List evals

client.evals.list(?, ?): CursorPage<EvalListResponse { id, created_at, data_source_config, 4 more } >

GET/evals

List evaluations for a project.

ParametersExpand Collapse

query: EvalListParams { after, limit, order, order_by }

after?: string

Identifier for the last eval from the previous pagination request.

limit?: number

Number of evals to retrieve.

order?: "asc" | "desc"

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

One of the following:

"asc"

"desc"

order_by?: "created_at" | "updated_at"

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

One of the following:

"created_at"

"updated_at"

ReturnsExpand Collapse

EvalListResponse { id, created_at, data_source_config, 4 more }

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

Improve the quality of my chatbot
See how well my chatbot handles customer support
Check if o4-mini is better at my usecase than gpt-4o

id: string

Unique identifier for the evaluation.

created_at: number

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

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

Configuration of data sources used in runs of the evaluation.

One of the following:

EvalCustomDataSourceConfig { schema, type }

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: Record<string, unknown>

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

type: "custom"

The type of data source. Always custom.

Logs { schema, type, metadata }

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: Record<string, unknown>

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

type: "logs"

The type of data source. Always logs.

metadata?: Metadata | null

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.

EvalStoredCompletionsDataSourceConfig { schema, type, metadata }

Deprecated in favor of LogsDataSourceConfig.

schema: Record<string, unknown>

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

type: "stored_completions"

The type of data source. Always stored_completions.

metadata?: Metadata | null

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

metadata: Metadata | null

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

The name of the evaluation.

object: "eval"

The object type.

testing_criteria: Array<LabelModelGrader { input, labels, model, 3 more } | StringCheckGrader { input, name, operation, 2 more } | EvalGraderTextSimilarity { pass_threshold } | 2 more>

A list of testing criteria.

One of the following:

LabelModelGrader { input, labels, model, 3 more }

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

input: Array<Input>

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

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

One of the following:

string

ResponseInputText { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail?: string

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

ResponseInputAudio { input_audio, type }

An audio input to the model.

input_audio: InputAudio { data, format }

data: string

Base64-encoded audio data.

format: "mp3" | "wav"

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

One of the following:

"mp3"

"wav"

type: "input_audio"

The type of the input item. Always input_audio.

GraderInputs = Array<string | ResponseInputText { text, type } | OutputText { text, type } | 2 more>

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

One of the following:

string

ResponseInputText { text, type }

A text input to the model.

text: string

The text input to the model.

type: "input_text"

The type of the input item. Always input_text.

OutputText { text, type }

A text output from the model.

text: string

The text output from the model.

type: "output_text"

The type of the output text. Always output_text.

InputImage { image_url, type, detail }

An image input block used within EvalItem content arrays.

image_url: string

The URL of the image input.

type: "input_image"

The type of the image input. Always input_image.

detail?: string

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

ResponseInputAudio { input_audio, type }

An audio input to the model.

input_audio: InputAudio { data, format }

data: string

Base64-encoded audio data.

format: "mp3" | "wav"

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

One of the following:

"mp3"

"wav"

type: "input_audio"

The type of the input item. Always input_audio.

role: "user" | "assistant" | "system" | "developer"

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

One of the following:

"user"

"assistant"

"system"

"developer"

type?: "message"

The type of the message input. Always message.

labels: Array<string>

The labels to assign to each item in the evaluation.

model: string

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

The name of the grader.

passing_labels: Array<string>

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

type: "label_model"

The object type, which is always label_model.

StringCheckGrader { input, name, operation, 2 more }

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

input: string

The input text. This may include template strings.

The name of the grader.

operation: "eq" | "ne" | "like" | "ilike"

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

One of the following:

"eq"

"ne"

"like"

"ilike"

reference: string

The reference text. This may include template strings.

type: "string_check"

The object type, which is always string_check.

EvalGraderTextSimilarity extends TextSimilarityGrader { evaluation_metric, input, name, 2 more } { pass_threshold }

A TextSimilarityGrader object which grades text based on similarity metrics.

pass_threshold: number

The threshold for the score.

EvalGraderPython extends PythonGrader { name, source, type, image_tag } { pass_threshold }

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

pass_threshold?: number

The threshold for the score.

EvalGraderScoreModel extends ScoreModelGrader { input, model, name, 3 more } { pass_threshold }

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

pass_threshold?: number

The threshold for the score.

List evals

import OpenAI from "openai";

const openai = new OpenAI();

const evals = await openai.evals.list({ limit: 1 });
console.log(evals);

{
  "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
}

Returns Examples

{
  "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
}

Suggested

List evals

ParametersExpand Collapse

ReturnsExpand Collapse

List evals

Returns Examples