Skip to main content

Label Studio annotation tool

Project description

Label Studio

Label Studio is an open-source, configurable data annotation tool.

Its purpose is to help you label different types of data using a simple interface with a standardized output format. It's mobile-friendly and fast.

WebsiteDocsTwitterJoin Slack Community

GitHub Build Status codecov GitHub release Gitter

Label Studio

Deploy in one click

Deploy Deploy to Azure Run on Google Cloud

Features ✨

Simple: Crafted with minimal UI design. A simple design is the best design.

Configurable: Using high-level jsx tags config, you can fully customize the interface for your data.

Embeddable: It's an NPM package too. You can include it into your projects.

Quick Labeling Guides

Coming Soon:

  • Time series
  • Video


Frontend package

npm install label-studio

Check documentation about frontend integration.

Backend and frontend

Check documentation about backend + frontend integration.


docker run -p 8200:8200 -t -i heartexlabs/label-studio -c config.json -l ../examples/chatbot_analysis/config.xml -i ../examples/chatbot_analysis/tasks.json -o output

Machine learning integration

You can easily connect your favorite machine learning framework with Label Studio by using Heartex SDK.

That gives you the opportunities to:

  • use model predictions as prelabeling
  • simultaneously update (retrain) your model while new annotations are coming
  • perform labeling in active learning mode
  • instantly create running production-ready prediction service

There is a quick example tutorial how to do that with simple image classification:

  1. Clone pyheartex, and start serving:
    git clone
    cd pyheartex/examples/docker
    docker-compose up -d
  2. Specify running server in your label config:
    "ml_backend": {
      "url": "http://localhost:9090",
      "model_name": "my_super_model"
  3. Launch Label Studio with image classification config:
    python -l ../examples/image_classification/config.xml

Once you're satisfied with prelabeling results, you can imediately send prediction requests via REST API:

curl -X POST -H 'Content-Type: application/json' -d '{"image_url": ""}' http://localhost:8200/predict

Feel free to play around any other models & frameworks apart from image classifiers! (see instructions here)


Detailed changes for each release are documented in the release notes.

Stay In Touch


Please make sure to read the

Label Studio for Teams, Startups, and Enterprises

Label Studio for Teams is our enterprise edition (cloud & on-prem), that includes a data manager, high-quality baseline models, active learning, collaborators support, and more. Please visit the website to learn more.


This software is licensed under the Apache 2.0 LICENSE © Heartex.

Happy Labeling!

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for label-studio, version 0.4.0rc5
Filename, size File type Python version Upload date Hashes
Filename, size label_studio-0.4.0rc5-py3-none-any.whl (6.9 MB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size label-studio-0.4.0rc5.tar.gz (6.8 MB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page