {{body}}', 'Rephrase this like a pirate: {{body}}'],
  providers: ['openai:gpt-3.5-turbo'],
  tests: [
    {
      vars: {
        body: 'Hello world',
      },
    },
    {
      vars: {
        body: "I'm hungry",
      },
    },
  ],
});

This code imports the promptfoo library, defines the evaluation options, and then calls the evaluate function with these options.

See the full example here, which includes an example results object.

Configuration

• Main guide: Learn about how to configure your YAML file, setup prompt files, etc.
• Configuring test cases: Learn more about how to configure expected outputs and test assertions.

Installation

See installation docs

API Providers

We support OpenAI's API as well as a number of open-source models. It's also to set up your own custom API provider. See Provider documentation for more details.

Development

Here's how to build and run locally:

git clone https://github.com/promptfoo/promptfoo.git
cd promptfoo

npm i
cd path/to/experiment-with-promptfoo   # contains your promptfooconfig.yaml
npx path/to/promptfoo-source eval

The web UI is located in src/web/nextui. To run it in dev mode, run npm run local:web. This will host the web UI at http://localhost:3000. The web UI expects promptfoo view to be running separately.

You may also have to set some placeholder envars (it is not necessary to sign up for a supabase account):

NEXT_PUBLIC_SUPABASE_URL=http://
NEXT_PUBLIC_SUPABASE_ANON_KEY=abc

Contributions are welcome! Please feel free to submit a pull request or open an issue.

promptfoo includes several npm scripts to make development easier and more efficient. To use these scripts, run npm run <script_name> in the project directory.

Here are some of the available scripts:

• build: Transpile TypeScript files to JavaScript
• build:watch: Continuously watch and transpile TypeScript files on changes
• test: Run test suite
• test:watch: Continuously run test suite on changes
• db:generate: Generate new db migrations (and create the db if it doesn't already exist). Note that after generating a new migration, you'll have to npm i to copy the migrations into dist/.
• db:migrate: Run existing db migrations (and create the db if it doesn't already exist)

» View full documentation «