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API Reference
Evals

List evals

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

List evaluations for a project.

ParametersExpand Collapse
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.

Accepts one of the following:
"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.

Accepts one of the following:
"created_at"
"updated_at"
ReturnsExpand Collapse
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.

Accepts one of the following:
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.

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.

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.

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.

testing_criteria: List[TestingCriterion]

A list of testing criteria.

Accepts one of the following:
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.

Accepts one of the following:
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.

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.

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.

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.

Accepts one of the following:
"mp3"
"wav"
type: Literal["input_audio"]

The type of the input item. Always input_audio.

List[GraderInputItem]
Accepts one of the following:
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.

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.

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.

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.

Accepts one of the following:
"mp3"
"wav"
type: Literal["input_audio"]

The type of the input item. Always input_audio.

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

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

Accepts one of the following:
"user"
"assistant"
"system"
"developer"
type: Optional[Literal["message"]]

The type of the message input. Always 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.

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.

Accepts one of the following:
"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.

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.

List evals

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)
{
  "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"
}
Returns Examples
{
  "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"
}