---
title: "List tasks"
method: GET
path: "/v2/tasks"
tags: ["Tasks"]
---

# List tasks

`GET /v2/tasks`

List tasks the user has access to, with cursor-based pagination.

Filter by space, space name, task name, project, dataset, or task type using query parameters.

<Note>This endpoint is in beta, read more [here](https://arize.com/docs/ax/rest-reference#api-version-stages).</Note>

## Query parameters

- `space_id` string — A universally unique identifier (base64-encoded opaque string).
- `space_name` string
- `name` string
- `project_id` string — A universally unique identifier (base64-encoded opaque string).
- `dataset_id` string — A universally unique identifier (base64-encoded opaque string).
- `type` 'TEMPLATE_EVALUATION' | 'CODE_EVALUATION' | 'RUN_EXPERIMENT' — The task type. - TEMPLATE_EVALUATION - An LLM template-based evaluation task. - CODE_EVALUATION - A code-based evaluation task. - RUN_EXPERIMENT - A task that runs experiments.
- `limit` integer
- `cursor` string

## Response `200`

Returns a list of task objects

- ListTasksResponse
  - `tasks` Task[], required — A list of tasks
    - `id` string, required — The unique identifier for the task
    - `name` string, required — The name of the task
    - `type` 'TEMPLATE_EVALUATION' | 'CODE_EVALUATION' | 'RUN_EXPERIMENT', required — The task type. - TEMPLATE_EVALUATION - An LLM template-based evaluation task. - CODE_EVALUATION - A code-based evaluation task. - RUN_EXPERIMENT - A task that runs experiments.
    - `project_id` string, nullable — The project identifier (base64). Present for project-based tasks.
    - `dataset_id` string, nullable — The dataset identifier (base64). Present for dataset-based tasks.
    - `sampling_rate` number, nullable — Sampling rate between 0 and 1. Only applicable for project-based tasks.
    - `is_continuous` boolean, required — Whether the task runs continuously on incoming data.
    - `query_filter` string, nullable, required — Task-level query filter applied to all data.
    - `evaluators` TaskEvaluator[], required — The evaluators attached to this task. Empty for run_experiment tasks.
      - `evaluator_id` string, required — Evaluator identifier (base64).
      - `evaluator_name` string, required — The name of the attached evaluator.
      - `evaluator_version_id` string, nullable, required — The evaluator version this attachment is pinned to (base64). Null is the default and means the attachment is not pinned, so it runs the evaluator's latest version.
      - `query_filter` string, nullable, required — Per-evaluator query filter, combined with the task-level filter (AND).
      - `column_mappings` object, nullable, required — Maps evaluator template variable names to data source column names.
    - `experiment_ids` string[], required — Experiment identifiers (base64) for dataset-based tasks.
    - `run_configuration` union — Experiment execution configuration for a `RUN_EXPERIMENT` task. Exactly one variant must be supplied, identified by `experiment_type`. All fields sit at the top level alongside `experiment_type` (flat — no wrapper sub-object).
      - LlmGenerationRunConfig — Configuration for running an LLM prompt against each dataset example.
        - `experiment_type` 'LLM_GENERATION', required — Discriminator. Must be `"LLM_GENERATION"`.
        - `ai_integration_id` string, required — AI integration identifier (base64).
        - `model_name` string — Model name (e.g. `gpt-4o`). Falls back to the integration's default if omitted.
        - `messages` LLMMessage[], required — Array of message objects (at least one).
          - `role` 'USER' | 'ASSISTANT' | 'SYSTEM' | 'TOOL', required — The role of the message author
          - `content` string, nullable — The content of the message
          - `tool_call_id` string — The ID of the tool call this message is responding to
          - `tool_calls` ToolCall[] — Tool calls generated by the model
            - `id` string — The ID of the tool call
            - `type` 'FUNCTION', required — The type of tool call
            - `function` ToolCallFunction, required — The function to call
              - …
        - `input_variable_format` 'F_STRING' | 'MUSTACHE' | 'NONE', required — The format for input variables in the prompt messages. Defaults to `F_STRING` if not provided. - `F_STRING`: Single curly braces ({variable_name}) - `MUSTACHE`: Double curly braces ({{variable_name}}) - `NONE`: **Deprecated.** Treated as `F_STRING`. Will be removed in a future version.
        - `invocation_parameters` InvocationParams — Parameters for the LLM invocation
          - `temperature` number — Sampling temperature (higher = more random)
          - `max_tokens` integer — Maximum number of tokens to generate
          - `max_completion_tokens` integer — Maximum number of completion tokens to generate
          - `top_p` number — Nucleus sampling parameter
          - `frequency_penalty` number — Frequency penalty (-2.0 to 2.0)
          - `presence_penalty` number — Presence penalty (-2.0 to 2.0)
          - `stop` string[] — Stop sequences
          - `response_format` ResponseFormat — Response format configuration
            - `type` 'TEXT' | 'JSON_OBJECT' | 'JSON_SCHEMA' — The response format type
            - `json_schema` JsonSchemaConfig — JSON schema configuration (when type is JSON_SCHEMA)
              - …
          - `tool_config` ToolConfig — Tool configuration for the LLM invocation
            - `tools` ToolDefinition[] — List of tool definitions available to the model
              - …
            - `tool_choice` unknown
          - `top_k` integer — Top-K sampling parameter. A top-K of 1 means the next selected token is the most probable (greedy decoding).
          - `thinking_level` string — Controls how much reasoning the model performs before responding. Supported by Gemini 3.x models. Accepted values: 'low', 'high'.
          - `thinking_budget` integer — Maximum tokens the model may use for internal reasoning. Supported by Gemini 2.5 models. Range: 0-24576 (Flash/Flash-Lite) or 128-32768 (Pro). Set 0 to disable thinking on Flash models.
          - `reasoning_effort` string — Controls how much reasoning the model performs before responding. Supported by OpenAI o-series and GPT-5 models. o-series: 'low' | 'medium' | 'high'. GPT-5: 'none' | 'low' | 'medium' | 'high' | 'xhigh'.
          - `verbosity` string — Controls the verbosity of model output. Supported by OpenAI GPT-5 series. Accepted values: 'low' | 'medium' | 'high'.
        - `provider_parameters` object — Provider-specific parameters. Defaults to `{}` (no overrides) if omitted.
        - `tool_config` ToolConfig — Tool configuration for the LLM invocation
          - `tools` ToolDefinition[] — List of tool definitions available to the model
          - `tool_choice` unknown
        - `prompt_version_id` string, nullable — Prompt version identifier (base64). Links to a Prompt Hub version for traceability.
      - TemplateEvaluationRunConfig — Configuration for running a template-based LLM evaluator against each dataset example.
        - `experiment_type` 'TEMPLATE_EVALUATION', required — Discriminator. Must be `"TEMPLATE_EVALUATION"`.
        - `ai_integration_id` string, required — AI integration identifier (base64). The LLM that judges each example.
        - `model_name` string — Model name (e.g. `gpt-4o`). Falls back to the integration's default if omitted.
        - `template` string, required — The evaluation prompt template. Use `{{variable}}` placeholders that map to dataset column paths via `column_mapping`.
        - `provide_explanation` boolean, required — Whether to ask the LLM to include a written explanation alongside the score/label.
        - `classification_choices` object — Map of choice label to numeric score (e.g. `{"relevant": 1, "irrelevant": 0}`).
        - `column_mapping` object — Maps template variable names to dataset column paths.
        - `evaluator_version_id` string, nullable — EvaluatorVersion identifier (base64). Links this run to an Eval Hub evaluator version.
        - `invocation_parameters` InvocationParams — Parameters for the LLM invocation
          - `temperature` number — Sampling temperature (higher = more random)
          - `max_tokens` integer — Maximum number of tokens to generate
          - `max_completion_tokens` integer — Maximum number of completion tokens to generate
          - `top_p` number — Nucleus sampling parameter
          - `frequency_penalty` number — Frequency penalty (-2.0 to 2.0)
          - `presence_penalty` number — Presence penalty (-2.0 to 2.0)
          - `stop` string[] — Stop sequences
          - `response_format` ResponseFormat — Response format configuration
            - `type` 'TEXT' | 'JSON_OBJECT' | 'JSON_SCHEMA' — The response format type
            - `json_schema` JsonSchemaConfig — JSON schema configuration (when type is JSON_SCHEMA)
              - …
          - `tool_config` ToolConfig — Tool configuration for the LLM invocation
            - `tools` ToolDefinition[] — List of tool definitions available to the model
              - …
            - `tool_choice` unknown
          - `top_k` integer — Top-K sampling parameter. A top-K of 1 means the next selected token is the most probable (greedy decoding).
          - `thinking_level` string — Controls how much reasoning the model performs before responding. Supported by Gemini 3.x models. Accepted values: 'low', 'high'.
          - `thinking_budget` integer — Maximum tokens the model may use for internal reasoning. Supported by Gemini 2.5 models. Range: 0-24576 (Flash/Flash-Lite) or 128-32768 (Pro). Set 0 to disable thinking on Flash models.
          - `reasoning_effort` string — Controls how much reasoning the model performs before responding. Supported by OpenAI o-series and GPT-5 models. o-series: 'low' | 'medium' | 'high'. GPT-5: 'none' | 'low' | 'medium' | 'high' | 'xhigh'.
          - `verbosity` string — Controls the verbosity of model output. Supported by OpenAI GPT-5 series. Accepted values: 'low' | 'medium' | 'high'.
        - `provider_parameters` object — Provider-specific parameters. Defaults to `{}` (no overrides) if omitted.
      - AgentCallRunConfig — Configuration for running an agent integration against each dataset example. The `input_template` is sent to the agent after Mustache substitution.
        - `experiment_type` 'AGENT_CALL', required — Discriminator. Must be `"AGENT_CALL"`.
        - `integration_id` string, required — Agent integration identifier (base64). The agent invoked for each dataset example. Must reference an integration of `type` `AGENT`; other integration types are rejected.
        - `input_template` object, required — JSON request body sent to the agent for each dataset example. Must be a JSON object whose values conform to the agent integration's input schema. Mustache placeholders (`{{column}}`) are substituted with each dataset row's values before the request is sent. The `dataset.` prefix is optional — `{{column}}` and `{{dataset.column}}` are equivalent, and responses (create, update, and read) always echo the normalized `{{column}}` form.
    - `last_run_at` string, date-time, nullable, required — When the task was last run.
    - `created_at` string, date-time, required — When the task was created.
    - `updated_at` string, date-time, required — When the task was last updated.
    - `created_by_user_id` string, nullable, required — The unique identifier for the user who created the task.
  - `pagination` PaginationMetadata, required — Cursor-based pagination metadata. Use `next_cursor` in the subsequent request's `cursor` query parameter.
    - `next_cursor` string — Opaque cursor for fetching the next page. Treat as an unreadable token. Present when `has_more` is true; omitted when `has_more` is false.
    - `has_more` boolean, required — True if another page of results is available.

## Other responses

- `400` — Invalid request
- `401` — Authentication is required
- `403` — Insufficient permissions to access this resource
- `404` — Not found
- `429` — Rate limit exceeded

---

[API](https://skmtc.net/arize-ai/apis/arize-rest-api.md) · [All operations](https://skmtc.net/arize-ai/apis/arize-rest-api/llms.txt) · [OpenAPI document](https://skmtc-service-staging.skmtc.workers.dev/v1/apis/arize-ai/arize-rest-api/versions/2ce448f1de13/schema)