May 2026

Documentation Index

Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt

Use this file to discover all available pages before exploring further.

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Build Arize-Powered Tooling in Go with the New Go SDK v2

• Core client — arize.NewClient wires HTTP transport, headers, and configuration with environment-variable overrides and secret masking.
• Resource resolution — Look up spaces, projects, datasets, experiments, prompts, evaluators, annotation configs, annotation queues, AI integrations, and tasks by name or ID.
• Typed errors and pre-release guardrails — Structured HTTP error types with Unwrap support, plus one-time warnings on alpha/beta endpoints so you know exactly which surfaces are stable.

package main

import (
    "log"
    "os"

    "github.com/Arize-ai/client-go-v2"
)

func main() {
    client, err := arize.NewClient(arize.Config{
        APIKey: os.Getenv("ARIZE_API_KEY"), // set ARIZE_API_KEY in your environment before running
    })
    if err != nil {
        log.Fatal(err)
    }
    // use client to make API calls
}

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Automatically Add Spans to Labeling Queues

• Project datasource — Select a project as the data source when creating a queue.
• Query filter — Optionally scope which spans are routed for labeling (for example, attributes.openinference.span.kind = 'LLM').
• Sampling rate — Route a representative slice of traffic when matching span volume exceeds annotation bandwidth.
• Continuous and backfill modes — Enable continuous ingestion to keep pace with new traffic, backfill to seed the queue from existing spans, or both.
• Queue cap — Set an optional cap to prevent unbounded growth and keep the queue manageable.

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Run an Evaluator on Selected Experiments in One Click

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Manage Users, Roles, and Invitations from the Python SDK

• Lifecycle operations — users.list(), get(), create(), update(), and delete() cover the standard CRUD flow.
• Invitation and password flows — users.resend_invitation() and users.reset_password() automate the most common admin chores.
• Typed domain models — User, organization, and space roles now return as Pydantic models with discriminated unions, so ax users list and other CLI surfaces produce clean to_df output instead of crashing on raw API types.

from arize.users.types import PredefinedUserRole

users = client.users.list(
    email="@acme.com",             # optional substring filter
    status=["active", "invited"],  # omit to return all statuses
)
for user in users:
    print(user.id, user.email)

user = client.users.create(
    name="Ada Lovelace",
    email="ada@example.com",                    # used as the idempotency key
    role=PredefinedUserRole(name="member"),      # "admin", "member", or "annotator"
    invite_mode="email_link",                    # "none", "email_link", or "temporary_password"
)

client.users.resend_invitation(user_id=user.id)  # target user must be in "invited" state
client.users.reset_password(user_id=user.id)     # user must authenticate via password, not SSO

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Fixes and Improvements

• Improvement PROJECT is now accepted as an alias for MODEL in custom metric SQL, so you can write FROM project to match how tracing projects are named elsewhere in the platform. Existing FROM model queries are unaffected.

• Improvement The Alyx home agent can now list traces directly, so you can ask for recent traces without having to switch surfaces first.
• Fix Editing a Prompt Playground prompt through Alyx no longer fails when the model returns the prompt as a JSON-encoded string—Alyx parses it automatically and retries less often.
• Fix The Alyx read_prompt tool validates prompt IDs before calling, eliminating a class of failed reads.
• Fix Re-opening an Alyx chat with a custom_trace_view widget no longer renders a fresh Accept button, so users can’t accidentally create duplicate views.

• Improvement A new background job soft-deletes annotation queue records that have been annotated or sat untouched for a year, keeping queue tables from growing unbounded as the auto-add-to-queue feature ramps up.
• Fix Creating an annotation queue via POST /v2/annotation-queues no longer throws 500 errors for accounts whose user names aren’t "admin"—the underlying SQL now references the correct column.

• Fix Toggling Enable Tracing on an existing template-eval online task now persists on save. Previously the field was silently dropped when patching legacy (pre–Eval Hub) tasks, so tracing reverted on reload.

• Improvement The integration setup flow now shows tooltips on each field, so it’s easier to understand what each value should be before submitting.

• Fix POST /v2/experiments now returns 400 Bad Request for schema mismatches such as an eval.*.score type mismatch, instead of a generic 500.

• Fix Switching to pretty JSON formatting in the trace view no longer causes UI issues on large payloads.

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Visualize Evaluator Score Distributions Across Spans and Experiments

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Review and Confirm Alyx Proposals Before They Take Effect

• Eval Form Proposals: When Alyx suggests creating or updating an evaluator, it now shows an editable drawer with the proposed name, display name, template, and classification choices. Edit any field before accepting.
• Task Creation: Alyx surfaces a review drawer when proposing a new evaluation task, showing the task name, evaluator, target project or dataset, run mode, and sampling rate before the task is created.
• Task Configuration: Configuring task parameters through Alyx now always routes through a confirmation drawer, whether you’re on the task-builder page or elsewhere in the platform.

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Control Annotation Queue Capacity with Per-Queue Record Limits

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Assign Multiple Annotation Queue Records to a Reviewer in Bulk

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Wire Experiment Runs into Automated Pipelines with the run_experiment REST API

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Fixes and Improvements

• Fix  Models and Integrations Azure OpenAI o-family models (o1, o3-mini, o4-mini) now work correctly in Prompt Playground and evals—the default API version is updated to 2025-04-01-preview so you no longer need to enter it manually.
• Fix  Datasets and Experiments The “View Experiment Traces” button now returns correct results for experiments run via the Arize Python SDK, which uses experiment_id rather than dataset_id.
• Fix  Evaluators Eval result columns in eval.<name>.<field> format generated by AX experiment evals are no longer dropped before the output is returned.
• Fix  Evaluators “View Task Logs” from an eval feedback tooltip now opens the exact task run instead of an approximate lookup that failed for renamed evaluators and older runs.