/arize-annotation

name: arize-annotation
description: Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.
metadata:
  author: arize
  version: "1.0"
compatibility: Requires the ax CLI and a configured Arize profile.

Arize Annotation Skill

> **`SPACE`** — All `--space` flags and the `ARIZE_SPACE` env var accept a space **name** (e.g., `my-workspace`) or a base64 space **ID** (e.g., `U3BhY2U6...`). Find yours with `ax spaces list`.

This skill covers **annotation configs** (the label schema) and **annotation queues** (human review workflows), as well as programmatically annotating project spans via the Python SDK.

**Direction:** Human labeling in Arize attaches values defined by configs to **spans**, **dataset examples**, **experiment-related records**, and **queue items** in the product UI. This skill covers: `ax annotation-configs`, `ax annotation-queues`, and bulk span updates with `ArizeClient.spans.update_annotations`.

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Prerequisites

Proceed directly with the task — run the `ax` command you need. Do NOT check versions, env vars, or profiles upfront.

If an `ax` command fails, troubleshoot based on the error:

• `command not found` or version error → see references/ax-setup.md
• `401 Unauthorized` / missing API key → run `ax profiles show` to inspect the current profile. If the profile is missing or the API key is wrong, follow references/ax-profiles.md to create/update it. If the user doesn't have their key, direct them to https://app.arize.com/admin > API Keys
• Space unknown → run `ax spaces list` to pick by name, or ask the user
• **Security:** Never read `.env` files or search the filesystem for credentials. Use `ax profiles` for Arize credentials and `ax ai-integrations` for LLM provider keys. If credentials are not available through these channels, ask the user.

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Concepts

What is an Annotation Config?

An **annotation config** defines the schema for a single type of human feedback label. Before anyone can annotate a span, dataset record, experiment output, or queue item, a config must exist for that label in the space.

| Field | Description | |-------|-------------| | **Name** | Descriptive identifier (e.g. `Correctness`, `Helpfulness`). Must be unique within the space. | | **Type** | `categorical` (pick from a list), `continuous` (numeric range), or `freeform` (free text). | | **Values** | For categorical: array of `{"label": str, "score": number}` pairs. | | **Min/Max Score** | For continuous: numeric bounds. | | **Optimization Direction** | Whether higher scores are better (`maximize`) or worse (`minimize`). Used to render trends in the UI. |

Where labels get applied (surfaces)

| Surface | Typical path | |---------|----------------| | **Project spans** | Python SDK `spans.update_annotations` (below) and/or the Arize UI | | **Dataset examples** | Arize UI (human labeling flows); configs must exist in the space | | **Experiment outputs** | Often reviewed alongside datasets or traces in the UI — see arize-experiment, arize-dataset | | **Annotation queue items** | `ax annotation-queues` CLI (below) and/or the Arize UI; configs must exist |

Always ensure the relevant **annotation config** exists in the space before expecting labels to persist.

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Basic CRUD: Annotation Configs

List

ax annotation-configs list --space SPACE
ax annotation-configs list --space SPACE -o json
ax annotation-configs list --space SPACE --limit 20

Create — Categorical

Categorical configs present a fixed set of labels for reviewers to choose from.

ax annotation-configs create \
  --name "Correctness" \
  --space SPACE \
  --type categorical \
  --value correct \
  --value incorrect \
  --optimization-direction maximize

Common binary label pairs:

• `correct` / `incorrect`
• `helpful` / `unhelpful`
• `safe` / `unsafe`
• `relevant` / `irrelevant`
• `pass` / `fail`

Create — Continuous

Continuous configs let reviewers enter a numeric score within a defined range.

ax annotation-configs create \
  --name "Quality Score" \
  --space SPACE \
  --type continuous \
  --min-score 0 \
  --max-score 10 \
  --optimization-direction maximize

Create — Freeform

Freeform configs collect open-ended text feedback. No additional flags needed beyond name, space, and type.

ax annotation-configs create \
  --name "Reviewer Notes" \
  --space SPACE \
  --type freeform

Get

ax annotation-configs get NAME_OR_ID
ax annotation-configs get NAME_OR_ID -o json
ax annotation-configs get NAME_OR_ID --space SPACE   # required when using name instead of ID

Delete

ax annotation-configs delete NAME_OR_ID
ax annotation-configs delete NAME_OR_ID --space SPACE   # required when using name instead of ID
ax annotation-configs delete NAME_OR_ID --force   # skip confirmation

**Note:** Deletion is irreversible. Any annotation queue associations to this config are also removed in the product (queues may remain; fix associations in the Arize UI if needed).

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Annotation Queues: `ax annotation-queues`

Annotation queues route records (spans, dataset examples, experiment runs) to human reviewers. Each queue is linked to one or more annotation configs that define what labels reviewers can apply.

List / Get

ax annotation-queues list --space SPACE
ax annotation-queues list --space SPACE -o json

ax annotation-queues get NAME_OR_ID --space SPACE
ax annotation-queues get NAME_OR_ID --space SPACE -o json

Create

At least one `--annotation-config-id` is required.

ax annotation-queues create \
  --name "Correctness Review" \
  --space SPACE \
  --annotat