Arize Annotation Skill

This skill focuses on annotation configs — the schema for human feedback — and on programmatically annotating project spans via the Python SDK. Human review in the Arize UI (including annotation queues, datasets, and experiments) still depends on these configs; there is no ax CLI for queues yet.

Direction: Human labeling in Arize attaches values defined by configs to spans, dataset examples, experiment-related records, and queue items in the product UI. What is documented here: ax annotation-configs 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: check .env for ARIZE_API_KEY and use it to create/update the profile via references/ax-profiles.md. If .env has no key either, ask the user for their Arize API key (https://app.arize.com/admin > API Keys)
• Space ID unknown → check .env for ARIZE_SPACE_ID, or run ax spaces list -o json, or 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	Arize UI; configs must exist — no ax queue commands documented here yet

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-id SPACE_ID
ax annotation-configs list --space-id SPACE_ID -o json
ax annotation-configs list --space-id SPACE_ID --limit 20

Create — Categorical

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

ax annotation-configs create \
  --name "Correctness" \
  --space-id SPACE_ID \
  --type categorical \
  --values '[{"label": "correct", "score": 1}, {"label": "incorrect", "score": 0}]' \
  --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-id SPACE_ID \
  --type continuous \
  --minimum-score 0 \
  --maximum-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-id SPACE_ID \
  --type freeform

Get

ax annotation-configs get ANNOTATION_CONFIG_ID
ax annotation-configs get ANNOTATION_CONFIG_ID -o json

Delete

ax annotation-configs delete ANNOTATION_CONFIG_ID
ax annotation-configs delete ANNOTATION_CONFIG_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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Applying Annotations to Spans (Python SDK)

Use the Python SDK to bulk-apply annotations to project spans when you already have labels (e.g., from a review export or an external labeling tool).

import pandas as pd
from arize import ArizeClient

import os

client = ArizeClient(api_key=os.environ["ARIZE_API_KEY"])

# Build a DataFrame with annotation columns
# Required: context.span_id + at least one annotation.<name>.label or annotation.<name>.score
annotations_df = pd.DataFrame([
    {
        "context.span_id": "span_001",
        "annotation.Correctness.label": "correct",
        "annotation.Correctness.updated_by": "reviewer@example.com",
    },
    {
        "context.span_id": "span_002",
        "annotation.Correctness.label": "incorrect",
        "annotation.Correctness.updated_by": "reviewer@example.com",
    },
])

response = client.spans.update_annotations(
    space_id=os.environ["ARIZE_SPACE_ID"],
    project_name="your-project",
    dataframe=annotations_df,
    validate=True,
)

DataFrame column schema:

Column	Required	Description
context.span_id	yes	The span to annotate
annotation.<name>.label	one of	Categorical or freeform label
annotation.<name>.score	one of	Numeric score
annotation.<name>.updated_by	no	Annotator identifier (email or name)
annotation.<name>.updated_at	no	Timestamp in milliseconds since epoch
annotation.notes	no	Freeform notes on the span

Limitation: Annotations apply only to spans within 31 days prior to submission.

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Troubleshooting

Problem	Solution
ax: command not found	See references/ax-setup.md
401 Unauthorized	API key may not have access to this space. Verify at https://app.arize.com/admin > API Keys
Annotation config not found	ax annotation-configs list --space-id SPACE_ID
409 Conflict on create	Name already exists in the space. Use a different name or get the existing config ID.
Human review / queues in UI	Use the Arize app; ensure configs exist — no ax annotation-queue CLI yet
Span SDK errors or missing spans	Confirm project_name, space_id, and span IDs; use arize-trace to export spans

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Related Skills

• arize-trace: Export spans to find span IDs and time ranges
• arize-dataset: Find dataset IDs and example IDs
• arize-evaluator: Automated LLM-as-judge alongside human annotation
• arize-experiment: Experiments tied to datasets and evaluation workflows
• arize-link: Deep links to annotation configs and queues in the Arize UI

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Save Credentials for Future Use

See references/ax-profiles.md § Save Credentials for Future Use.