Evals

List evals

evals.list(**kwargs) -> CursorPage<EvalListResponse>

get /evals

List evaluations for a project.

Parameters

• after: String

  Identifier for the last eval from the previous pagination request.

• limit: Integer

  Number of evals to retrieve.

• order: :asc | :desc

  Sort order for evals by timestamp. Use asc for ascending order or desc for descending order.

  • :asc

  • :desc

• order_by: :created_at | :updated_at

  Evals can be ordered by creation time or last updated time. Use created_at for creation time or updated_at for last updated time.

  • :created_at

  • :updated_at

Returns

• class EvalListResponse

  An Eval object with a data source config and testing criteria. An Eval represents a task to be done for your LLM integration. Like:

  • Improve the quality of my chatbot

  • See how well my chatbot handles customer support

  • Check if o4-mini is better at my usecase than gpt-4o

  • id: String

    Unique identifier for the evaluation.

  • created_at: Integer

    The Unix timestamp (in seconds) for when the eval was created.

  • data_source_config: EvalCustomDataSourceConfig | Logs{ schema, type, metadata} | EvalStoredCompletionsDataSourceConfig

    Configuration of data sources used in runs of the evaluation.

    • class EvalCustomDataSourceConfig

      A CustomDataSourceConfig which specifies the schema of your item and optionally sample namespaces. The response schema defines the shape of the data that will be:

      • Used to define your testing criteria and

      • What data is required when creating a run

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :custom

        The type of data source. Always custom.

        • :custom

    • class Logs

      A LogsDataSourceConfig which specifies the metadata property of your logs query. This is usually metadata like usecase=chatbot or prompt-version=v2, etc. The schema returned by this data source config is used to defined what variables are available in your evals. item and sample are both defined when using this data source config.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :logs

        The type of data source. Always logs.

        • :logs

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

    • class EvalStoredCompletionsDataSourceConfig

      Deprecated in favor of LogsDataSourceConfig.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :stored_completions

        The type of data source. Always stored_completions.

        • :stored_completions

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • metadata: Metadata

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • name: String

    The name of the evaluation.

  • object: :eval

    The object type.

    • :eval

  • testing_criteria: Array[LabelModelGrader | StringCheckGrader | TextSimilarityGrader & { pass_threshold} | 2 more]

    A list of testing criteria.

    • class LabelModelGrader

      A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

      • input: Array[Input{ content, role, type}]

        • content: String | ResponseInputText | OutputText{ text, type} | 3 more

          Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

          • String = String

            A text input to the model.

          • class ResponseInputText

            A text input to the model.

            • text: String

              The text input to the model.

            • type: :input_text

              The type of the input item. Always input_text.

              • :input_text

          • class OutputText

            A text output from the model.

            • text: String

              The text output from the model.

            • type: :output_text

              The type of the output text. Always output_text.

              • :output_text

          • class InputImage

            An image input block used within EvalItem content arrays.

            • image_url: String

              The URL of the image input.

            • type: :input_image

              The type of the image input. Always input_image.

              • :input_image

            • detail: String

              The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

          • class ResponseInputAudio

            An audio input to the model.

            • input_audio: InputAudio{ data, format_}

              • data: String

                Base64-encoded audio data.

              • format_: :mp3 | :wav

                The format of the audio data. Currently supported formats are mp3 and wav.

                • :mp3

                • :wav

            • type: :input_audio

              The type of the input item. Always input_audio.

              • :input_audio

          • GraderInputs = Array[GraderInputItem]

            A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

            • String = String

              A text input to the model.

            • class ResponseInputText

              A text input to the model.

            • class OutputText

              A text output from the model.

              • text: String

                The text output from the model.

              • type: :output_text

                The type of the output text. Always output_text.

                • :output_text

            • class InputImage

              An image input block used within EvalItem content arrays.

              • image_url: String

                The URL of the image input.

              • type: :input_image

                The type of the image input. Always input_image.

                • :input_image

              • detail: String

                The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

            • class ResponseInputAudio

              An audio input to the model.

        • role: :user | :assistant | :system | :developer

          The role of the message input. One of user, assistant, system, or developer.

          • :user

          • :assistant

          • :system

          • :developer

        • type: :message

          The type of the message input. Always message.

          • :message

      • labels: Array[String]

        The labels to assign to each item in the evaluation.

      • model: String

        The model to use for the evaluation. Must support structured outputs.

      • name: String

        The name of the grader.

      • passing_labels: Array[String]

        The labels that indicate a passing result. Must be a subset of labels.

      • type: :label_model

        The object type, which is always label_model.

        • :label_model

    • class StringCheckGrader

      A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

      • input: String

        The input text. This may include template strings.

      • name: String

        The name of the grader.

      • operation: :eq | :ne | :like | :ilike

        The string check operation to perform. One of eq, ne, like, or ilike.

        • :eq

        • :ne

        • :like

        • :ilike

      • reference: String

        The reference text. This may include template strings.

      • type: :string_check

        The object type, which is always string_check.

        • :string_check

    • class EvalGraderTextSimilarity

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderPython

      A PythonGrader object that runs a python script on the input.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderScoreModel

      A ScoreModelGrader object that uses a model to assign a score to the input.

      • pass_threshold: Float

        The threshold for the score.

Example

require "openai"

openai = OpenAI::Client.new(api_key: "My API Key")

page = openai.evals.list

puts(page)

Response

{
  "data": [
    {
      "id": "id",
      "created_at": 0,
      "data_source_config": {
        "schema": {
          "foo": "bar"
        },
        "type": "custom"
      },
      "metadata": {
        "foo": "string"
      },
      "name": "Chatbot effectiveness Evaluation",
      "object": "eval",
      "testing_criteria": [
        {
          "input": [
            {
              "content": "string",
              "role": "user",
              "type": "message"
            }
          ],
          "labels": [
            "string"
          ],
          "model": "model",
          "name": "name",
          "passing_labels": [
            "string"
          ],
          "type": "label_model"
        }
      ]
    }
  ],
  "first_id": "first_id",
  "has_more": true,
  "last_id": "last_id",
  "object": "list"
}

Create eval

evals.create(**kwargs) -> EvalCreateResponse

post /evals

Create the structure of an evaluation that can be used to test a model's performance. An evaluation is a set of testing criteria and the config for a data source, which dictates the schema of the data used in the evaluation. After creating an evaluation, you can run it on different models and model parameters. We support several types of graders and datasources. For more information, see the Evals guide.

Parameters

• data_source_config: Custom{ item_schema, type, include_sample_schema} | Logs{ type, metadata} | StoredCompletions{ type, metadata}

  The configuration for the data source used for the evaluation runs. Dictates the schema of the data used in the evaluation.

  • class Custom

    A CustomDataSourceConfig object that defines the schema for the data source used for the evaluation runs. This schema is used to define the shape of the data that will be:

    • Used to define your testing criteria and

    • What data is required when creating a run

    • item_schema: Hash[Symbol, untyped]

      The json schema for each row in the data source.

    • type: :custom

      The type of data source. Always custom.

      • :custom

    • include_sample_schema: bool

      Whether the eval should expect you to populate the sample namespace (ie, by generating responses off of your data source)

  • class Logs

    A data source config which specifies the metadata property of your logs query. This is usually metadata like usecase=chatbot or prompt-version=v2, etc.

    • type: :logs

      The type of data source. Always logs.

      • :logs

    • metadata: Hash[Symbol, untyped]

      Metadata filters for the logs data source.

  • class StoredCompletions

    Deprecated in favor of LogsDataSourceConfig.

    • type: :stored_completions

      The type of data source. Always stored_completions.

      • :stored_completions

    • metadata: Hash[Symbol, untyped]

      Metadata filters for the stored completions data source.

• testing_criteria: Array[LabelModel{ input, labels, model, 3 more} | StringCheckGrader | TextSimilarityGrader & { pass_threshold} | 2 more]

  A list of graders for all eval runs in this group. Graders can reference variables in the data source using double curly braces notation, like {{item.variable_name}}. To reference the model's output, use the sample namespace (ie, {{sample.output_text}}).

  • class LabelModel

    A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

    • input: Array[SimpleInputMessage{ content, role} | EvalItem{ content, role, type}]

      A list of chat messages forming the prompt or context. May include variable references to the item namespace, ie {{item.name}}.

      • class SimpleInputMessage

        • content: String

          The content of the message.

        • role: String

          The role of the message (e.g. "system", "assistant", "user").

      • class EvalItem

        A message input to the model with a role indicating instruction following hierarchy. Instructions given with the developer or system role take precedence over instructions given with the user role. Messages with the assistant role are presumed to have been generated by the model in previous interactions.

        • content: String | ResponseInputText | OutputText{ text, type} | 3 more

          Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

          • String = String

            A text input to the model.

          • class ResponseInputText

            A text input to the model.

            • text: String

              The text input to the model.

            • type: :input_text

              The type of the input item. Always input_text.

              • :input_text

          • class OutputText

            A text output from the model.

            • text: String

              The text output from the model.

            • type: :output_text

              The type of the output text. Always output_text.

              • :output_text

          • class InputImage

            An image input block used within EvalItem content arrays.

            • image_url: String

              The URL of the image input.

            • type: :input_image

              The type of the image input. Always input_image.

              • :input_image

            • detail: String

              The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

          • class ResponseInputAudio

            An audio input to the model.

            • input_audio: InputAudio{ data, format_}

              • data: String

                Base64-encoded audio data.

              • format_: :mp3 | :wav

                The format of the audio data. Currently supported formats are mp3 and wav.

                • :mp3

                • :wav

            • type: :input_audio

              The type of the input item. Always input_audio.

              • :input_audio

          • GraderInputs = Array[GraderInputItem]

            A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

            • String = String

              A text input to the model.

            • class ResponseInputText

              A text input to the model.

            • class OutputText

              A text output from the model.

              • text: String

                The text output from the model.

              • type: :output_text

                The type of the output text. Always output_text.

                • :output_text

            • class InputImage

              An image input block used within EvalItem content arrays.

              • image_url: String

                The URL of the image input.

              • type: :input_image

                The type of the image input. Always input_image.

                • :input_image

              • detail: String

                The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

            • class ResponseInputAudio

              An audio input to the model.

        • role: :user | :assistant | :system | :developer

          The role of the message input. One of user, assistant, system, or developer.

          • :user

          • :assistant

          • :system

          • :developer

        • type: :message

          The type of the message input. Always message.

          • :message

    • labels: Array[String]

      The labels to classify to each item in the evaluation.

    • model: String

      The model to use for the evaluation. Must support structured outputs.

    • name: String

      The name of the grader.

    • passing_labels: Array[String]

      The labels that indicate a passing result. Must be a subset of labels.

    • type: :label_model

      The object type, which is always label_model.

      • :label_model

  • class StringCheckGrader

    A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

    • input: String

      The input text. This may include template strings.

    • name: String

      The name of the grader.

    • operation: :eq | :ne | :like | :ilike

      The string check operation to perform. One of eq, ne, like, or ilike.

      • :eq

      • :ne

      • :like

      • :ilike

    • reference: String

      The reference text. This may include template strings.

    • type: :string_check

      The object type, which is always string_check.

      • :string_check

  • class TextSimilarity

    A TextSimilarityGrader object which grades text based on similarity metrics.

    • pass_threshold: Float

      The threshold for the score.

  • class Python

    A PythonGrader object that runs a python script on the input.

    • pass_threshold: Float

      The threshold for the score.

  • class ScoreModel

    A ScoreModelGrader object that uses a model to assign a score to the input.

    • pass_threshold: Float

      The threshold for the score.

• metadata: Metadata

  Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

  Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

• name: String

  The name of the evaluation.

Returns

• class EvalCreateResponse

  An Eval object with a data source config and testing criteria. An Eval represents a task to be done for your LLM integration. Like:

  • Improve the quality of my chatbot

  • See how well my chatbot handles customer support

  • Check if o4-mini is better at my usecase than gpt-4o

  • id: String

    Unique identifier for the evaluation.

  • created_at: Integer

    The Unix timestamp (in seconds) for when the eval was created.

  • data_source_config: EvalCustomDataSourceConfig | Logs{ schema, type, metadata} | EvalStoredCompletionsDataSourceConfig

    Configuration of data sources used in runs of the evaluation.

    • class EvalCustomDataSourceConfig

      A CustomDataSourceConfig which specifies the schema of your item and optionally sample namespaces. The response schema defines the shape of the data that will be:

      • Used to define your testing criteria and

      • What data is required when creating a run

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :custom

        The type of data source. Always custom.

        • :custom

    • class Logs

      A LogsDataSourceConfig which specifies the metadata property of your logs query. This is usually metadata like usecase=chatbot or prompt-version=v2, etc. The schema returned by this data source config is used to defined what variables are available in your evals. item and sample are both defined when using this data source config.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :logs

        The type of data source. Always logs.

        • :logs

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

    • class EvalStoredCompletionsDataSourceConfig

      Deprecated in favor of LogsDataSourceConfig.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :stored_completions

        The type of data source. Always stored_completions.

        • :stored_completions

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • metadata: Metadata

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • name: String

    The name of the evaluation.

  • object: :eval

    The object type.

    • :eval

  • testing_criteria: Array[LabelModelGrader | StringCheckGrader | TextSimilarityGrader & { pass_threshold} | 2 more]

    A list of testing criteria.

    • class LabelModelGrader

      A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

      • input: Array[Input{ content, role, type}]

        • content: String | ResponseInputText | OutputText{ text, type} | 3 more

          Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

          • String = String

            A text input to the model.

          • class ResponseInputText

            A text input to the model.

            • text: String

              The text input to the model.

            • type: :input_text

              The type of the input item. Always input_text.

              • :input_text

          • class OutputText

            A text output from the model.

            • text: String

              The text output from the model.

            • type: :output_text

              The type of the output text. Always output_text.

              • :output_text

          • class InputImage

            An image input block used within EvalItem content arrays.

            • image_url: String

              The URL of the image input.

            • type: :input_image

              The type of the image input. Always input_image.

              • :input_image

            • detail: String

              The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

          • class ResponseInputAudio

            An audio input to the model.

            • input_audio: InputAudio{ data, format_}

              • data: String

                Base64-encoded audio data.

              • format_: :mp3 | :wav

                The format of the audio data. Currently supported formats are mp3 and wav.

                • :mp3

                • :wav

            • type: :input_audio

              The type of the input item. Always input_audio.

              • :input_audio

          • GraderInputs = Array[GraderInputItem]

            A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

            • String = String

              A text input to the model.

            • class ResponseInputText

              A text input to the model.

            • class OutputText

              A text output from the model.

              • text: String

                The text output from the model.

              • type: :output_text

                The type of the output text. Always output_text.

                • :output_text

            • class InputImage

              An image input block used within EvalItem content arrays.

              • image_url: String

                The URL of the image input.

              • type: :input_image

                The type of the image input. Always input_image.

                • :input_image

              • detail: String

                The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

            • class ResponseInputAudio

              An audio input to the model.

        • role: :user | :assistant | :system | :developer

          The role of the message input. One of user, assistant, system, or developer.

          • :user

          • :assistant

          • :system

          • :developer

        • type: :message

          The type of the message input. Always message.

          • :message

      • labels: Array[String]

        The labels to assign to each item in the evaluation.

      • model: String

        The model to use for the evaluation. Must support structured outputs.

      • name: String

        The name of the grader.

      • passing_labels: Array[String]

        The labels that indicate a passing result. Must be a subset of labels.

      • type: :label_model

        The object type, which is always label_model.

        • :label_model

    • class StringCheckGrader

      A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

      • input: String

        The input text. This may include template strings.

      • name: String

        The name of the grader.

      • operation: :eq | :ne | :like | :ilike

        The string check operation to perform. One of eq, ne, like, or ilike.

        • :eq

        • :ne

        • :like

        • :ilike

      • reference: String

        The reference text. This may include template strings.

      • type: :string_check

        The object type, which is always string_check.

        • :string_check

    • class EvalGraderTextSimilarity

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderPython

      A PythonGrader object that runs a python script on the input.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderScoreModel

      A ScoreModelGrader object that uses a model to assign a score to the input.

      • pass_threshold: Float

        The threshold for the score.

Example

require "openai"

openai = OpenAI::Client.new(api_key: "My API Key")

eval_ = openai.evals.create(
  data_source_config: {item_schema: {foo: "bar"}, type: :custom},
  testing_criteria: [
    {
      input: [{content: "content", role: "role"}],
      labels: ["string"],
      model: "model",
      name: "name",
      passing_labels: ["string"],
      type: :label_model
    }
  ]
)

puts(eval_)

Response

{
  "id": "id",
  "created_at": 0,
  "data_source_config": {
    "schema": {
      "foo": "bar"
    },
    "type": "custom"
  },
  "metadata": {
    "foo": "string"
  },
  "name": "Chatbot effectiveness Evaluation",
  "object": "eval",
  "testing_criteria": [
    {
      "input": [
        {
          "content": "string",
          "role": "user",
          "type": "message"
        }
      ],
      "labels": [
        "string"
      ],
      "model": "model",
      "name": "name",
      "passing_labels": [
        "string"
      ],
      "type": "label_model"
    }
  ]
}

Get an eval

evals.retrieve(eval_id) -> EvalRetrieveResponse

get /evals/{eval_id}

Get an evaluation by ID.

Parameters

• eval_id: String

Returns

• class EvalRetrieveResponse

  An Eval object with a data source config and testing criteria. An Eval represents a task to be done for your LLM integration. Like:

  • Improve the quality of my chatbot

  • See how well my chatbot handles customer support

  • Check if o4-mini is better at my usecase than gpt-4o

  • id: String

    Unique identifier for the evaluation.

  • created_at: Integer

    The Unix timestamp (in seconds) for when the eval was created.

  • data_source_config: EvalCustomDataSourceConfig | Logs{ schema, type, metadata} | EvalStoredCompletionsDataSourceConfig

    Configuration of data sources used in runs of the evaluation.

    • class EvalCustomDataSourceConfig

      A CustomDataSourceConfig which specifies the schema of your item and optionally sample namespaces. The response schema defines the shape of the data that will be:

      • Used to define your testing criteria and

      • What data is required when creating a run

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :custom

        The type of data source. Always custom.

        • :custom

    • class Logs

      A LogsDataSourceConfig which specifies the metadata property of your logs query. This is usually metadata like usecase=chatbot or prompt-version=v2, etc. The schema returned by this data source config is used to defined what variables are available in your evals. item and sample are both defined when using this data source config.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :logs

        The type of data source. Always logs.

        • :logs

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

    • class EvalStoredCompletionsDataSourceConfig

      Deprecated in favor of LogsDataSourceConfig.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :stored_completions

        The type of data source. Always stored_completions.

        • :stored_completions

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • metadata: Metadata

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • name: String

    The name of the evaluation.

  • object: :eval

    The object type.

    • :eval

  • testing_criteria: Array[LabelModelGrader | StringCheckGrader | TextSimilarityGrader & { pass_threshold} | 2 more]

    A list of testing criteria.

    • class LabelModelGrader

      A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

      • input: Array[Input{ content, role, type}]

        • content: String | ResponseInputText | OutputText{ text, type} | 3 more

          Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

          • String = String

            A text input to the model.

          • class ResponseInputText

            A text input to the model.

            • text: String

              The text input to the model.

            • type: :input_text

              The type of the input item. Always input_text.

              • :input_text

          • class OutputText

            A text output from the model.

            • text: String

              The text output from the model.

            • type: :output_text

              The type of the output text. Always output_text.

              • :output_text

          • class InputImage

            An image input block used within EvalItem content arrays.

            • image_url: String

              The URL of the image input.

            • type: :input_image

              The type of the image input. Always input_image.

              • :input_image

            • detail: String

              The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

          • class ResponseInputAudio

            An audio input to the model.

            • input_audio: InputAudio{ data, format_}

              • data: String

                Base64-encoded audio data.

              • format_: :mp3 | :wav

                The format of the audio data. Currently supported formats are mp3 and wav.

                • :mp3

                • :wav

            • type: :input_audio

              The type of the input item. Always input_audio.

              • :input_audio

          • GraderInputs = Array[GraderInputItem]

            A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

            • String = String

              A text input to the model.

            • class ResponseInputText

              A text input to the model.

            • class OutputText

              A text output from the model.

              • text: String

                The text output from the model.

              • type: :output_text

                The type of the output text. Always output_text.

                • :output_text

            • class InputImage

              An image input block used within EvalItem content arrays.

              • image_url: String

                The URL of the image input.

              • type: :input_image

                The type of the image input. Always input_image.

                • :input_image

              • detail: String

                The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

            • class ResponseInputAudio

              An audio input to the model.

        • role: :user | :assistant | :system | :developer

          The role of the message input. One of user, assistant, system, or developer.

          • :user

          • :assistant

          • :system

          • :developer

        • type: :message

          The type of the message input. Always message.

          • :message

      • labels: Array[String]

        The labels to assign to each item in the evaluation.

      • model: String

        The model to use for the evaluation. Must support structured outputs.

      • name: String

        The name of the grader.

      • passing_labels: Array[String]

        The labels that indicate a passing result. Must be a subset of labels.

      • type: :label_model

        The object type, which is always label_model.

        • :label_model

    • class StringCheckGrader

      A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

      • input: String

        The input text. This may include template strings.

      • name: String

        The name of the grader.

      • operation: :eq | :ne | :like | :ilike

        The string check operation to perform. One of eq, ne, like, or ilike.

        • :eq

        • :ne

        • :like

        • :ilike

      • reference: String

        The reference text. This may include template strings.

      • type: :string_check

        The object type, which is always string_check.

        • :string_check

    • class EvalGraderTextSimilarity

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderPython

      A PythonGrader object that runs a python script on the input.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderScoreModel

      A ScoreModelGrader object that uses a model to assign a score to the input.

      • pass_threshold: Float

        The threshold for the score.

Example

require "openai"

openai = OpenAI::Client.new(api_key: "My API Key")

eval_ = openai.evals.retrieve("eval_id")

puts(eval_)

Response

{
  "id": "id",
  "created_at": 0,
  "data_source_config": {
    "schema": {
      "foo": "bar"
    },
    "type": "custom"
  },
  "metadata": {
    "foo": "string"
  },
  "name": "Chatbot effectiveness Evaluation",
  "object": "eval",
  "testing_criteria": [
    {
      "input": [
        {
          "content": "string",
          "role": "user",
          "type": "message"
        }
      ],
      "labels": [
        "string"
      ],
      "model": "model",
      "name": "name",
      "passing_labels": [
        "string"
      ],
      "type": "label_model"
    }
  ]
}

Update an eval

evals.update(eval_id, **kwargs) -> EvalUpdateResponse

post /evals/{eval_id}

Update certain properties of an evaluation.

Parameters

• eval_id: String

• metadata: Metadata

  Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

  Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

• name: String

  Rename the evaluation.

Returns

• class EvalUpdateResponse

  An Eval object with a data source config and testing criteria. An Eval represents a task to be done for your LLM integration. Like:

  • Improve the quality of my chatbot

  • See how well my chatbot handles customer support

  • Check if o4-mini is better at my usecase than gpt-4o

  • id: String

    Unique identifier for the evaluation.

  • created_at: Integer

    The Unix timestamp (in seconds) for when the eval was created.

  • data_source_config: EvalCustomDataSourceConfig | Logs{ schema, type, metadata} | EvalStoredCompletionsDataSourceConfig

    Configuration of data sources used in runs of the evaluation.

    • class EvalCustomDataSourceConfig

      A CustomDataSourceConfig which specifies the schema of your item and optionally sample namespaces. The response schema defines the shape of the data that will be:

      • Used to define your testing criteria and

      • What data is required when creating a run

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :custom

        The type of data source. Always custom.

        • :custom

    • class Logs

      A LogsDataSourceConfig which specifies the metadata property of your logs query. This is usually metadata like usecase=chatbot or prompt-version=v2, etc. The schema returned by this data source config is used to defined what variables are available in your evals. item and sample are both defined when using this data source config.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :logs

        The type of data source. Always logs.

        • :logs

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

    • class EvalStoredCompletionsDataSourceConfig

      Deprecated in favor of LogsDataSourceConfig.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :stored_completions

        The type of data source. Always stored_completions.

        • :stored_completions

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • metadata: Metadata

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • name: String

    The name of the evaluation.

  • object: :eval

    The object type.

    • :eval

  • testing_criteria: Array[LabelModelGrader | StringCheckGrader | TextSimilarityGrader & { pass_threshold} | 2 more]

    A list of testing criteria.

    • class LabelModelGrader

      A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

      • input: Array[Input{ content, role, type}]

        • content: String | ResponseInputText | OutputText{ text, type} | 3 more

          Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

          • String = String

            A text input to the model.

          • class ResponseInputText

            A text input to the model.

            • text: String

              The text input to the model.

            • type: :input_text

              The type of the input item. Always input_text.

              • :input_text

          • class OutputText

            A text output from the model.

            • text: String

              The text output from the model.

            • type: :output_text

              The type of the output text. Always output_text.

              • :output_text

          • class InputImage

            An image input block used within EvalItem content arrays.

            • image_url: String

              The URL of the image input.

            • type: :input_image

              The type of the image input. Always input_image.

              • :input_image

            • detail: String

              The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

          • class ResponseInputAudio

            An audio input to the model.

            • input_audio: InputAudio{ data, format_}

              • data: String

                Base64-encoded audio data.

              • format_: :mp3 | :wav

                The format of the audio data. Currently supported formats are mp3 and wav.

                • :mp3

                • :wav

            • type: :input_audio

              The type of the input item. Always input_audio.

              • :input_audio

          • GraderInputs = Array[GraderInputItem]

            A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

            • String = String

              A text input to the model.

            • class ResponseInputText

              A text input to the model.

            • class OutputText

              A text output from the model.

              • text: String

                The text output from the model.

              • type: :output_text

                The type of the output text. Always output_text.

                • :output_text

            • class InputImage

              An image input block used within EvalItem content arrays.

              • image_url: String

                The URL of the image input.

              • type: :input_image

                The type of the image input. Always input_image.

                • :input_image

              • detail: String

                The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

            • class ResponseInputAudio

              An audio input to the model.

        • role: :user | :assistant | :system | :developer

          The role of the message input. One of user, assistant, system, or developer.

          • :user

          • :assistant

          • :system

          • :developer

        • type: :message

          The type of the message input. Always message.

          • :message

      • labels: Array[String]

        The labels to assign to each item in the evaluation.

      • model: String

        The model to use for the evaluation. Must support structured outputs.

      • name: String

        The name of the grader.

      • passing_labels: Array[String]

        The labels that indicate a passing result. Must be a subset of labels.

      • type: :label_model

        The object type, which is always label_model.

        • :label_model

    • class StringCheckGrader

      A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

      • input: String

        The input text. This may include template strings.

      • name: String

        The name of the grader.

      • operation: :eq | :ne | :like | :ilike

        The string check operation to perform. One of eq, ne, like, or ilike.

        • :eq

        • :ne

        • :like

        • :ilike

      • reference: String

        The reference text. This may include template strings.

      • type: :string_check

        The object type, which is always string_check.

        • :string_check

    • class EvalGraderTextSimilarity

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderPython

      A PythonGrader object that runs a python script on the input.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderScoreModel

      A ScoreModelGrader object that uses a model to assign a score to the input.

      • pass_threshold: Float

        The threshold for the score.

Example

require "openai"

openai = OpenAI::Client.new(api_key: "My API Key")

eval_ = openai.evals.update("eval_id")

puts(eval_)

Response

{
  "id": "id",
  "created_at": 0,
  "data_source_config": {
    "schema": {
      "foo": "bar"
    },
    "type": "custom"
  },
  "metadata": {
    "foo": "string"
  },
  "name": "Chatbot effectiveness Evaluation",
  "object": "eval",
  "testing_criteria": [
    {
      "input": [
        {
          "content": "string",
          "role": "user",
          "type": "message"
        }
      ],
      "labels": [
        "string"
      ],
      "model": "model",
      "name": "name",
      "passing_labels": [
        "string"
      ],
      "type": "label_model"
    }
  ]
}

Delete an eval

evals.delete(eval_id) -> EvalDeleteResponse

delete /evals/{eval_id}

Delete an evaluation.

Parameters

• eval_id: String

Returns

• class EvalDeleteResponse

  • deleted: bool

  • eval_id: String

  • object: String

Example

require "openai"

openai = OpenAI::Client.new(api_key: "My API Key")

eval_ = openai.evals.delete("eval_id")

puts(eval_)

Response

{
  "deleted": true,
  "eval_id": "eval_abc123",
  "object": "eval.deleted"
}

Domain Types

Eval Custom Data Source Config

• class EvalCustomDataSourceConfig

  A CustomDataSourceConfig which specifies the schema of your item and optionally sample namespaces. The response schema defines the shape of the data that will be:

  • Used to define your testing criteria and

  • What data is required when creating a run

  • schema: Hash[Symbol, untyped]

    The json schema for the run data source items. Learn how to build JSON schemas here.

  • type: :custom

    The type of data source. Always custom.

    • :custom

Eval Stored Completions Data Source Config

• class EvalStoredCompletionsDataSourceConfig

  Deprecated in favor of LogsDataSourceConfig.

  • schema: Hash[Symbol, untyped]

    The json schema for the run data source items. Learn how to build JSON schemas here.

  • type: :stored_completions

    The type of data source. Always stored_completions.

    • :stored_completions

  • metadata: Metadata

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

Eval List Response

• class EvalListResponse

  An Eval object with a data source config and testing criteria. An Eval represents a task to be done for your LLM integration. Like:

  • Improve the quality of my chatbot

  • See how well my chatbot handles customer support

  • Check if o4-mini is better at my usecase than gpt-4o

  • id: String

    Unique identifier for the evaluation.

  • created_at: Integer

    The Unix timestamp (in seconds) for when the eval was created.

  • data_source_config: EvalCustomDataSourceConfig | Logs{ schema, type, metadata} | EvalStoredCompletionsDataSourceConfig

    Configuration of data sources used in runs of the evaluation.

    • class EvalCustomDataSourceConfig

      A CustomDataSourceConfig which specifies the schema of your item and optionally sample namespaces. The response schema defines the shape of the data that will be:

      • Used to define your testing criteria and

      • What data is required when creating a run

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :custom

        The type of data source. Always custom.

        • :custom

    • class Logs

      A LogsDataSourceConfig which specifies the metadata property of your logs query. This is usually metadata like usecase=chatbot or prompt-version=v2, etc. The schema returned by this data source config is used to defined what variables are available in your evals. item and sample are both defined when using this data source config.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :logs

        The type of data source. Always logs.

        • :logs

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

    • class EvalStoredCompletionsDataSourceConfig

      Deprecated in favor of LogsDataSourceConfig.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :stored_completions

        The type of data source. Always stored_completions.

        • :stored_completions

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • metadata: Metadata

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • name: String

    The name of the evaluation.

  • object: :eval

    The object type.

    • :eval

  • testing_criteria: Array[LabelModelGrader | StringCheckGrader | TextSimilarityGrader & { pass_threshold} | 2 more]

    A list of testing criteria.

    • class LabelModelGrader

      A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

      • input: Array[Input{ content, role, type}]

        • content: String | ResponseInputText | OutputText{ text, type} | 3 more

          Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

          • String = String

            A text input to the model.

          • class ResponseInputText

            A text input to the model.

            • text: String

              The text input to the model.

            • type: :input_text

              The type of the input item. Always input_text.

              • :input_text

          • class OutputText

            A text output from the model.

            • text: String

              The text output from the model.

            • type: :output_text

              The type of the output text. Always output_text.

              • :output_text

          • class InputImage

            An image input block used within EvalItem content arrays.

            • image_url: String

              The URL of the image input.

            • type: :input_image

              The type of the image input. Always input_image.

              • :input_image

            • detail: String

              The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

          • class ResponseInputAudio

            An audio input to the model.

            • input_audio: InputAudio{ data, format_}

              • data: String

                Base64-encoded audio data.

              • format_: :mp3 | :wav

                The format of the audio data. Currently supported formats are mp3 and wav.

                • :mp3

                • :wav

            • type: :input_audio

              The type of the input item. Always input_audio.

              • :input_audio

          • GraderInputs = Array[GraderInputItem]

            A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

            • String = String

              A text input to the model.

            • class ResponseInputText

              A text input to the model.

            • class OutputText

              A text output from the model.

              • text: String

                The text output from the model.

              • type: :output_text

                The type of the output text. Always output_text.

                • :output_text

            • class InputImage

              An image input block used within EvalItem content arrays.

              • image_url: String

                The URL of the image input.

              • type: :input_image

                The type of the image input. Always input_image.

                • :input_image

              • detail: String

                The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

            • class ResponseInputAudio

              An audio input to the model.

        • role: :user | :assistant | :system | :developer

          The role of the message input. One of user, assistant, system, or developer.

          • :user

          • :assistant

          • :system

          • :developer

        • type: :message

          The type of the message input. Always message.

          • :message

      • labels: Array[String]

        The labels to assign to each item in the evaluation.

      • model: String

        The model to use for the evaluation. Must support structured outputs.

      • name: String

        The name of the grader.

      • passing_labels: Array[String]

        The labels that indicate a passing result. Must be a subset of labels.

      • type: :label_model

        The object type, which is always label_model.

        • :label_model

    • class StringCheckGrader

      A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

      • input: String

        The input text. This may include template strings.

      • name: String

        The name of the grader.

      • operation: :eq | :ne | :like | :ilike

        The string check operation to perform. One of eq, ne, like, or ilike.

        • :eq

        • :ne

        • :like

        • :ilike

      • reference: String

        The reference text. This may include template strings.

      • type: :string_check

        The object type, which is always string_check.

        • :string_check

    • class EvalGraderTextSimilarity

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderPython

      A PythonGrader object that runs a python script on the input.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderScoreModel

      A ScoreModelGrader object that uses a model to assign a score to the input.

      • pass_threshold: Float

        The threshold for the score.

Eval Create Response

• class EvalCreateResponse

  An Eval object with a data source config and testing criteria. An Eval represents a task to be done for your LLM integration. Like:

  • Improve the quality of my chatbot

  • See how well my chatbot handles customer support

  • Check if o4-mini is better at my usecase than gpt-4o

  • id: String

    Unique identifier for the evaluation.

  • created_at: Integer

    The Unix timestamp (in seconds) for when the eval was created.

  • data_source_config: EvalCustomDataSourceConfig | Logs{ schema, type, metadata} | EvalStoredCompletionsDataSourceConfig

    Configuration of data sources used in runs of the evaluation.

    • class EvalCustomDataSourceConfig

      A CustomDataSourceConfig which specifies the schema of your item and optionally sample namespaces. The response schema defines the shape of the data that will be:

      • Used to define your testing criteria and

      • What data is required when creating a run

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :custom

        The type of data source. Always custom.

        • :custom

    • class Logs

      A LogsDataSourceConfig which specifies the metadata property of your logs query. This is usually metadata like usecase=chatbot or prompt-version=v2, etc. The schema returned by this data source config is used to defined what variables are available in your evals. item and sample are both defined when using this data source config.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :logs

        The type of data source. Always logs.

        • :logs

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

    • class EvalStoredCompletionsDataSourceConfig

      Deprecated in favor of LogsDataSourceConfig.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :stored_completions

        The type of data source. Always stored_completions.

        • :stored_completions

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • metadata: Metadata

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • name: String

    The name of the evaluation.

  • object: :eval

    The object type.

    • :eval

  • testing_criteria: Array[LabelModelGrader | StringCheckGrader | TextSimilarityGrader & { pass_threshold} | 2 more]

    A list of testing criteria.

    • class LabelModelGrader

      A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

      • input: Array[Input{ content, role, type}]

        • content: String | ResponseInputText | OutputText{ text, type} | 3 more

          Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

          • String = String

            A text input to the model.

          • class ResponseInputText

            A text input to the model.

            • text: String

              The text input to the model.

            • type: :input_text

              The type of the input item. Always input_text.

              • :input_text

          • class OutputText

            A text output from the model.

            • text: String

              The text output from the model.

            • type: :output_text

              The type of the output text. Always output_text.

              • :output_text

          • class InputImage

            An image input block used within EvalItem content arrays.

            • image_url: String

              The URL of the image input.

            • type: :input_image

              The type of the image input. Always input_image.

              • :input_image

            • detail: String

              The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

          • class ResponseInputAudio

            An audio input to the model.

            • input_audio: InputAudio{ data, format_}

              • data: String

                Base64-encoded audio data.

              • format_: :mp3 | :wav

                The format of the audio data. Currently supported formats are mp3 and wav.

                • :mp3

                • :wav

            • type: :input_audio

              The type of the input item. Always input_audio.

              • :input_audio

          • GraderInputs = Array[GraderInputItem]

            A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

            • String = String

              A text input to the model.

            • class ResponseInputText

              A text input to the model.

            • class OutputText

              A text output from the model.

              • text: String

                The text output from the model.

              • type: :output_text

                The type of the output text. Always output_text.

                • :output_text

            • class InputImage

              An image input block used within EvalItem content arrays.

              • image_url: String

                The URL of the image input.

              • type: :input_image

                The type of the image input. Always input_image.

                • :input_image

              • detail: String

                The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

            • class ResponseInputAudio

              An audio input to the model.

        • role: :user | :assistant | :system | :developer

          The role of the message input. One of user, assistant, system, or developer.

          • :user

          • :assistant

          • :system

          • :developer

        • type: :message

          The type of the message input. Always message.

          • :message

      • labels: Array[String]

        The labels to assign to each item in the evaluation.

      • model: String

        The model to use for the evaluation. Must support structured outputs.

      • name: String

        The name of the grader.

      • passing_labels: Array[String]

        The labels that indicate a passing result. Must be a subset of labels.

      • type: :label_model

        The object type, which is always label_model.

        • :label_model

    • class StringCheckGrader

      A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

      • input: String

        The input text. This may include template strings.

      • name: String

        The name of the grader.

      • operation: :eq | :ne | :like | :ilike

        The string check operation to perform. One of eq, ne, like, or ilike.

        • :eq

        • :ne

        • :like

        • :ilike

      • reference: String

        The reference text. This may include template strings.

      • type: :string_check

        The object type, which is always string_check.

        • :string_check

    • class EvalGraderTextSimilarity

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderPython

      A PythonGrader object that runs a python script on the input.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderScoreModel

      A ScoreModelGrader object that uses a model to assign a score to the input.

      • pass_threshold: Float

        The threshold for the score.

Eval Retrieve Response

• class EvalRetrieveResponse

  An Eval object with a data source config and testing criteria. An Eval represents a task to be done for your LLM integration. Like:

  • Improve the quality of my chatbot

  • See how well my chatbot handles customer support

  • Check if o4-mini is better at my usecase than gpt-4o

  • id: String

    Unique identifier for the evaluation.

  • created_at: Integer

    The Unix timestamp (in seconds) for when the eval was created.

  • data_source_config: EvalCustomDataSourceConfig | Logs{ schema, type, metadata} | EvalStoredCompletionsDataSourceConfig

    Configuration of data sources used in runs of the evaluation.

    • class EvalCustomDataSourceConfig

      A CustomDataSourceConfig which specifies the schema of your item and optionally sample namespaces. The response schema defines the shape of the data that will be:

      • Used to define your testing criteria and

      • What data is required when creating a run

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :custom

        The type of data source. Always custom.

        • :custom

    • class Logs

      A LogsDataSourceConfig which specifies the metadata property of your logs query. This is usually metadata like usecase=chatbot or prompt-version=v2, etc. The schema returned by this data source config is used to defined what variables are available in your evals. item and sample are both defined when using this data source config.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :logs

        The type of data source. Always logs.

        • :logs

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

    • class EvalStoredCompletionsDataSourceConfig

      Deprecated in favor of LogsDataSourceConfig.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :stored_completions

        The type of data source. Always stored_completions.

        • :stored_completions

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • metadata: Metadata

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • name: String

    The name of the evaluation.

  • object: :eval

    The object type.

    • :eval

  • testing_criteria: Array[LabelModelGrader | StringCheckGrader | TextSimilarityGrader & { pass_threshold} | 2 more]

    A list of testing criteria.

    • class LabelModelGrader

      A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

      • input: Array[Input{ content, role, type}]

        • content: String | ResponseInputText | OutputText{ text, type} | 3 more

          Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

          • String = String

            A text input to the model.

          • class ResponseInputText

            A text input to the model.

            • text: String

              The text input to the model.

            • type: :input_text

              The type of the input item. Always input_text.

              • :input_text

          • class OutputText

            A text output from the model.

            • text: String

              The text output from the model.

            • type: :output_text

              The type of the output text. Always output_text.

              • :output_text

          • class InputImage

            An image input block used within EvalItem content arrays.

            • image_url: String

              The URL of the image input.

            • type: :input_image

              The type of the image input. Always input_image.

              • :input_image

            • detail: String

              The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

          • class ResponseInputAudio

            An audio input to the model.

            • input_audio: InputAudio{ data, format_}

              • data: String

                Base64-encoded audio data.

              • format_: :mp3 | :wav

                The format of the audio data. Currently supported formats are mp3 and wav.

                • :mp3

                • :wav

            • type: :input_audio

              The type of the input item. Always input_audio.

              • :input_audio

          • GraderInputs = Array[GraderInputItem]

            A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

            • String = String

              A text input to the model.

            • class ResponseInputText

              A text input to the model.

            • class OutputText

              A text output from the model.

              • text: String

                The text output from the model.

              • type: :output_text

                The type of the output text. Always output_text.

                • :output_text

            • class InputImage

              An image input block used within EvalItem content arrays.

              • image_url: String

                The URL of the image input.

              • type: :input_image

                The type of the image input. Always input_image.

                • :input_image

              • detail: String

                The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

            • class ResponseInputAudio

              An audio input to the model.

        • role: :user | :assistant | :system | :developer

          The role of the message input. One of user, assistant, system, or developer.

          • :user

          • :assistant

          • :system

          • :developer

        • type: :message

          The type of the message input. Always message.

          • :message

      • labels: Array[String]

        The labels to assign to each item in the evaluation.

      • model: String

        The model to use for the evaluation. Must support structured outputs.

      • name: String

        The name of the grader.

      • passing_labels: Array[String]

        The labels that indicate a passing result. Must be a subset of labels.

      • type: :label_model

        The object type, which is always label_model.

        • :label_model

    • class StringCheckGrader

      A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

      • input: String

        The input text. This may include template strings.

      • name: String

        The name of the grader.

      • operation: :eq | :ne | :like | :ilike

        The string check operation to perform. One of eq, ne, like, or ilike.

        • :eq

        • :ne

        • :like

        • :ilike

      • reference: String

        The reference text. This may include template strings.

      • type: :string_check

        The object type, which is always string_check.

        • :string_check

    • class EvalGraderTextSimilarity

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderPython

      A PythonGrader object that runs a python script on the input.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderScoreModel

      A ScoreModelGrader object that uses a model to assign a score to the input.

      • pass_threshold: Float

        The threshold for the score.

Eval Update Response

• class EvalUpdateResponse

  An Eval object with a data source config and testing criteria. An Eval represents a task to be done for your LLM integration. Like:

  • Improve the quality of my chatbot

  • See how well my chatbot handles customer support

  • Check if o4-mini is better at my usecase than gpt-4o

  • id: String

    Unique identifier for the evaluation.

  • created_at: Integer

    The Unix timestamp (in seconds) for when the eval was created.

  • data_source_config: EvalCustomDataSourceConfig | Logs{ schema, type, metadata} | EvalStoredCompletionsDataSourceConfig

    Configuration of data sources used in runs of the evaluation.

    • class EvalCustomDataSourceConfig

      A CustomDataSourceConfig which specifies the schema of your item and optionally sample namespaces. The response schema defines the shape of the data that will be:

      • Used to define your testing criteria and

      • What data is required when creating a run

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :custom

        The type of data source. Always custom.

        • :custom

    • class Logs

      A LogsDataSourceConfig which specifies the metadata property of your logs query. This is usually metadata like usecase=chatbot or prompt-version=v2, etc. The schema returned by this data source config is used to defined what variables are available in your evals. item and sample are both defined when using this data source config.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :logs

        The type of data source. Always logs.

        • :logs

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

    • class EvalStoredCompletionsDataSourceConfig

      Deprecated in favor of LogsDataSourceConfig.

      • schema: Hash[Symbol, untyped]

        The json schema for the run data source items. Learn how to build JSON schemas here.

      • type: :stored_completions

        The type of data source. Always stored_completions.

        • :stored_completions

      • metadata: Metadata

        Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

        Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • metadata: Metadata

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • name: String

    The name of the evaluation.

  • object: :eval

    The object type.

    • :eval

  • testing_criteria: Array[LabelModelGrader | StringCheckGrader | TextSimilarityGrader & { pass_threshold} | 2 more]

    A list of testing criteria.

    • class LabelModelGrader

      A LabelModelGrader object which uses a model to assign labels to each item in the evaluation.

      • input: Array[Input{ content, role, type}]

        • content: String | ResponseInputText | OutputText{ text, type} | 3 more

          Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

          • String = String

            A text input to the model.

          • class ResponseInputText

            A text input to the model.

            • text: String

              The text input to the model.

            • type: :input_text

              The type of the input item. Always input_text.

              • :input_text

          • class OutputText

            A text output from the model.

            • text: String

              The text output from the model.

            • type: :output_text

              The type of the output text. Always output_text.

              • :output_text

          • class InputImage

            An image input block used within EvalItem content arrays.

            • image_url: String

              The URL of the image input.

            • type: :input_image

              The type of the image input. Always input_image.

              • :input_image

            • detail: String

              The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

          • class ResponseInputAudio

            An audio input to the model.

            • input_audio: InputAudio{ data, format_}

              • data: String

                Base64-encoded audio data.

              • format_: :mp3 | :wav

                The format of the audio data. Currently supported formats are mp3 and wav.

                • :mp3

                • :wav

            • type: :input_audio

              The type of the input item. Always input_audio.

              • :input_audio

          • GraderInputs = Array[GraderInputItem]

            A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

            • String = String

              A text input to the model.

            • class ResponseInputText

              A text input to the model.

            • class OutputText

              A text output from the model.

              • text: String

                The text output from the model.

              • type: :output_text

                The type of the output text. Always output_text.

                • :output_text

            • class InputImage

              An image input block used within EvalItem content arrays.

              • image_url: String

                The URL of the image input.

              • type: :input_image

                The type of the image input. Always input_image.

                • :input_image

              • detail: String

                The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

            • class ResponseInputAudio

              An audio input to the model.

        • role: :user | :assistant | :system | :developer

          The role of the message input. One of user, assistant, system, or developer.

          • :user

          • :assistant

          • :system

          • :developer

        • type: :message

          The type of the message input. Always message.

          • :message

      • labels: Array[String]

        The labels to assign to each item in the evaluation.

      • model: String

        The model to use for the evaluation. Must support structured outputs.

      • name: String

        The name of the grader.

      • passing_labels: Array[String]

        The labels that indicate a passing result. Must be a subset of labels.

      • type: :label_model

        The object type, which is always label_model.

        • :label_model

    • class StringCheckGrader

      A StringCheckGrader object that performs a string comparison between input and reference using a specified operation.

      • input: String

        The input text. This may include template strings.

      • name: String

        The name of the grader.

      • operation: :eq | :ne | :like | :ilike

        The string check operation to perform. One of eq, ne, like, or ilike.

        • :eq

        • :ne

        • :like

        • :ilike

      • reference: String

        The reference text. This may include template strings.

      • type: :string_check

        The object type, which is always string_check.

        • :string_check

    • class EvalGraderTextSimilarity

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderPython

      A PythonGrader object that runs a python script on the input.

      • pass_threshold: Float

        The threshold for the score.

    • class EvalGraderScoreModel

      A ScoreModelGrader object that uses a model to assign a score to the input.

      • pass_threshold: Float

        The threshold for the score.

Eval Delete Response

• class EvalDeleteResponse

  • deleted: bool

  • eval_id: String

  • object: String

Runs

Get eval runs

evals.runs.list(eval_id, **kwargs) -> CursorPage<RunListResponse>

get /evals/{eval_id}/runs

Get a list of runs for an evaluation.

Parameters

• eval_id: String

• after: String

  Identifier for the last run from the previous pagination request.

• limit: Integer

  Number of runs to retrieve.

• order: :asc | :desc

  Sort order for runs by timestamp. Use asc for ascending order or desc for descending order. Defaults to asc.

  • :asc

  • :desc

• status: :queued | :in_progress | :completed | 2 more

  Filter runs by status. One of queued | in_progress | failed | completed | canceled.

  • :queued

  • :in_progress

  • :completed

  • :canceled

  • :failed

Returns

• class RunListResponse

  A schema representing an evaluation run.

  • id: String

    Unique identifier for the evaluation run.

  • created_at: Integer

    Unix timestamp (in seconds) when the evaluation run was created.

  • data_source: CreateEvalJSONLRunDataSource | CreateEvalCompletionsRunDataSource | Responses{ source, type, input_messages, 2 more}

    Information about the run's data source.

    • class CreateEvalJSONLRunDataSource

      A JsonlRunDataSource object with that specifies a JSONL file that matches the eval

      • source: FileContent{ content, type} | FileID{ id, type}

        Determines what populates the item namespace in the data source.

        • class FileContent

          • content: Array[Content{ item, sample}]

            The content of the jsonl file.

            • item: Hash[Symbol, untyped]

            • sample: Hash[Symbol, untyped]

          • type: :file_content

            The type of jsonl source. Always file_content.

            • :file_content

        • class FileID

          • id: String

            The identifier of the file.

          • type: :file_id

            The type of jsonl source. Always file_id.

            • :file_id

      • type: :jsonl

        The type of data source. Always jsonl.

        • :jsonl

    • class CreateEvalCompletionsRunDataSource

      A CompletionsRunDataSource object describing a model sampling configuration.

      • source: FileContent{ content, type} | FileID{ id, type} | StoredCompletions{ type, created_after, created_before, 3 more}

        Determines what populates the item namespace in this run's data source.

        • class FileContent

          • content: Array[Content{ item, sample}]

            The content of the jsonl file.

            • item: Hash[Symbol, untyped]

            • sample: Hash[Symbol, untyped]

          • type: :file_content

            The type of jsonl source. Always file_content.

            • :file_content

        • class FileID

          • id: String

            The identifier of the file.

          • type: :file_id

            The type of jsonl source. Always file_id.

            • :file_id

        • class StoredCompletions

          A StoredCompletionsRunDataSource configuration describing a set of filters

          • type: :stored_completions

            The type of source. Always stored_completions.

            • :stored_completions

          • created_after: Integer

            An optional Unix timestamp to filter items created after this time.

          • created_before: Integer

            An optional Unix timestamp to filter items created before this time.

          • limit: Integer

            An optional maximum number of items to return.

          • metadata: Metadata

            Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

            Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

          • model: String

            An optional model to filter by (e.g., 'gpt-4o').

      • type: :completions

        The type of run data source. Always completions.

        • :completions

      • input_messages: Template{ template, type} | ItemReference{ item_reference, type}

        Used when sampling from a model. Dictates the structure of the messages passed into the model. Can either be a reference to a prebuilt trajectory (ie, item.input_trajectory), or a template with variable references to the item namespace.

        • class Template

          • template: Array[EasyInputMessage | EvalItem{ content, role, type}]

            A list of chat messages forming the prompt or context. May include variable references to the item namespace, ie {{item.name}}.

            • class EasyInputMessage

              A message input to the model with a role indicating instruction following hierarchy. Instructions given with the developer or system role take precedence over instructions given with the user role. Messages with the assistant role are presumed to have been generated by the model in previous interactions.

              • content: String | ResponseInputMessageContentList

                Text, image, or audio input to the model, used to generate a response. Can also contain previous assistant responses.

                • String = String

                  A text input to the model.

                • ResponseInputMessageContentList = Array[ResponseInputContent]

                  A list of one or many input items to the model, containing different content types.

                  • class ResponseInputText

                    A text input to the model.

                    • text: String

                      The text input to the model.

                    • type: :input_text

                      The type of the input item. Always input_text.

                      • :input_text

                  • class ResponseInputImage

                    An image input to the model. Learn about image inputs.

                    • detail: :low | :high | :auto | :original

                      The detail level of the image to be sent to the model. One of high, low, auto, or original. Defaults to auto.

                      • :low

                      • :high

                      • :auto

                      • :original

                    • type: :input_image

                      The type of the input item. Always input_image.

                      • :input_image

                    • file_id: String

                      The ID of the file to be sent to the model.

                    • image_url: String

                      The URL of the image to be sent to the model. A fully qualified URL or base64 encoded image in a data URL.

                  • class ResponseInputFile

                    A file input to the model.

                    • type: :input_file

                      The type of the input item. Always input_file.

                      • :input_file

                    • detail: :low | :high

                      The detail level of the file to be sent to the model. Use low for the default rendering behavior, or high to render the file at higher quality. Defaults to low.

                      • :low

                      • :high

                    • file_data: String

                      The content of the file to be sent to the model.

                    • file_id: String

                      The ID of the file to be sent to the model.

                    • file_url: String

                      The URL of the file to be sent to the model.

                    • filename: String

                      The name of the file to be sent to the model.

              • role: :user | :assistant | :system | :developer

                The role of the message input. One of user, assistant, system, or developer.

                • :user

                • :assistant

                • :system

                • :developer

              • phase: :commentary | :final_answer

                Labels an assistant message as intermediate commentary (commentary) or the final answer (final_answer). For models like gpt-5.3-codex and beyond, when sending follow-up requests, preserve and resend phase on all assistant messages — dropping it can degrade performance. Not used for user messages.

                • :commentary

                • :final_answer

              • type: :message

                The type of the message input. Always message.

                • :message

            • class EvalItem

              A message input to the model with a role indicating instruction following hierarchy. Instructions given with the developer or system role take precedence over instructions given with the user role. Messages with the assistant role are presumed to have been generated by the model in previous interactions.

              • content: String | ResponseInputText | OutputText{ text, type} | 3 more

                Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                • String = String

                  A text input to the model.

                • class ResponseInputText

                  A text input to the model.

                • class OutputText

                  A text output from the model.

                  • text: String

                    The text output from the model.

                  • type: :output_text

                    The type of the output text. Always output_text.

                    • :output_text

                • class InputImage

                  An image input block used within EvalItem content arrays.

                  • image_url: String

                    The URL of the image input.

                  • type: :input_image

                    The type of the image input. Always input_image.

                    • :input_image

                  • detail: String

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • class ResponseInputAudio

                  An audio input to the model.

                  • input_audio: InputAudio{ data, format_}

                    • data: String

                      Base64-encoded audio data.

                    • format_: :mp3 | :wav

                      The format of the audio data. Currently supported formats are mp3 and wav.

                      • :mp3

                      • :wav

                  • type: :input_audio

                    The type of the input item. Always input_audio.

                    • :input_audio

                • GraderInputs = Array[GraderInputItem]

                  A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                  • String = String

                    A text input to the model.

                  • class ResponseInputText

                    A text input to the model.

                  • class OutputText

                    A text output from the model.

                    • text: String

                      The text output from the model.

                    • type: :output_text

                      The type of the output text. Always output_text.

                      • :output_text

                  • class InputImage

                    An image input block used within EvalItem content arrays.

                    • image_url: String

                      The URL of the image input.

                    • type: :input_image

                      The type of the image input. Always input_image.

                      • :input_image

                    • detail: String

                      The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                  • class ResponseInputAudio

                    An audio input to the model.

              • role: :user | :assistant | :system | :developer

                The role of the message input. One of user, assistant, system, or developer.

                • :user

                • :assistant

                • :system

                • :developer

              • type: :message

                The type of the message input. Always message.

                • :message

          • type: :template

            The type of input messages. Always template.

            • :template

        • class ItemReference

          • item_reference: String

            A reference to a variable in the item namespace. Ie, "item.input_trajectory"

          • type: :item_reference

            The type of input messages. Always item_reference.

            • :item_reference

      • model: String

        The name of the model to use for generating completions (e.g. "o3-mini").

      • sampling_params: SamplingParams{ max_completion_tokens, reasoning_effort, response_format, 4 more}

        • max_completion_tokens: Integer

          The maximum number of tokens in the generated output.

        • reasoning_effort: ReasoningEffort

          Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

          • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.

          • All models before gpt-5.1 default to medium reasoning effort, and do not support none.

          • The gpt-5-pro model defaults to (and only supports) high reasoning effort.

          • xhigh is supported for all models after gpt-5.1-codex-max.

          • :none

          • :minimal

          • :low

          • :medium

          • :high

          • :xhigh

        • response_format: ResponseFormatText | ResponseFormatJSONSchema | ResponseFormatJSONObject

          An object specifying the format that the model must output.

          Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.

          Setting to { "type": "json_object" } enables the older JSON mode, which ensures the message the model generates is valid JSON. Using json_schema is preferred for models that support it.

          • class ResponseFormatText

            Default response format. Used to generate text responses.

            • type: :text

              The type of response format being defined. Always text.

              • :text

          • class ResponseFormatJSONSchema

            JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.

            • json_schema: JSONSchema{ name, description, schema, strict}

              Structured Outputs configuration options, including a JSON Schema.

              • name: String

                The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

              • description: String

                A description of what the response format is for, used by the model to determine how to respond in the format.

              • schema: Hash[Symbol, untyped]

                The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.

              • strict: bool

                Whether to enable strict schema adherence when generating the output. If set to true, the model will always follow the exact schema defined in the schema field. Only a subset of JSON Schema is supported when strict is true. To learn more, read the Structured Outputs guide.

            • type: :json_schema

              The type of response format being defined. Always json_schema.

              • :json_schema

          • class ResponseFormatJSONObject

            JSON object response format. An older method of generating JSON responses. Using json_schema is recommended for models that support it. Note that the model will not generate JSON without a system or user message instructing it to do so.

            • type: :json_object

              The type of response format being defined. Always json_object.

              • :json_object

        • seed: Integer

          A seed value to initialize the randomness, during sampling.

        • temperature: Float

          A higher temperature increases randomness in the outputs.

        • tools: Array[ChatCompletionFunctionTool]

          A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. A max of 128 functions are supported.

          • function: FunctionDefinition

            • name: String

              The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

            • description: String

              A description of what the function does, used by the model to choose when and how to call the function.

            • parameters: FunctionParameters

              The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.

              Omitting parameters defines a function with an empty parameter list.

            • strict: bool

              Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.

          • type: :function

            The type of the tool. Currently, only function is supported.

            • :function

        • top_p: Float

          An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

    • class Responses

      A ResponsesRunDataSource object describing a model sampling configuration.

      • source: FileContent{ content, type} | FileID{ id, type} | Responses{ type, created_after, created_before, 8 more}

        Determines what populates the item namespace in this run's data source.

        • class FileContent

          • content: Array[Content{ item, sample}]

            The content of the jsonl file.

            • item: Hash[Symbol, untyped]

            • sample: Hash[Symbol, untyped]

          • type: :file_content

            The type of jsonl source. Always file_content.

            • :file_content

        • class FileID

          • id: String

            The identifier of the file.

          • type: :file_id

            The type of jsonl source. Always file_id.

            • :file_id

        • class Responses

          A EvalResponsesSource object describing a run data source configuration.

          • type: :responses

            The type of run data source. Always responses.

            • :responses

          • created_after: Integer

            Only include items created after this timestamp (inclusive). This is a query parameter used to select responses.

          • created_before: Integer

            Only include items created before this timestamp (inclusive). This is a query parameter used to select responses.

          • instructions_search: String

            Optional string to search the 'instructions' field. This is a query parameter used to select responses.

          • metadata: untyped

            Metadata filter for the responses. This is a query parameter used to select responses.

          • model: String

            The name of the model to find responses for. This is a query parameter used to select responses.

          • reasoning_effort: ReasoningEffort

            Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

            • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.
            • All models before gpt-5.1 default to medium reasoning effort, and do not support none.
            • The gpt-5-pro model defaults to (and only supports) high reasoning effort.
            • xhigh is supported for all models after gpt-5.1-codex-max.

          • temperature: Float

            Sampling temperature. This is a query parameter used to select responses.

          • tools: Array[String]

            List of tool names. This is a query parameter used to select responses.

          • top_p: Float

            Nucleus sampling parameter. This is a query parameter used to select responses.

          • users: Array[String]

            List of user identifiers. This is a query parameter used to select responses.

      • type: :responses

        The type of run data source. Always responses.

        • :responses

      • input_messages: Template{ template, type} | ItemReference{ item_reference, type}

        Used when sampling from a model. Dictates the structure of the messages passed into the model. Can either be a reference to a prebuilt trajectory (ie, item.input_trajectory), or a template with variable references to the item namespace.

        • class Template

          • template: Array[ChatMessage{ content, role} | EvalItem{ content, role, type}]

            A list of chat messages forming the prompt or context. May include variable references to the item namespace, ie {{item.name}}.

            • class ChatMessage

              • content: String

                The content of the message.

              • role: String

                The role of the message (e.g. "system", "assistant", "user").

            • class EvalItem

              A message input to the model with a role indicating instruction following hierarchy. Instructions given with the developer or system role take precedence over instructions given with the user role. Messages with the assistant role are presumed to have been generated by the model in previous interactions.

              • content: String | ResponseInputText | OutputText{ text, type} | 3 more

                Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

                • String = String

                  A text input to the model.

                • class ResponseInputText

                  A text input to the model.

                • class OutputText

                  A text output from the model.

                  • text: String

                    The text output from the model.

                  • type: :output_text

                    The type of the output text. Always output_text.

                    • :output_text

                • class InputImage

                  An image input block used within EvalItem content arrays.

                  • image_url: String

                    The URL of the image input.

                  • type: :input_image

                    The type of the image input. Always input_image.

                    • :input_image

                  • detail: String

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • class ResponseInputAudio

                  An audio input to the model.

                • GraderInputs = Array[GraderInputItem]

                  A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

              • role: :user | :assistant | :system | :developer

                The role of the message input. One of user, assistant, system, or developer.

                • :user

                • :assistant

                • :system

                • :developer

              • type: :message

                The type of the message input. Always message.

                • :message

          • type: :template

            The type of input messages. Always template.

            • :template

        • class ItemReference

          • item_reference: String

            A reference to a variable in the item namespace. Ie, "item.name"

          • type: :item_reference

            The type of input messages. Always item_reference.

            • :item_reference

      • model: String

        The name of the model to use for generating completions (e.g. "o3-mini").

      • sampling_params: SamplingParams{ max_completion_tokens, reasoning_effort, seed, 4 more}

        • max_completion_tokens: Integer

          The maximum number of tokens in the generated output.

        • reasoning_effort: ReasoningEffort

          Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

          • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.
          • All models before gpt-5.1 default to medium reasoning effort, and do not support none.
          • The gpt-5-pro model defaults to (and only supports) high reasoning effort.
          • xhigh is supported for all models after gpt-5.1-codex-max.

        • seed: Integer

          A seed value to initialize the randomness, during sampling.

        • temperature: Float

          A higher temperature increases randomness in the outputs.

        • text: Text{ format_}

          Configuration options for a text response from the model. Can be plain text or structured JSON data. Learn more:

          • Text inputs and outputs

          • Structured Outputs

          • format_: ResponseFormatTextConfig

            An object specifying the format that the model must output.

            Configuring { "type": "json_schema" } enables Structured Outputs, which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.

            The default format is { "type": "text" } with no additional options.

            Not recommended for gpt-4o and newer models:

            Setting to { "type": "json_object" } enables the older JSON mode, which ensures the message the model generates is valid JSON. Using json_schema is preferred for models that support it.

            • class ResponseFormatText

              Default response format. Used to generate text responses.

            • class ResponseFormatTextJSONSchemaConfig

              JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.

              • name: String

                The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

              • schema: Hash[Symbol, untyped]

                The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.

              • type: :json_schema

                The type of response format being defined. Always json_schema.

                • :json_schema

              • description: String

                A description of what the response format is for, used by the model to determine how to respond in the format.

              • strict: bool

                Whether to enable strict schema adherence when generating the output. If set to true, the model will always follow the exact schema defined in the schema field. Only a subset of JSON Schema is supported when strict is true. To learn more, read the Structured Outputs guide.

            • class ResponseFormatJSONObject

              JSON object response format. An older method of generating JSON responses. Using json_schema is recommended for models that support it. Note that the model will not generate JSON without a system or user message instructing it to do so.

        • tools: Array[Tool]

          An array of tools the model may call while generating a response. You can specify which tool to use by setting the tool_choice parameter.

          The two categories of tools you can provide the model are:

          • Built-in tools: Tools that are provided by OpenAI that extend the model's capabilities, like web search or file search. Learn more about built-in tools.

          • Function calls (custom tools): Functions that are defined by you, enabling the model to call your own code. Learn more about function calling.

          • class FunctionTool

            Defines a function in your own code the model can choose to call. Learn more about function calling.

            • name: String

              The name of the function to call.

            • parameters: Hash[Symbol, untyped]

              A JSON schema object describing the parameters of the function.

            • strict: bool

              Whether to enforce strict parameter validation. Default true.

            • type: :function

              The type of the function tool. Always function.

              • :function

            • defer_loading: bool

              Whether this function is deferred and loaded via tool search.

            • description: String

              A description of the function. Used by the model to determine whether or not to call the function.

          • class FileSearchTool

            A tool that searches for relevant content from uploaded files. Learn more about the file search tool.

            • type: :file_search

              The type of the file search tool. Always file_search.

              • :file_search

            • vector_store_ids: Array[String]

              The IDs of the vector stores to search.

            • filters: ComparisonFilter | CompoundFilter

              A filter to apply.

              • class ComparisonFilter

                A filter used to compare a specified attribute key to a given value using a defined comparison operation.

                • key: String

                  The key to compare against the value.

                • type: :eq | :ne | :gt | 5 more

                  Specifies the comparison operator: eq, ne, gt, gte, lt, lte, in, nin.

                  • eq: equals

                  • ne: not equal

                  • gt: greater than

                  • gte: greater than or equal

                  • lt: less than

                  • lte: less than or equal

                  • in: in

                  • nin: not in

                  • :eq

                  • :ne

                  • :gt

                  • :gte

                  • :lt

                  • :lte

                  • :in

                  • :nin

                • value: String | Float | bool | Array[String | Float]

                  The value to compare against the attribute key; supports string, number, or boolean types.

                  • String = String

                  • Float = Float

                  • UnionMember2 = bool

                  • UnionMember3 = Array[String | Float]

                    • String = String

                    • Float = Float

              • class CompoundFilter

                Combine multiple filters using and or or.

                • filters: Array[ComparisonFilter | untyped]

                  Array of filters to combine. Items can be ComparisonFilter or CompoundFilter.

                  • class ComparisonFilter

                    A filter used to compare a specified attribute key to a given value using a defined comparison operation.

                  • UnionMember1 = untyped

                • type: :and | :or

                  Type of operation: and or or.

                  • :and

                  • :or

            • max_num_results: Integer

              The maximum number of results to return. This number should be between 1 and 50 inclusive.

            • ranking_options: RankingOptions{ hybrid_search, ranker, score_threshold}

              Ranking options for search.

              • hybrid_search: HybridSearch{ embedding_weight, text_weight}

                Weights that control how reciprocal rank fusion balances semantic embedding matches versus sparse keyword matches when hybrid search is enabled.

                • embedding_weight: Float

                  The weight of the embedding in the reciprocal ranking fusion.

                • text_weight: Float

                  The weight of the text in the reciprocal ranking fusion.

              • ranker: :auto | :"default-2024-11-15"

                The ranker to use for the file search.

                • :auto

                • :"default-2024-11-15"

              • score_threshold: Float

                The score threshold for the file search, a number between 0 and 1. Numbers closer to 1 will attempt to return only the most relevant results, but may return fewer results.

          • class ComputerTool

            A tool that controls a virtual computer. Learn more about the computer tool.

            • type: :computer

              The type of the computer tool. Always computer.

              • :computer

          • class ComputerUsePreviewTool

            A tool that controls a virtual computer. Learn more about the computer tool.

            • display_height: Integer

              The height of the computer display.

            • display_width: Integer

              The width of the computer display.

            • environment: :windows | :mac | :linux | 2 more

              The type of computer environment to control.

              • :windows

              • :mac

              • :linux

              • :ubuntu

              • :browser

            • type: :computer_use_preview

              The type of the computer use tool. Always computer_use_preview.

              • :computer_use_preview

          • class WebSearchTool

            Search the Internet for sources related to the prompt. Learn more about the web search tool.

            • type: :web_search | :web_search_2025_08_26

              The type of the web search tool. One of web_search or web_search_2025_08_26.

              • :web_search

              • :web_search_2025_08_26

            • filters: Filters{ allowed_domains}

              Filters for the search.

              • allowed_domains: Array[String]

                Allowed domains for the search. If not provided, all domains are allowed. Subdomains of the provided domains are allowed as well.

                Example: ["pubmed.ncbi.nlm.nih.gov"]

            • search_context_size: :low | :medium | :high

              High level guidance for the amount of context window space to use for the search. One of low, medium, or high. medium is the default.

              • :low

              • :medium

              • :high

            • user_location: UserLocation{ city, country, region, 2 more}

              The approximate location of the user.

              • city: String

                Free text input for the city of the user, e.g. San Francisco.

              • country: String

                The two-letter ISO country code of the user, e.g. US.

              • region: String

                Free text input for the region of the user, e.g. California.

              • timezone: String

                The IANA timezone of the user, e.g. America/Los_Angeles.

              • type: :approximate

                The type of location approximation. Always approximate.

                • :approximate

          • class Mcp

            Give the model access to additional tools via remote Model Context Protocol (MCP) servers. Learn more about MCP.

            • server_label: String

              A label for this MCP server, used to identify it in tool calls.

            • type: :mcp

              The type of the MCP tool. Always mcp.

              • :mcp

            • allowed_tools: Array[String] | McpToolFilter{ read_only, tool_names}

              List of allowed tool names or a filter object.

              • McpAllowedTools = Array[String]

                A string array of allowed tool names

              • class McpToolFilter

                A filter object to specify which tools are allowed.

                • read_only: bool

                  Indicates whether or not a tool modifies data or is read-only. If an MCP server is annotated with readOnlyHint, it will match this filter.

                • tool_names: Array[String]

                  List of allowed tool names.

            • authorization: String

              An OAuth access token that can be used with a remote MCP server, either with a custom MCP server URL or a service connector. Your application must handle the OAuth authorization flow and provide the token here.

            • connector_id: :connector_dropbox | :connector_gmail | :connector_googlecalendar | 5 more

              Identifier for service connectors, like those available in ChatGPT. One of server_url, connector_id, or tunnel_id must be provided. Learn more about service connectors here.

              Currently supported connector_id values are:

              • Dropbox: connector_dropbox

              • Gmail: connector_gmail

              • Google Calendar: connector_googlecalendar

              • Google Drive: connector_googledrive

              • Microsoft Teams: connector_microsoftteams

              • Outlook Calendar: connector_outlookcalendar

              • Outlook Email: connector_outlookemail

              • SharePoint: connector_sharepoint

              • :connector_dropbox

              • :connector_gmail

              • :connector_googlecalendar

              • :connector_googledrive

              • :connector_microsoftteams

              • :connector_outlookcalendar

              • :connector_outlookemail

              • :connector_sharepoint

            • defer_loading: bool

              Whether this MCP tool is deferred and discovered via tool search.

            • headers: Hash[Symbol, String]

              Optional HTTP headers to send to the MCP server. Use for authentication or other purposes.

            • require_approval: McpToolApprovalFilter{ always, never} | :always | :never

              Specify which of the MCP server's tools require approval.

              • class McpToolApprovalFilter

                Specify which of the MCP server's tools require approval. Can be always, never, or a filter object associated with tools that require approval.

                • always: Always{ read_only, tool_names}

                  A filter object to specify which tools are allowed.

                  • read_only: bool

                    Indicates whether or not a tool modifies data or is read-only. If an MCP server is annotated with readOnlyHint, it will match this filter.

                  • tool_names: Array[String]

                    List of allowed tool names.

                • never: Never{ read_only, tool_names}

                  A filter object to specify which tools are allowed.

                  • read_only: bool

                    Indicates whether or not a tool modifies data or is read-only. If an MCP server is annotated with readOnlyHint, it will match this filter.

                  • tool_names: Array[String]

                    List of allowed tool names.

              • McpToolApprovalSetting = :always | :never

                Specify a single approval policy for all tools. One of always or never. When set to always, all tools will require approval. When set to never, all tools will not require approval.

                • :always

                • :never

            • server_description: String

              Optional description of the MCP server, used to provide more context.

            • server_url: String

              The URL for the MCP server. One of server_url, connector_id, or tunnel_id must be provided.

            • tunnel_id: String

              The Secure MCP Tunnel ID to use instead of a direct server URL. One of server_url, connector_id, or tunnel_id must be provided.

          • class CodeInterpreter

            A tool that runs Python code to help generate a response to a prompt.

            • container: String | CodeInterpreterToolAuto{ type, file_ids, memory_limit, network_policy}

              The code interpreter container. Can be a container ID or an object that specifies uploaded file IDs to make available to your code, along with an optional memory_limit setting.

              • String = String

                The container ID.

              • class CodeInterpreterToolAuto

                Configuration for a code interpreter container. Optionally specify the IDs of the files to run the code on.

                • type: :auto

                  Always auto.

                  • :auto

                • file_ids: Array[String]

                  An optional list of uploaded files to make available to your code.

                • memory_limit: :"1g" | :"4g" | :"16g" | :"64g"

                  The memory limit for the code interpreter container.

                  • :"1g"

                  • :"4g"

                  • :"16g"

                  • :"64g"

                • network_policy: ContainerNetworkPolicyDisabled | ContainerNetworkPolicyAllowlist

                  Network access policy for the container.

                  • class ContainerNetworkPolicyDisabled

                    • type: :disabled

                      Disable outbound network access. Always disabled.

                      • :disabled

                  • class ContainerNetworkPolicyAllowlist

                    • allowed_domains: Array[String]

                      A list of allowed domains when type is allowlist.

                    • type: :allowlist

                      Allow outbound network access only to specified domains. Always allowlist.

                      • :allowlist

                    • domain_secrets: Array[ContainerNetworkPolicyDomainSecret]

                      Optional domain-scoped secrets for allowlisted domains.

                      • domain: String

                        The domain associated with the secret.

                      • name: String

                        The name of the secret to inject for the domain.

                      • value: String

                        The secret value to inject for the domain.

            • type: :code_interpreter

              The type of the code interpreter tool. Always code_interpreter.

              • :code_interpreter

          • class ImageGeneration

            A tool that generates images using the GPT image models.

            • type: :image_generation

              The type of the image generation tool. Always image_generation.

              • :image_generation

            • action: :generate | :edit | :auto

              Whether to generate a new image or edit an existing image. Default: auto.

              • :generate

              • :edit

              • :auto

            • background: :transparent | :opaque | :auto

              Allows to set transparency for the background of the generated image(s). This parameter is only supported for GPT image models that support transparent backgrounds. Must be one of transparent, opaque, or auto (default value). When auto is used, the model will automatically determine the best background for the image.

              gpt-image-2 and gpt-image-2-2026-04-21 do not support transparent backgrounds. Requests with background set to transparent will return an error for these models; use opaque or auto instead.

              If transparent, the output format needs to support transparency, so it should be set to either png (default value) or webp.

              • :transparent

              • :opaque

              • :auto

            • input_fidelity: :high | :low

              Control how much effort the model will exert to match the style and features, especially facial features, of input images. This parameter is only supported for gpt-image-1 and gpt-image-1.5 and later models, unsupported for gpt-image-1-mini. Supports high and low. Defaults to low.

              • :high

              • :low

            • input_image_mask: InputImageMask{ file_id, image_url}

              Optional mask for inpainting. Contains image_url (string, optional) and file_id (string, optional).

              • file_id: String

                File ID for the mask image.

              • image_url: String

                Base64-encoded mask image.

            • model: String | :"gpt-image-1" | :"gpt-image-1-mini" | :"gpt-image-2" | 3 more

              The image generation model to use. Default: gpt-image-1.

              • String = String

              • Model = :"gpt-image-1" | :"gpt-image-1-mini" | :"gpt-image-2" | 3 more

                The image generation model to use. Default: gpt-image-1.

                • :"gpt-image-1"

                • :"gpt-image-1-mini"

                • :"gpt-image-2"

                • :"gpt-image-2-2026-04-21"

                • :"gpt-image-1.5"

                • :"chatgpt-image-latest"

            • moderation: :auto | :low

              Moderation level for the generated image. Default: auto.

              • :auto

              • :low

            • output_compression: Integer

              Compression level for the output image. Default: 100.

            • output_format: :png | :webp | :jpeg

              The output format of the generated image. One of png, webp, or jpeg. Default: png.

              • :png

              • :webp

              • :jpeg

            • partial_images: Integer

              Number of partial images to generate in streaming mode, from 0 (default value) to 3.

            • quality: :low | :medium | :high | :auto

              The quality of the generated image. One of low, medium, high, or auto. Default: auto.

              • :low

              • :medium

              • :high

              • :auto

            • size: String | :"1024x1024" | :"1024x1536" | :"1536x1024" | :auto

              The size of the generated images. For gpt-image-2 and gpt-image-2-2026-04-21, arbitrary resolutions are supported as WIDTHxHEIGHT strings, for example 1536x864. Width and height must both be divisible by 16 and the requested aspect ratio must be between 1:3 and 3:1. Resolutions above 2560x1440 are experimental, and the maximum supported resolution is 3840x2160. The requested size must also satisfy the model's current pixel and edge limits. The standard sizes 1024x1024, 1536x1024, and 1024x1536 are supported by the GPT image models; auto is supported for models that allow automatic sizing. For dall-e-2, use one of 256x256, 512x512, or 1024x1024. For dall-e-3, use one of 1024x1024, 1792x1024, or 1024x1792.

              • String = String

              • Size = :"1024x1024" | :"1024x1536" | :"1536x1024" | :auto

                The size of the generated images. For gpt-image-2 and gpt-image-2-2026-04-21, arbitrary resolutions are supported as WIDTHxHEIGHT strings, for example 1536x864. Width and height must both be divisible by 16 and the requested aspect ratio must be between 1:3 and 3:1. Resolutions above 2560x1440 are experimental, and the maximum supported resolution is 3840x2160. The requested size must also satisfy the model's current pixel and edge limits. The standard sizes 1024x1024, 1536x1024, and 1024x1536 are supported by the GPT image models; auto is supported for models that allow automatic sizing. For dall-e-2, use one of 256x256, 512x512, or 1024x1024. For dall-e-3, use one of 1024x1024, 1792x1024, or 1024x1792.

                • :"1024x1024"

                • :"1024x1536"

                • :"1536x1024"

                • :auto

          • class LocalShell

            A tool that allows the model to execute shell commands in a local environment.

            • type: :local_shell

              The type of the local shell tool. Always local_shell.

              • :local_shell

          • class FunctionShellTool

            A tool that allows the model to execute shell commands.

            • type: :shell

              The type of the shell tool. Always shell.

              • :shell

            • environment: ContainerAuto | LocalEnvironment | ContainerReference

              • class ContainerAuto

                • type: :container_auto

                  Automatically creates a container for this request

                  • :container_auto

                • file_ids: Array[String]

                  An optional list of uploaded files to make available to your code.

                • memory_limit: :"1g" | :"4g" | :"16g" | :"64g"

                  The memory limit for the container.

                  • :"1g"

                  • :"4g"

                  • :"16g"

                  • :"64g"

                • network_policy: ContainerNetworkPolicyDisabled | ContainerNetworkPolicyAllowlist

                  Network access policy for the container.

                  • class ContainerNetworkPolicyDisabled

                  • class ContainerNetworkPolicyAllowlist

                • skills: Array[SkillReference | InlineSkill]

                  An optional list of skills referenced by id or inline data.

                  • class SkillReference

                    • skill_id: String

                      The ID of the referenced skill.

                    • type: :skill_reference

                      References a skill created with the /v1/skills endpoint.

                      • :skill_reference

                    • version: String

                      Optional skill version. Use a positive integer or 'latest'. Omit for default.

                  • class InlineSkill

                    • description: String

                      The description of the skill.

                    • name: String

                      The name of the skill.

                    • source: InlineSkillSource

                      Inline skill payload

                      • data: String

                        Base64-encoded skill zip bundle.

                      • media_type: :"application/zip"

                        The media type of the inline skill payload. Must be application/zip.

                        • :"application/zip"

                      • type: :base64

                        The type of the inline skill source. Must be base64.

                        • :base64

                    • type: :inline

                      Defines an inline skill for this request.

                      • :inline

              • class LocalEnvironment

                • type: :local

                  Use a local computer environment.

                  • :local

                • skills: Array[LocalSkill]

                  An optional list of skills.

                  • description: String

                    The description of the skill.

                  • name: String

                    The name of the skill.

                  • path: String

                    The path to the directory containing the skill.

              • class ContainerReference

                • container_id: String

                  The ID of the referenced container.

                • type: :container_reference

                  References a container created with the /v1/containers endpoint

                  • :container_reference

          • class CustomTool

            A custom tool that processes input using a specified format. Learn more about custom tools

            • name: String

              The name of the custom tool, used to identify it in tool calls.

            • type: :custom

              The type of the custom tool. Always custom.

              • :custom

            • defer_loading: bool

              Whether this tool should be deferred and discovered via tool search.

            • description: String

              Optional description of the custom tool, used to provide more context.

            • format_: CustomToolInputFormat

              The input format for the custom tool. Default is unconstrained text.

              • class Text

                Unconstrained free-form text.

                • type: :text

                  Unconstrained text format. Always text.

                  • :text

              • class Grammar

                A grammar defined by the user.

                • definition: String

                  The grammar definition.

                • syntax: :lark | :regex

                  The syntax of the grammar definition. One of lark or regex.

                  • :lark

                  • :regex

                • type: :grammar

                  Grammar format. Always grammar.

                  • :grammar

          • class NamespaceTool

            Groups function/custom tools under a shared namespace.

            • description: String

              A description of the namespace shown to the model.

            • name: String

              The namespace name used in tool calls (for example, crm).

            • tools: Array[Function{ name, type, defer_loading, 3 more} | CustomTool]

              The function/custom tools available inside this namespace.

              • class Function

                • name: String

                • type: :function

                  • :function

                • defer_loading: bool

                  Whether this function should be deferred and discovered via tool search.

                • description: String

                • parameters: untyped

                • strict: bool

              • class CustomTool

                A custom tool that processes input using a specified format. Learn more about custom tools

            • type: :namespace

              The type of the tool. Always namespace.

              • :namespace

          • class ToolSearchTool

            Hosted or BYOT tool search configuration for deferred tools.

            • type: :tool_search

              The type of the tool. Always tool_search.

              • :tool_search

            • description: String

              Description shown to the model for a client-executed tool search tool.

            • execution: :server | :client

              Whether tool search is executed by the server or by the client.

              • :server

              • :client

            • parameters: untyped

              Parameter schema for a client-executed tool search tool.

          • class WebSearchPreviewTool

            This tool searches the web for relevant results to use in a response. Learn more about the web search tool.

            • type: :web_search_preview | :web_search_preview_2025_03_11

              The type of the web search tool. One of web_search_preview or web_search_preview_2025_03_11.

              • :web_search_preview

              • :web_search_preview_2025_03_11

            • search_content_types: Array[:text | :image]

              • :text

              • :image

            • search_context_size: :low | :medium | :high

              High level guidance for the amount of context window space to use for the search. One of low, medium, or high. medium is the default.

              • :low

              • :medium

              • :high

            • user_location: UserLocation{ type, city, country, 2 more}

              The user's location.

              • type: :approximate

                The type of location approximation. Always approximate.

                • :approximate

              • city: String

                Free text input for the city of the user, e.g. San Francisco.

              • country: String

                The two-letter ISO country code of the user, e.g. US.

              • region: String

                Free text input for the region of the user, e.g. California.

              • timezone: String

                The IANA timezone of the user, e.g. America/Los_Angeles.

          • class ApplyPatchTool

            Allows the assistant to create, delete, or update files using unified diffs.

            • type: :apply_patch

              The type of the tool. Always apply_patch.

              • :apply_patch

        • top_p: Float

          An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

  • error: EvalAPIError

    An object representing an error response from the Eval API.

    • code: String

      The error code.

    • message: String

      The error message.

  • eval_id: String

    The identifier of the associated evaluation.

  • metadata: Metadata

    Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

    Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

  • model: String

    The model that is evaluated, if applicable.

  • name: String

    The name of the evaluation run.

  • object: :"eval.run"

    The type of the object. Always "eval.run".

    • :"eval.run"

  • per_model_usage: Array[PerModelUsage{ cached_tokens, completion_tokens, invocation_count, 3 more}]

    Usage statistics for each model during the evaluation run.

    • cached_tokens: Integer

      The number of tokens retrieved from cache.

    • completion_tokens: Integer

      The number of completion tokens generated.

    • invocation_count: Integer

      The number of invocations.

    • model_name: String

      The name of the model.

    • prompt_tokens: Integer

      The number of prompt tokens used.

    • total_tokens: Integer

      The total number of tokens used.

  • per_testing_criteria_results: Array[PerTestingCriteriaResult{ failed, passed, testing_criteria}]

    Results per testing criteria applied during the evaluation run.

    • failed: Integer

      Number of tests failed for this criteria.

    • passed: Integer

      Number of tests passed for this criteria.

    • testing_criteria: String

      A description of the testing criteria.

  • report_url: String

    The URL to the rendered evaluation run report on the UI dashboard.

  • result_counts: ResultCounts{ errored, failed, passed, total}

    Counters summarizing the outcomes of the evaluation run.

    • errored: Integer

      Number of output items that resulted in an error.

    • failed: Integer

      Number of output items that failed to pass the evaluation.

    • passed: Integer

      Number of output items that passed the evaluation.

    • total: Integer

      Total number of executed output items.

  • status: String

    The status of the evaluation run.

Example

require "openai"

openai = OpenAI::Client.new(api_key: "My API Key")

page = openai.evals.runs.list("eval_id")

puts(page)

Response

{
  "data": [
    {
      "id": "id",
      "created_at": 0,
      "data_source": {
        "source": {
          "content": [
            {
              "item": {
                "foo": "bar"
              },
              "sample": {
                "foo": "bar"
              }
            }
          ],
          "type": "file_content"
        },
        "type": "jsonl"
      },
      "error": {
        "code": "code",
        "message": "message"
      },
      "eval_id": "eval_id",
      "metadata": {
        "foo": "string"
      },
      "model": "model",
      "name": "name",
      "object": "eval.run",
      "per_model_usage": [
        {
          "cached_tokens": 0,
          "completion_tokens": 0,
          "invocation_count": 0,
          "model_name": "model_name",
          "prompt_tokens": 0,
          "total_tokens": 0
        }
      ],
      "per_testing_criteria_results": [
        {
          "failed": 0,
          "passed": 0,
          "testing_criteria": "testing_criteria"
        }
      ],
      "report_url": "https://example.com",
      "result_counts": {
        "errored": 0,
        "failed": 0,
        "passed": 0,
        "total": 0
      },
      "status": "status"
    }
  ],
  "first_id": "first_id",
  "has_more": true,
  "last_id": "last_id",
  "object": "list"
}

Create eval run

evals.runs.create(eval_id, **kwargs) -> RunCreateResponse

post /evals/{eval_id}/runs

Kicks off a new run for a given evaluation, specifying the data source, and what model configuration to use to test. The datasource will be validated against the schema specified in the config of the evaluation.

Parameters

• eval_id: String

• data_source: CreateEvalJSONLRunDataSource | CreateEvalCompletionsRunDataSource | CreateEvalResponsesRunDataSource{ source, type, input_messages, 2 more}

  Details about the run's data source.

  • class CreateEvalJSONLRunDataSource

    A JsonlRunDataSource object with that specifies a JSONL file that matches the eval

    • source: FileContent{ content, type} | FileID{ id, type}

      Determines what populates the item namespace in the data source.

      • class FileContent

        • content: Array[Content{ item, sample}]

          The content of the jsonl file.

          • item: Hash[Symbol, untyped]

          • sample: Hash[Symbol, untyped]

        • type: :file_content

          The type of jsonl source. Always file_content.

          • :file_content

      • class FileID

        • id: String

          The identifier of the file.

        • type: :file_id

          The type of jsonl source. Always file_id.

          • :file_id

    • type: :jsonl

      The type of data source. Always jsonl.

      • :jsonl

  • class CreateEvalCompletionsRunDataSource

    A CompletionsRunDataSource object describing a model sampling configuration.

    • source: FileContent{ content, type} | FileID{ id, type} | StoredCompletions{ type, created_after, created_before, 3 more}

      Determines what populates the item namespace in this run's data source.

      • class FileContent

        • content: Array[Content{ item, sample}]

          The content of the jsonl file.

          • item: Hash[Symbol, untyped]

          • sample: Hash[Symbol, untyped]

        • type: :file_content

          The type of jsonl source. Always file_content.

          • :file_content

      • class FileID

        • id: String

          The identifier of the file.

        • type: :file_id

          The type of jsonl source. Always file_id.

          • :file_id

      • class StoredCompletions

        A StoredCompletionsRunDataSource configuration describing a set of filters

        • type: :stored_completions

          The type of source. Always stored_completions.

          • :stored_completions

        • created_after: Integer

          An optional Unix timestamp to filter items created after this time.

        • created_before: Integer

          An optional Unix timestamp to filter items created before this time.

        • limit: Integer

          An optional maximum number of items to return.

        • metadata: Metadata

          Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

          Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

        • model: String

          An optional model to filter by (e.g., 'gpt-4o').

    • type: :completions

      The type of run data source. Always completions.

      • :completions

    • input_messages: Template{ template, type} | ItemReference{ item_reference, type}

      Used when sampling from a model. Dictates the structure of the messages passed into the model. Can either be a reference to a prebuilt trajectory (ie, item.input_trajectory), or a template with variable references to the item namespace.

      • class Template

        • template: Array[EasyInputMessage | EvalItem{ content, role, type}]

          A list of chat messages forming the prompt or context. May include variable references to the item namespace, ie {{item.name}}.

          • class EasyInputMessage

            A message input to the model with a role indicating instruction following hierarchy. Instructions given with the developer or system role take precedence over instructions given with the user role. Messages with the assistant role are presumed to have been generated by the model in previous interactions.

            • content: String | ResponseInputMessageContentList

              Text, image, or audio input to the model, used to generate a response. Can also contain previous assistant responses.

              • String = String

                A text input to the model.

              • ResponseInputMessageContentList = Array[ResponseInputContent]

                A list of one or many input items to the model, containing different content types.

                • class ResponseInputText

                  A text input to the model.

                  • text: String

                    The text input to the model.

                  • type: :input_text

                    The type of the input item. Always input_text.

                    • :input_text

                • class ResponseInputImage

                  An image input to the model. Learn about image inputs.

                  • detail: :low | :high | :auto | :original

                    The detail level of the image to be sent to the model. One of high, low, auto, or original. Defaults to auto.

                    • :low

                    • :high

                    • :auto

                    • :original

                  • type: :input_image

                    The type of the input item. Always input_image.

                    • :input_image

                  • file_id: String

                    The ID of the file to be sent to the model.

                  • image_url: String

                    The URL of the image to be sent to the model. A fully qualified URL or base64 encoded image in a data URL.

                • class ResponseInputFile

                  A file input to the model.

                  • type: :input_file

                    The type of the input item. Always input_file.

                    • :input_file

                  • detail: :low | :high

                    The detail level of the file to be sent to the model. Use low for the default rendering behavior, or high to render the file at higher quality. Defaults to low.

                    • :low

                    • :high

                  • file_data: String

                    The content of the file to be sent to the model.

                  • file_id: String

                    The ID of the file to be sent to the model.

                  • file_url: String

                    The URL of the file to be sent to the model.

                  • filename: String

                    The name of the file to be sent to the model.

            • role: :user | :assistant | :system | :developer

              The role of the message input. One of user, assistant, system, or developer.

              • :user

              • :assistant

              • :system

              • :developer

            • phase: :commentary | :final_answer

              Labels an assistant message as intermediate commentary (commentary) or the final answer (final_answer). For models like gpt-5.3-codex and beyond, when sending follow-up requests, preserve and resend phase on all assistant messages — dropping it can degrade performance. Not used for user messages.

              • :commentary

              • :final_answer

            • type: :message

              The type of the message input. Always message.

              • :message

          • class EvalItem

            A message input to the model with a role indicating instruction following hierarchy. Instructions given with the developer or system role take precedence over instructions given with the user role. Messages with the assistant role are presumed to have been generated by the model in previous interactions.

            • content: String | ResponseInputText | OutputText{ text, type} | 3 more

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              • String = String

                A text input to the model.

              • class ResponseInputText

                A text input to the model.

              • class OutputText

                A text output from the model.

                • text: String

                  The text output from the model.

                • type: :output_text

                  The type of the output text. Always output_text.

                  • :output_text

              • class InputImage

                An image input block used within EvalItem content arrays.

                • image_url: String

                  The URL of the image input.

                • type: :input_image

                  The type of the image input. Always input_image.

                  • :input_image

                • detail: String

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • class ResponseInputAudio

                An audio input to the model.

                • input_audio: InputAudio{ data, format_}

                  • data: String

                    Base64-encoded audio data.

                  • format_: :mp3 | :wav

                    The format of the audio data. Currently supported formats are mp3 and wav.

                    • :mp3

                    • :wav

                • type: :input_audio

                  The type of the input item. Always input_audio.

                  • :input_audio

              • GraderInputs = Array[GraderInputItem]

                A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

                • String = String

                  A text input to the model.

                • class ResponseInputText

                  A text input to the model.

                • class OutputText

                  A text output from the model.

                  • text: String

                    The text output from the model.

                  • type: :output_text

                    The type of the output text. Always output_text.

                    • :output_text

                • class InputImage

                  An image input block used within EvalItem content arrays.

                  • image_url: String

                    The URL of the image input.

                  • type: :input_image

                    The type of the image input. Always input_image.

                    • :input_image

                  • detail: String

                    The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

                • class ResponseInputAudio

                  An audio input to the model.

            • role: :user | :assistant | :system | :developer

              The role of the message input. One of user, assistant, system, or developer.

              • :user

              • :assistant

              • :system

              • :developer

            • type: :message

              The type of the message input. Always message.

              • :message

        • type: :template

          The type of input messages. Always template.

          • :template

      • class ItemReference

        • item_reference: String

          A reference to a variable in the item namespace. Ie, "item.input_trajectory"

        • type: :item_reference

          The type of input messages. Always item_reference.

          • :item_reference

    • model: String

      The name of the model to use for generating completions (e.g. "o3-mini").

    • sampling_params: SamplingParams{ max_completion_tokens, reasoning_effort, response_format, 4 more}

      • max_completion_tokens: Integer

        The maximum number of tokens in the generated output.

      • reasoning_effort: ReasoningEffort

        Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

        • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.

        • All models before gpt-5.1 default to medium reasoning effort, and do not support none.

        • The gpt-5-pro model defaults to (and only supports) high reasoning effort.

        • xhigh is supported for all models after gpt-5.1-codex-max.

        • :none

        • :minimal

        • :low

        • :medium

        • :high

        • :xhigh

      • response_format: ResponseFormatText | ResponseFormatJSONSchema | ResponseFormatJSONObject

        An object specifying the format that the model must output.

        Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.

        Setting to { "type": "json_object" } enables the older JSON mode, which ensures the message the model generates is valid JSON. Using json_schema is preferred for models that support it.

        • class ResponseFormatText

          Default response format. Used to generate text responses.

          • type: :text

            The type of response format being defined. Always text.

            • :text

        • class ResponseFormatJSONSchema

          JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.

          • json_schema: JSONSchema{ name, description, schema, strict}

            Structured Outputs configuration options, including a JSON Schema.

            • name: String

              The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

            • description: String

              A description of what the response format is for, used by the model to determine how to respond in the format.

            • schema: Hash[Symbol, untyped]

              The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.

            • strict: bool

              Whether to enable strict schema adherence when generating the output. If set to true, the model will always follow the exact schema defined in the schema field. Only a subset of JSON Schema is supported when strict is true. To learn more, read the Structured Outputs guide.

          • type: :json_schema

            The type of response format being defined. Always json_schema.

            • :json_schema

        • class ResponseFormatJSONObject

          JSON object response format. An older method of generating JSON responses. Using json_schema is recommended for models that support it. Note that the model will not generate JSON without a system or user message instructing it to do so.

          • type: :json_object

            The type of response format being defined. Always json_object.

            • :json_object

      • seed: Integer

        A seed value to initialize the randomness, during sampling.

      • temperature: Float

        A higher temperature increases randomness in the outputs.

      • tools: Array[ChatCompletionFunctionTool]

        A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. A max of 128 functions are supported.

        • function: FunctionDefinition

          • name: String

            The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

          • description: String

            A description of what the function does, used by the model to choose when and how to call the function.

          • parameters: FunctionParameters

            The parameters the functions accepts, described as a JSON Schema object. See the guide for examples, and the JSON Schema reference for documentation about the format.

            Omitting parameters defines a function with an empty parameter list.

          • strict: bool

            Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is true. Learn more about Structured Outputs in the function calling guide.

        • type: :function

          The type of the tool. Currently, only function is supported.

          • :function

      • top_p: Float

        An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

  • class CreateEvalResponsesRunDataSource

    A ResponsesRunDataSource object describing a model sampling configuration.

    • source: FileContent{ content, type} | FileID{ id, type} | Responses{ type, created_after, created_before, 8 more}

      Determines what populates the item namespace in this run's data source.

      • class FileContent

        • content: Array[Content{ item, sample}]

          The content of the jsonl file.

          • item: Hash[Symbol, untyped]

          • sample: Hash[Symbol, untyped]

        • type: :file_content

          The type of jsonl source. Always file_content.

          • :file_content

      • class FileID

        • id: String

          The identifier of the file.

        • type: :file_id

          The type of jsonl source. Always file_id.

          • :file_id

      • class Responses

        A EvalResponsesSource object describing a run data source configuration.

        • type: :responses

          The type of run data source. Always responses.

          • :responses

        • created_after: Integer

          Only include items created after this timestamp (inclusive). This is a query parameter used to select responses.

        • created_before: Integer

          Only include items created before this timestamp (inclusive). This is a query parameter used to select responses.

        • instructions_search: String

          Optional string to search the 'instructions' field. This is a query parameter used to select responses.

        • metadata: untyped

          Metadata filter for the responses. This is a query parameter used to select responses.

        • model: String

          The name of the model to find responses for. This is a query parameter used to select responses.

        • reasoning_effort: ReasoningEffort

          Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

          • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.
          • All models before gpt-5.1 default to medium reasoning effort, and do not support none.
          • The gpt-5-pro model defaults to (and only supports) high reasoning effort.
          • xhigh is supported for all models after gpt-5.1-codex-max.

        • temperature: Float

          Sampling temperature. This is a query parameter used to select responses.

        • tools: Array[String]

          List of tool names. This is a query parameter used to select responses.

        • top_p: Float

          Nucleus sampling parameter. This is a query parameter used to select responses.

        • users: Array[String]

          List of user identifiers. This is a query parameter used to select responses.

    • type: :responses

      The type of run data source. Always responses.

      • :responses

    • input_messages: Template{ template, type} | ItemReference{ item_reference, type}

      Used when sampling from a model. Dictates the structure of the messages passed into the model. Can either be a reference to a prebuilt trajectory (ie, item.input_trajectory), or a template with variable references to the item namespace.

      • class Template

        • template: Array[ChatMessage{ content, role} | EvalItem{ content, role, type}]

          A list of chat messages forming the prompt or context. May include variable references to the item namespace, ie {{item.name}}.

          • class ChatMessage

            • content: String

              The content of the message.

            • role: String

              The role of the message (e.g. "system", "assistant", "user").

          • class EvalItem

            A message input to the model with a role indicating instruction following hierarchy. Instructions given with the developer or system role take precedence over instructions given with the user role. Messages with the assistant role are presumed to have been generated by the model in previous interactions.

            • content: String | ResponseInputText | OutputText{ text, type} | 3 more

              Inputs to the model - can contain template strings. Supports text, output text, input images, and input audio, either as a single item or an array of items.

              • String = String

                A text input to the model.

              • class ResponseInputText

                A text input to the model.

              • class OutputText

                A text output from the model.

                • text: String

                  The text output from the model.

                • type: :output_text

                  The type of the output text. Always output_text.

                  • :output_text

              • class InputImage

                An image input block used within EvalItem content arrays.

                • image_url: String

                  The URL of the image input.

                • type: :input_image

                  The type of the image input. Always input_image.

                  • :input_image

                • detail: String

                  The detail level of the image to be sent to the model. One of high, low, or auto. Defaults to auto.

              • class ResponseInputAudio

                An audio input to the model.

              • GraderInputs = Array[GraderInputItem]

                A list of inputs, each of which may be either an input text, output text, input image, or input audio object.

            • role: :user | :assistant | :system | :developer

              The role of the message input. One of user, assistant, system, or developer.

              • :user

              • :assistant

              • :system

              • :developer

            • type: :message

              The type of the message input. Always message.

              • :message

        • type: :template

          The type of input messages. Always template.

          • :template

      • class ItemReference

        • item_reference: String

          A reference to a variable in the item namespace. Ie, "item.name"

        • type: :item_reference

          The type of input messages. Always item_reference.

          • :item_reference

    • model: String

      The name of the model to use for generating completions (e.g. "o3-mini").

    • sampling_params: SamplingParams{ max_completion_tokens, reasoning_effort, seed, 4 more}

      • max_completion_tokens: Integer

        The maximum number of tokens in the generated output.

      • reasoning_effort: ReasoningEffort

        Constrains effort on reasoning for reasoning models. Currently supported values are none, minimal, low, medium, high, and xhigh. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

        • gpt-5.1 defaults to none, which does not perform reasoning. The supported reasoning values for gpt-5.1 are none, low, medium, and high. Tool calls are supported for all reasoning values in gpt-5.1.
        • All models before gpt-5.1 default to medium reasoning effort, and do not support none.
        • The gpt-5-pro model defaults to (and only supports) high reasoning effort.
        • xhigh is supported for all models after gpt-5.1-codex-max.

      • seed: Integer

        A seed value to initialize the randomness, during sampling.

      • temperature: Float

        A higher temperature increases randomness in the outputs.

      • text: Text{ format_}

        Configuration options for a text response from the model. Can be plain text or structured JSON data. Learn more:

        • Text inputs and outputs

        • Structured Outputs

        • format_: ResponseFormatTextConfig

          An object specifying the format that the model must output.

          Configuring { "type": "json_schema" } enables Structured Outputs, which ensures the model will match your supplied JSON schema. Learn more in the Structured Outputs guide.

          The default format is { "type": "text" } with no additional options.

          Not recommended for gpt-4o and newer models:

          Setting to { "type": "json_object" } enables the older JSON mode, which ensures the message the model generates is valid JSON. Using json_schema is preferred for models that support it.

          • class ResponseFormatText

            Default response format. Used to generate text responses.

          • class ResponseFormatTextJSONSchemaConfig

            JSON Schema response format. Used to generate structured JSON responses. Learn more about Structured Outputs.

            • name: String

              The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

            • schema: Hash[Symbol, untyped]

              The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas here.

            • type: :json_schema

              The type of response format being defined. Always json_schema.

              • :json_schema

            • description: String

              A description of what the response format is for, used by the model to determine how to respond in the format.

            • strict: bool

              Whether to enable strict schema adherence when generating the output. If set to true, the model will always follow the exact schema defined in the schema field. Only a subset of JSON Schema is supported when strict is true. To learn more, read the Structured Outputs guide.

          • class ResponseFormatJSONObject

            JSON object response format. An older method of generating JSON responses. Using json_schema is recommended for models that support it. Note that the model will not generate JSON without a system or user message instructing it to do so.

      • tools: Array[Tool]

        An array of tools the model may call while generating a response. You can specify which tool to use by setting the tool_choice parameter.

        The two categories of tools you can provide the model are:

        • Built-in tools: Tools that are provided by OpenAI that extend the model's capabilities, like web search or file search. Learn more about built-in tools.

        • Function calls (custom tools): Functions that are defined by you, enabling the model to call your own code. Learn more about function calling.

        • class FunctionTool

          Defines a function in your own code the model can choose to call. Learn more about function calling.

          • name: String

            The name of the function to call.

          • parameters: Hash[Symbol, untyped]

            A JSON schema object describing the parameters of the function.

          • strict: bool

            Whether to enforce strict parameter validation. Default true.

          • type: :function

            The type of the function tool. Always function.

            • :function

          • defer_loading: bool

            Whether this function is deferred and loaded via tool search.

          • description: String

            A description of the function. Used by the model to determine whether or not to call the function.

        • class FileSearchTool

          A tool that searches for relevant content from uploaded files. Learn more about the file search tool.

          • type: :file_search

            The type of the file search tool. Always file_search.

            • :file_search

          • vector_store_ids: Array[String]

            The IDs of the vector stores to search.

          • filters: ComparisonFilter | CompoundFilter

            A filter to apply.

            • class ComparisonFilter

              A filter used to compare a specified attribute key to a given value using a defined comparison operation.

              • key: String

                The key to compare against the value.

              • type: :eq | :ne | :gt | 5 more

                Specifies the comparison operator: eq, ne, gt, gte, lt, lte, in, nin.

                • eq: equals

                • ne: not equal

                • gt: greater than

                • gte: greater than or equal

                • lt: less than

                • lte: less than or equal

                • in: in

                • nin: not in

                • :eq

                • :ne

                • :gt

                • :gte

                • :lt

                • :lte

                • :in

                • :nin

              • value: String | Float | bool | Array[String | Float]

                The value to compare against the attribute key; supports string, number, or boolean types.

                • String = String

                • Float = Float

                • UnionMember2 = bool

                • UnionMember3 = Array[String | Float]

                  • String = String

                  • Float = Float

            • class CompoundFilter

              Combine multiple filters using and or or.

              • filters: Array[ComparisonFilter | untyped]

                Array of filters to combine. Items can be ComparisonFilter or CompoundFilter.

                • class ComparisonFilter

                  A filter used to compare a specified attribute key to a given value using a defined comparison operation.

                • UnionMember1 = untyped

              • type: :and | :or

                Type of operation: and or or.

                • :and

                • :or

          • max_num_results: Integer

            The maximum number of results to return. This number should be between 1 and 50 inclusive.

          • ranking_options: RankingOptions{ hybrid_search, ranker, score_threshold}

            Ranking options for search.

            • hybrid_search: HybridSearch{ embedding_weight, text_weight}

              Weights that control how reciprocal rank fusion balances semantic embedding matches versus sparse keyword matches when hybrid search is enabled.

              • embedding_weight: Float

                The weight of the embedding in the reciprocal ranking fusion.

              • text_weight: Float

                The weight of the text in the reciprocal ranking fusion.

            • ranker: :auto | :"default-2024-11-15"

              The ranker to use for the file search.

              • :auto

              • :"default-2024-11-15"

            • score_threshold: Float

              The score threshold for the file search, a number between 0 and 1. Numbers closer to 1 will attempt to return only the most relevant results, but may return fewer results.

        • class ComputerTool

          A tool that controls a virtual computer. Learn more about the computer tool.

          • type: :computer

            The type of the computer tool. Always computer.

            • :computer

        • class ComputerUsePreviewTool

          A tool that controls a virtual computer. Learn more about the computer tool.

          • display_height: Integer

            The height of the computer display.

          • display_width: Integer

            The width of the computer display.

          • environment: :windows | :mac | :linux | 2 more

            The type of computer environment to control.

            • :windows

            • :mac

            • :linux

            • :ubuntu

            • :browser

          • type: :computer_use_preview

            The type of the computer use tool. Always computer_use_preview.

            • :computer_use_preview

        • class WebSearchTool

          Search the Internet for sources related to the prompt. Learn more about the web search tool.

          • type: :web_search | :web_search_2025_08_26

            The type of the web search tool. One of web_search or web_search_2025_08_26.

            • :web_search

            • :web_search_2025_08_26

          • filters: Filters{ allowed_domains}

            Filters for the search.

            • allowed_domains: Array[String]

              Allowed domains for the search. If not provided, all domains are allowed. Subdomains of the provided domains are allowed as well.

              Example: ["pubmed.ncbi.nlm.nih.gov"]

          • search_context_size: :low | :medium | :high

            High level guidance for the amount of context window space to use for the search. One of low, medium, or high. medium is the default.

            • :low

            • :medium

            • :high

          • user_location: UserLocation{ city, country, region, 2 more}

            The approximate location of the user.

            • city: String

              Free text input for the city of the user, e.g. San Francisco.

            • country: String

              The two-letter ISO country code of the user, e.g. US.

            • region: String

              Free text input for the region of the user, e.g. California.

            • timezone: String

              The IANA timezone of the user, e.g. America/Los_Angeles.

            • type: :approximate

              The type of location approximation. Always approximate.

              • :approximate

        • class Mcp

          Give the model access to additional tools via remote Model Context Protocol (MCP) servers. Learn more about MCP.

          • server_label: String

            A label for this MCP server, used to identify it in tool calls.

          • type: :mcp

            The type of the MCP tool. Always mcp.

            • :mcp

          • allowed_tools: Array[String] | McpToolFilter{ read_only, tool_names}

            List of allowed tool names or a filter object.

            • McpAllowedTools = Array[String]

              A string array of allowed tool names

            • class McpToolFilter

              A filter object to specify which tools are allowed.

              • read_only: bool

                Indicates whether or not a tool modifies data or is read-only. If an MCP server is annotated with readOnlyHint, it will match this filter.

              • tool_names: Array[String]

                List of allowed tool names.

          • authorization: String

            An OAuth access token that can be used with a remote MCP server, either with a custom MCP server URL or a service connector. Your application must handle the OAuth authorization flow and provide the token here.

          • connector_id: :connector_dropbox | :connector_gmail | :connector_googlecalendar | 5 more

            Identifier for service connectors, like those available in ChatGPT. One of server_url, connector_id, or tunnel_id must be provided. Learn more about service connectors here.

            Currently supported connector_id values are:

            • Dropbox: connector_dropbox

            • Gmail: connector_gmail

            • Google Calendar: connector_googlecalendar

            • Google Drive: connector_googledrive

            • Microsoft Teams: connector_microsoftteams

            • Outlook Calendar: connector_outlookcalendar

            • Outlook Email: connector_outlookemail

            • SharePoint: connector_sharepoint

            • :connector_dropbox

            • :connector_gmail

            • :connector_googlecalendar

            • :connector_googledrive

            • :connector_microsoftteams

            • :connector_outlookcalendar

            • :connector_outlookemail

            • :connector_sharepoint

          • defer_loading: bool

            Whether this MCP tool is deferred and discovered via tool search.

          • headers: Hash[Symbol, String]

            Optional HTTP headers to send to the MCP server. Use for authentication or other purposes.

          • require_approval: McpToolApprovalFilter{ always, never} | :always | :never

            Specify which of the MCP server's tools require approval.

            • class McpToolApprovalFilter

              Specify which of the MCP server's tools require approval. Can be always, never, or a filter object associated with tools that require approval.

              • always: Always{ read_only, tool_names}

                A filter object to specify which tools are allowed.

                • read_only: bool

                  Indicates whether or not a tool modifies data or is read-only. If an MCP server is annotated with readOnlyHint, it will match this filter.

                • tool_names: Array[String]

                  List of allowed tool names.

              • never: Never{ read_only, tool_names}

                A filter object to specify which tools are allowed.

                • read_only: bool

                  Indicates whether or not a tool modifies data or is read-only. If an MCP server is annotated with readOnlyHint, it will match this filter.

                • tool_names: Array[String]

                  List of allowed tool names.

            • McpToolApprovalSetting = :always | :never

              Specify a single approval policy for all tools. One of always or never. When set to always, all tools will require approval. When set to never, all tools will not require approval.

              • :always

              • :never

          • server_description: String

            Optional description of the MCP server, used to provide more context.

          • server_url: String

            The URL for the MCP server. One of server_url, connector_id, or tunnel_id must be provided.

          • tunnel_id: String

            The Secure MCP Tunnel ID to use instead of a direct server URL. One of server_url, connector_id, or tunnel_id must be provided.

        • class CodeInterpreter

          A tool that runs Python code to help generate a response to a prompt.

          • container: String | CodeInterpreterToolAuto{ type, file_ids, memory_limit, network_policy}

            The code interpreter container. Can be a container ID or an object that specifies uploaded file IDs to make available to your code, along with an optional memory_limit setting.

            • String = String

              The container ID.

            • class CodeInterpreterToolAuto

              Configuration for a code interpreter container. Optionally specify the IDs of the files to run the code on.

              • type: :auto

                Always auto.

                • :auto

              • file_ids: Array[String]

                An optional list of uploaded files to make available to your code.

              • memory_limit: :"1g" | :"4g" | :"16g" | :"64g"

                The memory limit for the code interpreter container.

                • :"1g"

                • :"4g"

                • :"16g"

                • :"64g"

              • network_policy: ContainerNetworkPolicyDisabled | ContainerNetworkPolicyAllowlist

                Network access policy for the container.

                • class ContainerNetworkPolicyDisabled

                  • type: :disabled

                    Disable outbound network access. Always disabled.

                    • :disabled

                • class ContainerNetworkPolicyAllowlist

                  • allowed_domains: Array[String]

                    A list of allowed domains when type is allowlist.

                  • type: :allowlist

                    Allow outbound network access only to specified domains. Always allowlist.

                    • :allowlist

                  • domain_secrets: Array[ContainerNetworkPolicyDomainSecret]

                    Optional domain-scoped secrets for allowlisted domains.

                    • domain: String

                      The domain associated with the secret.

                    • name: String

                      The name of the secret to inject for the domain.

                    • value: String

                      The secret value to inject for the domain.

          • type: :code_interpreter

            The type of the code interpreter tool. Always code_interpreter.

            • :code_interpreter

        • class ImageGeneration

          A tool that generates images using the GPT image models.

          • type: :image_generation

            The type of the image generation tool. Always image_generation.

            • :image_generation

          • action: :generate | :edit | :auto

            Whether to generate a new image or edit an existing image. Default: auto.

            • :generate

            • :edit

            • :auto

          • background: :transparent | :opaque | :auto

            Allows to set transparency for the background of the generated image(s). This parameter is only supported for GPT image models that support transparent backgrounds. Must be one of transparent, opaque, or auto (default value). When auto is used, the model will automatically determine the best background for the image.

            gpt-image-2 and gpt-image-2-2026-04-21 do not support transparent backgrounds. Requests with background set to transparent will return an error for these models; use opaque or auto instead.

            If transparent, the output format needs to support transparency, so it should be set to either png (default value) or webp.

            • :transparent

            • :opaque

            • :auto

          • input_fidelity: :high | :low

            Control how much effort the model will exert to match the style and features, especially facial features, of input images. This parameter is only supported for gpt-image-1 and gpt-image-1.5 and later models, unsupported for gpt-image-1-mini. Supports high and low. Defaults to low.

            • :high

            • :low

          • input_image_mask: InputImageMask{ file_id, image_url}

            Optional mask for inpainting. Contains image_url (string, optional) and file_id (string, optional).

            • file_id: String

              File ID for the mask image.

            • image_url: String

              Base64-encoded mask image.

          • model: String | :"gpt-image-1" | :"gpt-image-1-mini" | :"gpt-image-2" | 3 more

            The image generation model to use. Default: gpt-image-1.

            • String = String

            • Model = :"gpt-image-1" | :"gpt-image-1-mini" | :"gpt-image-2" | 3 more

              The image generation model to use. Default: gpt-image-1.

              • :"gpt-image-1"

              • :"gpt-image-1-mini"

              • :"gpt-image-2"

              • :"gpt-image-2-2026-04-21"

              • :"gpt-image-1.5"

              • :"chatgpt-image-latest"

          • moderation: :auto | :low

            Moderation level for the generated image. Default: auto.

            • :auto

            • :low

          • output_compression: Integer

            Compression level for the output image. Default: 100.

          • output_format: :png | :webp | :jpeg

            The output format of the generated image. One of png, webp, or jpeg. Default: png.

            • :png

            • :webp

            • :jpeg

          • partial_images: Integer

            Number of partial images to generate in streaming mode, from 0 (default value) to 3.

          • quality: :low | :medium | :high | :auto

            The quality of the generated image. One of low, medium, high, or auto. Default: auto.

            • :low

            • :medium

            • :high

            • :auto

          • size: String | :"1024x1024" | :"1024x1536" | :"1536x1024" | :auto

            The size of the generated images. For gpt-image-2 and gpt-image-2-2026-04-21, arbitrary resolutions are supported as WIDTHxHEIGHT strings, for example 1536x864. Width and height must both be divisible by 16 and the requested aspect ratio must be between 1:3 and 3:1. Resolutions above 2560x1440 are experimental, and the maximum supported resolution is 3840x2160. The requested size must also satisfy the model's current pixel and edge limits. The standard sizes 1024x1024, 1536x1024, and 1024x1536 are supported by the GPT image models; auto is supported for models that allow automatic sizing. For dall-e-2, use one of 256x256, 512x512, or 1024x1024. For dall-e-3, use one of 1024x1024, 1792x1024, or 1024x1792.

            • String = String

            • Size = :"1024x1024" | :"1024x1536" | :"1536x1024" | :auto

              The size of the generated images. For gpt-image-2 and gpt-image-2-2026-04-21, arbitrary resolutions are supported as WIDTHxHEIGHT strings, for example 1536x864. Width and height must both be divisible by 16 and the requested aspect ratio must be between 1:3 and 3:1. Resolutions above 2560x1440 are experimental, and the maximum supported resolution is 3840x2160. The requested size must also satisfy the model's current pixel and edge limits. The standard sizes 1024x1024, 1536x1024, and 1024x1536 are supported by the GPT image models; auto is supported for models that allow automatic sizing. For dall-e-2, use one of 256x256, 512x512, or 1024x1024. For dall-e-3, use one of 1024x1024, 1792x1024, or 1024x1792.

              • :"1024x1024"

              • :"1024x1536"

              • :"1536x1024"

              • :auto

        • class LocalShell

          A tool that allows the model to execute shell commands in a local environment.

          • type: :local_shell

            The type of the local shell tool. Always local_shell.

            • :local_shell

        • class FunctionShellTool

          A tool that allows the model to execute shell commands.

          • type: :shell

            The type of the shell tool. Always shell.

            • :shell

          • environment: ContainerAuto | LocalEnvironment | ContainerReference

            • class ContainerAuto

              • type: :container_auto

                Automatically creates a container for this request

                • :container_auto

              • file_ids: Array[String]

                An optional list of uploaded files to make available to your code.

              • memory_limit: :"1g" | :"4g" | :"16g" | :"64g"

                The memory limit for the container.

                • :"1g"

                • :"4g"

                • :"16g"

                • :"64g"

              • network_policy: ContainerNetworkPolicyDisabled | ContainerNetworkPolicyAllowlist

                Network access policy for the container.

                • class ContainerNetworkPolicyDisabled

                • class ContainerNetworkPolicyAllowlist

              • skills: Array[SkillReference | InlineSkill]

                An optional list of skills referenced by id or inline data.

                • class SkillReference

                  • skill_id: String

                    The ID of the referenced skill.

                  • type: :skill_reference

                    References a skill created with the /v1/skills endpoint.

                    • :skill_reference

                  • version: String

                    Optional skill version. Use a positive integer or 'latest'. Omit for default.

                • class InlineSkill

                  • description: String

                    The description of the skill.

                  • name: String

                    The name of the skill.

                  • source: InlineSkillSource

                    Inline skill payload

                    • data: String

                      Base64-encoded skill zip bundle.

                    • media_type: :"application/zip"

                      The media type of the inline skill payload. Must be application/zip.

                      • :"application/zip"

                    • type: :base64

                      The type of the inline skill source. Must be base64.

                      • :base64

                  • type: :inline

                    Defines an inline skill for this request.

                    • :inline

            • class LocalEnvironment

              • type: :local

                Use a local computer environment.

                • :local

              • skills: Array[LocalSkill]

                An optional list of skills.

                • description: String

                  The description of the skill.

                • name: String

                  The name of the skill.

                • path: String

                  The path to the directory containing the skill.

            • class ContainerReference

              • container_id: String

                The ID of the referenced container.

              • type: :container_reference

                References a container created with the /v1/containers endpoint

                • :container_reference

        • class CustomTool

          A custom tool that processes input using a specified format. Learn more about custom tools

          • name: String

            The name of the custom tool, used to identify it in tool calls.

          • type: :custom

            The type of the custom tool. Always custom.

            • :custom

          • defer_loading: bool

            Whether this tool should be deferred and discovered via tool search.

          • description: String

            Optional description of the custom tool, used to provide more context.

          • format_: CustomToolInputFormat

            The input format for the custom tool. Default is unconstrained text.

            • class Text

              Unconstrained free-form text.

              • type: :text

                Unconstrained text format. Always text.

                • :text

            • class Grammar

              A grammar defined by the user.

              • definition: String

                The grammar definition.

              • syntax: :lark | :regex

                The syntax of the grammar definition. One of lark or regex.

                • :lark

                • :regex

              • type: :grammar

                Grammar format. Always grammar.

                • :grammar

        • class NamespaceTool

          Groups function/custom tools under a shared namespace.

          • description: String

            A description of the namespace shown to the model.

          • name: String

            The namespace name used in tool calls (for example, crm).

          • tools: Array[Function{ name, type, defer_loading, 3 more} | CustomTool]

            The function/custom tools available inside this namespace.

            • class Function

              • name: String

              • type: :function

                • :function

              • defer_loading: bool

                Whether this function should be deferred and discovered via tool search.

              • description: String

              • parameters: untyped

              • strict: bool

            • class CustomTool

              A custom tool that processes input using a specified format. Learn more about custom tools

          • type: :namespace

            The type of the tool. Always namespace.

            • :namespace

        • class ToolSearchTool

          Hosted or BYOT tool search configuration for deferred tools.

          • type: :tool_search

            The type of the tool. Always tool_search.

            • :tool_search

          • description: String

            Description shown to the model for a client-executed tool search tool.

          • execution: :server | :client

            Whether tool search is executed by the server or by the client.

            • :server

            • :client

          • parameters: untyped

            Parameter schema for a client-executed tool search tool.

        • class WebSearchPreviewTool

          This tool searches the web for relevant results to use in a response. Learn more about the web search tool.

          • type: :web_search_preview | :web_search_preview_2025_03_11

            The type of the web search tool. One of web_search_preview or web_search_preview_2025_03_11.

            • :web_search_preview

            • :web_search_preview_2025_03_11

          • search_content_types: Array[:text | :image]

            • :text

            • :image

          • search_context_size: :low | :medium | :high

            High level guidance for the amount of context window space to use for the search. One of low, medium, or high. medium is the default.

            • :low

            • :medium

            • :high

          • user_location: UserLocation{ type, city, country, 2 more}

            The user's location.

            • type: :approximate

              The type of location approximation. Always approximate.

              • :approximate

            • city: String

              Free text input for the city of the user, e.g. San Francisco.

            • country: String

              The two-letter ISO country code of the user, e.g. US.

            • region: String

              Free text input for the region of the user, e.g. California.

            • timezone: String

              The IANA timezone of the user, e.g. America/Los_Angeles.

        • class ApplyPatchTool

          Allows the assistant to create, delete, or update files using unified diffs.

          • type: :apply_patch

            The type of the tool. Always apply_patch.

            • :apply_patch

      • top_p: Float

        An alternative to temperature for nucleus sampling; 1.0 includes all tokens.

• metadata: Metadata

  Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.

  Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.

• name: String

  The name of the run.