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Arize Annotation

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Manage annotation configurations and workflows seamlessly.

by github37.7k stars on github/awesome-copilot
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Updated Aug 10, 2026
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What Arize Annotation does

The Arize Annotation skill provides a comprehensive toolset for creating and managing annotation configurations and queues in Arize. This skill allows users to define various label schemas, including categorical, continuous, and freeform types, which are essential for human feedback in machine learning projects. By utilizing the Python SDK, users can programmatically apply these annotations to project spans, dataset examples, and experiment-related records, streamlining the annotation process.

Users can create annotation configs that define the schema for human feedback labels, ensuring that all necessary configurations are in place before any annotations are made. The skill supports bulk updates to annotation spans, making it easier to manage large datasets and apply consistent labeling across multiple records. Additionally, it facilitates the creation and management of annotation queues, which route records to human reviewers, ensuring that feedback is collected efficiently and effectively.

This skill is particularly useful for data scientists, machine learning engineers, and project managers who require a structured approach to gathering human feedback on their models. By leveraging the Arize Annotation skill, users can enhance the quality of their datasets and improve the performance of their machine learning models through systematic human review and annotation processes.

When to use it

Use this skill when you need to create, manage, and apply annotation configurations and queues for human review in Arize.

When not to use it

This skill may not be suitable for users who do not work with Arize or do not require human feedback for their machine learning projects.

What you can build with it

Creating Annotation Configs

Define your labeling schema by creating categorical, continuous, or freeform configs to standardize human feedback.

Managing Annotation Queues

Set up annotation queues to efficiently route records to human reviewers, ensuring structured feedback collection.

Bulk Applying Annotations

Use the Python SDK to apply annotations in bulk to project spans, streamlining the labeling process for large datasets.

How to install Arize Annotation

View source

1. Install with the skills CLI

npx skills add github/awesome-copilot/arize-annotation --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by github

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.


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.

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.

FieldDescription
NameDescriptive identifier (e.g. Correctness, Helpfulness). Must be unique within the space.
Typecategorical (pick from a list), continuous (numeric range), or freeform (free text).
ValuesFor categorical: array of {"label": str, "score": number} pairs.
Min/Max ScoreFor continuous: numeric bounds.
Optimization DirectionWhether higher scores are better (maximize) or worse (minimize). Used to render trends in the UI.

Where labels get applied (surfaces)

SurfaceTypical path
Project spansPython SDK spans.update_annotations (below) and/or the Arize UI
Dataset examplesArize UI (human labeling flows); configs must exist in the space
Experiment outputsOften reviewed alongside datasets or traces in the UI — see arize-experiment, arize-dataset
Annotation queue itemsax 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

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).


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 \
  --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.

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:

ax annotation-queues assign-record NAME_OR_ID RECORD_ID --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"],
    project_name="your-project",
    dataframe=annotations_df,
    validate=True,
)

DataFrame column schema:

ColumnRequiredDescription
context.span_idyesThe span to annotate
annotation.<name>.labelone ofCategorical or freeform label
annotation.<name>.scoreone ofNumeric score
annotation.<name>.updated_bynoAnnotator identifier (email or name)
annotation.<name>.updated_atnoTimestamp in milliseconds since epoch
annotation.notesnoFreeform notes on the span

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


Troubleshooting

ProblemSolution
ax: command not foundSee references/ax-setup.md
401 UnauthorizedAPI key may not have access to this space. Verify at https://app.arize.com/admin > API Keys
Annotation config not foundax annotation-configs list --space SPACE (or use ax annotation-configs get NAME_OR_ID --space SPACE)
409 Conflict on createName already exists in the space. Use a different name or get the existing config ID.
Queue not foundax annotation-queues list --space SPACE; verify the queue name or ID
Record not appearing in queueEnsure the annotation config linked to the queue exists; check ax annotation-configs list --space SPACE
Span SDK errors or missing spansConfirm project_name, space_id, and span IDs; use arize-trace to export spans

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.

Frequently asked questions about Arize Annotation

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