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 (≥ 0.33.0) and a configured Arize profile.

Arize Annotation Skill

SPACE — --space flags 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.

---

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. Never ask the user to paste secrets into chat. For missing credentials, see references/ax-profiles.md.

---

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 (MINIMIZE) or worse (MAXIMIZE). 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.

---

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
ax annotation-configs list --space SPACE --name "Correctness"   # substring filter

Create — Categorical

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

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

Create — Freeform

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

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

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

Update

Each config type has its own update command. Only changed fields are updated.

Freeform:

ax annotation-configs update freeform NAME_OR_ID --space SPACE --new-name "Updated Name"

Continuous:

ax annotation-configs update continuous NAME_OR_ID --space SPACE --min-score 1 --max-score 5 --optimization-direction MINIMIZE

Categorical:

ax annotation-configs update categorical NAME_OR_ID --space SPACE --value correct --value incorrect --optimization-direction MAXIMIZE

--space is required when using a name instead of ID. Each --value supplies the complete categorical label list: it replaces existing labels rather than appending to them. Repeat --value for every label that should remain, or omitted labels are removed.

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).

---

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 list --space SPACE --name "Review"   # substring filter

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 \
  --annotation-config-id CONFIG_ID \
  --annotator-email reviewer@example.com \
  --instructions "Label each response as correct or incorrect." \
  --assignment-method ALL   # or: RANDOM

Repeat --annotation-config-id and --annotator-email to attach multiple configs or reviewers.

Add Records

Add records to an existing queue after creation. Records can come from spans (by project and time range) or dataset examples.

ax annotation-queues add-records NAME_OR_ID --space SPACE --record-sources sources.json

ax annotation-queues add-records NAME_OR_ID --space SPACE \
  --record-sources '[{"record_type": "EXAMPLE", "dataset_id": "ds-1", "example_ids": ["ex-1", "ex-2"]}]'

--record-sources is required and accepts a JSON file path or inline JSON array. Each source specifies record_type (SPAN or EXAMPLE) plus type-specific fields:

• Span: project_id, start_time (ISO 8601), end_time (ISO 8601), optional span_ids
• Example: dataset_id, example_ids

Update

List flags (--annotation-config-id, --annotator-email) fully replace existing values when provided — pass all desired values, not just the new ones.

ax annotation-queues update NAME_OR_ID --space SPACE --name "New Name"
ax annotation-queues update NAME_OR_ID --space SPACE --instructions "Updated instructions"
ax annotation-queues update NAME_OR_ID --space SPACE \
  --annotation-config-id CONFIG_ID_A \
  --annotation-config-id CONFIG_ID_B

Delete

ax annotation-queues delete NAME_OR_ID --space SPACE
ax annotation-queues delete NAME_OR_ID --space SPACE --force   # skip confirmation

List Records

ax annotation-queues list-records NAME_OR_ID --space SPACE
ax annotation-queues list-records NAME_OR_ID --space SPACE --limit 50 -o json

Submit an Annotation for a Record

Annotations are upserted by config name — call once per annotation config. Supply at least one of --score, --label, or --text.

ax annotation-queues annotate-record NAME_OR_ID RECORD_ID \
  --annotation-name "Correctness" \
  --label "correct" \
  --space SPACE

ax annotation-queues annotate-record NAME_OR_ID RECORD_ID \
  --annotation-name "Quality Score" \
  --score 8.5 \
  --text "Response was accurate but slightly verbose." \
  --space SPACE

Assign a Record

Assign users to review a specific record by repeating --email; this fully replaces existing assignments, so pass every assignee each time (passing no --email flags clears all assignments):

ax annotation-queues assign-record NAME_OR_ID RECORD_ID --email user@example.com --space SPACE

Delete Records

ax annotation-queues delete-records NAME_OR_ID --space SPACE

---

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.

---

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 SPACE (or use ax annotation-configs get NAME_OR_ID --space SPACE)
409 Conflict on create	Name already exists in the space. Use a different name or get the existing config ID.
Queue not found	ax annotation-queues list --space SPACE; verify the queue name or ID
Record not appearing in queue	Ensure the annotation config linked to the queue exists; check ax annotation-configs list --space SPACE
Span SDK errors or missing spans	Confirm project_name, space_id, and span IDs; use arize-trace to export spans

---

Batch Annotate via CLI

The ax CLI provides batch annotation commands for writing annotations at scale without the Python SDK. All commands accept a file (CSV, JSON, JSONL, or Parquet) with up to 1000 annotations per request and use upsert semantics (existing annotations with the same key are updated; new ones are created).

Resource	Command	Skill
Spans	ax spans annotate PROJECT --file annotations.json	arize-trace
Dataset examples	ax datasets annotate-examples NAME_OR_ID --file annotations.json	arize-dataset
Experiment runs	ax experiments annotate-runs NAME_OR_ID --file annotations.json --dataset DATASET	arize-experiment

All three commands support --space SPACE. See the linked skills for full flag tables and file format details.

---

Related Skills

• arize-trace: Export spans to find span IDs and time ranges; batch annotate spans via ax spans annotate
• arize-dataset: Find dataset IDs and example IDs; batch annotate examples via ax datasets annotate-examples
• arize-evaluator: Automated LLM-as-judge alongside human annotation
• arize-experiment: Experiments tied to datasets and evaluation workflows; batch annotate runs via ax experiments annotate-runs
• arize-prompts: Manage prompt templates; annotate prompt outputs for quality tracking
• 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.