Arize REST API
79
AI ReadinessAI-Ready (A-)79/100
Agent-ready OpenAPI document · curated by JenticAI/MLMl Inferencebearer73 EndpointsREST
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For Agents
Manage AI observability resources on Arize: datasets, prompts, projects, evaluators, experiments, annotation queues, and traces for LLM and ML evaluation.
Use for: I need to create a new dataset in Arize for evaluation, Add examples to an existing Arize dataset, List all evaluators configured in my Arize space, Set up an annotation queue for human review of LLM outputs
Not supported: Does not host model inference, train models, or store raw production application logs - use for AI evaluation, observability, and annotation workflows only.
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.
Install Jentic One Beta
Connect the Arize REST API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Arize REST API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Farize.com%2Farize" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Farize.com%2Farize" | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with Arize REST 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
Issue, refresh, and revoke API keys and assign roles to team members
Read trace spans and tasks generated by instrumented applications
Wire up AI integrations (model providers, vector stores) inside Arize
Use Cases
Patterns agents use Arize REST 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
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
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
A
Arize REST API
- AI-Ready (A-)79/100
87
Foundational Compliance63
Developer Experience & Jentic Compatibility86
AI-Readiness & Agent Experience90
Agent Usability60
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
87
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: ASignals: 4
98%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
100%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
50%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
63
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: BSignals: 4
3%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
50%
Response Coverage
Percentage of operations with complete response definitions (success, client error, server error).
100%
Tooling Readiness
Health of API ingestion, bundling, and resolution within Jentic pipelines.
86
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: ASignals: 4
80%
Description Coverage
Coverage of descriptions across API elements.
100%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
100%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
65%
Summary Coverage
Coverage of summaries across operations/tags/info.
90
Agent Usability
Functional utility, complexity comfort, and AI orchestration readiness.
Grade: A+Signals: 1
90%
Complexity Comfort
Agent comfort level based on API operational and structural complexity.
60
Security
Trust, risk posture, and security compliance.
Grade: B-Signals: 1
60%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
100
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: A+Signals: 1
100%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the Arize REST API by hand means learning its bearer auth and mapping its datasets, evaluators, experiments, annotation-queues, and spans surface yourself. Through Jentic you install once, import the Arize REST API from the API Directory, store the key once, and your agent calls it.
Permission scoping
Arize puts the dataset and api-key ids in the URL path (/v2/datasets/{dataset_id}/examples, /v2/api-keys/{api_key_id}/refresh), so a rule can pin your agent to a specific dataset for reads and example writes. You choose the operations it may call, so key rotation via the refresh endpoint is not included unless you add it.
Credential isolation
Your Arize API key is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'run an Arize experiment' or 'add examples to a dataset', and Jentic returns the matching Arize operation with its input schema so the agent calls the right endpoint without mapping paths and parameters by hand.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Arize REST 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 with Jentic One, the self-hosted execution layer.
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.
Can I limit what my agent is allowed to do with the Arize REST API?
Yes. Because you run Jentic One yourself, your own rules decide which Arize operations and credentials the agent may use. Since Arize puts dataset and API-key ids in the URL path (for example /v2/datasets/{dataset_id}/examples and /v2/api-keys/{api_key_id}/refresh), you can pin the agent to a specific dataset for reads and example writes. You choose the exact operations it may call, so key rotation through the refresh endpoint stays off unless you explicitly grant it.
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