## Update or Restore Evaluation

**patch** `/v5/evaluations/{evaluation_id}`

Update an evaluation's mutable fields, or restore it from the archive.

The action is selected by the request body: a restore request un-archives the evaluation and
cascades the restore to its items and dashboards, while any other body applies a partial update
to fields such as name, description, tags, and metadata (metadata is applied as an RFC 7396
merge patch). Updating an already-archived evaluation is rejected — restore it first. The
evaluation row is locked for the duration of the write to avoid concurrent-update races.

### Path Parameters

- `evaluation_id: string`

### Body Parameters

- `evaluation: object { description, metadata, name, tags }  or RestoreRequest`

  - `PartialEvaluationUpdateRequest object { description, metadata, name, tags }`

    - `description: optional string`

    - `metadata: optional map[unknown]`

      Optional metadata key-value pairs for the evaluation

    - `name: optional string`

    - `tags: optional array of string`

      The tags associated with the evaluation

  - `RestoreRequest object { restore }`

    - `restore: true`

      Set to true to restore the entity from the database.

      - `true`

### Returns

- `Evaluation object { id, created_at, created_by, 12 more }`

  - `id: string`

    The unique identifier of the entity.

  - `created_at: string`

    The date and time when the entity was created in ISO format.

  - `created_by: Identity`

    The identity that created the entity.

    - `id: string`

    - `type: "user" or "service_account"`

      - `"user"`

      - `"service_account"`

    - `object: optional "identity"`

      - `"identity"`

  - `datasets: array of Dataset`

    - `id: string`

      The unique identifier of the entity.

    - `created_at: string`

      The date and time when the entity was created in ISO format.

    - `created_by: Identity`

      The identity that created the entity.

    - `current_version_num: number`

    - `name: string`

    - `tags: array of string`

      The tags associated with the entity

    - `archived_at: optional string`

      The date and time when the entity was archived in ISO format.

    - `description: optional string`

    - `object: optional "dataset"`

      - `"dataset"`

  - `name: string`

  - `status: "failed" or "completed" or "running"`

    - `"failed"`

    - `"completed"`

    - `"running"`

  - `tags: array of string`

    The tags associated with the entity

  - `archived_at: optional string`

    The date and time when the entity was archived in ISO format.

  - `description: optional string`

  - `error_count: optional number`

    Number of task errors across all items in this evaluation.

  - `metadata: optional map[unknown]`

    Metadata key-value pairs for the evaluation

  - `object: optional "evaluation"`

    - `"evaluation"`

  - `progress: optional EvaluationTasksProgressSchema`

    Progress of the evaluation's underlying async job

    - `items: optional object { failed, pending, successful, 2 more }`

      - `failed: number`

      - `pending: number`

      - `successful: number`

      - `total: number`

      - `failed_items: optional array of object { item_id, error, error_type }`

        - `item_id: string`

        - `error: optional string`

        - `error_type: optional string`

    - `workflows: optional object { completed, failed, pending, total }`

      - `completed: number`

      - `failed: number`

      - `pending: number`

      - `total: number`

  - `status_reason: optional string`

    Reason for evaluation status

  - `tasks: optional array of EvaluationTask`

    Tasks executed during evaluation. Populated with optional `task` view.

    - `ChatCompletion object { configuration, alias, task_type }`

      - `configuration: object { messages, model, audio, 24 more }`

        - `messages: array of map[unknown] or ItemLocator`

          openai standard message format

          - `array of map[unknown]`

          - `ItemLocator = string`

        - `model: string`

          model specified as `model_vendor/model`, for example `openai/gpt-4o`

        - `audio: optional map[unknown] or ItemLocator`

          Parameters for audio output. Required when audio output is requested with modalities: ['audio'].

          - `map[unknown]`

          - `ItemLocator = string`

        - `frequency_penalty: optional number or ItemLocator`

          Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far.

          - `number`

          - `ItemLocator = string`

        - `function_call: optional map[unknown] or ItemLocator`

          Deprecated in favor of tool_choice. Controls which function is called by the model.

          - `map[unknown]`

          - `ItemLocator = string`

        - `functions: optional array of map[unknown] or ItemLocator`

          Deprecated in favor of tools. A list of functions the model may generate JSON inputs for.

          - `array of map[unknown]`

          - `ItemLocator = string`

        - `logit_bias: optional map[number] or ItemLocator`

          Modify the likelihood of specified tokens appearing in the completion. Maps tokens to bias values from -100 to 100.

          - `map[number]`

          - `ItemLocator = string`

        - `logprobs: optional boolean or ItemLocator`

          Whether to return log probabilities of the output tokens or not.

          - `boolean`

          - `ItemLocator = string`

        - `max_completion_tokens: optional number or ItemLocator`

          An upper bound for the number of tokens that can be generated, including visible output tokens and reasoning tokens.

          - `number`

          - `ItemLocator = string`

        - `max_tokens: optional number or ItemLocator`

          Deprecated in favor of max_completion_tokens. The maximum number of tokens to generate.

          - `number`

          - `ItemLocator = string`

        - `metadata: optional map[string] or ItemLocator`

          Developer-defined tags and values used for filtering completions in the dashboard.

          - `map[string]`

          - `ItemLocator = string`

        - `modalities: optional array of string or ItemLocator`

          Output types that you would like the model to generate for this request.

          - `array of string`

          - `ItemLocator = string`

        - `n: optional number or ItemLocator`

          How many chat completion choices to generate for each input message.

          - `number`

          - `ItemLocator = string`

        - `parallel_tool_calls: optional boolean or ItemLocator`

          Whether to enable parallel function calling during tool use.

          - `boolean`

          - `ItemLocator = string`

        - `prediction: optional map[unknown] or ItemLocator`

          Static predicted output content, such as the content of a text file being regenerated.

          - `map[unknown]`

          - `ItemLocator = string`

        - `presence_penalty: optional number or ItemLocator`

          Number between -2.0 and 2.0. Positive values penalize tokens based on whether they appear in the text so far.

          - `number`

          - `ItemLocator = string`

        - `reasoning_effort: optional string`

          For o1 models only. Constrains effort on reasoning. Values: low, medium, high.

        - `response_format: optional map[unknown] or ItemLocator`

          An object specifying the format that the model must output.

          - `map[unknown]`

          - `ItemLocator = string`

        - `seed: optional number or ItemLocator`

          If specified, system will attempt to sample deterministically for repeated requests with same seed.

          - `number`

          - `ItemLocator = string`

        - `stop: optional string or array of string`

          Up to 4 sequences where the API will stop generating further tokens.

          - `string`

          - `array of string`

        - `store: optional boolean or ItemLocator`

          Whether to store the output for use in model distillation or evals products.

          - `boolean`

          - `ItemLocator = string`

        - `temperature: optional number or ItemLocator`

          What sampling temperature to use. Higher values make output more random, lower more focused.

          - `number`

          - `ItemLocator = string`

        - `tool_choice: optional string or map[unknown]`

          Controls which tool is called by the model. Values: none, auto, required, or specific tool.

          - `string`

          - `map[unknown]`

        - `tools: optional array of map[unknown] or ItemLocator`

          A list of tools the model may call. Currently, only functions are supported. Max 128 functions.

          - `array of map[unknown]`

          - `ItemLocator = string`

        - `top_k: optional number or ItemLocator`

          Only sample from the top K options for each subsequent token

          - `number`

          - `ItemLocator = string`

        - `top_logprobs: optional number or ItemLocator`

          Number of most likely tokens to return at each position, with associated log probability.

          - `number`

          - `ItemLocator = string`

        - `top_p: optional number or ItemLocator`

          Alternative to temperature. Only tokens comprising top_p probability mass are considered.

          - `number`

          - `ItemLocator = string`

      - `alias: optional string`

        Alias to title the results column. Defaults to the `chat_completion`

      - `task_type: optional "chat_completion"`

        - `"chat_completion"`

    - `Inference object { configuration, alias, task_type }`

      - `configuration: object { model, args, inference_configuration }`

        - `model: string`

          model specified as `vendor/name` (ex. openai/gpt-5)

        - `args: optional map[unknown] or ItemLocator`

          Arguments passed into model

          - `map[unknown]`

          - `ItemLocator = string`

        - `inference_configuration: optional LaunchInferenceConfiguration or ItemLocator`

          Vendor specific configuration

          - `LaunchInferenceConfiguration object { num_retries, timeout_seconds }`

            - `num_retries: optional number`

            - `timeout_seconds: optional number`

          - `ItemLocator = string`

      - `alias: optional string`

        Alias to title the results column. Defaults to the `inference`

      - `task_type: optional "inference"`

        - `"inference"`

    - `ApplicationVariant object { configuration, alias, task_type }`

      - `configuration: object { application_variant_id, inputs, history, 2 more }`

        - `application_variant_id: string`

        - `inputs: map[unknown] or ItemLocator`

          Input data for the application. For agents service variants, you must provide inputs as a mapping from `{input_name: input_value}`. For V0 variants, you must specify the node your input should be passed to, structuring your input as `{node_id: {input_name: input_value}}`.

          - `map[unknown]`

          - `ItemLocator = string`

        - `history: optional array of object { request, response, session_data }  or ItemLocator`

          History of the application

          - `ApplicationRequestResponsePairArray = array of object { request, response, session_data }`

            - `request: string`

              Request inputs

            - `response: string`

              Response outputs

            - `session_data: optional map[unknown]`

              Session data corresponding to the request response pair

          - `ItemLocator = string`

        - `operation_metadata: optional map[unknown] or ItemLocator`

          Arbitrary user-defined metadata that can be attached to the process operations and will be registered in the interaction.

          - `map[unknown]`

          - `ItemLocator = string`

        - `overrides: optional object { concurrent, initial_state, partial_trace, 2 more }  or map[object { artifact_ids_filter, artifact_name_regex, type } ] or ItemLocator`

          Optional overrides for the application

          - `AgenticApplicationOverrides object { concurrent, initial_state, partial_trace, 2 more }`

            Execution override options for agentic applications

            - `concurrent: optional boolean`

            - `initial_state: optional object { current_node, state }`

              - `current_node: string`

              - `state: map[unknown]`

            - `partial_trace: optional array of object { duration_ms, node_id, operation_input, 5 more }`

              - `duration_ms: number`

              - `node_id: string`

              - `operation_input: string`

              - `operation_output: string`

              - `operation_type: string`

              - `start_timestamp: string`

              - `workflow_id: string`

              - `operation_metadata: optional map[unknown]`

            - `return_span: optional boolean`

            - `use_channels: optional boolean`

          - `map[object { artifact_ids_filter, artifact_name_regex, type } ]`

            - `artifact_ids_filter: optional array of string`

            - `artifact_name_regex: optional array of string`

            - `type: optional "knowledge_base_schema"`

              - `"knowledge_base_schema"`

          - `ItemLocator = string`

      - `alias: optional string`

        Alias to title the results column. Defaults to the `application_variant`

      - `task_type: optional "application_variant"`

        - `"application_variant"`

    - `AgentexOutput object { configuration, alias, task_type }`

      - `configuration: object { agentex_agent_id, input_column, agent_task_params, 6 more }`

        - `agentex_agent_id: string`

          The ID of the Agentex agent to use

        - `input_column: string or map[unknown] or array of unknown`

          The dataset column to use as input for the agent

          - `string`

          - `map[unknown]`

          - `array of unknown`

        - `agent_task_params: optional map[unknown] or ItemLocator`

          Extra params merged into the Agentex `task/create` call's `params` object and forwarded verbatim to the agent. Required by agents that demand configuration at task creation -- the golden agent, for example, rejects any task whose params omit `config_id`. SGP always pins `is_eval: true`; a caller-supplied `description` overrides the SGP default. Nested `item.`-prefixed strings and `{{item.x}}` templates are resolved per evaluation item, so a per-row `config_id` can come from a dataset column.

          - `map[unknown]`

          - `ItemLocator = string`

        - `completion_mode: optional "first_message" or "turn_quiescence"`

          How the agent's first turn is judged finished. `first_message` (the default) grades the first non-empty agent text message after the input, which is cheap but grades a streaming harness on whatever text block streamed first. `turn_quiescence` keeps listening while the agent is still producing messages and grades once at least one agent text message exists and nothing new has arrived for `quiescence_seconds` -- the right choice for tool-using agents. Neither mode requires the agent to mark the task complete; a terminal task status always ends the wait, and `timeout_seconds` always bounds it.

          - `"first_message"`

          - `"turn_quiescence"`

        - `deployment_id: optional string`

          Optional Agentex deployment ID to pin the eval to a specific deployment. When set, RPC traffic routes through /agents/{agent_id}/deployments/{deployment_id}/rpc. When unset, traffic uses the agent's default RPC endpoint, which resolves through the agent's current routing rules on the Agentex side.

        - `include_traces: optional boolean or ItemLocator`

          Whether to include trace data in the evaluation results. Traces are read from SGP's own span store for the agent's trace, not from Agentex.

          - `boolean`

          - `ItemLocator = string`

        - `input_mode: optional "text" or "data"`

          How the resolved `input_column` is delivered to the agent. `text` (the default) sends a TextContent message with the value stringified. `data` sends a DataContent message whose `data` is the value as a JSON object; the resolved value must be an object, or a string that parses to one. Most agents accept text only and reject `data`.

          - `"text"`

          - `"data"`

        - `quiescence_seconds: optional number or ItemLocator`

          Seconds of no new messages before `completion_mode: turn_quiescence` considers the turn finished. Ignored in `first_message` mode. Should exceed the agent's longest expected gap between messages (a slow tool call), or the turn is graded early.

          - `number`

          - `ItemLocator = string`

        - `timeout_seconds: optional number or ItemLocator`

          Maximum seconds to wait for the agent's first-turn response per item. If not set, the server-side default of 600s applies. Capped at 1500s to stay within the evaluation item activity's 1800s start-to-close budget.

          - `number`

          - `ItemLocator = string`

      - `alias: optional string`

        Alias to title the results column. Defaults to the `agentex_output`

      - `task_type: optional "agentex_output"`

        - `"agentex_output"`

    - `Metric object { configuration, alias, task_type }`

      - `configuration: object { candidate, reference, type }  or object { candidate, reference, type }  or object { candidate, reference, type }  or 4 more`

        - `Bleu object { candidate, reference, type }`

          - `candidate: string`

          - `reference: string`

          - `type: "bleu"`

            - `"bleu"`

        - `Meteor object { candidate, reference, type }`

          - `candidate: string`

          - `reference: string`

          - `type: "meteor"`

            - `"meteor"`

        - `CosineSimilarity object { candidate, reference, type }`

          - `candidate: string`

          - `reference: string`

          - `type: "cosine_similarity"`

            - `"cosine_similarity"`

        - `F1 object { candidate, reference, type }`

          - `candidate: string`

          - `reference: string`

          - `type: "f1"`

            - `"f1"`

        - `Rouge1 object { candidate, reference, type }`

          - `candidate: string`

          - `reference: string`

          - `type: "rouge1"`

            - `"rouge1"`

        - `Rouge2 object { candidate, reference, type }`

          - `candidate: string`

          - `reference: string`

          - `type: "rouge2"`

            - `"rouge2"`

        - `RougeL object { candidate, reference, type }`

          - `candidate: string`

          - `reference: string`

          - `type: "rougeL"`

            - `"rougeL"`

      - `alias: optional string`

        Alias to title the results column. Defaults to the metric type specified in the configuration

      - `task_type: optional "metric"`

        - `"metric"`

    - `AutoEvaluationQuestion object { configuration, alias, task_type }`

      - `configuration: object { model, prompt, question_id }`

        - `model: string`

          model specified as `model_vendor/model_name`

        - `prompt: string`

        - `question_id: string`

          question to be evaluated

      - `alias: optional string`

        Alias to title the results column. Defaults to the `auto_evaluation_question`

      - `task_type: optional "auto_evaluation.question"`

        - `"auto_evaluation.question"`

    - `AutoEvaluationGuidedDecoding object { configuration, alias, task_type }`

      - `configuration: object { model, prompt, response_format, 3 more }  or object { choices, model, prompt, 3 more }  or AutoEvaluationAgentTaskRequestWithItemLocator`

        - `AutoEvaluationStructuredOutputTaskRequestWithItemLocator object { model, prompt, response_format, 3 more }`

          - `model: string`

            model specified as `model_vendor/model_name`

          - `prompt: string`

          - `response_format: map[unknown]`

            JSON schema used for structuring the model response

          - `inference_args: optional map[unknown]`

            Additional arguments to pass to the inference request

          - `run_condition: optional object { op, value }  or object { path, op }  or EqEvaluationRunCondition or 12 more`

            - `Const object { op, value }`

              - `op: optional "const"`

                - `"const"`

              - `value: optional string or number or boolean`

                - `string`

                - `number`

                - `boolean`

            - `Var object { path, op }`

              - `path: string`

              - `op: optional "var"`

                - `"var"`

            - `EqEvaluationRunCondition object { left, right, op }`

              - `left: unknown`

              - `right: unknown`

              - `op: optional "eq"`

                - `"eq"`

            - `NeEvaluationRunCondition object { left, right, op }`

              - `left: unknown`

              - `right: unknown`

              - `op: optional "ne"`

                - `"ne"`

            - `LtEvaluationRunCondition object { left, right, op }`

              - `left: unknown`

              - `right: unknown`

              - `op: optional "lt"`

                - `"lt"`

            - `LteEvaluationRunCondition object { left, right, op }`

              - `left: unknown`

              - `right: unknown`

              - `op: optional "lte"`

                - `"lte"`

            - `GtEvaluationRunCondition object { left, right, op }`

              - `left: unknown`

              - `right: unknown`

              - `op: optional "gt"`

                - `"gt"`

            - `GteEvaluationRunCondition object { left, right, op }`

              - `left: unknown`

              - `right: unknown`

              - `op: optional "gte"`

                - `"gte"`

            - `AndEvaluationRunCondition object { operands, op }`

              - `operands: array of unknown`

              - `op: optional "and"`

                - `"and"`

            - `OrEvaluationRunCondition object { operands, op }`

              - `operands: array of unknown`

              - `op: optional "or"`

                - `"or"`

            - `InEvaluationRunCondition object { left, operands, op }`

              - `left: unknown`

              - `operands: array of unknown`

              - `op: optional "in"`

                - `"in"`

            - `NotInEvaluationRunCondition object { left, operands, op }`

              - `left: unknown`

              - `operands: array of unknown`

              - `op: optional "not_in"`

                - `"not_in"`

            - `NotEvaluationRunCondition object { operands, op }`

              - `operands: array of unknown`

              - `op: optional "not"`

                - `"not"`

            - `IsNullEvaluationRunCondition object { operands, op }`

              - `operands: array of unknown`

              - `op: optional "is_null"`

                - `"is_null"`

            - `IsNotNullEvaluationRunCondition object { operands, op }`

              - `operands: array of unknown`

              - `op: optional "is_not_null"`

                - `"is_not_null"`

          - `system_prompt: optional string`

        - `AutoEvaluationGuidedDecodingTaskRequestWithItemLocator object { choices, model, prompt, 3 more }`

          - `choices: array of string`

            Choices array cannot be empty

          - `model: string`

            model specified as `model_vendor/model_name`

          - `prompt: string`

          - `inference_args: optional map[unknown]`

            Additional arguments to pass to the inference request

          - `run_condition: optional object { op, value }  or object { path, op }  or EqEvaluationRunCondition or 12 more`

            - `Const object { op, value }`

              - `op: optional "const"`

                - `"const"`

              - `value: optional string or number or boolean`

                - `string`

                - `number`

                - `boolean`

            - `Var object { path, op }`

              - `path: string`

              - `op: optional "var"`

                - `"var"`

            - `EqEvaluationRunCondition object { left, right, op }`

            - `NeEvaluationRunCondition object { left, right, op }`

            - `LtEvaluationRunCondition object { left, right, op }`

            - `LteEvaluationRunCondition object { left, right, op }`

            - `GtEvaluationRunCondition object { left, right, op }`

            - `GteEvaluationRunCondition object { left, right, op }`

            - `AndEvaluationRunCondition object { operands, op }`

            - `OrEvaluationRunCondition object { operands, op }`

            - `InEvaluationRunCondition object { left, operands, op }`

            - `NotInEvaluationRunCondition object { left, operands, op }`

            - `NotEvaluationRunCondition object { operands, op }`

            - `IsNullEvaluationRunCondition object { operands, op }`

            - `IsNotNullEvaluationRunCondition object { operands, op }`

          - `system_prompt: optional string`

        - `AutoEvaluationAgentTaskRequestWithItemLocator object { definition, name, output_rules, 6 more }`

          - `definition: string`

          - `name: string`

          - `output_rules: array of string`

          - `data_fields: optional array of string`

          - `designated_to: optional object { config, agent_name }  or object { config, agent_name }  or object { config, agent_name }  or object { config, agent_name }`

            - `ApeAgent object { config, agent_name }`

              - `config: object { model, temperature }`

                - `model: optional string`

                - `temperature: optional number`

              - `agent_name: optional "APEAgent"`

                - `"APEAgent"`

            - `IfAgent object { config, agent_name }`

              - `config: object { model }`

                - `model: optional string`

              - `agent_name: optional "IFAgent"`

                - `"IFAgent"`

            - `TruthfulnessAgent object { config, agent_name }`

              - `config: object { model }`

                - `model: optional string`

              - `agent_name: optional "TruthfulnessAgent"`

                - `"TruthfulnessAgent"`

            - `BaseAgent object { config, agent_name }`

              - `config: object { model }`

                - `model: optional string`

              - `agent_name: optional "BaseAgent"`

                - `"BaseAgent"`

          - `output_type: optional "text" or "integer" or "float" or "boolean"`

            - `"text"`

            - `"integer"`

            - `"float"`

            - `"boolean"`

          - `output_values: optional array of string or number or boolean`

            - `string`

            - `number`

            - `boolean`

          - `rubric_id: optional string`

          - `rubric_version: optional number`

      - `alias: optional string`

        Alias to title the results column. Defaults to the `auto_evaluation_guided_decoding`

      - `task_type: optional "auto_evaluation.guided_decoding"`

        - `"auto_evaluation.guided_decoding"`

    - `AutoEvaluationAgent object { configuration, alias, task_type }`

      - `configuration: AutoEvaluationAgentTaskRequestWithItemLocator`

      - `alias: optional string`

        Alias to title the results column. Defaults to the `auto_evaluation_agent`

      - `task_type: optional "auto_evaluation.agent"`

        - `"auto_evaluation.agent"`

    - `ContributorEvaluationQuestion object { configuration, alias, task_type }`

      - `configuration: object { layout, question_id, prefill_from, 3 more }`

        - `layout: Container`

          - `children: array of Container or Component`

            The children to be displayed within the container

            - `Container object { children, direction }`

            - `Component object { data, label }`

              - `data: ItemLocator`

                A pointer to the data in each evaluation item to be displayed within the component

              - `label: optional string`

          - `direction: optional "row" or "column"`

            The axis that children are placed in the container. Based on CSS `flex-direction` (see: https://developer.mozilla.org/en-US/docs/Web/CSS/flex-direction)

            - `"row"`

            - `"column"`

        - `question_id: string`

        - `prefill_from: optional string`

          Dataset column to prefill contributor question task result

        - `queue_id: optional string`

          The contributor annotation queue to include this task in. Defaults to `default`

        - `required: optional boolean`

          Whether the question is required to be answered

        - `rubric_id: optional string`

          ID of the rubric to use for scoring this evaluation question

      - `alias: optional string`

        Alias to title the results column. Defaults to the `contributor_evaluation_question`

      - `task_type: optional "contributor_evaluation.question"`

        - `"contributor_evaluation.question"`

    - `CustomFunction object { configuration, alias, task_type }`

      - `configuration: object { function_source, arg_mapping, config_args, outputs }`

        Configuration for a custom Python function evaluation task.

        - `function_source: string`

          Python function source code

        - `arg_mapping: optional map[string]`

          Mapping of function parameter names to item locators (e.g. item.field). Auto-derived from function signature if not provided.

        - `config_args: optional map[unknown]`

          Literal argument values for function parameters, such as thresholds or RNG seeds. Serialized JSON must be at most 10000 characters.

        - `outputs: optional array of object { path, alias }`

          Optional output paths to materialize as separate result columns. If omitted, the function return value is stored only under the task alias/data key.

          - `path: string`

            Dot path in the custom function return value to materialize.

          - `alias: optional string`

            Result column alias. Defaults to path with dots replaced by underscores.

      - `alias: optional string`

        Alias to title the results column. Defaults to the function name.

      - `task_type: optional "custom_function"`

        - `"custom_function"`

### Example

```http
curl https://api.egp.scale.com/v5/evaluations/$EVALUATION_ID \
    -X PATCH \
    -H 'Content-Type: application/json' \
    -H "x-api-key: $SGP_API_KEY" \
    -d '{
          "description": "description",
          "metadata": {
            "foo": "bar"
          },
          "name": "x",
          "tags": [
            "x"
          ]
        }'
```

#### Response

```json
{
  "id": "id",
  "created_at": "2019-12-27T18:11:19.117Z",
  "created_by": {
    "id": "id",
    "type": "user",
    "object": "identity"
  },
  "datasets": [
    {
      "id": "id",
      "created_at": "2019-12-27T18:11:19.117Z",
      "created_by": {
        "id": "id",
        "type": "user",
        "object": "identity"
      },
      "current_version_num": 0,
      "name": "name",
      "tags": [
        "string"
      ],
      "archived_at": "2019-12-27T18:11:19.117Z",
      "description": "description",
      "object": "dataset"
    }
  ],
  "name": "name",
  "status": "failed",
  "tags": [
    "string"
  ],
  "archived_at": "2019-12-27T18:11:19.117Z",
  "description": "description",
  "error_count": 0,
  "metadata": {
    "foo": "bar"
  },
  "object": "evaluation",
  "progress": {
    "items": {
      "failed": 0,
      "pending": 0,
      "successful": 0,
      "total": 0,
      "failed_items": [
        {
          "item_id": "item_id",
          "error": "error",
          "error_type": "error_type"
        }
      ]
    },
    "workflows": {
      "completed": 0,
      "failed": 0,
      "pending": 0,
      "total": 0
    }
  },
  "status_reason": "status_reason",
  "tasks": [
    {
      "configuration": {
        "messages": [
          {
            "foo": "bar"
          }
        ],
        "model": "model",
        "audio": {
          "foo": "bar"
        },
        "frequency_penalty": -2,
        "function_call": {
          "foo": "bar"
        },
        "functions": [
          {
            "foo": "bar"
          }
        ],
        "logit_bias": {
          "foo": 0
        },
        "logprobs": true,
        "max_completion_tokens": 0,
        "max_tokens": 0,
        "metadata": {
          "foo": "string"
        },
        "modalities": [
          "string"
        ],
        "n": 0,
        "parallel_tool_calls": true,
        "prediction": {
          "foo": "bar"
        },
        "presence_penalty": -2,
        "reasoning_effort": "reasoning_effort",
        "response_format": {
          "foo": "bar"
        },
        "seed": 0,
        "stop": "string",
        "store": true,
        "temperature": 0,
        "tool_choice": "string",
        "tools": [
          {
            "foo": "bar"
          }
        ],
        "top_k": 0,
        "top_logprobs": 0,
        "top_p": 0
      },
      "alias": "alias",
      "task_type": "chat_completion"
    }
  ]
}
```