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

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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:

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

Assertion Type	Returns true if...
`equals`	output matches exactly
`contains`	output contains substring
`icontains`	output contains substring, case insensitive
`regex`	output matches regex
`starts-with`	output starts with string
`contains-any`	output contains any of the listed substrings
`contains-all`	output contains all list of substrings
`icontains-any`	output contains any of the listed substrings, case insensitive
`icontains-all`	output contains all list of substrings, case insensitive
`is-json`	output is valid json (optional json schema validation)
`contains-json`	output contains valid json (optional json schema validation)
`javascript`	provided Javascript function validates the output
`python`	provided Python function validates the output
`webhook`	provided webhook returns `{pass: true}`
`rouge-n`	Rouge-N score is above a given threshold
`levenshtein`	Levenshtein distance is below a threshold
`latency`	Latency is below a threshold (milliseconds)
`perplexity`	Perplexity is below a threshold
`cost`	Cost is below a threshold (for models with cost info such as GPT)
`is-valid-openai-function-call`	Ensure that the function call matches the function's JSON schema
`is-valid-openai-tools-call`	Ensure that all tool calls match the tools JSON schema

Model-assisted eval metrics

Assertion Type	Method
similar	Embeddings and cosine similarity are above a threshold
classifier	Run LLM output through a classifier
llm-rubric	LLM output matches a given rubric, using a Language Model to grade output
answer-relevance	Ensure that LLM output is related to original query
context-faithfulness	Ensure that LLM output uses the context
context-recall	Ensure that ground truth appears in context
context-relevance	Ensure that context is relevant to original query
factuality	LLM output adheres to the given facts, using Factuality method from OpenAI eval
model-graded-closedqa	LLM output adheres to given criteria, using Closed QA method from OpenAI eval
moderation	Make sure outputs are safe
select-best	Compare multiple outputs for a test case and pick the best one

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.

Option	Description
`-p, --prompts <paths...>`	Paths to prompt files, directory, or glob
`-r, --providers <name or path...>`	One of: openai:chat, openai:completion, openai:model-name, localai:chat:model-name, localai:completion:model-name. See API providers
`-o, --output <path>`	Path to output file (csv, json, yaml, html)
`--tests <path>`	Path to external test file
`-c, --config <paths>`	Path to one or more configuration files. `promptfooconfig.js/json/yaml` is automatically loaded if present
`-j, --max-concurrency <number>`	Maximum number of concurrent API calls
`--table-cell-max-length <number>`	Truncate console table cells to this length
`--prompt-prefix <path>`	This prefix is prepended to every prompt
`--prompt-suffix <path>`	This suffix is append to every prompt
`--grader`	Provider that will conduct the evaluation, if you are using LLM to grade your output

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, substituting 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;

  // Override the provider for this test
  provider?: string | ProviderOptions | ApiProvider;
}

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

# Optionally use the Node.js version specified in the .nvmrc file - make sure you are on node >= 18
nvm use

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.

In order to build the next.js app, you'll have to set some placeholder envars (it is not necessary to sign up for a supabase account). You can edit src/web/nextui/.env to include the following placeholders:

DATABASE_URL="postgresql://..."

NEXT_PUBLIC_PROMPTFOO_WITH_DATABASE=1
NEXT_PUBLIC_SUPABASE_URL=https://placeholder.promptfoo.dev
NEXT_PUBLIC_SUPABASE_ANON_KEY=abc123

Then run:

npm run build

The build has some side effects such as e.g. copying HTML templates, migrations, etc.

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)

To run the CLI during development you can run a command like: npm run local -- eval --config $(readlink -f ./examples/cloudflare-ai/chat_config.yaml), where any parts of the command after -- are passed through to our CLI entrypoint. Since the Next dev server isn't supported in this mode, see the instructions above for running the web server.

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Adding a New Provider

Create an implementation in src/providers/SOME_PROVIDER_FILE
Update loadApiProvider in src/providers.ts to load your provider via string
Add test cases in test/providers.test.ts
1. Test the actual provider implementation
2. Test loading the provider via a loadApiProvider test

vrushankportkey/promptfoo

promptfoo: test your LLM app locally

» View full documentation «

Why choose promptfoo?

Workflow

Usage

Configuration

Supported assertion types

Tests from spreadsheet

Command-line

Examples

Prompt quality

Model quality

Usage (node package)

Example

Configuration

Installation

API Providers

Development

» View full documentation «

Adding a New Provider