
Arize Annotation
OfficialFreeManage annotation configurations and workflows seamlessly.
Free · Opens the source repo
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 source1. Install with the skills CLI
npx skills add github/awesome-copilot/arize-annotation --agent claude-code2. 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 githubArize Annotation Skill
SPACE— All--spaceflags and theARIZE_SPACEenv var accept a space name (e.g.,my-workspace) or a base64 space ID (e.g.,U3BhY2U6...). Find yours withax 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 foundor version error → see references/ax-setup.md401 Unauthorized/ missing API key → runax profiles showto 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 listto pick by name, or ask the user - Security: Never read
.envfiles or search the filesystem for credentials. Useax profilesfor Arize credentials andax ai-integrationsfor 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.
| 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.
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/incorrecthelpful/unhelpfulsafe/unsaferelevant/irrelevantpass/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:
| 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 |
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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