> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.site/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# List 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.
This endpoint is in alpha, read more [here](https://arize.com/docs/ax/rest-reference#api-version-stages).
## OpenAPI
````yaml https://api.arize.com/v2/spec.yaml get /v2/tasks
openapi: 3.0.3
info:
title: Arize REST API
version: 2.0.0
description: |
API specification for the backend data server. The API is hosted globally
at https://api.arize.com/v2 or in your own environment.
license:
name: Apache-2.0
url: https://www.apache.org/licenses/LICENSE-2.0
servers:
- description: Global
url: https://api.arize.com
- description: Regional
url: https://api.{region}.arize.com
variables:
region:
default: eu-west-1a
enum:
- eu-west-1a
- ca-central-1a
- description: Custom Host
url: https://{host}
variables:
host:
default: api.arize.com
security:
- bearerAuth: []
tags:
- name: AI Integrations
description: |
AI integrations configure access to external LLM providers (e.g. OpenAI,
Azure OpenAI, AWS Bedrock, Vertex AI). Integrations can be scoped to the
entire account, a specific organization, or a specific space.
- name: Annotation Configs
description: >
Annotation configs allow you to define consistent annotation schemas that
can be reused across your workspace, ensuring evaluations are structured
and
comparable over time.
- name: Annotation Queues
description: >
Annotation queues help you organize and manage human evaluation workflows.
Use queues to assign spans or examples to annotators for review and
labeling.
- name: API Keys
description: >
API keys are used to authenticate requests to the Arize API. List your
keys
to view metadata; the raw secret is never returned after creation.
- name: Datasets
description: |
Datasets are structured, version-controlled example collections you use to
run, evaluate, and track LLM experiments.
- name: Evaluators
description: >
Evaluators are reusable evaluation configurations used to assess the
quality
of LLM outputs. They can be template-based (using LLM judges) or
code-based.
- name: Experiments
description: >
Experiments let you systematically test prompt/model changes using
datasets,
tasks, and evaluators.
- name: Integrations
description: >
Integrations configure access to external LLM providers (e.g. OpenAI,
Azure OpenAI, AWS Bedrock, Vertex AI), notifications services (e.g.
PagerDuty, Slack), and
your own agents. Integrations can be scoped to the entire account, a
specific
organization, or a specific space.
- name: Organizations
description: >
Organizations are top-level containers within an Arize AX account for
grouping spaces.
- name: Projects
description: |
Projects represent LLM applications being monitored in Arize where you can
observe traces and spans.
- name: Prompts
description: >
Prompts are reusable, versioned templates for LLM interactions. Use
prompts
to standardize and manage how you interact with LLMs across your
application.
- name: Resource Restrictions
description: |
Endpoints for restricting and unrestricting resources (projects, models).
- name: Role Bindings
description: |
Role bindings assign a role to a user on a resource. REST currently
supports space- and project-scoped bindings.
- name: Roles
description: >
Roles define sets of permissions that can be assigned to users within an
account. Create custom roles to tailor access control to your team's
needs.
- name: Spaces
description: >
Spaces are containers within an organization for grouping related
projects,
datasets, and experiments, enabling collaboration or isolated
experimentation
with role-based access control.
- name: Spans
description: |
Spans represent individual operations within a trace. A span captures the
timing, status, and attributes of a single operation in your application.
- name: Tasks
description: |
Tasks are configurable units of work that tie one or more evaluators to a
data source (project or dataset). Use tasks to automate evaluation of LLM
outputs, with support for continuous evaluation and backfill runs.
- name: Users
description: >
Users represent members of an account. The Users endpoints allow creating,
listing, updating (display name), and removing users from the account
programmatically.
paths:
/v2/tasks:
get:
tags:
- Tasks
summary: List tasks
description: >
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.
This endpoint is in alpha, read more
[here](https://arize.com/docs/ax/rest-reference#api-version-stages).
operationId: tasks_list
parameters:
- $ref: '#/components/parameters/SpaceIdQueryParam'
- $ref: '#/components/parameters/SpaceNameQueryParam'
- $ref: '#/components/parameters/NameSearchQueryParam'
- $ref: '#/components/parameters/ProjectIdQueryParam'
- $ref: '#/components/parameters/DatasetIdQueryParam'
- $ref: '#/components/parameters/TaskTypeQueryParam'
- $ref: '#/components/parameters/LimitQueryParamMax100'
- $ref: '#/components/parameters/CursorQueryParam'
responses:
'200':
$ref: '#/components/responses/TaskList'
'400':
$ref: '#/components/responses/BadRequest'
'401':
$ref: '#/components/responses/Unauthorized'
'403':
$ref: '#/components/responses/Forbidden'
'404':
$ref: '#/components/responses/NotFound'
'429':
$ref: '#/components/responses/RateLimitExceeded'
components:
parameters:
SpaceIdQueryParam:
name: space_id
in: query
description: Filter search results to a particular space ID
schema:
$ref: '#/components/schemas/Id'
example: U3BhY2U6MTIzNDU=
SpaceNameQueryParam:
name: space_name
in: query
description: |
Case-insensitive substring filter on the space name. Narrows results
to resources in spaces whose name contains the given string. If omitted,
no space name filtering is applied and all resources are returned.
schema:
type: string
maxLength: 255
example: my-space
NameSearchQueryParam:
name: name
in: query
description: |
Case-insensitive substring filter on the resource name. Returns only
resources whose name contains the given string. For example,
`name=prod` matches "production", "my-prod-dataset", etc. If omitted,
no name filtering is applied and all resources are returned.
schema:
type: string
maxLength: 255
example: production
ProjectIdQueryParam:
name: project_id
in: query
description: Filter to tasks for a specific project (base64 identifier (base64))
schema:
$ref: '#/components/schemas/Id'
example: UHJvamVjdDoxMjM0NQ==
DatasetIdQueryParam:
name: dataset_id
in: query
description: Filter to a specific dataset (base64 identifier (base64))
schema:
$ref: '#/components/schemas/Id'
example: RGF0YXNldDoxMjM0NQ==
TaskTypeQueryParam:
name: type
in: query
description: >-
Filter by task type: template_evaluation, code_evaluation, or
run_experiment
schema:
$ref: '#/components/schemas/TaskType'
example: template_evaluation
LimitQueryParamMax100:
name: limit
in: query
description: Maximum items to return
schema:
type: integer
minimum: 1
maximum: 100
default: 50
CursorQueryParam:
name: cursor
in: query
description: |
Opaque pagination cursor returned from a previous response
(`pagination.next_cursor`). Treat it as an unreadable token; do not
attempt to parse or construct it.
schema:
type: string
responses:
TaskList:
description: Returns a list of task objects
content:
application/json:
schema:
$ref: '#/components/schemas/TaskListResponse'
example:
tasks:
- id: T25saW5lVGFzazo0NTpxUndY
name: Production Hallucination Check
type: template_evaluation
project_id: TW9kZWw6MTIzOmFCY0Q=
dataset_id: null
sampling_rate: 1
is_continuous: true
query_filter: null
evaluators:
- evaluator_id: RXZhbHVhdG9yOjEyOmFCY0Q=
evaluator_name: Hallucination Eval
query_filter: null
column_mappings:
input: attributes.input.value
output: attributes.output.value
experiment_ids: []
last_run_at: '2026-03-01T14:30:00.000Z'
created_at: '2026-02-20T10:00:00.000Z'
updated_at: '2026-02-20T10:00:00.500Z'
created_by_user_id: VXNlcjoxOm5OYkM=
pagination:
has_more: false
BadRequest:
description: Invalid request
content:
application/problem+json:
schema:
$ref: '#/components/schemas/Problem'
example:
status: 400
title: Invalid request parameters
detail: The 'name' field is required and must be a non-empty string.
instance: /resource
type: https://arize.com/docs/ax/rest-reference/errors#invalid-request
Unauthorized:
description: Authentication is required
content:
application/problem+json:
schema:
$ref: '#/components/schemas/Problem'
example:
status: 401
title: Authentication required
detail: You must be authenticated to access this resource.
instance: /resource
type: >-
https://arize.com/docs/ax/rest-reference/errors#authentication-required
Forbidden:
description: Insufficient permissions to access this resource
content:
application/problem+json:
schema:
$ref: '#/components/schemas/Problem'
example:
status: 403
title: Access forbidden
detail: You do not have permission to access this resource.
instance: /resource/12345
type: https://arize.com/docs/ax/rest-reference/errors#access-forbidden
NotFound:
description: Not found
content:
application/problem+json:
schema:
$ref: '#/components/schemas/Problem'
example:
status: 404
title: Resource not found
detail: The requested resource with ID '12345' was not found.
instance: /resource/12345
type: https://arize.com/docs/ax/rest-reference/errors#resource-not-found
RateLimitExceeded:
description: Rate limit exceeded
headers:
Retry-After:
description: |
When throttled (429), how long to wait before retrying. Value is
either a delta-seconds integer.
schema:
type: integer
minimum: 0
example: 42
content:
application/problem+json:
schema:
$ref: '#/components/schemas/Problem'
example:
status: 429
title: Rate limit exceeded
detail: >-
You have exceeded the allowed number of requests. Please try again
later.
instance: /resource
type: >-
https://arize.com/docs/ax/rest-reference/errors#rate-limit-exceeded
schemas:
Id:
type: string
description: A universally unique identifier (base64-encoded opaque string).
example: RW50aXR5OjEyMzQ1
TaskType:
type: string
enum:
- template_evaluation
- code_evaluation
- run_experiment
description: |
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.
TaskListResponse:
type: object
required:
- tasks
- pagination
properties:
tasks:
type: array
items:
$ref: '#/components/schemas/Task'
description: A list of tasks
pagination:
$ref: '#/components/schemas/PaginationMetadata'
description: Pagination metadata for cursor-based navigation
additionalProperties: false
Problem:
type: object
description: RFC 9457 Problem Details
properties:
title:
type: string
description: A short, human-readable summary of the problem type
status:
type: integer
description: >-
The HTTP status code generated by the origin server for this
occurrence of the problem
type:
type: string
format: uri-reference
description: A URI reference that identifies the problem type
detail:
type: string
description: >-
A human-readable explanation specific to this occurrence of the
problem
instance:
type: string
format: uri-reference
description: >-
A URI reference that identifies the specific occurrence of the
problem
required:
- title
- status
additionalProperties: false
Task:
type: object
required:
- id
- name
- type
- is_continuous
- query_filter
- evaluators
- experiment_ids
- last_run_at
- created_at
- updated_at
- created_by_user_id
properties:
id:
type: string
description: The unique identifier for the task
name:
type: string
description: The name of the task
type:
$ref: '#/components/schemas/TaskType'
project_id:
type: string
nullable: true
description: The project identifier (base64). Present for project-based tasks.
dataset_id:
type: string
nullable: true
description: The dataset identifier (base64). Present for dataset-based tasks.
sampling_rate:
type: number
nullable: true
minimum: 0
maximum: 1
description: >-
Sampling rate between 0 and 1. Only applicable for project-based
tasks.
is_continuous:
type: boolean
description: Whether the task runs continuously on incoming data.
query_filter:
type: string
nullable: true
description: Task-level query filter applied to all data.
evaluators:
type: array
items:
$ref: '#/components/schemas/TaskEvaluator'
description: >-
The evaluators attached to this task. Empty for run_experiment
tasks.
experiment_ids:
type: array
items:
type: string
description: Experiment identifiers (base64) for dataset-based tasks.
run_configuration:
type: object
nullable: true
allOf:
- $ref: '#/components/schemas/RunConfiguration'
description: |
The run configuration for a `run_experiment` task. Present only when
`type` is `run_experiment`. Null for all other task types.
last_run_at:
type: string
format: date-time
nullable: true
description: When the task was last run.
created_at:
type: string
format: date-time
description: When the task was created.
updated_at:
type: string
format: date-time
description: When the task was last updated.
created_by_user_id:
type: string
nullable: true
description: The unique identifier for the user who created the task.
description: >
A task is a typed, configurable unit of work that ties one or more
evaluators
to a data source (project or dataset). `run_experiment` tasks
additionally
carry a `run_configuration` that defines the LLM or evaluator settings
for
each triggered run.
additionalProperties: false
PaginationMetadata:
required:
- has_more
type: object
properties:
next_cursor:
type: string
description: >
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:
type: boolean
description: True if another page of results is available.
description: |
Cursor-based pagination metadata. Use `next_cursor` in the subsequent
request's `cursor` query parameter.
additionalProperties: false
TaskEvaluator:
type: object
required:
- evaluator_id
- evaluator_name
- query_filter
- column_mappings
properties:
evaluator_id:
type: string
description: Evaluator identifier (base64).
evaluator_name:
type: string
description: The name of the attached evaluator.
query_filter:
type: string
nullable: true
description: >-
Per-evaluator query filter, combined with the task-level filter
(AND).
column_mappings:
type: object
nullable: true
additionalProperties:
type: string
description: Maps evaluator template variable names to data source column names.
additionalProperties: false
RunConfiguration:
oneOf:
- $ref: '#/components/schemas/LlmGenerationRunConfig'
- $ref: '#/components/schemas/TemplateEvaluationRunConfig'
discriminator:
propertyName: experiment_type
mapping:
llm_generation:
$ref: '#/components/schemas/LlmGenerationRunConfig'
template_evaluation:
$ref: '#/components/schemas/TemplateEvaluationRunConfig'
description: >
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:
type: object
required:
- experiment_type
- ai_integration_id
- messages
- input_variable_format
properties:
experiment_type:
type: string
enum:
- llm_generation
description: Discriminator. Must be `"llm_generation"`.
ai_integration_id:
type: string
description: AI integration identifier (base64).
model_name:
type: string
description: >-
Model name (e.g. `gpt-4o`). Falls back to the integration's default
if omitted.
messages:
type: array
minItems: 1
items:
$ref: '#/components/schemas/LLMMessage'
description: Array of message objects (at least one).
input_variable_format:
$ref: '#/components/schemas/InputVariableFormat'
invocation_parameters:
$ref: '#/components/schemas/InvocationParams'
provider_parameters:
type: object
description: >-
Provider-specific parameters. Defaults to `{}` (no overrides) if
omitted.
tool_config:
$ref: '#/components/schemas/ToolConfig'
prompt_version_id:
type: string
nullable: true
description: >-
Prompt version identifier (base64). Links to a Prompt Hub version
for traceability.
additionalProperties: false
description: Configuration for running an LLM prompt against each dataset example.
TemplateEvaluationRunConfig:
type: object
required:
- experiment_type
- ai_integration_id
- template
- provide_explanation
properties:
experiment_type:
type: string
enum:
- template_evaluation
description: Discriminator. Must be `"template_evaluation"`.
ai_integration_id:
type: string
description: >-
AI integration identifier (base64). The LLM that judges each
example.
model_name:
type: string
description: >-
Model name (e.g. `gpt-4o`). Falls back to the integration's default
if omitted.
template:
type: string
minLength: 1
description: >
The evaluation prompt template. Use `{{variable}}` placeholders that
map to dataset
column paths via `column_mapping`.
provide_explanation:
type: boolean
description: >-
Whether to ask the LLM to include a written explanation alongside
the score/label.
classification_choices:
type: object
additionalProperties:
type: number
description: >-
Map of choice label to numeric score (e.g. `{"relevant": 1,
"irrelevant": 0}`).
column_mapping:
type: object
additionalProperties:
type: string
description: Maps template variable names to dataset column paths.
evaluator_version_id:
type: string
nullable: true
description: >-
EvaluatorVersion identifier (base64). Links this run to an Eval Hub
evaluator version.
invocation_parameters:
$ref: '#/components/schemas/InvocationParams'
provider_parameters:
type: object
description: >-
Provider-specific parameters. Defaults to `{}` (no overrides) if
omitted.
additionalProperties: false
description: >-
Configuration for running a template-based LLM evaluator against each
dataset example.
LLMMessage:
type: object
description: A message in the prompt template
required:
- role
properties:
role:
$ref: '#/components/schemas/MessageRole'
content:
type: string
nullable: true
description: The content of the message
tool_call_id:
type: string
description: The ID of the tool call this message is responding to
tool_calls:
type: array
description: Tool calls generated by the model
items:
$ref: '#/components/schemas/ToolCall'
InputVariableFormat:
type: string
enum:
- f_string
- mustache
- none
description: >
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.
InvocationParams:
type: object
description: Parameters for the LLM invocation
properties:
temperature:
type: number
description: Sampling temperature (higher = more random)
max_tokens:
type: integer
description: Maximum number of tokens to generate
max_completion_tokens:
type: integer
description: Maximum number of completion tokens to generate
top_p:
type: number
description: Nucleus sampling parameter
frequency_penalty:
type: number
description: Frequency penalty (-2.0 to 2.0)
presence_penalty:
type: number
description: Presence penalty (-2.0 to 2.0)
stop:
type: array
items:
type: string
description: Stop sequences
response_format:
allOf:
- $ref: '#/components/schemas/ResponseFormat'
description: >-
Response format configuration. Optional. When omitted, no structured
output constraint is applied (the provider's default plain-text
behavior is used).
tool_config:
allOf:
- $ref: '#/components/schemas/ToolConfig'
description: >-
Tool configuration for the LLM invocation. Optional. When omitted,
no tools are made available to the model.
top_k:
type: integer
description: >-
Top-K sampling parameter. A top-K of 1 means the next selected token
is the most probable (greedy decoding).
thinking_level:
type: string
description: >-
Controls how much reasoning the model performs before responding.
Supported by Gemini 3.x models. Accepted values: 'low', 'high'.
thinking_budget:
type: integer
description: >-
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:
type: string
description: >-
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:
type: string
description: >-
Controls the verbosity of model output. Supported by OpenAI GPT-5
series. Accepted values: 'low' | 'medium' | 'high'.
additionalProperties: true
ToolConfig:
type: object
description: Tool configuration for the LLM invocation
properties:
tools:
type: array
description: List of tool definitions available to the model
items:
type: object
tool_choice:
description: Tool choice configuration
MessageRole:
type: string
enum:
- user
- assistant
- system
- tool
description: The role of the message author
ToolCall:
type: object
description: A tool call generated by the model
required:
- type
- function
properties:
id:
type: string
description: The ID of the tool call
type:
$ref: '#/components/schemas/ToolCallType'
function:
$ref: '#/components/schemas/ToolCallFunction'
ResponseFormat:
type: object
description: Response format configuration
properties:
type:
allOf:
- $ref: '#/components/schemas/ResponseFormatType'
default: text
description: The response format type. Defaults to `text` if not specified.
json_schema:
type: object
description: JSON schema configuration (when type is json_schema)
properties:
name:
type: string
description: The name of the JSON schema
description:
type: string
description: A description of the JSON schema
schema:
type: object
description: The JSON schema object
strict:
type: boolean
default: false
description: >-
Whether to enforce strict schema validation. Defaults to
`false`.
ToolCallType:
type: string
enum:
- function
description: The type of tool call
ToolCallFunction:
type: object
description: The function to call
required:
- name
- arguments
properties:
name:
type: string
description: The name of the function
arguments:
type: string
description: The arguments to the function as a JSON string
ResponseFormatType:
type: string
enum:
- text
- json_object
- json_schema
description: The response format type
securitySchemes:
bearerAuth:
type: http
scheme: bearer
bearerFormat:
description: >
Most Arize AI endpoints require authentication. For those endpoints that
require authentication, include your API key in the request header using
the format
``` Authorization: Bearer
```
````