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arize-annotation

Name: Arize Annotation
Author: Arize-ai

// 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.

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updated:28 de mayo de 2026, 19:46

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arize-admin.md

from "Arize-ai/arize-skills"

Manages Arize users, organizations, spaces, projects, roles, role bindings, resource restrictions, and API keys via the ax CLI. Use for enterprise admin workflows: inviting and offboarding users, onboarding new teams, creating custom roles for SAML/SSO mappings, assigning roles to users, restricting project-level access, and managing service keys for multi-tenant architectures. Covers ax users, ax organizations, ax spaces, ax projects, ax roles, ax role-bindings, and ax api-keys.

2026-05-2830

arize-ai-provider-integration.md

from "Arize-ai/arize-skills"

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

2026-05-2830

arize-dataset.md

from "Arize-ai/arize-skills"

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

2026-05-2830

arize-evaluator.md

from "Arize-ai/arize-skills"

Handles LLM-as-judge and code evaluator workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, code evaluator, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

2026-05-2830

arize-experiment.md

from "Arize-ai/arize-skills"

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

2026-05-2830

arize-instrumentation.md

from "Arize-ai/arize-skills"

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

2026-05-2830

package.json

"author": "Arize-ai"

"repository": "Arize-ai/arize-skills"

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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 and a configured Arize profile.

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.

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
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 \
  --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 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.

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

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.

arize-annotation

Más de este repositorio

Más de este repositorio

Arize Annotation Skill

Prerequisites

Concepts

What is an Annotation Config?

Where labels get applied (surfaces)

Basic CRUD: Annotation Configs

List

Create — Categorical

Create — Continuous

Create — Freeform

Get

Delete

Annotation Queues: ax annotation-queues

List / Get

Create

Update

Delete

List Records

Submit an Annotation for a Record

Assign a Record

Delete Records

Applying Annotations to Spans (Python SDK)

Troubleshooting

Batch Annotate via CLI

Related Skills

Save Credentials for Future Use

Arize Annotation Skill

Prerequisites

Concepts

What is an Annotation Config?

Where labels get applied (surfaces)

Basic CRUD: Annotation Configs

List

Create — Categorical

Create — Continuous

Create — Freeform

Get

Delete

Annotation Queues: ax annotation-queues

List / Get

Create

Update

Delete

List Records

Submit an Annotation for a Record

Assign a Record

Delete Records

Applying Annotations to Spans (Python SDK)

Troubleshooting

Batch Annotate via CLI

Related Skills

Save Credentials for Future Use

Annotation Queues: `ax annotation-queues`

Annotation Queues: `ax annotation-queues`