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.

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

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.

Where labels get applied (surfaces)

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

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).

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:

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

Troubleshooting

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

Save Credentials for Future Use

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