Label Studio

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Tagline:The open source platform for data labeling, AI evaluation, and human-in-the-loop workflows.

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What is Label Studio?

Label Studio is an open source data labeling and annotation platform for multi-modal data. It supports computer vision, NLP, document AI, audio transcription, time series, and LLM evaluation workflows including agent traces, RLHF, and RAG QA. API, Python SDK, and webhooks connect projects to your ML pipeline for pre-labeling, active learning, and model evaluation.

How to use Label Studio?

Install with pip (pip install -U label-studio), then run label-studio to open the app at http://localhost:8080. Sign up, create a project, import data, choose a labeling template, customize labels, and start annotating.

Label Studio Core Features

Programmable Interfaces

Custom layouts and templates adapt to your data, tasks, and evaluation criteria.

Native to your ML / AI Pipeline

API, Python SDK, and webhooks let you create projects, stream predictions, and trigger training, active learning, and evaluation workflows in real time.

Connect Any Data or Models

Sync data from any storage and connect any model to power AI-assisted labeling, benchmarking, and continuous model evaluation.

LLM & Agent Evaluation

Connect observability tools for human-in-the-loop review, collect human preferences for RLHF, create custom benchmarks, and evaluate RAG retrieval and generated answers.

Multi-Modal Labeling

Label images, text, audio, video, time series, and multi-modal tasks including classification, object detection, segmentation, NER, transcription, and dialogue processing.

Label Studio Use Cases

Human-in-the-loop review of agent traces and LLM outputs

Computer vision annotation for object detection and semantic segmentation

Document OCR and named entity recognition for NLP projects

Audio transcription and speaker diarization at scale

RLHF preference collection and LLM benchmark evaluation

Label Studio Frequently Asked Questions

What are the system requirements to install Label Studio?

Label Studio runs on Linux, Windows, or macOS with Python 3.8 or later. The team recommends 16GB RAM, 50GB disk for production, and PostgreSQL 13+ or SQLite 3.35+. Port 8080 is used by default.

How do I install Label Studio?

Run pip install label-studio in a Python virtual environment, then start the server with label-studio. Docker, Homebrew, and source installs are also documented on the official site.

What data types can I label with Label Studio?

Label Studio supports image, text, audio, video, HTML, time series, multi-modal, and agentic trace data with templates for classification, detection, segmentation, NER, transcription, and LLM evaluation.

Label Studio Product Background & Official Links

Label Studio Support Email & Customer service contact & Refund contact etc.

More Contact, visit the about us page(https://labelstud.io/docs)

Label Studio Company Info

Label Studio Company name: HumanSignal

Label Studio Developer Type: company

More about Label Studio, Please visit the about us page(https://labelstud.io/docs)

Label Studio Pricing & Plans

Community Edition

Free

Open source on-prem deployment
Multi-modal labeling platform
Configurable labeling interface
Templates for common data types
API, SDK, and webhooks
Community Slack and GitHub support

Subscribe & upgrade now

Starter Cloud

Recurring

$99 /month

Fully hosted service
Up to 12 users
$49 per month for each additional user
Role-based access control
Quality workflows and automated task distribution
Dedicated support portal

Subscribe & upgrade now

Enterprise

contact

Contact sales

SOC2 and HIPAA compliant cloud or on-prem
Single Sign-On with SAML or LDAP
Programmable and embeddable interfaces
LLM-as-a-judge, auto-labeling, and bulk labeling
Highest priority SLAs
Available on AWS Marketplace

Subscribe & upgrade now

Label Studio Tags

Open Source Free Freemium API Available Self-Hosted Cloud-based Team Collaboration Web App

Best Suited Occupations for Label Studio

Software Developers Data Scientists Product Managers Project & Program Managers

Label Studio

Active

Tagline:The open source platform for data labeling, AI evaluation, and human-in-the-loop workflows.

Updated:-

Social Media:

Open Website

Visit Website

What is Label Studio?

How to use Label Studio?

Label Studio Core Features

Programmable Interfaces

Custom layouts and templates adapt to your data, tasks, and evaluation criteria.

Native to your ML / AI Pipeline

API, Python SDK, and webhooks let you create projects, stream predictions, and trigger training, active learning, and evaluation workflows in real time.

Connect Any Data or Models

Sync data from any storage and connect any model to power AI-assisted labeling, benchmarking, and continuous model evaluation.

LLM & Agent Evaluation

Connect observability tools for human-in-the-loop review, collect human preferences for RLHF, create custom benchmarks, and evaluate RAG retrieval and generated answers.

Multi-Modal Labeling

Label images, text, audio, video, time series, and multi-modal tasks including classification, object detection, segmentation, NER, transcription, and dialogue processing.

Label Studio Use Cases

Human-in-the-loop review of agent traces and LLM outputs

Computer vision annotation for object detection and semantic segmentation

Document OCR and named entity recognition for NLP projects

Audio transcription and speaker diarization at scale

RLHF preference collection and LLM benchmark evaluation

Label Studio Frequently Asked Questions

What are the system requirements to install Label Studio?

Label Studio runs on Linux, Windows, or macOS with Python 3.8 or later. The team recommends 16GB RAM, 50GB disk for production, and PostgreSQL 13+ or SQLite 3.35+. Port 8080 is used by default.

How do I install Label Studio?

Run pip install label-studio in a Python virtual environment, then start the server with label-studio. Docker, Homebrew, and source installs are also documented on the official site.