Arize REST API

Name: Arize REST API API
Brand: Arize REST API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferencebearer73 EndpointsREST

For Agents

Manage AI observability resources on Arize: datasets, prompts, projects, evaluators, experiments, annotation queues, and traces for LLM and ML evaluation.

Quickstart

Get started with Arize REST API in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"create an Arize dataset for evaluation"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with Arize REST API API.

Create and manage datasets and dataset examples used to evaluate LLM and ML systems

Configure evaluators and run experiments against datasets

Operate annotation queues and configurations for human-in-the-loop labelling

Manage prompts, projects, and spaces that scope evaluation work

GET STARTED

Start building with Arize REST API API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for Arize REST API, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Arize REST API, keeping it validated and agent-ready. The Arize REST API is the programmatic interface to Arize's AI observability and evaluation platform, covering datasets, prompts, projects, spaces, evaluators, experiments, annotation queues, traces (spans), and AI integration management. Seventy-three endpoints let teams script the full lifecycle of evaluating LLM and ML systems — creating datasets, running experiments, annotating outputs, and managing API keys, roles, and integrations across regions or self-hosted deployments.

Use Cases

Patterns agents use Arize REST API API for, with concrete tasks.

★ LLM Evaluation Pipeline

Build a continuous LLM evaluation pipeline that creates a dataset of test inputs, registers evaluators, runs experiments against new prompt versions, and reads back metrics — all driven from CI. The /v2/datasets, /v2/evaluators, and /v2/experiments endpoints provide the primitives so a release pipeline can block deploys when an evaluator regresses on the test dataset.

Create a dataset called 'qa-regression-2026', add 50 examples via /v2/datasets/{dataset_id}/examples, then trigger an experiment that runs evaluator id 7 against prompt id 21

Human-in-the-Loop Annotation

Stand up a human review workflow by creating annotation configs, queues, and records via /v2/annotation-configs, /v2/annotation-queues, and /v2/annotation-queues/{annotation_queue_id}/records. Reviewers label model outputs and the resulting labels feed back into evaluators, closing the loop between production traces and improved evaluation criteria.

Create an annotation config for 'response-quality' with three labels, attach it to a new annotation queue, and push 200 trace spans into the queue's records endpoint

Trace and Span Observability

Pull production trace spans from /v2/spans into a downstream warehouse or alerting system. The endpoint exposes telemetry from instrumented LLM applications, so a monitoring agent can surface latency spikes, tool-call failures, or unusual token usage without standing up a separate tracing backend.

Query /v2/spans for the last hour of spans on project id 5 and write them to S3 as Parquet for downstream analysis

Workspace and Access Management

Provision spaces, assign roles, and rotate API keys programmatically when onboarding teams onto Arize. The /v2/spaces, /v2/roles, and /v2/api-keys endpoints let an internal IT or platform agent provision new evaluation workspaces without console clicks.

Create a new space, add three users with the 'editor' role, and refresh the API key for the platform service account via /v2/api-keys/{api_key_id}/refresh

Agent-Driven Eval Loop via Jentic

Let an evaluation agent create datasets and trigger experiments on Arize whenever a new prompt is committed. Through Jentic the agent searches once for 'run an Arize experiment', loads the schema for /v2/experiments, and executes — reusing one credential vault entry across runs.

Search Jentic for 'run an Arize experiment', load the schema, and execute against dataset id 12 with evaluator id 4

Key Endpoints

73 endpoints — jentic publishes the only available openapi specification for arize rest api, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

GET

/v2/datasets

List datasets in a space

POST

/v2/datasets

Create a new dataset

POST

/v2/datasets/{dataset_id}/examples

Add examples to a dataset

GET

/v2/evaluators

List configured evaluators

GET

/v2/experiments

List experiment runs

GET

/v2/annotation-queues

List annotation queues

GET

/v2/spans

Query trace spans

POST

/v2/api-keys/{api_key_id}/refresh

Refresh an API key

GET

/v2/datasets

List datasets in a space

POST

/v2/datasets

Create a new dataset

POST

/v2/datasets/{dataset_id}/examples

Add examples to a dataset

GET

/v2/evaluators

List configured evaluators

GET

/v2/experiments

List experiment runs

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Arize API keys are stored encrypted in the Jentic vault and injected at execution. Agents never see the raw key, and keys can be rotated through /v2/api-keys/{api_key_id}/refresh without redeploying agent code.

Intent-based discovery

Agents search Jentic by intent (e.g., 'run an Arize experiment', 'add examples to a dataset') and Jentic returns the matching Arize operation with input schema, so the agent doesn't need to map paths and parameters by hand.

Time to first call

Direct Arize integration across 73 endpoints: 1-2 weeks for auth, paging, and schema modelling. Through Jentic: under an hour to wire up the operations an agent actually needs.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Langfuse API

Langfuse covers similar LLM tracing, prompt management, and evaluation territory as Arize

Choose Langfuse for an open-source-leaning stack with strong prompt management. Choose Arize for deeper ML observability features (drift, embeddings) alongside LLM eval.

Complementary

OpenAI API

OpenAI generates the responses that Arize evaluates and traces

Use alongside Arize when the agent is generating outputs with OpenAI and pushing the resulting prompts/responses into Arize datasets and spans for evaluation.

Complementary

Anthropic Messages API

Anthropic produces model completions; Arize captures them as spans and feeds them into evaluators

Pair when an evaluation pipeline runs Claude completions and stores them as Arize examples for offline scoring.

Complementary

New Relic API

New Relic monitors infrastructure and app metrics; Arize monitors AI/ML model behaviour

Pair to give an SRE agent both system-level alerts (New Relic) and model-level evaluation regressions (Arize) in one workflow.

FAQs

Specific to using Arize REST API API through Jentic.

Why is there no official OpenAPI spec for Arize REST API?

Arize does not publish a unified OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Arize REST API via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Arize REST API use?

Arize uses HTTP Bearer authentication. You pass a personal or service API key in the Authorization header. Keys are managed under /v2/api-keys and can be refreshed via /v2/api-keys/{api_key_id}/refresh. Through Jentic, the key is encrypted in the vault and never enters the agent context.

Can I run experiments against a dataset with the Arize REST API?

Yes. Create a dataset under /v2/datasets, add examples via /v2/datasets/{dataset_id}/examples, then trigger experiments through /v2/experiments referencing your dataset, prompt, and evaluator ids.

What are the rate limits for the Arize REST API?

Arize does not publish a single global rate limit in the spec; in practice limits are tied to the workspace plan. For high-volume span ingestion use the streaming/SDK ingest path; the REST API is intended for control-plane operations rather than per-event traces.

How do I create a new dataset on Arize through Jentic?

Run pip install jentic, then search Jentic with 'create an Arize dataset', load the schema for POST /v2/datasets, and execute with the dataset name and space id. Add examples in a follow-up call to /v2/datasets/{dataset_id}/examples.

Can I export trace spans from Arize?

Yes. GET /v2/spans returns spans for a given project and time window, which you can page through and write to a downstream store like S3 or BigQuery for offline analysis.

Can the Arize REST API manage roles and access?

Yes. /v2/roles and /v2/spaces let you assign roles to users at the space level. Combined with /v2/api-keys, this is enough to script onboarding and offboarding of evaluation team members.