Evals

List evals

evals.list(EvalListParams**kwargs) -> SyncCursorPage[EvalListResponse]

get /evals

List evaluations for a project.

Parameters

• after: Optional[str]

  Identifier for the last eval from the previous pagination request.

• limit: Optional[int]

  Number of evals to retrieve.

• order: Optional[Literal["asc", "desc"]]

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

  • "asc"

  • "desc"

• order_by: Optional[Literal["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: str

    Unique identifier for the evaluation.

  • created_at: int

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

  • data_source_config: DataSourceConfig

    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: Dict[str, object]

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

      • type: Literal["custom"]

        The type of data source. Always custom.

        • "custom"

    • class DataSourceConfigLogs: …

      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: Dict[str, object]

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

      • type: Literal["logs"]

        The type of data source. Always logs.

        • "logs"

      • metadata: Optional[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: Dict[str, object]

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

      • type: Literal["stored_completions"]

        The type of data source. Always stored_completions.

        • "stored_completions"

      • metadata: Optional[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: Optional[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: str

    The name of the evaluation.

  • object: Literal["eval"]

    The object type.

    • "eval"

  • testing_criteria: List[TestingCriterion]

    A list of testing criteria.

    • class LabelModelGrader: …

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

      • input: List[Input]

        • content: InputContent

          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.

          • str

            A text input to the model.

          • class ResponseInputText: …

            A text input to the model.

            • text: str

              The text input to the model.

            • type: Literal["input_text"]

              The type of the input item. Always input_text.

              • "input_text"

          • class InputContentOutputText: …

            A text output from the model.

            • text: str

              The text output from the model.

            • type: Literal["output_text"]

              The type of the output text. Always output_text.

              • "output_text"

          • class InputContentInputImage: …

            An image input block used within EvalItem content arrays.

            • image_url: str

              The URL of the image input.

            • type: Literal["input_image"]

              The type of the image input. Always input_image.

              • "input_image"

            • detail: Optional[str]

              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: str

                Base64-encoded audio data.

              • format: Literal["mp3", "wav"]

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

                • "mp3"

                • "wav"

            • type: Literal["input_audio"]

              The type of the input item. Always input_audio.

              • "input_audio"

          • List[GraderInputItem]

            • str

              A text input to the model.

            • class ResponseInputText: …

              A text input to the model.

            • class GraderInputItemOutputText: …

              A text output from the model.

              • text: str

                The text output from the model.

              • type: Literal["output_text"]

                The type of the output text. Always output_text.

                • "output_text"

            • class GraderInputItemInputImage: …

              An image input block used within EvalItem content arrays.

              • image_url: str

                The URL of the image input.

              • type: Literal["input_image"]

                The type of the image input. Always input_image.

                • "input_image"

              • detail: Optional[str]

                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: Literal["user", "assistant", "system", "developer"]

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

          • "user"

          • "assistant"

          • "system"

          • "developer"

        • type: Optional[Literal["message"]]

          The type of the message input. Always message.

          • "message"

      • labels: List[str]

        The labels to assign to each item in the evaluation.

      • model: str

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

      • name: str

        The name of the grader.

      • passing_labels: List[str]

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

      • type: Literal["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: str

        The input text. This may include template strings.

      • name: str

        The name of the grader.

      • operation: Literal["eq", "ne", "like", "ilike"]

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

        • "eq"

        • "ne"

        • "like"

        • "ilike"

      • reference: str

        The reference text. This may include template strings.

      • type: Literal["string_check"]

        The object type, which is always string_check.

        • "string_check"

    • class TestingCriterionEvalGraderTextSimilarity: …

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: float

        The threshold for the score.

    • class TestingCriterionEvalGraderPython: …

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

      • pass_threshold: Optional[float]

        The threshold for the score.

    • class TestingCriterionEvalGraderScoreModel: …

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

      • pass_threshold: Optional[float]

        The threshold for the score.

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
page = client.evals.list()
page = page.data[0]
print(page.id)

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"
}

Example

from openai import OpenAI
client = OpenAI()

evals = client.evals.list(limit=1)
print(evals)

Response

{
  "object": "list",
  "data": [
    {
      "id": "eval_67abd54d9b0081909a86353f6fb9317a",
      "object": "eval",
      "data_source_config": {
        "type": "stored_completions",
        "metadata": {
          "usecase": "push_notifications_summarizer"
        },
        "schema": {
          "type": "object",
          "properties": {
            "item": {
              "type": "object"
            },
            "sample": {
              "type": "object"
            }
          },
          "required": [
            "item",
            "sample"
          ]
        }
      },
      "testing_criteria": [
        {
          "name": "Push Notification Summary Grader",
          "id": "Push Notification Summary Grader-9b876f24-4762-4be9-aff4-db7a9b31c673",
          "type": "label_model",
          "model": "o3-mini",
          "input": [
            {
              "type": "message",
              "role": "developer",
              "content": {
                "type": "input_text",
                "text": "\nLabel the following push notification summary as either correct or incorrect.\nThe push notification and the summary will be provided below.\nA good push notificiation summary is concise and snappy.\nIf it is good, then label it as correct, if not, then incorrect.\n"
              }
            },
            {
              "type": "message",
              "role": "user",
              "content": {
                "type": "input_text",
                "text": "\nPush notifications: {{item.input}}\nSummary: {{sample.output_text}}\n"
              }
            }
          ],
          "passing_labels": [
            "correct"
          ],
          "labels": [
            "correct",
            "incorrect"
          ],
          "sampling_params": null
        }
      ],
      "name": "Push Notification Summary Grader",
      "created_at": 1739314509,
      "metadata": {
        "description": "A stored completions eval for push notification summaries"
      }
    }
  ],
  "first_id": "eval_67abd54d9b0081909a86353f6fb9317a",
  "last_id": "eval_67aa884cf6688190b58f657d4441c8b7",
  "has_more": true
}

Create eval

evals.create(EvalCreateParams**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: DataSourceConfig

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

  • class DataSourceConfigCustom: …

    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: Dict[str, object]

      The json schema for each row in the data source.

    • type: Literal["custom"]

      The type of data source. Always custom.

      • "custom"

    • include_sample_schema: Optional[bool]

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

  • class DataSourceConfigLogs: …

    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: Literal["logs"]

      The type of data source. Always logs.

      • "logs"

    • metadata: Optional[Dict[str, object]]

      Metadata filters for the logs data source.

  • class DataSourceConfigStoredCompletions: …

    Deprecated in favor of LogsDataSourceConfig.

    • type: Literal["stored_completions"]

      The type of data source. Always stored_completions.

      • "stored_completions"

    • metadata: Optional[Dict[str, object]]

      Metadata filters for the stored completions data source.

• testing_criteria: Iterable[TestingCriterion]

  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 TestingCriterionLabelModel: …

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

    • input: Iterable[TestingCriterionLabelModelInput]

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

      • class TestingCriterionLabelModelInputSimpleInputMessage: …

        • content: str

          The content of the message.

        • role: str

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

      • class TestingCriterionLabelModelInputEvalItem: …

        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: TestingCriterionLabelModelInputEvalItemContent

          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.

          • str

            A text input to the model.

          • class ResponseInputText: …

            A text input to the model.

            • text: str

              The text input to the model.

            • type: Literal["input_text"]

              The type of the input item. Always input_text.

              • "input_text"

          • class TestingCriterionLabelModelInputEvalItemContentOutputText: …

            A text output from the model.

            • text: str

              The text output from the model.

            • type: Literal["output_text"]

              The type of the output text. Always output_text.

              • "output_text"

          • class TestingCriterionLabelModelInputEvalItemContentInputImage: …

            An image input block used within EvalItem content arrays.

            • image_url: str

              The URL of the image input.

            • type: Literal["input_image"]

              The type of the image input. Always input_image.

              • "input_image"

            • detail: Optional[str]

              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: str

                Base64-encoded audio data.

              • format: Literal["mp3", "wav"]

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

                • "mp3"

                • "wav"

            • type: Literal["input_audio"]

              The type of the input item. Always input_audio.

              • "input_audio"

          • Sequence[GraderInputsParamItem]

            • str

              A text input to the model.

            • class ResponseInputText: …

              A text input to the model.

            • class GraderInputItemOutputText: …

              A text output from the model.

              • text: str

                The text output from the model.

              • type: Literal["output_text"]

                The type of the output text. Always output_text.

                • "output_text"

            • class GraderInputItemInputImage: …

              An image input block used within EvalItem content arrays.

              • image_url: str

                The URL of the image input.

              • type: Literal["input_image"]

                The type of the image input. Always input_image.

                • "input_image"

              • detail: Optional[str]

                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: Literal["user", "assistant", "system", "developer"]

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

          • "user"

          • "assistant"

          • "system"

          • "developer"

        • type: Optional[Literal["message"]]

          The type of the message input. Always message.

          • "message"

    • labels: Sequence[str]

      The labels to classify to each item in the evaluation.

    • model: str

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

    • name: str

      The name of the grader.

    • passing_labels: Sequence[str]

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

    • type: Literal["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: str

      The input text. This may include template strings.

    • name: str

      The name of the grader.

    • operation: Literal["eq", "ne", "like", "ilike"]

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

      • "eq"

      • "ne"

      • "like"

      • "ilike"

    • reference: str

      The reference text. This may include template strings.

    • type: Literal["string_check"]

      The object type, which is always string_check.

      • "string_check"

  • class TestingCriterionTextSimilarity: …

    A TextSimilarityGrader object which grades text based on similarity metrics.

    • pass_threshold: float

      The threshold for the score.

  • class TestingCriterionPython: …

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

    • pass_threshold: Optional[float]

      The threshold for the score.

  • class TestingCriterionScoreModel: …

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

    • pass_threshold: Optional[float]

      The threshold for the score.

• metadata: Optional[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: Optional[str]

  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: str

    Unique identifier for the evaluation.

  • created_at: int

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

  • data_source_config: DataSourceConfig

    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: Dict[str, object]

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

      • type: Literal["custom"]

        The type of data source. Always custom.

        • "custom"

    • class DataSourceConfigLogs: …

      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: Dict[str, object]

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

      • type: Literal["logs"]

        The type of data source. Always logs.

        • "logs"

      • metadata: Optional[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: Dict[str, object]

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

      • type: Literal["stored_completions"]

        The type of data source. Always stored_completions.

        • "stored_completions"

      • metadata: Optional[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: Optional[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: str

    The name of the evaluation.

  • object: Literal["eval"]

    The object type.

    • "eval"

  • testing_criteria: List[TestingCriterion]

    A list of testing criteria.

    • class LabelModelGrader: …

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

      • input: List[Input]

        • content: InputContent

          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.

          • str

            A text input to the model.

          • class ResponseInputText: …

            A text input to the model.

            • text: str

              The text input to the model.

            • type: Literal["input_text"]

              The type of the input item. Always input_text.

              • "input_text"

          • class InputContentOutputText: …

            A text output from the model.

            • text: str

              The text output from the model.

            • type: Literal["output_text"]

              The type of the output text. Always output_text.

              • "output_text"

          • class InputContentInputImage: …

            An image input block used within EvalItem content arrays.

            • image_url: str

              The URL of the image input.

            • type: Literal["input_image"]

              The type of the image input. Always input_image.

              • "input_image"

            • detail: Optional[str]

              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: str

                Base64-encoded audio data.

              • format: Literal["mp3", "wav"]

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

                • "mp3"

                • "wav"

            • type: Literal["input_audio"]

              The type of the input item. Always input_audio.

              • "input_audio"

          • List[GraderInputItem]

            • str

              A text input to the model.

            • class ResponseInputText: …

              A text input to the model.

            • class GraderInputItemOutputText: …

              A text output from the model.

              • text: str

                The text output from the model.

              • type: Literal["output_text"]

                The type of the output text. Always output_text.

                • "output_text"

            • class GraderInputItemInputImage: …

              An image input block used within EvalItem content arrays.

              • image_url: str

                The URL of the image input.

              • type: Literal["input_image"]

                The type of the image input. Always input_image.

                • "input_image"

              • detail: Optional[str]

                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: Literal["user", "assistant", "system", "developer"]

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

          • "user"

          • "assistant"

          • "system"

          • "developer"

        • type: Optional[Literal["message"]]

          The type of the message input. Always message.

          • "message"

      • labels: List[str]

        The labels to assign to each item in the evaluation.

      • model: str

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

      • name: str

        The name of the grader.

      • passing_labels: List[str]

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

      • type: Literal["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: str

        The input text. This may include template strings.

      • name: str

        The name of the grader.

      • operation: Literal["eq", "ne", "like", "ilike"]

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

        • "eq"

        • "ne"

        • "like"

        • "ilike"

      • reference: str

        The reference text. This may include template strings.

      • type: Literal["string_check"]

        The object type, which is always string_check.

        • "string_check"

    • class TestingCriterionEvalGraderTextSimilarity: …

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: float

        The threshold for the score.

    • class TestingCriterionEvalGraderPython: …

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

      • pass_threshold: Optional[float]

        The threshold for the score.

    • class TestingCriterionEvalGraderScoreModel: …

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

      • pass_threshold: Optional[float]

        The threshold for the score.

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
eval = client.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",
    }],
)
print(eval.id)

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"
    }
  ]
}

Example

from openai import OpenAI
client = OpenAI()

eval_obj = client.evals.create(
  name="Sentiment",
  data_source_config={
    "type": "stored_completions",
    "metadata": {"usecase": "chatbot"}
  },
  testing_criteria=[
    {
      "type": "label_model",
      "model": "o3-mini",
      "input": [
        {"role": "developer", "content": "Classify the sentiment of the following statement as one of 'positive', 'neutral', or 'negative'"},
        {"role": "user", "content": "Statement: {{item.input}}"}
      ],
      "passing_labels": ["positive"],
      "labels": ["positive", "neutral", "negative"],
      "name": "Example label grader"
    }
  ]
)
print(eval_obj)

Response

{
  "object": "eval",
  "id": "eval_67b7fa9a81a88190ab4aa417e397ea21",
  "data_source_config": {
    "type": "stored_completions",
    "metadata": {
      "usecase": "chatbot"
    },
    "schema": {
      "type": "object",
      "properties": {
        "item": {
          "type": "object"
        },
        "sample": {
          "type": "object"
        }
      },
      "required": [
        "item",
        "sample"
      ]
  },
  "testing_criteria": [
    {
      "name": "Example label grader",
      "type": "label_model",
      "model": "o3-mini",
      "input": [
        {
          "type": "message",
          "role": "developer",
          "content": {
            "type": "input_text",
            "text": "Classify the sentiment of the following statement as one of positive, neutral, or negative"
          }
        },
        {
          "type": "message",
          "role": "user",
          "content": {
            "type": "input_text",
            "text": "Statement: {{item.input}}"
          }
        }
      ],
      "passing_labels": [
        "positive"
      ],
      "labels": [
        "positive",
        "neutral",
        "negative"
      ]
    }
  ],
  "name": "Sentiment",
  "created_at": 1740110490,
  "metadata": {
    "description": "An eval for sentiment analysis"
  }
}

Get an eval

evals.retrieve(streval_id) -> EvalRetrieveResponse

get /evals/{eval_id}

Get an evaluation by ID.

Parameters

• eval_id: str

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: str

    Unique identifier for the evaluation.

  • created_at: int

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

  • data_source_config: DataSourceConfig

    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: Dict[str, object]

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

      • type: Literal["custom"]

        The type of data source. Always custom.

        • "custom"

    • class DataSourceConfigLogs: …

      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: Dict[str, object]

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

      • type: Literal["logs"]

        The type of data source. Always logs.

        • "logs"

      • metadata: Optional[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: Dict[str, object]

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

      • type: Literal["stored_completions"]

        The type of data source. Always stored_completions.

        • "stored_completions"

      • metadata: Optional[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: Optional[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: str

    The name of the evaluation.

  • object: Literal["eval"]

    The object type.

    • "eval"

  • testing_criteria: List[TestingCriterion]

    A list of testing criteria.

    • class LabelModelGrader: …

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

      • input: List[Input]

        • content: InputContent

          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.

          • str

            A text input to the model.

          • class ResponseInputText: …

            A text input to the model.

            • text: str

              The text input to the model.

            • type: Literal["input_text"]

              The type of the input item. Always input_text.

              • "input_text"

          • class InputContentOutputText: …

            A text output from the model.

            • text: str

              The text output from the model.

            • type: Literal["output_text"]

              The type of the output text. Always output_text.

              • "output_text"

          • class InputContentInputImage: …

            An image input block used within EvalItem content arrays.

            • image_url: str

              The URL of the image input.

            • type: Literal["input_image"]

              The type of the image input. Always input_image.

              • "input_image"

            • detail: Optional[str]

              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: str

                Base64-encoded audio data.

              • format: Literal["mp3", "wav"]

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

                • "mp3"

                • "wav"

            • type: Literal["input_audio"]

              The type of the input item. Always input_audio.

              • "input_audio"

          • List[GraderInputItem]

            • str

              A text input to the model.

            • class ResponseInputText: …

              A text input to the model.

            • class GraderInputItemOutputText: …

              A text output from the model.

              • text: str

                The text output from the model.

              • type: Literal["output_text"]

                The type of the output text. Always output_text.

                • "output_text"

            • class GraderInputItemInputImage: …

              An image input block used within EvalItem content arrays.

              • image_url: str

                The URL of the image input.

              • type: Literal["input_image"]

                The type of the image input. Always input_image.

                • "input_image"

              • detail: Optional[str]

                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: Literal["user", "assistant", "system", "developer"]

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

          • "user"

          • "assistant"

          • "system"

          • "developer"

        • type: Optional[Literal["message"]]

          The type of the message input. Always message.

          • "message"

      • labels: List[str]

        The labels to assign to each item in the evaluation.

      • model: str

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

      • name: str

        The name of the grader.

      • passing_labels: List[str]

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

      • type: Literal["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: str

        The input text. This may include template strings.

      • name: str

        The name of the grader.

      • operation: Literal["eq", "ne", "like", "ilike"]

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

        • "eq"

        • "ne"

        • "like"

        • "ilike"

      • reference: str

        The reference text. This may include template strings.

      • type: Literal["string_check"]

        The object type, which is always string_check.

        • "string_check"

    • class TestingCriterionEvalGraderTextSimilarity: …

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: float

        The threshold for the score.

    • class TestingCriterionEvalGraderPython: …

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

      • pass_threshold: Optional[float]

        The threshold for the score.

    • class TestingCriterionEvalGraderScoreModel: …

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

      • pass_threshold: Optional[float]

        The threshold for the score.

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
eval = client.evals.retrieve(
    "eval_id",
)
print(eval.id)

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"
    }
  ]
}

Example

from openai import OpenAI
client = OpenAI()

eval_obj = client.evals.retrieve("eval_67abd54d9b0081909a86353f6fb9317a")
print(eval_obj)

Response

{
  "object": "eval",
  "id": "eval_67abd54d9b0081909a86353f6fb9317a",
  "data_source_config": {
    "type": "custom",
    "schema": {
      "type": "object",
      "properties": {
        "item": {
          "type": "object",
          "properties": {
            "input": {
              "type": "string"
            },
            "ground_truth": {
              "type": "string"
            }
          },
          "required": [
            "input",
            "ground_truth"
          ]
        }
      },
      "required": [
        "item"
      ]
    }
  },
  "testing_criteria": [
    {
      "name": "String check",
      "id": "String check-2eaf2d8d-d649-4335-8148-9535a7ca73c2",
      "type": "string_check",
      "input": "{{item.input}}",
      "reference": "{{item.ground_truth}}",
      "operation": "eq"
    }
  ],
  "name": "External Data Eval",
  "created_at": 1739314509,
  "metadata": {},
}

Update an eval

evals.update(streval_id, EvalUpdateParams**kwargs) -> EvalUpdateResponse

post /evals/{eval_id}

Update certain properties of an evaluation.

Parameters

• eval_id: str

• metadata: Optional[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: Optional[str]

  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: str

    Unique identifier for the evaluation.

  • created_at: int

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

  • data_source_config: DataSourceConfig

    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: Dict[str, object]

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

      • type: Literal["custom"]

        The type of data source. Always custom.

        • "custom"

    • class DataSourceConfigLogs: …

      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: Dict[str, object]

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

      • type: Literal["logs"]

        The type of data source. Always logs.

        • "logs"

      • metadata: Optional[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: Dict[str, object]

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

      • type: Literal["stored_completions"]

        The type of data source. Always stored_completions.

        • "stored_completions"

      • metadata: Optional[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: Optional[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: str

    The name of the evaluation.

  • object: Literal["eval"]

    The object type.

    • "eval"

  • testing_criteria: List[TestingCriterion]

    A list of testing criteria.

    • class LabelModelGrader: …

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

      • input: List[Input]

        • content: InputContent

          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.

          • str

            A text input to the model.

          • class ResponseInputText: …

            A text input to the model.

            • text: str

              The text input to the model.

            • type: Literal["input_text"]

              The type of the input item. Always input_text.

              • "input_text"

          • class InputContentOutputText: …

            A text output from the model.

            • text: str

              The text output from the model.

            • type: Literal["output_text"]

              The type of the output text. Always output_text.

              • "output_text"

          • class InputContentInputImage: …

            An image input block used within EvalItem content arrays.

            • image_url: str

              The URL of the image input.

            • type: Literal["input_image"]

              The type of the image input. Always input_image.

              • "input_image"

            • detail: Optional[str]

              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: str

                Base64-encoded audio data.

              • format: Literal["mp3", "wav"]

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

                • "mp3"

                • "wav"

            • type: Literal["input_audio"]

              The type of the input item. Always input_audio.

              • "input_audio"

          • List[GraderInputItem]

            • str

              A text input to the model.

            • class ResponseInputText: …

              A text input to the model.

            • class GraderInputItemOutputText: …

              A text output from the model.

              • text: str

                The text output from the model.

              • type: Literal["output_text"]

                The type of the output text. Always output_text.

                • "output_text"

            • class GraderInputItemInputImage: …

              An image input block used within EvalItem content arrays.

              • image_url: str

                The URL of the image input.

              • type: Literal["input_image"]

                The type of the image input. Always input_image.

                • "input_image"

              • detail: Optional[str]

                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: Literal["user", "assistant", "system", "developer"]

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

          • "user"

          • "assistant"

          • "system"

          • "developer"

        • type: Optional[Literal["message"]]

          The type of the message input. Always message.

          • "message"

      • labels: List[str]

        The labels to assign to each item in the evaluation.

      • model: str

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

      • name: str

        The name of the grader.

      • passing_labels: List[str]

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

      • type: Literal["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: str

        The input text. This may include template strings.

      • name: str

        The name of the grader.

      • operation: Literal["eq", "ne", "like", "ilike"]

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

        • "eq"

        • "ne"

        • "like"

        • "ilike"

      • reference: str

        The reference text. This may include template strings.

      • type: Literal["string_check"]

        The object type, which is always string_check.

        • "string_check"

    • class TestingCriterionEvalGraderTextSimilarity: …

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: float

        The threshold for the score.

    • class TestingCriterionEvalGraderPython: …

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

      • pass_threshold: Optional[float]

        The threshold for the score.

    • class TestingCriterionEvalGraderScoreModel: …

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

      • pass_threshold: Optional[float]

        The threshold for the score.

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
eval = client.evals.update(
    eval_id="eval_id",
)
print(eval.id)

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"
    }
  ]
}

Example

from openai import OpenAI
client = OpenAI()

updated_eval = client.evals.update(
  "eval_67abd54d9b0081909a86353f6fb9317a",
  name="Updated Eval",
  metadata={"description": "Updated description"}
)
print(updated_eval)

Response

{
  "object": "eval",
  "id": "eval_67abd54d9b0081909a86353f6fb9317a",
  "data_source_config": {
    "type": "custom",
    "schema": {
      "type": "object",
      "properties": {
        "item": {
          "type": "object",
          "properties": {
            "input": {
              "type": "string"
            },
            "ground_truth": {
              "type": "string"
            }
          },
          "required": [
            "input",
            "ground_truth"
          ]
        }
      },
      "required": [
        "item"
      ]
    }
  },
  "testing_criteria": [
    {
      "name": "String check",
      "id": "String check-2eaf2d8d-d649-4335-8148-9535a7ca73c2",
      "type": "string_check",
      "input": "{{item.input}}",
      "reference": "{{item.ground_truth}}",
      "operation": "eq"
    }
  ],
  "name": "Updated Eval",
  "created_at": 1739314509,
  "metadata": {"description": "Updated description"},
}

Delete an eval

evals.delete(streval_id) -> EvalDeleteResponse

delete /evals/{eval_id}

Delete an evaluation.

Parameters

• eval_id: str

Returns

• class EvalDeleteResponse: …

  • deleted: bool

  • eval_id: str

  • object: str

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
eval = client.evals.delete(
    "eval_id",
)
print(eval.eval_id)

Response

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

Example

from openai import OpenAI
client = OpenAI()

deleted = client.evals.delete("eval_abc123")
print(deleted)

Response

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

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: Dict[str, object]

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

  • type: Literal["custom"]

    The type of data source. Always custom.

    • "custom"

Eval Stored Completions Data Source Config

• class EvalStoredCompletionsDataSourceConfig: …

  Deprecated in favor of LogsDataSourceConfig.

  • schema: Dict[str, object]

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

  • type: Literal["stored_completions"]

    The type of data source. Always stored_completions.

    • "stored_completions"

  • metadata: Optional[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: str

    Unique identifier for the evaluation.

  • created_at: int

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

  • data_source_config: DataSourceConfig

    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: Dict[str, object]

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

      • type: Literal["custom"]

        The type of data source. Always custom.

        • "custom"

    • class DataSourceConfigLogs: …

      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: Dict[str, object]

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

      • type: Literal["logs"]

        The type of data source. Always logs.

        • "logs"

      • metadata: Optional[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: Dict[str, object]

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

      • type: Literal["stored_completions"]

        The type of data source. Always stored_completions.

        • "stored_completions"

      • metadata: Optional[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: Optional[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: str

    The name of the evaluation.

  • object: Literal["eval"]

    The object type.

    • "eval"

  • testing_criteria: List[TestingCriterion]

    A list of testing criteria.

    • class LabelModelGrader: …

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

      • input: List[Input]

        • content: InputContent

          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.

          • str

            A text input to the model.

          • class ResponseInputText: …

            A text input to the model.

            • text: str

              The text input to the model.

            • type: Literal["input_text"]

              The type of the input item. Always input_text.

              • "input_text"

          • class InputContentOutputText: …

            A text output from the model.

            • text: str

              The text output from the model.

            • type: Literal["output_text"]

              The type of the output text. Always output_text.

              • "output_text"

          • class InputContentInputImage: …

            An image input block used within EvalItem content arrays.

            • image_url: str

              The URL of the image input.

            • type: Literal["input_image"]

              The type of the image input. Always input_image.

              • "input_image"

            • detail: Optional[str]

              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: str

                Base64-encoded audio data.

              • format: Literal["mp3", "wav"]

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

                • "mp3"

                • "wav"

            • type: Literal["input_audio"]

              The type of the input item. Always input_audio.

              • "input_audio"

          • List[GraderInputItem]

            • str

              A text input to the model.

            • class ResponseInputText: …

              A text input to the model.

            • class GraderInputItemOutputText: …

              A text output from the model.

              • text: str

                The text output from the model.

              • type: Literal["output_text"]

                The type of the output text. Always output_text.

                • "output_text"

            • class GraderInputItemInputImage: …

              An image input block used within EvalItem content arrays.

              • image_url: str

                The URL of the image input.

              • type: Literal["input_image"]

                The type of the image input. Always input_image.

                • "input_image"

              • detail: Optional[str]

                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: Literal["user", "assistant", "system", "developer"]

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

          • "user"

          • "assistant"

          • "system"

          • "developer"

        • type: Optional[Literal["message"]]

          The type of the message input. Always message.

          • "message"

      • labels: List[str]

        The labels to assign to each item in the evaluation.

      • model: str

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

      • name: str

        The name of the grader.

      • passing_labels: List[str]

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

      • type: Literal["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: str

        The input text. This may include template strings.

      • name: str

        The name of the grader.

      • operation: Literal["eq", "ne", "like", "ilike"]

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

        • "eq"

        • "ne"

        • "like"

        • "ilike"

      • reference: str

        The reference text. This may include template strings.

      • type: Literal["string_check"]

        The object type, which is always string_check.

        • "string_check"

    • class TestingCriterionEvalGraderTextSimilarity: …

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: float

        The threshold for the score.

    • class TestingCriterionEvalGraderPython: …

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

      • pass_threshold: Optional[float]

        The threshold for the score.

    • class TestingCriterionEvalGraderScoreModel: …

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

      • pass_threshold: Optional[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: str

    Unique identifier for the evaluation.

  • created_at: int

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

  • data_source_config: DataSourceConfig

    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: Dict[str, object]

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

      • type: Literal["custom"]

        The type of data source. Always custom.

        • "custom"

    • class DataSourceConfigLogs: …

      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: Dict[str, object]

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

      • type: Literal["logs"]

        The type of data source. Always logs.

        • "logs"

      • metadata: Optional[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: Dict[str, object]

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

      • type: Literal["stored_completions"]

        The type of data source. Always stored_completions.

        • "stored_completions"

      • metadata: Optional[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: Optional[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: str

    The name of the evaluation.

  • object: Literal["eval"]

    The object type.

    • "eval"

  • testing_criteria: List[TestingCriterion]

    A list of testing criteria.

    • class LabelModelGrader: …

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

      • input: List[Input]

        • content: InputContent

          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.

          • str

            A text input to the model.

          • class ResponseInputText: …

            A text input to the model.

            • text: str

              The text input to the model.

            • type: Literal["input_text"]

              The type of the input item. Always input_text.

              • "input_text"

          • class InputContentOutputText: …

            A text output from the model.

            • text: str

              The text output from the model.

            • type: Literal["output_text"]

              The type of the output text. Always output_text.

              • "output_text"

          • class InputContentInputImage: …

            An image input block used within EvalItem content arrays.

            • image_url: str

              The URL of the image input.

            • type: Literal["input_image"]

              The type of the image input. Always input_image.

              • "input_image"

            • detail: Optional[str]

              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: str

                Base64-encoded audio data.

              • format: Literal["mp3", "wav"]

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

                • "mp3"

                • "wav"

            • type: Literal["input_audio"]

              The type of the input item. Always input_audio.

              • "input_audio"

          • List[GraderInputItem]

            • str

              A text input to the model.

            • class ResponseInputText: …

              A text input to the model.

            • class GraderInputItemOutputText: …

              A text output from the model.

              • text: str

                The text output from the model.

              • type: Literal["output_text"]

                The type of the output text. Always output_text.

                • "output_text"

            • class GraderInputItemInputImage: …

              An image input block used within EvalItem content arrays.

              • image_url: str

                The URL of the image input.

              • type: Literal["input_image"]

                The type of the image input. Always input_image.

                • "input_image"

              • detail: Optional[str]

                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: Literal["user", "assistant", "system", "developer"]

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

          • "user"

          • "assistant"

          • "system"

          • "developer"

        • type: Optional[Literal["message"]]

          The type of the message input. Always message.

          • "message"

      • labels: List[str]

        The labels to assign to each item in the evaluation.

      • model: str

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

      • name: str

        The name of the grader.

      • passing_labels: List[str]

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

      • type: Literal["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: str

        The input text. This may include template strings.

      • name: str

        The name of the grader.

      • operation: Literal["eq", "ne", "like", "ilike"]

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

        • "eq"

        • "ne"

        • "like"

        • "ilike"

      • reference: str

        The reference text. This may include template strings.

      • type: Literal["string_check"]

        The object type, which is always string_check.

        • "string_check"

    • class TestingCriterionEvalGraderTextSimilarity: …

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: float

        The threshold for the score.

    • class TestingCriterionEvalGraderPython: …

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

      • pass_threshold: Optional[float]

        The threshold for the score.

    • class TestingCriterionEvalGraderScoreModel: …

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

      • pass_threshold: Optional[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: str

    Unique identifier for the evaluation.

  • created_at: int

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

  • data_source_config: DataSourceConfig

    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: Dict[str, object]

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

      • type: Literal["custom"]

        The type of data source. Always custom.

        • "custom"

    • class DataSourceConfigLogs: …

      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: Dict[str, object]

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

      • type: Literal["logs"]

        The type of data source. Always logs.

        • "logs"

      • metadata: Optional[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: Dict[str, object]

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

      • type: Literal["stored_completions"]

        The type of data source. Always stored_completions.

        • "stored_completions"

      • metadata: Optional[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: Optional[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: str

    The name of the evaluation.

  • object: Literal["eval"]

    The object type.

    • "eval"

  • testing_criteria: List[TestingCriterion]

    A list of testing criteria.

    • class LabelModelGrader: …

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

      • input: List[Input]

        • content: InputContent

          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.

          • str

            A text input to the model.

          • class ResponseInputText: …

            A text input to the model.

            • text: str

              The text input to the model.

            • type: Literal["input_text"]

              The type of the input item. Always input_text.

              • "input_text"

          • class InputContentOutputText: …

            A text output from the model.

            • text: str

              The text output from the model.

            • type: Literal["output_text"]

              The type of the output text. Always output_text.

              • "output_text"

          • class InputContentInputImage: …

            An image input block used within EvalItem content arrays.

            • image_url: str

              The URL of the image input.

            • type: Literal["input_image"]

              The type of the image input. Always input_image.

              • "input_image"

            • detail: Optional[str]

              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: str

                Base64-encoded audio data.

              • format: Literal["mp3", "wav"]

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

                • "mp3"

                • "wav"

            • type: Literal["input_audio"]

              The type of the input item. Always input_audio.

              • "input_audio"

          • List[GraderInputItem]

            • str

              A text input to the model.

            • class ResponseInputText: …

              A text input to the model.

            • class GraderInputItemOutputText: …

              A text output from the model.

              • text: str

                The text output from the model.

              • type: Literal["output_text"]

                The type of the output text. Always output_text.

                • "output_text"

            • class GraderInputItemInputImage: …

              An image input block used within EvalItem content arrays.

              • image_url: str

                The URL of the image input.

              • type: Literal["input_image"]

                The type of the image input. Always input_image.

                • "input_image"

              • detail: Optional[str]

                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: Literal["user", "assistant", "system", "developer"]

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

          • "user"

          • "assistant"

          • "system"

          • "developer"

        • type: Optional[Literal["message"]]

          The type of the message input. Always message.

          • "message"

      • labels: List[str]

        The labels to assign to each item in the evaluation.

      • model: str

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

      • name: str

        The name of the grader.

      • passing_labels: List[str]

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

      • type: Literal["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: str

        The input text. This may include template strings.

      • name: str

        The name of the grader.

      • operation: Literal["eq", "ne", "like", "ilike"]

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

        • "eq"

        • "ne"

        • "like"

        • "ilike"

      • reference: str

        The reference text. This may include template strings.

      • type: Literal["string_check"]

        The object type, which is always string_check.

        • "string_check"

    • class TestingCriterionEvalGraderTextSimilarity: …

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: float

        The threshold for the score.

    • class TestingCriterionEvalGraderPython: …

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

      • pass_threshold: Optional[float]

        The threshold for the score.

    • class TestingCriterionEvalGraderScoreModel: …

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

      • pass_threshold: Optional[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: str

    Unique identifier for the evaluation.

  • created_at: int

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

  • data_source_config: DataSourceConfig

    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: Dict[str, object]

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

      • type: Literal["custom"]

        The type of data source. Always custom.

        • "custom"

    • class DataSourceConfigLogs: …

      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: Dict[str, object]

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

      • type: Literal["logs"]

        The type of data source. Always logs.

        • "logs"

      • metadata: Optional[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: Dict[str, object]

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

      • type: Literal["stored_completions"]

        The type of data source. Always stored_completions.

        • "stored_completions"

      • metadata: Optional[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: Optional[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: str

    The name of the evaluation.

  • object: Literal["eval"]

    The object type.

    • "eval"

  • testing_criteria: List[TestingCriterion]

    A list of testing criteria.

    • class LabelModelGrader: …

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

      • input: List[Input]

        • content: InputContent

          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.

          • str

            A text input to the model.

          • class ResponseInputText: …

            A text input to the model.

            • text: str

              The text input to the model.

            • type: Literal["input_text"]

              The type of the input item. Always input_text.

              • "input_text"

          • class InputContentOutputText: …

            A text output from the model.

            • text: str

              The text output from the model.

            • type: Literal["output_text"]

              The type of the output text. Always output_text.

              • "output_text"

          • class InputContentInputImage: …

            An image input block used within EvalItem content arrays.

            • image_url: str

              The URL of the image input.

            • type: Literal["input_image"]

              The type of the image input. Always input_image.

              • "input_image"

            • detail: Optional[str]

              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: str

                Base64-encoded audio data.

              • format: Literal["mp3", "wav"]

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

                • "mp3"

                • "wav"

            • type: Literal["input_audio"]

              The type of the input item. Always input_audio.

              • "input_audio"

          • List[GraderInputItem]

            • str

              A text input to the model.

            • class ResponseInputText: …

              A text input to the model.

            • class GraderInputItemOutputText: …

              A text output from the model.

              • text: str

                The text output from the model.

              • type: Literal["output_text"]

                The type of the output text. Always output_text.

                • "output_text"

            • class GraderInputItemInputImage: …

              An image input block used within EvalItem content arrays.

              • image_url: str

                The URL of the image input.

              • type: Literal["input_image"]

                The type of the image input. Always input_image.

                • "input_image"

              • detail: Optional[str]

                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: Literal["user", "assistant", "system", "developer"]

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

          • "user"

          • "assistant"

          • "system"

          • "developer"

        • type: Optional[Literal["message"]]

          The type of the message input. Always message.

          • "message"

      • labels: List[str]

        The labels to assign to each item in the evaluation.

      • model: str

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

      • name: str

        The name of the grader.

      • passing_labels: List[str]

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

      • type: Literal["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: str

        The input text. This may include template strings.

      • name: str

        The name of the grader.

      • operation: Literal["eq", "ne", "like", "ilike"]

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

        • "eq"

        • "ne"

        • "like"

        • "ilike"

      • reference: str

        The reference text. This may include template strings.

      • type: Literal["string_check"]

        The object type, which is always string_check.

        • "string_check"

    • class TestingCriterionEvalGraderTextSimilarity: …

      A TextSimilarityGrader object which grades text based on similarity metrics.

      • pass_threshold: float

        The threshold for the score.

    • class TestingCriterionEvalGraderPython: …

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

      • pass_threshold: Optional[float]

        The threshold for the score.

    • class TestingCriterionEvalGraderScoreModel: …

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

      • pass_threshold: Optional[float]

        The threshold for the score.

Eval Delete Response

• class EvalDeleteResponse: …

  • deleted: bool

  • eval_id: str

  • object: str

Runs

Get eval runs

evals.runs.list(streval_id, RunListParams**kwargs) -> SyncCursorPage[RunListResponse]

get /evals/{eval_id}/runs

Get a list of runs for an evaluation.

Parameters

• eval_id: str

• after: Optional[str]

  Identifier for the last run from the previous pagination request.

• limit: Optional[int]

  Number of runs to retrieve.

• order: Optional[Literal["asc", "desc"]]

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

  • "asc"

  • "desc"

• status: Optional[Literal["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: str

    Unique identifier for the evaluation run.

  • created_at: int

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

  • data_source: DataSource

    Information about the run's data source.

    • class CreateEvalJSONLRunDataSource: …

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

      • source: Source

        Determines what populates the item namespace in the data source.

        • class SourceFileContent: …

          • content: List[SourceFileContentContent]

            The content of the jsonl file.

            • item: Dict[str, object]

            • sample: Optional[Dict[str, object]]

          • type: Literal["file_content"]

            The type of jsonl source. Always file_content.

            • "file_content"

        • class SourceFileID: …

          • id: str

            The identifier of the file.

          • type: Literal["file_id"]

            The type of jsonl source. Always file_id.

            • "file_id"

      • type: Literal["jsonl"]

        The type of data source. Always jsonl.

        • "jsonl"

    • class CreateEvalCompletionsRunDataSource: …

      A CompletionsRunDataSource object describing a model sampling configuration.

      • source: Source

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

        • class SourceFileContent: …

          • content: List[SourceFileContentContent]

            The content of the jsonl file.

            • item: Dict[str, object]

            • sample: Optional[Dict[str, object]]

          • type: Literal["file_content"]

            The type of jsonl source. Always file_content.

            • "file_content"

        • class SourceFileID: …

          • id: str

            The identifier of the file.

          • type: Literal["file_id"]

            The type of jsonl source. Always file_id.

            • "file_id"

        • class SourceStoredCompletions: …

          A StoredCompletionsRunDataSource configuration describing a set of filters

          • type: Literal["stored_completions"]

            The type of source. Always stored_completions.

            • "stored_completions"

          • created_after: Optional[int]

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

          • created_before: Optional[int]

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

          • limit: Optional[int]

            An optional maximum number of items to return.

          • metadata: Optional[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: Optional[str]

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

      • type: Literal["completions"]

        The type of run data source. Always completions.

        • "completions"

      • input_messages: Optional[InputMessages]

        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 InputMessagesTemplate: …

          • template: List[InputMessagesTemplateTemplate]

            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: Union[str, ResponseInputMessageContentList]

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

                • str

                  A text input to the model.

                • List[ResponseInputContent]

                  • class ResponseInputText: …

                    A text input to the model.

                    • text: str

                      The text input to the model.

                    • type: Literal["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: Literal["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: Literal["input_image"]

                      The type of the input item. Always input_image.

                      • "input_image"

                    • file_id: Optional[str]

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

                    • image_url: Optional[str]

                      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: Literal["input_file"]

                      The type of the input item. Always input_file.

                      • "input_file"

                    • detail: Optional[Literal["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: Optional[str]

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

                    • file_id: Optional[str]

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

                    • file_url: Optional[str]

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

                    • filename: Optional[str]

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

              • role: Literal["user", "assistant", "system", "developer"]

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

                • "user"

                • "assistant"

                • "system"

                • "developer"

              • phase: Optional[Literal["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: Optional[Literal["message"]]

                The type of the message input. Always message.

                • "message"

            • class InputMessagesTemplateTemplateEvalItem: …

              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: InputMessagesTemplateTemplateEvalItemContent

                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.

                • str

                  A text input to the model.

                • class ResponseInputText: …

                  A text input to the model.

                • class InputMessagesTemplateTemplateEvalItemContentOutputText: …

                  A text output from the model.

                  • text: str

                    The text output from the model.

                  • type: Literal["output_text"]

                    The type of the output text. Always output_text.

                    • "output_text"

                • class InputMessagesTemplateTemplateEvalItemContentInputImage: …

                  An image input block used within EvalItem content arrays.

                  • image_url: str

                    The URL of the image input.

                  • type: Literal["input_image"]

                    The type of the image input. Always input_image.

                    • "input_image"

                  • detail: Optional[str]

                    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: str

                      Base64-encoded audio data.

                    • format: Literal["mp3", "wav"]

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

                      • "mp3"

                      • "wav"

                  • type: Literal["input_audio"]

                    The type of the input item. Always input_audio.

                    • "input_audio"

                • List[GraderInputItem]

                  • str

                    A text input to the model.

                  • class ResponseInputText: …

                    A text input to the model.

                  • class GraderInputItemOutputText: …

                    A text output from the model.

                    • text: str

                      The text output from the model.

                    • type: Literal["output_text"]

                      The type of the output text. Always output_text.

                      • "output_text"

                  • class GraderInputItemInputImage: …

                    An image input block used within EvalItem content arrays.

                    • image_url: str

                      The URL of the image input.

                    • type: Literal["input_image"]

                      The type of the image input. Always input_image.

                      • "input_image"

                    • detail: Optional[str]

                      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: Literal["user", "assistant", "system", "developer"]

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

                • "user"

                • "assistant"

                • "system"

                • "developer"

              • type: Optional[Literal["message"]]

                The type of the message input. Always message.

                • "message"

          • type: Literal["template"]

            The type of input messages. Always template.

            • "template"

        • class InputMessagesItemReference: …

          • item_reference: str

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

          • type: Literal["item_reference"]

            The type of input messages. Always item_reference.

            • "item_reference"

      • model: Optional[str]

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

      • sampling_params: Optional[SamplingParams]

        • max_completion_tokens: Optional[int]

          The maximum number of tokens in the generated output.

        • reasoning_effort: Optional[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: Optional[SamplingParamsResponseFormat]

          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: Literal["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

              Structured Outputs configuration options, including a JSON Schema.

              • name: str

                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: Optional[str]

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

              • schema: Optional[Dict[str, object]]

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

              • strict: Optional[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: Literal["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: Literal["json_object"]

              The type of response format being defined. Always json_object.

              • "json_object"

        • seed: Optional[int]

          A seed value to initialize the randomness, during sampling.

        • temperature: Optional[float]

          A higher temperature increases randomness in the outputs.

        • tools: Optional[List[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: str

              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: Optional[str]

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

            • parameters: Optional[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: Optional[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: Literal["function"]

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

            • "function"

        • top_p: Optional[float]

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

    • class DataSourceResponses: …

      A ResponsesRunDataSource object describing a model sampling configuration.

      • source: DataSourceResponsesSource

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

        • class DataSourceResponsesSourceFileContent: …

          • content: List[DataSourceResponsesSourceFileContentContent]

            The content of the jsonl file.

            • item: Dict[str, object]

            • sample: Optional[Dict[str, object]]

          • type: Literal["file_content"]

            The type of jsonl source. Always file_content.

            • "file_content"

        • class DataSourceResponsesSourceFileID: …

          • id: str

            The identifier of the file.

          • type: Literal["file_id"]

            The type of jsonl source. Always file_id.

            • "file_id"

        • class DataSourceResponsesSourceResponses: …

          A EvalResponsesSource object describing a run data source configuration.

          • type: Literal["responses"]

            The type of run data source. Always responses.

            • "responses"

          • created_after: Optional[int]

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

          • created_before: Optional[int]

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

          • instructions_search: Optional[str]

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

          • metadata: Optional[object]

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

          • model: Optional[str]

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

          • reasoning_effort: Optional[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: Optional[float]

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

          • tools: Optional[List[str]]

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

          • top_p: Optional[float]

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

          • users: Optional[List[str]]

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

      • type: Literal["responses"]

        The type of run data source. Always responses.

        • "responses"

      • input_messages: Optional[DataSourceResponsesInputMessages]

        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 DataSourceResponsesInputMessagesTemplate: …

          • template: List[DataSourceResponsesInputMessagesTemplateTemplate]

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

            • class DataSourceResponsesInputMessagesTemplateTemplateChatMessage: …

              • content: str

                The content of the message.

              • role: str

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

            • class DataSourceResponsesInputMessagesTemplateTemplateEvalItem: …

              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: DataSourceResponsesInputMessagesTemplateTemplateEvalItemContent

                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.

                • str

                  A text input to the model.

                • class ResponseInputText: …

                  A text input to the model.

                • class DataSourceResponsesInputMessagesTemplateTemplateEvalItemContentOutputText: …

                  A text output from the model.

                  • text: str

                    The text output from the model.

                  • type: Literal["output_text"]

                    The type of the output text. Always output_text.

                    • "output_text"

                • class DataSourceResponsesInputMessagesTemplateTemplateEvalItemContentInputImage: …

                  An image input block used within EvalItem content arrays.

                  • image_url: str

                    The URL of the image input.

                  • type: Literal["input_image"]

                    The type of the image input. Always input_image.

                    • "input_image"

                  • detail: Optional[str]

                    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.

                • List[GraderInputItem]

                  • str

                    A text input to the model.

                  • class ResponseInputText: …

                    A text input to the model.

                  • class GraderInputItemOutputText: …

                    A text output from the model.

                  • class GraderInputItemInputImage: …

                    An image input block used within EvalItem content arrays.

                  • class ResponseInputAudio: …

                    An audio input to the model.

              • role: Literal["user", "assistant", "system", "developer"]

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

                • "user"

                • "assistant"

                • "system"

                • "developer"

              • type: Optional[Literal["message"]]

                The type of the message input. Always message.

                • "message"

          • type: Literal["template"]

            The type of input messages. Always template.

            • "template"

        • class DataSourceResponsesInputMessagesItemReference: …

          • item_reference: str

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

          • type: Literal["item_reference"]

            The type of input messages. Always item_reference.

            • "item_reference"

      • model: Optional[str]

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

      • sampling_params: Optional[DataSourceResponsesSamplingParams]

        • max_completion_tokens: Optional[int]

          The maximum number of tokens in the generated output.

        • reasoning_effort: Optional[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: Optional[int]

          A seed value to initialize the randomness, during sampling.

        • temperature: Optional[float]

          A higher temperature increases randomness in the outputs.

        • text: Optional[DataSourceResponsesSamplingParamsText]

          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: Optional[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: str

                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: Dict[str, object]

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

              • type: Literal["json_schema"]

                The type of response format being defined. Always json_schema.

                • "json_schema"

              • description: Optional[str]

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

              • strict: Optional[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: Optional[List[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: str

              The name of the function to call.

            • parameters: Optional[Dict[str, object]]

              A JSON schema object describing the parameters of the function.

            • strict: Optional[bool]

              Whether to enforce strict parameter validation. Default true.

            • type: Literal["function"]

              The type of the function tool. Always function.

              • "function"

            • defer_loading: Optional[bool]

              Whether this function is deferred and loaded via tool search.

            • description: Optional[str]

              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: Literal["file_search"]

              The type of the file search tool. Always file_search.

              • "file_search"

            • vector_store_ids: List[str]

              The IDs of the vector stores to search.

            • filters: Optional[Filters]

              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: str

                  The key to compare against the value.

                • type: Literal["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: Union[str, float, bool, List[Union[str, float]]]

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

                  • str

                  • float

                  • bool

                  • List[Union[str, float]]

                    • str

                    • float

              • class CompoundFilter: …

                Combine multiple filters using and or or.

                • filters: List[Filter]

                  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.

                  • object

                • type: Literal["and", "or"]

                  Type of operation: and or or.

                  • "and"

                  • "or"

            • max_num_results: Optional[int]

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

            • ranking_options: Optional[RankingOptions]

              Ranking options for search.

              • hybrid_search: Optional[RankingOptionsHybridSearch]

                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: Optional[Literal["auto", "default-2024-11-15"]]

                The ranker to use for the file search.

                • "auto"

                • "default-2024-11-15"

              • score_threshold: Optional[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: Literal["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: int

              The height of the computer display.

            • display_width: int

              The width of the computer display.

            • environment: Literal["windows", "mac", "linux", 2 more]

              The type of computer environment to control.

              • "windows"

              • "mac"

              • "linux"

              • "ubuntu"

              • "browser"

            • type: Literal["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: Literal["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: Optional[Filters]

              Filters for the search.

              • allowed_domains: Optional[List[str]]

                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: Optional[Literal["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: Optional[UserLocation]

              The approximate location of the user.

              • city: Optional[str]

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

              • country: Optional[str]

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

              • region: Optional[str]

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

              • timezone: Optional[str]

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

              • type: Optional[Literal["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: str

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

            • type: Literal["mcp"]

              The type of the MCP tool. Always mcp.

              • "mcp"

            • allowed_tools: Optional[McpAllowedTools]

              List of allowed tool names or a filter object.

              • List[str]

                A string array of allowed tool names

              • class McpAllowedToolsMcpToolFilter: …

                A filter object to specify which tools are allowed.

                • read_only: Optional[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: Optional[List[str]]

                  List of allowed tool names.

            • authorization: Optional[str]

              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: Optional[Literal["connector_dropbox", "connector_gmail", "connector_googlecalendar", 5 more]]

              Identifier for service connectors, like those available in ChatGPT. One of server_url or connector_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: Optional[bool]

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

            • headers: Optional[Dict[str, str]]

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

            • require_approval: Optional[McpRequireApproval]

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

              • class McpRequireApprovalMcpToolApprovalFilter: …

                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: Optional[McpRequireApprovalMcpToolApprovalFilterAlways]

                  A filter object to specify which tools are allowed.

                  • read_only: Optional[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: Optional[List[str]]

                    List of allowed tool names.

                • never: Optional[McpRequireApprovalMcpToolApprovalFilterNever]

                  A filter object to specify which tools are allowed.

                  • read_only: Optional[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: Optional[List[str]]

                    List of allowed tool names.

              • Literal["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: Optional[str]

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

            • server_url: Optional[str]

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

          • class CodeInterpreter: …

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

            • container: CodeInterpreterContainer

              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.

              • str

                The container ID.

              • class CodeInterpreterContainerCodeInterpreterToolAuto: …

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

                • type: Literal["auto"]

                  Always auto.

                  • "auto"

                • file_ids: Optional[List[str]]

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

                • memory_limit: Optional[Literal["1g", "4g", "16g", "64g"]]

                  The memory limit for the code interpreter container.

                  • "1g"

                  • "4g"

                  • "16g"

                  • "64g"

                • network_policy: Optional[CodeInterpreterContainerCodeInterpreterToolAutoNetworkPolicy]

                  Network access policy for the container.

                  • class ContainerNetworkPolicyDisabled: …

                    • type: Literal["disabled"]

                      Disable outbound network access. Always disabled.

                      • "disabled"

                  • class ContainerNetworkPolicyAllowlist: …

                    • allowed_domains: List[str]

                      A list of allowed domains when type is allowlist.

                    • type: Literal["allowlist"]

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

                      • "allowlist"

                    • domain_secrets: Optional[List[ContainerNetworkPolicyDomainSecret]]

                      Optional domain-scoped secrets for allowlisted domains.

                      • domain: str

                        The domain associated with the secret.

                      • name: str

                        The name of the secret to inject for the domain.

                      • value: str

                        The secret value to inject for the domain.

            • type: Literal["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: Literal["image_generation"]

              The type of the image generation tool. Always image_generation.

              • "image_generation"

            • action: Optional[Literal["generate", "edit", "auto"]]

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

              • "generate"

              • "edit"

              • "auto"

            • background: Optional[Literal["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: Optional[Literal["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: Optional[ImageGenerationInputImageMask]

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

              • file_id: Optional[str]

                File ID for the mask image.

              • image_url: Optional[str]

                Base64-encoded mask image.

            • model: Optional[Union[str, Literal["gpt-image-1", "gpt-image-1-mini", "gpt-image-2", 3 more], null]]

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

              • str

              • Literal["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: Optional[Literal["auto", "low"]]

              Moderation level for the generated image. Default: auto.

              • "auto"

              • "low"

            • output_compression: Optional[int]

              Compression level for the output image. Default: 100.

            • output_format: Optional[Literal["png", "webp", "jpeg"]]

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

              • "png"

              • "webp"

              • "jpeg"

            • partial_images: Optional[int]

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

            • quality: Optional[Literal["low", "medium", "high", "auto"]]

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

              • "low"

              • "medium"

              • "high"

              • "auto"

            • size: Optional[Union[str, Literal["1024x1024", "1024x1536", "1536x1024", "auto"], null]]

              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.

              • str

              • Literal["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: Literal["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: Literal["shell"]

              The type of the shell tool. Always shell.

              • "shell"

            • environment: Optional[Environment]

              • class ContainerAuto: …

                • type: Literal["container_auto"]

                  Automatically creates a container for this request

                  • "container_auto"

                • file_ids: Optional[List[str]]

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

                • memory_limit: Optional[Literal["1g", "4g", "16g", "64g"]]

                  The memory limit for the container.

                  • "1g"

                  • "4g"

                  • "16g"

                  • "64g"

                • network_policy: Optional[NetworkPolicy]

                  Network access policy for the container.

                  • class ContainerNetworkPolicyDisabled: …

                  • class ContainerNetworkPolicyAllowlist: …

                • skills: Optional[List[Skill]]

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

                  • class SkillReference: …

                    • skill_id: str

                      The ID of the referenced skill.

                    • type: Literal["skill_reference"]

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

                      • "skill_reference"

                    • version: Optional[str]

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

                  • class InlineSkill: …

                    • description: str

                      The description of the skill.

                    • name: str

                      The name of the skill.

                    • source: InlineSkillSource

                      Inline skill payload

                      • data: str

                        Base64-encoded skill zip bundle.

                      • media_type: Literal["application/zip"]

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

                        • "application/zip"

                      • type: Literal["base64"]

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

                        • "base64"

                    • type: Literal["inline"]

                      Defines an inline skill for this request.

                      • "inline"

              • class LocalEnvironment: …

                • type: Literal["local"]

                  Use a local computer environment.

                  • "local"

                • skills: Optional[List[LocalSkill]]

                  An optional list of skills.

                  • description: str

                    The description of the skill.

                  • name: str

                    The name of the skill.

                  • path: str

                    The path to the directory containing the skill.

              • class ContainerReference: …

                • container_id: str

                  The ID of the referenced container.

                • type: Literal["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: str

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

            • type: Literal["custom"]

              The type of the custom tool. Always custom.

              • "custom"

            • defer_loading: Optional[bool]

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

            • description: Optional[str]

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

            • format: Optional[CustomToolInputFormat]

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

              • class Text: …

                Unconstrained free-form text.

                • type: Literal["text"]

                  Unconstrained text format. Always text.

                  • "text"

              • class Grammar: …

                A grammar defined by the user.

                • definition: str

                  The grammar definition.

                • syntax: Literal["lark", "regex"]

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

                  • "lark"

                  • "regex"

                • type: Literal["grammar"]

                  Grammar format. Always grammar.

                  • "grammar"

          • class NamespaceTool: …

            Groups function/custom tools under a shared namespace.

            • description: str

              A description of the namespace shown to the model.

            • name: str

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

            • tools: List[Tool]

              The function/custom tools available inside this namespace.

              • class ToolFunction: …

                • name: str

                • type: Literal["function"]

                  • "function"

                • defer_loading: Optional[bool]

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

                • description: Optional[str]

                • parameters: Optional[object]

                • strict: Optional[bool]

              • class CustomTool: …

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

            • type: Literal["namespace"]

              The type of the tool. Always namespace.

              • "namespace"

          • class ToolSearchTool: …

            Hosted or BYOT tool search configuration for deferred tools.

            • type: Literal["tool_search"]

              The type of the tool. Always tool_search.

              • "tool_search"

            • description: Optional[str]

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

            • execution: Optional[Literal["server", "client"]]

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

              • "server"

              • "client"

            • parameters: Optional[object]

              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: Literal["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: Optional[List[Literal["text", "image"]]]

              • "text"

              • "image"

            • search_context_size: Optional[Literal["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: Optional[UserLocation]

              The user's location.

              • type: Literal["approximate"]

                The type of location approximation. Always approximate.

                • "approximate"

              • city: Optional[str]

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

              • country: Optional[str]

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

              • region: Optional[str]

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

              • timezone: Optional[str]

                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: Literal["apply_patch"]

              The type of the tool. Always apply_patch.

              • "apply_patch"

        • top_p: Optional[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: str

      The error code.

    • message: str

      The error message.

  • eval_id: str

    The identifier of the associated evaluation.

  • metadata: Optional[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: str

    The model that is evaluated, if applicable.

  • name: str

    The name of the evaluation run.

  • object: Literal["eval.run"]

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

    • "eval.run"

  • per_model_usage: List[PerModelUsage]

    Usage statistics for each model during the evaluation run.

    • cached_tokens: int

      The number of tokens retrieved from cache.

    • completion_tokens: int

      The number of completion tokens generated.

    • invocation_count: int

      The number of invocations.

    • model_name: str

      The name of the model.

    • prompt_tokens: int

      The number of prompt tokens used.

    • total_tokens: int

      The total number of tokens used.

  • per_testing_criteria_results: List[PerTestingCriteriaResult]

    Results per testing criteria applied during the evaluation run.

    • failed: int

      Number of tests failed for this criteria.

    • passed: int

      Number of tests passed for this criteria.

    • testing_criteria: str

      A description of the testing criteria.

  • report_url: str

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

  • result_counts: ResultCounts

    Counters summarizing the outcomes of the evaluation run.

    • errored: int

      Number of output items that resulted in an error.

    • failed: int

      Number of output items that failed to pass the evaluation.

    • passed: int

      Number of output items that passed the evaluation.

    • total: int

      Total number of executed output items.

  • status: str

    The status of the evaluation run.

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
page = client.evals.runs.list(
    eval_id="eval_id",
)
page = page.data[0]
print(page.id)

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"
}

Example

from openai import OpenAI
client = OpenAI()

runs = client.evals.runs.list("egroup_67abd54d9b0081909a86353f6fb9317a")
print(runs)

Response

{
  "object": "list",
  "data": [
    {
      "object": "eval.run",
      "id": "evalrun_67e0c7d31560819090d60c0780591042",
      "eval_id": "eval_67e0c726d560819083f19a957c4c640b",
      "report_url": "https://platform.openai.com/evaluations/eval_67e0c726d560819083f19a957c4c640b",
      "status": "completed",
      "model": "o3-mini",
      "name": "bulk_with_negative_examples_o3-mini",
      "created_at": 1742784467,
      "result_counts": {
        "total": 1,
        "errored": 0,
        "failed": 0,
        "passed": 1
      },
      "per_model_usage": [
        {
          "model_name": "o3-mini",
          "invocation_count": 1,
          "prompt_tokens": 563,
          "completion_tokens": 874,
          "total_tokens": 1437,
          "cached_tokens": 0
        }
      ],
      "per_testing_criteria_results": [
        {
          "testing_criteria": "Push Notification Summary Grader-1808cd0b-eeec-4e0b-a519-337e79f4f5d1",
          "passed": 1,
          "failed": 0
        }
      ],
      "data_source": {
        "type": "completions",
        "source": {
          "type": "file_content",
          "content": [
            {
              "item": {
                "notifications": "\n- New message from Sarah: \"Can you call me later?\"\n- Your package has been delivered!\n- Flash sale: 20% off electronics for the next 2 hours!\n"
              }
            }
          ]
        },
        "input_messages": {
          "type": "template",
          "template": [
            {
              "type": "message",
              "role": "developer",
              "content": {
                "type": "input_text",
                "text": "\n\n\n\nYou are a helpful assistant that takes in an array of push notifications and returns a collapsed summary of them.\nThe push notification will be provided as follows:\n<push_notifications>\n...notificationlist...\n</push_notifications>\n\nYou should return just the summary and nothing else.\n\n\nYou should return a summary that is concise and snappy.\n\n\nHere is an example of a good summary:\n<push_notifications>\n- Traffic alert: Accident reported on Main Street.- Package out for delivery: Expected by 5 PM.- New friend suggestion: Connect with Emma.\n</push_notifications>\n<summary>\nTraffic alert, package expected by 5pm, suggestion for new friend (Emily).\n</summary>\n\n\nHere is an example of a bad summary:\n<push_notifications>\n- Traffic alert: Accident reported on Main Street.- Package out for delivery: Expected by 5 PM.- New friend suggestion: Connect with Emma.\n</push_notifications>\n<summary>\nTraffic alert reported on main street. You have a package that will arrive by 5pm, Emily is a new friend suggested for you.\n</summary>\n"
              }
            },
            {
              "type": "message",
              "role": "user",
              "content": {
                "type": "input_text",
                "text": "<push_notifications>{{item.notifications}}</push_notifications>"
              }
            }
          ]
        },
        "model": "o3-mini",
        "sampling_params": null
      },
      "error": null,
      "metadata": {}
    }
  ],
  "first_id": "evalrun_67e0c7d31560819090d60c0780591042",
  "last_id": "evalrun_67e0c7d31560819090d60c0780591042",
  "has_more": true
}

Create eval run

evals.runs.create(streval_id, RunCreateParams**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: str

• data_source: DataSource

  Details about the run's data source.

  • class CreateEvalJSONLRunDataSource: …

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

    • source: Source

      Determines what populates the item namespace in the data source.

      • class SourceFileContent: …

        • content: List[SourceFileContentContent]

          The content of the jsonl file.

          • item: Dict[str, object]

          • sample: Optional[Dict[str, object]]

        • type: Literal["file_content"]

          The type of jsonl source. Always file_content.

          • "file_content"

      • class SourceFileID: …

        • id: str

          The identifier of the file.

        • type: Literal["file_id"]

          The type of jsonl source. Always file_id.

          • "file_id"

    • type: Literal["jsonl"]

      The type of data source. Always jsonl.

      • "jsonl"

  • class CreateEvalCompletionsRunDataSource: …

    A CompletionsRunDataSource object describing a model sampling configuration.

    • source: Source

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

      • class SourceFileContent: …

        • content: List[SourceFileContentContent]

          The content of the jsonl file.

          • item: Dict[str, object]

          • sample: Optional[Dict[str, object]]

        • type: Literal["file_content"]

          The type of jsonl source. Always file_content.

          • "file_content"

      • class SourceFileID: …

        • id: str

          The identifier of the file.

        • type: Literal["file_id"]

          The type of jsonl source. Always file_id.

          • "file_id"

      • class SourceStoredCompletions: …

        A StoredCompletionsRunDataSource configuration describing a set of filters

        • type: Literal["stored_completions"]

          The type of source. Always stored_completions.

          • "stored_completions"

        • created_after: Optional[int]

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

        • created_before: Optional[int]

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

        • limit: Optional[int]

          An optional maximum number of items to return.

        • metadata: Optional[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: Optional[str]

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

    • type: Literal["completions"]

      The type of run data source. Always completions.

      • "completions"

    • input_messages: Optional[InputMessages]

      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 InputMessagesTemplate: …

        • template: List[InputMessagesTemplateTemplate]

          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: Union[str, ResponseInputMessageContentList]

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

              • str

                A text input to the model.

              • List[ResponseInputContent]

                • class ResponseInputText: …

                  A text input to the model.

                  • text: str

                    The text input to the model.

                  • type: Literal["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: Literal["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: Literal["input_image"]

                    The type of the input item. Always input_image.

                    • "input_image"

                  • file_id: Optional[str]

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

                  • image_url: Optional[str]

                    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: Literal["input_file"]

                    The type of the input item. Always input_file.

                    • "input_file"

                  • detail: Optional[Literal["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: Optional[str]

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

                  • file_id: Optional[str]

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

                  • file_url: Optional[str]

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

                  • filename: Optional[str]

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

            • role: Literal["user", "assistant", "system", "developer"]

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

              • "user"

              • "assistant"

              • "system"

              • "developer"

            • phase: Optional[Literal["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: Optional[Literal["message"]]

              The type of the message input. Always message.

              • "message"

          • class InputMessagesTemplateTemplateEvalItem: …

            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: InputMessagesTemplateTemplateEvalItemContent

              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.

              • str

                A text input to the model.

              • class ResponseInputText: …

                A text input to the model.

              • class InputMessagesTemplateTemplateEvalItemContentOutputText: …

                A text output from the model.

                • text: str

                  The text output from the model.

                • type: Literal["output_text"]

                  The type of the output text. Always output_text.

                  • "output_text"

              • class InputMessagesTemplateTemplateEvalItemContentInputImage: …

                An image input block used within EvalItem content arrays.

                • image_url: str

                  The URL of the image input.

                • type: Literal["input_image"]

                  The type of the image input. Always input_image.

                  • "input_image"

                • detail: Optional[str]

                  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: str

                    Base64-encoded audio data.

                  • format: Literal["mp3", "wav"]

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

                    • "mp3"

                    • "wav"

                • type: Literal["input_audio"]

                  The type of the input item. Always input_audio.

                  • "input_audio"

              • List[GraderInputItem]

                • str

                  A text input to the model.

                • class ResponseInputText: …

                  A text input to the model.

                • class GraderInputItemOutputText: …

                  A text output from the model.

                  • text: str

                    The text output from the model.

                  • type: Literal["output_text"]

                    The type of the output text. Always output_text.

                    • "output_text"

                • class GraderInputItemInputImage: …

                  An image input block used within EvalItem content arrays.

                  • image_url: str

                    The URL of the image input.

                  • type: Literal["input_image"]

                    The type of the image input. Always input_image.

                    • "input_image"

                  • detail: Optional[str]

                    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: Literal["user", "assistant", "system", "developer"]

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

              • "user"

              • "assistant"

              • "system"

              • "developer"

            • type: Optional[Literal["message"]]

              The type of the message input. Always message.

              • "message"

        • type: Literal["template"]

          The type of input messages. Always template.

          • "template"

      • class InputMessagesItemReference: …

        • item_reference: str

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

        • type: Literal["item_reference"]

          The type of input messages. Always item_reference.

          • "item_reference"

    • model: Optional[str]

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

    • sampling_params: Optional[SamplingParams]

      • max_completion_tokens: Optional[int]

        The maximum number of tokens in the generated output.

      • reasoning_effort: Optional[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: Optional[SamplingParamsResponseFormat]

        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: Literal["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

            Structured Outputs configuration options, including a JSON Schema.

            • name: str

              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: Optional[str]

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

            • schema: Optional[Dict[str, object]]

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

            • strict: Optional[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: Literal["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: Literal["json_object"]

            The type of response format being defined. Always json_object.

            • "json_object"

      • seed: Optional[int]

        A seed value to initialize the randomness, during sampling.

      • temperature: Optional[float]

        A higher temperature increases randomness in the outputs.

      • tools: Optional[List[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: str

            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: Optional[str]

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

          • parameters: Optional[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: Optional[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: Literal["function"]

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

          • "function"

      • top_p: Optional[float]

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

  • class DataSourceCreateEvalResponsesRunDataSource: …

    A ResponsesRunDataSource object describing a model sampling configuration.

    • source: DataSourceCreateEvalResponsesRunDataSourceSource

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

      • class DataSourceCreateEvalResponsesRunDataSourceSourceFileContent: …

        • content: Iterable[DataSourceCreateEvalResponsesRunDataSourceSourceFileContentContent]

          The content of the jsonl file.

          • item: Dict[str, object]

          • sample: Optional[Dict[str, object]]

        • type: Literal["file_content"]

          The type of jsonl source. Always file_content.

          • "file_content"

      • class DataSourceCreateEvalResponsesRunDataSourceSourceFileID: …

        • id: str

          The identifier of the file.

        • type: Literal["file_id"]

          The type of jsonl source. Always file_id.

          • "file_id"

      • class DataSourceCreateEvalResponsesRunDataSourceSourceResponses: …

        A EvalResponsesSource object describing a run data source configuration.

        • type: Literal["responses"]

          The type of run data source. Always responses.

          • "responses"

        • created_after: Optional[int]

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

        • created_before: Optional[int]

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

        • instructions_search: Optional[str]

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

        • metadata: Optional[object]

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

        • model: Optional[str]

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

        • reasoning_effort: Optional[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: Optional[float]

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

        • tools: Optional[Sequence[str]]

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

        • top_p: Optional[float]

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

        • users: Optional[Sequence[str]]

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

    • type: Literal["responses"]

      The type of run data source. Always responses.

      • "responses"

    • input_messages: Optional[DataSourceCreateEvalResponsesRunDataSourceInputMessages]

      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 DataSourceCreateEvalResponsesRunDataSourceInputMessagesTemplate: …

        • template: Iterable[DataSourceCreateEvalResponsesRunDataSourceInputMessagesTemplateTemplate]

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

          • class DataSourceCreateEvalResponsesRunDataSourceInputMessagesTemplateTemplateChatMessage: …

            • content: str

              The content of the message.

            • role: str

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

          • class DataSourceCreateEvalResponsesRunDataSourceInputMessagesTemplateTemplateEvalItem: …

            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: DataSourceCreateEvalResponsesRunDataSourceInputMessagesTemplateTemplateEvalItemContent

              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.

              • str

                A text input to the model.

              • class ResponseInputText: …

                A text input to the model.

              • class DataSourceCreateEvalResponsesRunDataSourceInputMessagesTemplateTemplateEvalItemContentOutputText: …

                A text output from the model.

                • text: str

                  The text output from the model.

                • type: Literal["output_text"]

                  The type of the output text. Always output_text.

                  • "output_text"

              • class DataSourceCreateEvalResponsesRunDataSourceInputMessagesTemplateTemplateEvalItemContentInputImage: …

                An image input block used within EvalItem content arrays.

                • image_url: str

                  The URL of the image input.

                • type: Literal["input_image"]

                  The type of the image input. Always input_image.

                  • "input_image"

                • detail: Optional[str]

                  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.

              • Sequence[GraderInputsParamItem]

                • str

                  A text input to the model.

                • class ResponseInputText: …

                  A text input to the model.

                • class GraderInputItemOutputText: …

                  A text output from the model.

                • class GraderInputItemInputImage: …

                  An image input block used within EvalItem content arrays.

                • class ResponseInputAudio: …

                  An audio input to the model.

            • role: Literal["user", "assistant", "system", "developer"]

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

              • "user"

              • "assistant"

              • "system"

              • "developer"

            • type: Optional[Literal["message"]]

              The type of the message input. Always message.

              • "message"

        • type: Literal["template"]

          The type of input messages. Always template.

          • "template"

      • class DataSourceCreateEvalResponsesRunDataSourceInputMessagesItemReference: …

        • item_reference: str

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

        • type: Literal["item_reference"]

          The type of input messages. Always item_reference.

          • "item_reference"

    • model: Optional[str]

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

    • sampling_params: Optional[DataSourceCreateEvalResponsesRunDataSourceSamplingParams]

      • max_completion_tokens: Optional[int]

        The maximum number of tokens in the generated output.

      • reasoning_effort: Optional[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: Optional[int]

        A seed value to initialize the randomness, during sampling.

      • temperature: Optional[float]

        A higher temperature increases randomness in the outputs.

      • text: Optional[DataSourceCreateEvalResponsesRunDataSourceSamplingParamsText]

        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: Optional[ResponseFormatTextConfigParam]

          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: str

              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: Dict[str, object]

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

            • type: Literal["json_schema"]

              The type of response format being defined. Always json_schema.

              • "json_schema"

            • description: Optional[str]

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

            • strict: Optional[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: Optional[Iterable[ToolParam]]

        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: str

            The name of the function to call.

          • parameters: Optional[Dict[str, object]]

            A JSON schema object describing the parameters of the function.

          • strict: Optional[bool]

            Whether to enforce strict parameter validation. Default true.

          • type: Literal["function"]

            The type of the function tool. Always function.

            • "function"

          • defer_loading: Optional[bool]

            Whether this function is deferred and loaded via tool search.

          • description: Optional[str]

            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: Literal["file_search"]

            The type of the file search tool. Always file_search.

            • "file_search"

          • vector_store_ids: List[str]

            The IDs of the vector stores to search.

          • filters: Optional[Filters]

            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: str

                The key to compare against the value.

              • type: Literal["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: Union[str, float, bool, List[Union[str, float]]]

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

                • str

                • float

                • bool

                • List[Union[str, float]]

                  • str

                  • float

            • class CompoundFilter: …

              Combine multiple filters using and or or.

              • filters: List[Filter]

                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.

                • object

              • type: Literal["and", "or"]

                Type of operation: and or or.

                • "and"

                • "or"

          • max_num_results: Optional[int]

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

          • ranking_options: Optional[RankingOptions]

            Ranking options for search.

            • hybrid_search: Optional[RankingOptionsHybridSearch]

              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: Optional[Literal["auto", "default-2024-11-15"]]

              The ranker to use for the file search.

              • "auto"

              • "default-2024-11-15"

            • score_threshold: Optional[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: Literal["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: int

            The height of the computer display.

          • display_width: int

            The width of the computer display.

          • environment: Literal["windows", "mac", "linux", 2 more]

            The type of computer environment to control.

            • "windows"

            • "mac"

            • "linux"

            • "ubuntu"

            • "browser"

          • type: Literal["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: Literal["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: Optional[Filters]

            Filters for the search.

            • allowed_domains: Optional[List[str]]

              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: Optional[Literal["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: Optional[UserLocation]

            The approximate location of the user.

            • city: Optional[str]

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

            • country: Optional[str]

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

            • region: Optional[str]

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

            • timezone: Optional[str]

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

            • type: Optional[Literal["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: str

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

          • type: Literal["mcp"]

            The type of the MCP tool. Always mcp.

            • "mcp"

          • allowed_tools: Optional[McpAllowedTools]

            List of allowed tool names or a filter object.

            • List[str]

              A string array of allowed tool names

            • class McpAllowedToolsMcpToolFilter: …

              A filter object to specify which tools are allowed.

              • read_only: Optional[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: Optional[List[str]]

                List of allowed tool names.

          • authorization: Optional[str]

            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: Optional[Literal["connector_dropbox", "connector_gmail", "connector_googlecalendar", 5 more]]

            Identifier for service connectors, like those available in ChatGPT. One of server_url or connector_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: Optional[bool]

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

          • headers: Optional[Dict[str, str]]

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

          • require_approval: Optional[McpRequireApproval]

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

            • class McpRequireApprovalMcpToolApprovalFilter: …

              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: Optional[McpRequireApprovalMcpToolApprovalFilterAlways]

                A filter object to specify which tools are allowed.

                • read_only: Optional[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: Optional[List[str]]

                  List of allowed tool names.

              • never: Optional[McpRequireApprovalMcpToolApprovalFilterNever]

                A filter object to specify which tools are allowed.

                • read_only: Optional[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: Optional[List[str]]

                  List of allowed tool names.

            • Literal["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: Optional[str]

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

          • server_url: Optional[str]

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

        • class CodeInterpreter: …

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

          • container: CodeInterpreterContainer

            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.

            • str

              The container ID.

            • class CodeInterpreterContainerCodeInterpreterToolAuto: …

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

              • type: Literal["auto"]

                Always auto.

                • "auto"

              • file_ids: Optional[List[str]]

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

              • memory_limit: Optional[Literal["1g", "4g", "16g", "64g"]]

                The memory limit for the code interpreter container.

                • "1g"

                • "4g"

                • "16g"

                • "64g"

              • network_policy: Optional[CodeInterpreterContainerCodeInterpreterToolAutoNetworkPolicy]

                Network access policy for the container.

                • class ContainerNetworkPolicyDisabled: …

                  • type: Literal["disabled"]

                    Disable outbound network access. Always disabled.

                    • "disabled"

                • class ContainerNetworkPolicyAllowlist: …

                  • allowed_domains: List[str]

                    A list of allowed domains when type is allowlist.

                  • type: Literal["allowlist"]

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

                    • "allowlist"

                  • domain_secrets: Optional[List[ContainerNetworkPolicyDomainSecret]]

                    Optional domain-scoped secrets for allowlisted domains.

                    • domain: str

                      The domain associated with the secret.

                    • name: str

                      The name of the secret to inject for the domain.

                    • value: str

                      The secret value to inject for the domain.

          • type: Literal["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: Literal["image_generation"]

            The type of the image generation tool. Always image_generation.

            • "image_generation"

          • action: Optional[Literal["generate", "edit", "auto"]]

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

            • "generate"

            • "edit"

            • "auto"

          • background: Optional[Literal["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: Optional[Literal["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: Optional[ImageGenerationInputImageMask]

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

            • file_id: Optional[str]

              File ID for the mask image.

            • image_url: Optional[str]

              Base64-encoded mask image.

          • model: Optional[Union[str, Literal["gpt-image-1", "gpt-image-1-mini", "gpt-image-2", 3 more], null]]

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

            • str

            • Literal["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: Optional[Literal["auto", "low"]]

            Moderation level for the generated image. Default: auto.

            • "auto"

            • "low"

          • output_compression: Optional[int]

            Compression level for the output image. Default: 100.

          • output_format: Optional[Literal["png", "webp", "jpeg"]]

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

            • "png"

            • "webp"

            • "jpeg"

          • partial_images: Optional[int]

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

          • quality: Optional[Literal["low", "medium", "high", "auto"]]

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

            • "low"

            • "medium"

            • "high"

            • "auto"

          • size: Optional[Union[str, Literal["1024x1024", "1024x1536", "1536x1024", "auto"], null]]

            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.

            • str

            • Literal["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: Literal["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: Literal["shell"]

            The type of the shell tool. Always shell.

            • "shell"

          • environment: Optional[Environment]

            • class ContainerAuto: …

              • type: Literal["container_auto"]

                Automatically creates a container for this request

                • "container_auto"

              • file_ids: Optional[List[str]]

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

              • memory_limit: Optional[Literal["1g", "4g", "16g", "64g"]]

                The memory limit for the container.

                • "1g"

                • "4g"

                • "16g"

                • "64g"

              • network_policy: Optional[NetworkPolicy]

                Network access policy for the container.

                • class ContainerNetworkPolicyDisabled: …

                • class ContainerNetworkPolicyAllowlist: …

              • skills: Optional[List[Skill]]

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

                • class SkillReference: …

                  • skill_id: str

                    The ID of the referenced skill.

                  • type: Literal["skill_reference"]

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

                    • "skill_reference"

                  • version: Optional[str]

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

                • class InlineSkill: …

                  • description: str

                    The description of the skill.

                  • name: str

                    The name of the skill.

                  • source: InlineSkillSource

                    Inline skill payload

                    • data: str

                      Base64-encoded skill zip bundle.

                    • media_type: Literal["application/zip"]

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

                      • "application/zip"

                    • type: Literal["base64"]

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

                      • "base64"

                  • type: Literal["inline"]

                    Defines an inline skill for this request.

                    • "inline"

            • class LocalEnvironment: …

              • type: Literal["local"]

                Use a local computer environment.

                • "local"

              • skills: Optional[List[LocalSkill]]

                An optional list of skills.

                • description: str

                  The description of the skill.

                • name: str

                  The name of the skill.

                • path: str

                  The path to the directory containing the skill.

            • class ContainerReference: …

              • container_id: str

                The ID of the referenced container.

              • type: Literal["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: str

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

          • type: Literal["custom"]

            The type of the custom tool. Always custom.

            • "custom"

          • defer_loading: Optional[bool]

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

          • description: Optional[str]

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

          • format: Optional[CustomToolInputFormat]

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

            • class Text: …

              Unconstrained free-form text.

              • type: Literal["text"]

                Unconstrained text format. Always text.

                • "text"

            • class Grammar: …

              A grammar defined by the user.

              • definition: str

                The grammar definition.

              • syntax: Literal["lark", "regex"]

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

                • "lark"

                • "regex"

              • type: Literal["grammar"]

                Grammar format. Always grammar.

                • "grammar"

        • class NamespaceTool: …

          Groups function/custom tools under a shared namespace.

          • description: str

            A description of the namespace shown to the model.

          • name: str

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

          • tools: List[Tool]

            The function/custom tools available inside this namespace.

            • class ToolFunction: …

              • name: str

              • type: Literal["function"]

                • "function"

              • defer_loading: Optional[bool]

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

              • description: Optional[str]

              • parameters: Optional[object]

              • strict: Optional[bool]

            • class CustomTool: …

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

          • type: Literal["namespace"]

            The type of the tool. Always namespace.

            • "namespace"

        • class ToolSearchTool: …

          Hosted or BYOT tool search configuration for deferred tools.

          • type: Literal["tool_search"]

            The type of the tool. Always tool_search.

            • "tool_search"

          • description: Optional[str]

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

          • execution: Optional[Literal["server", "client"]]

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

            • "server"

            • "client"

          • parameters: Optional[object]

            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: Literal["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: Optional[List[Literal["text", "image"]]]

            • "text"

            • "image"

          • search_context_size: Optional[Literal["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: Optional[UserLocation]

            The user's location.

            • type: Literal["approximate"]

              The type of location approximation. Always approximate.

              • "approximate"

            • city: Optional[str]

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

            • country: Optional[str]

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

            • region: Optional[str]

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

            • timezone: Optional[str]

              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: Literal["apply_patch"]

            The type of the tool. Always apply_patch.

            • "apply_patch"

      • top_p: Optional[float]

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

• metadata: Optional[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: Optional[str]

  The name of the run.