OpenAI

To use the OpenAI API, set the OPENAI_API_KEY environment variable, specify via apiKey field in the configuration file or pass the API key as an argument to the constructor.

Example:

export OPENAI_API_KEY=your_api_key_here

The OpenAI provider supports the following model formats:

openai:chat - defaults to gpt-4o-mini
openai:completion - defaults to text-davinci-003
openai:<model name> - uses a specific model name (mapped automatically to chat or completion endpoint)
openai:chat:<model name> - uses any model name against the /v1/chat/completions endpoint
openai:chat:ft:gpt-4o-mini:company-name:ID - example of a fine-tuned chat completion model
openai:completion:<model name> - uses any model name against the /v1/completions endpoint
openai:embeddings:<model name> - uses any model name against the /v1/embeddings endpoint
openai:assistant:<assistant id> - use an assistant

The openai:<endpoint>:<model name> construction is useful if OpenAI releases a new model, or if you have a custom model. For example, if OpenAI releases gpt-5 chat completion, you could begin using it immediately with openai:chat:gpt-5.

The OpenAI provider supports a handful of configuration options, such as temperature, functions, and tools, which can be used to customize the behavior of the model like so:

providers:
  - id: openai:gpt-4o-mini
    config:
      temperature: 0
      max_tokens: 1024

Formatting chat messages

For information on setting up chat conversation, see chat threads.

Configuring parameters

The providers list takes a config key that allows you to set parameters like temperature, max_tokens, and others. For example:

providers:
  - id: openai:gpt-4o-mini
    config:
      temperature: 0
      max_tokens: 128
      apiKey: sk-abc123

Supported parameters include:

Parameter	Description
`apiBaseUrl`	The base URL of the OpenAI API, please also read `OPENAI_BASE_URL` below.
`apiHost`	The hostname of the OpenAI API, please also read `OPENAI_API_HOST` below.
`apiKey`	Your OpenAI API key, equivalent to `OPENAI_API_KEY` environment variable
`apiKeyEnvar`	An environment variable that contains the API key
`best_of`	Controls the number of alternative outputs to generate and select from.
`frequency_penalty`	Applies a penalty to frequent tokens, making them less likely to appear in the output.
`function_call`	Controls whether the AI should call functions. Can be either 'none', 'auto', or an object with a `name` that specifies the function to call.
`functions`	Allows you to define custom functions. Each function should be an object with a `name`, optional `description`, and `parameters`.
`functionToolCallbacks`	A map of function tool names to function callbacks. Each callback should accept a string and return a string or a `Promise<string>`.
`headers`	Additional headers to include in the request.
`max_tokens`	Controls the maximum length of the output in tokens.
`organization`	Your OpenAI organization key.
`passthrough`	Additional parameters to pass through to the API.
`presence_penalty`	Applies a penalty to new tokens (tokens that haven't appeared in the input), making them less likely to appear in the output.
`response_format`	Specifies the desired output format, including `json_object` and `json_schema`. Can also be specified in the prompt config. If specified in both, the prompt config takes precedence.
`seed`	Seed used for deterministic output.
`stop`	Defines a list of tokens that signal the end of the output.
`temperature`	Controls the randomness of the AI's output. Higher values (close to 1) make the output more random, while lower values (close to 0) make it more deterministic.
`tool_choice`	Controls whether the AI should use a tool. See OpenAI Tools documentation
`tools`	Allows you to define custom tools. See OpenAI Tools documentation
`top_p`	Controls the nucleus sampling, a method that helps control the randomness of the AI's output.

Here are the type declarations of config parameters:

interface OpenAiConfig {
  // Completion parameters
  temperature?: number;
  max_tokens?: number;
  top_p?: number;
  frequency_penalty?: number;
  presence_penalty?: number;
  best_of?: number;
  functions?: OpenAiFunction[];
  function_call?: 'none' | 'auto' | { name: string };
  tools?: OpenAiTool[];
  tool_choice?: 'none' | 'auto' | 'required' | { type: 'function'; function?: { name: string } };
  response_format?: { type: 'json_object' | 'json_schema'; json_schema?: object };
  stop?: string[];
  seed?: number;
  passthrough?: object;

  // Function tool callbacks
  functionToolCallbacks?: Record<
    OpenAI.FunctionDefinition['name'],
    (arg: string) => Promise<string>
  >;

  // General OpenAI parameters
  apiKey?: string;
  apiKeyEnvar?: string;
  apiHost?: string;
  apiBaseUrl?: string;
  organization?: string;
  headers?: { [key: string]: string };
}

Images

Sending images in prompts

You can include images in the prompt by using content blocks. For example, here's an example config:

prompts:
  - prompt.json

providers:
  - openai:gpt-4o

tests:
  - vars:
      question: 'What do you see?'
      url: 'https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg'
  # ...

And an example prompt.json:

[
  {
    "role": "user",
    "content": [
      {
        "type": "text",
        "text": "{{question}}"
      },
      {
        "type": "image_url",
        "image_url": {
          "url": "{{url}}"
        }
      }
    ]
  }
]

See the OpenAI vision example.

Generating images

OpenAI supports Dall-E generations via openai:image:dall-e-3. See the OpenAI Dall-E example.

prompts:
  - 'In the style of Van Gogh: {{subject}}'
  - 'In the style of Dali: {{subject}}'

providers:
  - openai:image:dall-e-3

tests:
  - vars:
      subject: bananas
  - vars:
      subject: new york city

To display images in the web viewer, wrap vars or outputs in markdown image tags like so:

![](/path/to/myimage.png)

Then, enable 'Render markdown' under Table Settings.

Using tools and functions

OpenAI tools and functions are supported. See OpenAI tools example and OpenAI functions example.

Using tools

To set tools on an OpenAI provider, use the provider's config key. Add your function definitions under this key.

prompts:
  - file://prompt.txt
providers:
  - id: openai:chat:gpt-4o-mini
    config:
      tools: [
        {
        "type": "function",
          "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
              "type": "object",
                "properties": {
                  "location": {
                    "type": "string",
                      "description": "The city and state, e.g. San Francisco, CA"
                    },
                    "unit": {
                      "type": "string",
                      "enum": ["celsius", "fahrenheit"]
                    }
                  },
              "required": ["location"]
            }
          }
        }
      ]
      tool_choice: 'auto'

tests:
   - vars:
        city: Boston
     assert:
        - type: is-json
        - type: is-valid-openai-tools-call
        - type: javascript
          value: output[0].function.name === 'get_current_weather'
        - type: javascript
          value: JSON.parse(output[0].function.arguments).location === 'Boston, MA'

   - vars:
        city: New York
# ...

Sometimes OpenAI function calls don't match tools schemas. Use is-valid-openai-tools-call or is-valid-openai-tools-call assertions to enforce an exact schema match between tools and the function definition.

To further test tools definitions, you can use the javascript assertion and/or transform directives. For example:

tests:
  - vars:
      city: Boston
    assert:
      - type: is-json
      - type: is-valid-openai-tools-call
      - type: javascript
        value: output[0].function.name === 'get_current_weather'
      - type: javascript
        value: JSON.parse(output[0].function.arguments).location === 'Boston, MA'

  - vars:
      city: New York
      # transform returns only the 'name' property
    transform: output[0].function.name
    assert:
      - type: is-json
      - type: similar
        value: NYC

tip

Functions can use variables from test cases:

{
  type: "function",
  function: {
    description: "Get temperature in {{city}}"
    // ...
  }
}

They can also include functions that dynamically reference vars:

{
  type: "function",
  function: {
    name: "get_temperature",
    parameters: {
      type: "object",
        properties: {
          unit: {
            type: "string",
            enum: (vars) => vars.units,
          }
        },
    }
  }
}

Using functions

functions and function_call is deprecated in favor of tools and tool_choice, see detail in OpenAI API reference.

Use the functions config to define custom functions. Each function should be an object with a name, optional description, and parameters. For example:

prompts:
  - file://prompt.txt
providers:
  - id: openai:chat:gpt-4o-mini
    config:
      functions:
        [
          {
            'name': 'get_current_weather',
            'description': 'Get the current weather in a given location',
            'parameters':
              {
                'type': 'object',
                'properties':
                  {
                    'location':
                      {
                        'type': 'string',
                        'description': 'The city and state, e.g. San Francisco, CA',
                      },
                    'unit': { 'type': 'string', 'enum': ['celsius', 'fahrenheit'] },
                  },
                'required': ['location'],
              },
          },
        ]
tests:
  - vars:
      city: Boston
    assert:
      - type: is-valid-openai-function-call
  - vars:
      city: New York
  # ...

Sometimes OpenAI function calls don't match functions schemas. Use is-valid-openai-function-call assertions to enforce an exact schema match between function calls and the function definition.

To further test function call definitions, you can use the javascript assertion and/or transform directives. For example:

tests:
  - vars:
      city: Boston
    assert:
      - type: is-valid-openai-function-call
      - type: javascript
        value: output.name === 'get_current_weather'
      - type: javascript
        value: JSON.parse(output.arguments).location === 'Boston, MA'

  - vars:
      city: New York
    # transform returns only the 'name' property for this test case
    transform: output.name
    assert:
      - type: is-json
      - type: similar
        value: NYC

Loading tools/functions from a file

Instead of duplicating function definitions across multiple configurations, you can reference an external YAML (or JSON) file that contains your functions. This allows you to maintain a single source of truth for your functions, which is particularly useful if you have multiple versions or regular changes to definitions.

To load your functions from a file, specify the file path in your provider configuration like so:

providers:
  - file://./path/to/provider_with_function.yaml

You can also use a pattern to load multiple files:

providers:
  - file://./path/to/provider_*.yaml

Here's an example of how your provider_with_function.yaml might look:

id: openai:chat:gpt-4o-mini
config:
  functions:
    - name: get_current_weather
      description: Get the current weather in a given location
      parameters:
        type: object
        properties:
          location:
            type: string
            description: The city and state, e.g. San Francisco, CA
          unit:
            type: string
            enum:
              - celsius
              - fahrenheit
            description: The unit in which to return the temperature
        required:
          - location

Using `response_format`

Promptfoo supports the response_format parameter, which allows you to specify the expected output format.

response_format can be included in the provider config, or in the prompt config.

Prompt config example

prompts:
  - label: 'Prompt #1'
    raw: 'You are a helpful math tutor. Solve {{problem}}'
    config:
      response_format:
        type: json_schema
        json_schema: ...

Provider config example

providers:
  - id: openai:chat:gpt-4o-mini
    config:
      response_format:
        type: json_schema
        json_schema: ...

External file references

To make it easier to manage large JSON schemas, external file references are supported:

config:
  response_format: file://./path/to/response_format.json

Supported environment variables

These OpenAI-related environment variables are supported:

Variable	Description
`OPENAI_TEMPERATURE`	Temperature model parameter, defaults to 0. Not supported by o1-models.
`OPENAI_MAX_TOKENS`	Max_tokens model parameter, defaults to 1024. Not supported by o1-models.
`OPENAI_MAX_COMPLETION_TOKENS`	Max_completion_tokens model parameter, defaults to 1024. Used by o1-models.
`OPENAI_API_HOST`	The hostname to use (useful if you're using an API proxy). Takes priority over `OPENAI_BASE_URL`.
`OPENAI_BASE_URL`	The base URL (protocol + hostname + port) to use, this is a more general option than `OPENAI_API_HOST`.
`OPENAI_API_KEY`	OpenAI API key.
`OPENAI_ORGANIZATION`	The OpenAI organization key to use.
`PROMPTFOO_DELAY_MS`	Number of milliseconds to delay between API calls. Useful if you are hitting OpenAI rate limits (defaults to 0).
`PROMPTFOO_REQUEST_BACKOFF_MS`	Base number of milliseconds to backoff and retry if a request fails (defaults to 5000).

Evaluating assistants

To test out an Assistant via OpenAI's Assistants API, first create an Assistant in the API playground.

Set functions, code interpreter, and files for retrieval as necessary.

Then, include the assistant in your config:

prompts:
  - 'Write a tweet about {{topic}}'
providers:
  - openai:assistant:asst_fEhNN3MClMamLfKLkIaoIpgZ
tests:
  - vars:
      topic: bananas
  # ...

Code interpreter, function calls, and retrievals will be included in the output alongside chat messages. Note that the evaluator creates a new thread for each eval.

The following properties can be overwritten in provider config:

model - OpenAI model to use
instructions - System prompt
tools - Enabled tools
thread.messages - A list of message objects that the thread is created with.
temperature - Temperature for the model
toolChoice - Controls whether the AI should use a tool
attachments - File attachments to include in messages - see Assistant v2 attachments

Here's an example of a more detailed config:

prompts:
  - 'Write a tweet about {{topic}}'
providers:
  - id: openai:assistant:asst_fEhNN3MClMamLfKLkIaoIpgZ
    config:
      model: gpt-4o
      instructions: "You always speak like a pirate"
      temperature: 0.2
      toolChoice:
        type: file_search
      tools:
        - type: code_interpreter
        - type: file_search
      thread:
        messages:
          - role: user
            content: "Hello world"
          - role: assistant
            content: "Greetings from the high seas"
tests:
  - vars:
      topic: bananas
  # ...

Automatically handling function tool calls

You can specify JavaScript callbacks that are automatically called to create the output of a function tool call.

This requires defining your config in a JavaScript file instead of YAML.

module.exports = /** @type {import('promptfoo').TestSuiteConfig} */ ({
  prompts: 'Please add the following numbers together: {{a}} and {{b}}',
  providers: [
    {
      id: 'openai:assistant:asst_fEhNN3MClMamLfKLkIaoIpgZ',
      config:
        /** @type {InstanceType<import('promptfoo')["providers"]["OpenAiAssistantProvider"]>["config"]} */ ({
          model: 'gpt-4o',
          instructions: 'You can add two numbers together using the `addNumbers` tool',
          tools: [
            {
              type: 'function',
              function: {
                name: 'addNumbers',
                description: 'Add two numbers together',
                parameters: {
                  type: 'object',
                  properties: {
                    a: { type: 'number' },
                    b: { type: 'number' },
                  },
                  required: ['a', 'b'],
                },
              },
            },
          ],
          /**
           * Map of function tool names to function callback.
           */
          functionToolCallbacks: {
            // this function should accept a string, and return a string
            // or a `Promise<string>`.
            addNumbers: (parametersJsonString) => {
              const { a, b } = JSON.parse(parametersJsonString);
              return JSON.stringify(a + b);
            },
          },
        }),
    },
  ],
  tests: [
    {
      vars: { a: 5, b: 6 },
    },
  ],
});

Troubleshooting

OpenAI rate limits

There are a few things you can do if you encounter OpenAI rate limits (most commonly with GPT-4):

Reduce concurrency to 1 by setting --max-concurrency 1 in the CLI, or by setting evaluateOptions.maxConcurrency in the config.
Set a delay between requests by setting --delay 3000 (3000 ms) in the CLI, or by setting evaluateOptions.delay in the config, or with the environment variable PROMPTFOO_DELAY_MS (all values are in milliseconds).
Adjust the exponential backoff for failed requests by setting the environment variable PROMPTFOO_REQUEST_BACKOFF_MS. This defaults to 5000 milliseconds and retries exponential up to 4 times. You can increase this value if requests are still failing, but note that this can significantly increase end-to-end test time.

OpenAI flakiness

To retry HTTP requests that are Internal Server errors, set the PROMPTFOO_RETRY_5XX environment variable to 1.

Formatting chat messages​

Configuring parameters​

Images​

Sending images in prompts​

Generating images​

Using tools and functions​

Using tools​

Using functions​

Loading tools/functions from a file​

Using response_format​

Prompt config example​

Provider config example​

External file references​

Supported environment variables​

Evaluating assistants​

Automatically handling function tool calls​

Troubleshooting​

OpenAI rate limits​

OpenAI flakiness​