Label Studio

AI & ML

Manage data labeling projects and annotations through conversation — Neotask uses OpenClaw to operate your Label Studio annotation infrastructure.

What You Can Do

Create and Configure Labeling Projects

Tell Neotask to create a Label Studio project with a specific task type — image classification, NER, bounding boxes, sentiment, or custom — and configure the labeling interface. Provide the label schema in plain English; Neotask translates it into the correct Label Studio XML configuration.

Import Data for Annotation

Ask Neotask to import tasks into a Label Studio project from a URL list, S3 path, or JSON payload. It handles the import API call and returns the number of tasks created.

Track Annotation Progress

Ask Neotask for the annotation progress on any project: how many tasks are total, how many are annotated, how many are in review, and how many are skipped.

Export Labeled Data

Ask Neotask to export annotations from a project in your preferred format — JSON, CSV, COCO, YOLO, Pascal VOC — and receive the data or a download link.

Manage Predictions and Pre-Annotations

Import model predictions as pre-annotations into a Label Studio project to speed up human review. Ask Neotask to upload predictions and convert them to annotations or leave them as suggestions.

Assign and Manage Annotators

Ask Neotask to assign annotators to specific tasks, set up annotation queues, or check which annotator has the most tasks pending.

Try Asking

  • "Create a new Label Studio project for sentiment classification with three labels: Positive, Negative, Neutral"
  • "Import 500 text tasks from this S3 URL into project 42"
  • "What is the annotation progress on project customer-feedback-q3?"
  • "Export all annotations from project 15 in COCO format"
  • "Upload these model predictions to project 42 as pre-annotations"
  • "Who has the most incomplete tasks in project invoice-ner?"
  • "List all projects and their current status"
  • "Delete all skipped tasks from project 22 so annotators can re-attempt them"

    • Pro Tips

  • Use pre-annotations to speed labeling — import your model's predictions as pre-annotations before humans review; annotators confirm or correct rather than labeling from scratch, which cuts annotation time by 50-70% for mature models.
  • Export early and often — do not wait until a project is 100% done to export; ask Neotask to export partial datasets for initial model training runs while labeling continues in parallel.
  • Consistent label schemas matter — define your label schema carefully before importing data; changing labels mid-project forces re-annotation.
  • Filter tasks by status — when reviewing quality, ask Neotask to list tasks with disagreements or low annotation confidence first; addressing uncertainty early improves overall dataset quality.
  • API tokens per annotator — each Label Studio user has their own API token; for audit trails, ensure individual annotators use their own credentials rather than a shared account.

    • Works Well With