#1111th of 14 in Data and scraping for AI
label-studio
Multi-user data labeling tool for audio, text, images, video and time series
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- Stars
- 28.4k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Sep 2026
- Language
- TypeScript
Missed the latest GitHub check; these facts may be older.
Overview
Label Studio is a web-based annotation tool for preparing and correcting ML training data across images, audio, text, HTML, video and time series. Labeling interfaces are defined in a configuration language, with included templates, and results export to various model formats. It can connect to an ML backend for pre-labeling, online learning and active learning, and has a REST API for pipelines.
Who it is for: ML teams that need to annotate and manage training data
Strengths
- Handles images, audio, text, HTML, video and time series in one instance
- Installs via pip, Docker, or a Compose stack with Nginx and PostgreSQL
- ML backend SDK supports pre-labeling, online learning and active learning
- Imports from S3, GCS and files; REST API for pipeline integration
Weaknesses
- Default install stores data in SQLite; PostgreSQL needs the Compose setup
- Some features are tied to paid editions; README links a comparison page
- ML integration needs a separately run backend server
- Windows install requires manually downloading a prebuilt lxml wheel
What it needs
- no GPU
- Docker + Compose
- Needs PostgreSQL (optional, Compose), Nginx (optional, Compose), MinIO (optional)
- Models: Custom ML backends via Label Studio ML SDK
- port 8080
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