> ## 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.
# Get task
> Returns a single task by its ID.
This endpoint is in beta, 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/{task_id}
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: Audit Logs
description: >
Audit logs record authenticated user actions within an account, providing
a
chronological trail for security and compliance review. Access requires
account admin privileges and audit logging to be enabled.
- 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: Monitors
description: >
Monitors continuously track a metric over your model or LLM application
data
and alert you when it crosses a threshold. Each monitor watches a single
metric - data quality, model performance, drift, a custom metric, or a
tracing metric - and moves between statuses as the metric passes in and
out of its
healthy range.
- 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: Traces
description: |
A trace is the collection of spans sharing a trace ID, representing a
single end-to-end request through an LLM application. Use the Traces
endpoint to retrieve traces with all of their spans in one call.
- 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.
- name: Webhooks
description: >
Webhooks are organization-owned destinations that receive event deliveries
over HTTPS. Deliveries are authenticated with a bearer token or signed
with
an HMAC signing secret — the secret is returned exactly once, when the
webhook is created. Delivery attempts are recorded and can be listed for
debugging. To choose which events a webhook receives, manage its
subscriptions through the prompt and evaluator webhook-subscription
endpoints.
paths:
/v2/tasks/{task_id}:
get:
tags:
- Tasks
summary: Get task
description: >
Returns a single task by its ID.
This endpoint is in beta, read more
[here](https://arize.com/docs/ax/rest-reference#api-version-stages).
operationId: get_task
parameters:
- $ref: '#/components/parameters/TaskIdPathParam'
responses:
'200':
$ref: '#/components/responses/Task'
'400':
$ref: '#/components/responses/BadRequest'
'401':
$ref: '#/components/responses/Unauthorized'
'404':
$ref: '#/components/responses/NotFound'
'429':
$ref: '#/components/responses/RateLimitExceeded'
components:
parameters:
TaskIdPathParam:
name: task_id
in: path
required: true
description: The unique task identifier (base64)
schema:
$ref: '#/components/schemas/Id'
example: VGFzazoxMjM0NQ==
responses:
Task:
description: Returns a single task object
content:
application/json:
schema:
$ref: '#/components/schemas/Task'
example:
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=
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
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
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, evaluator, or agent
settings for each triggered run.
additionalProperties: false
x-forward-compatible: true
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
x-forward-compatible: true
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.
TaskEvaluator:
type: object
required:
- evaluator_id
- evaluator_name
- evaluator_version_id
- query_filter
- column_mappings
properties:
evaluator_id:
type: string
description: Evaluator identifier (base64).
evaluator_name:
type: string
description: The name of the attached evaluator.
evaluator_version_id:
type: string
nullable: true
description: >
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:
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
x-forward-compatible: true
RunConfiguration:
oneOf:
- $ref: '#/components/schemas/LlmGenerationRunConfig'
- $ref: '#/components/schemas/TemplateEvaluationRunConfig'
- $ref: '#/components/schemas/AgentCallRunConfig'
discriminator:
propertyName: experiment_type
mapping:
LLM_GENERATION:
$ref: '#/components/schemas/LlmGenerationRunConfig'
TEMPLATE_EVALUATION:
$ref: '#/components/schemas/TemplateEvaluationRunConfig'
AGENT_CALL:
$ref: '#/components/schemas/AgentCallRunConfig'
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.
description: Configuration for running an LLM prompt against each dataset example.
additionalProperties: true
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.
description: >-
Configuration for running a template-based LLM evaluator against each
dataset example.
additionalProperties: true
AgentCallRunConfig:
type: object
required:
- experiment_type
- integration_id
- input_template
properties:
experiment_type:
type: string
enum:
- AGENT_CALL
description: Discriminator. Must be `"AGENT_CALL"`.
integration_id:
type: string
description: |
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:
type: object
additionalProperties: true
description: >
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.
description: |
Configuration for running an agent integration against each dataset
example. The `input_template` is sent to the agent after Mustache
substitution.
additionalProperties: true
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'
additionalProperties: true
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:
$ref: '#/components/schemas/ToolDefinition'
tool_choice:
description: Tool choice configuration
additionalProperties: true
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'
additionalProperties: false
x-forward-compatible: true
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:
allOf:
- $ref: '#/components/schemas/JsonSchemaConfig'
description: JSON schema configuration (when type is JSON_SCHEMA)
additionalProperties: false
x-forward-compatible: true
ToolDefinition:
type: object
description: A tool definition available to the model
additionalProperties: true
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
additionalProperties: false
x-forward-compatible: true
ResponseFormatType:
type: string
enum:
- TEXT
- JSON_OBJECT
- JSON_SCHEMA
description: The response format type
JsonSchemaConfig:
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`.
additionalProperties: false
x-forward-compatible: true
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
```
````