Project and workflow management
Create projects for labeling and annotation, then manage them through the data manager, project settings, and labeling guide in the documentation.
Label Studio
RevendiquerLabel Studio is an open source data labeling and AI evaluation platform for annotation, human-in-the-loop review, and model evaluation workflows.
Overview
Label Studio is an open source data labeling and AI evaluation platform for building annotation workflows across many data types. The product site positions it for human-in-the-loop labeling, AI evaluation, and model training data preparation.
Its documentation and template gallery show support for computer vision, document AI, NLP, audio transcription, time series, conversational AI, RLHF, agent traces, RAG and retrieval evaluation, and other multi-modal workflows. Teams can start from templates, customize interfaces, connect storage and models, and export annotations for downstream use.
Features
Templates and configurable interfaces
Use pre-built labeling templates and customizable tags to start faster and adapt interfaces to different data types and evaluation criteria.
Import and export workflows
Import data from local sources and external storage, and export annotations and data when labeling is complete.
ML and automation integration
Connect machine learning models through the documented machine learning integration, custom ML backends, API, Python SDK, and webhooks.
Multi-modal labeling and evaluation
Work across computer vision, NLP, documents, audio, time series, multi-modal data, and agent or LLM evaluation workflows.
Ecosystem integrations
Use the integrations directory to connect Label Studio with storage, model, observability, and infrastructure tools such as S3, GCS, Azure Blob Storage, Docker, Kubernetes, TensorFlow, PyTorch, and LangSmith.
Use Cases
Computer vision annotation
Prepare labeled datasets for image classification, object detection, segmentation, keypoint labeling, and OCR using the computer vision templates and workflow examples.
NLP, document, and audio labeling
Label text, documents, chat data, and speech for named entity recognition, sentiment analysis, question answering, audio transcription, speaker diarization, and related workflows.
LLM and agent evaluation
Build evaluation workflows for LLMs, RAG systems, and agent traces, including side-by-side comparisons, custom rubrics, and human preference collection.
ML pipeline integration
Sync data from external storage, connect models, and export annotations to support training, active learning, benchmarking, and continuous evaluation pipelines.
Custom task interfaces
Adapt a template or programmable interface for specialized layouts such as robotics, structured data parsing, rank-and-score tasks, and multi-turn conversation review.
Pros and Cons
Pros
- Covers a broad set of labeling and evaluation tasks from vision and documents to audio, time series, and LLM workflows.
- Offers pre-built templates that help teams start with common labeling and evaluation setups.
- Supports customization through configurable interfaces, tags, templates, API, Python SDK, and webhooks.
- Connects to external storage, model, and observability tools through a published integrations directory.
Cons
- The pricing page at `/pricing` returns a 404, so public pricing and plan details are not available from the provided sources.
- Some capabilities are presented through documentation and templates rather than dedicated product pages, so implementation depth may vary by workflow.
FAQ
What is Label Studio used for?
Label Studio is designed for creating and managing labeling projects, importing data, configuring labeling interfaces, and exporting annotations for downstream use.
How is Label Studio installed and deployed?
The documentation lists quick-start installation, Kubernetes installation, airgapped server setup, database setup, persistent storage, and upgrade and security guidance.
Can Label Studio connect to machine learning systems?
Yes. The documentation includes machine learning integration, a way to write your own ML backend, an API, Python SDK, and webhooks for connecting labeling and evaluation workflows to external systems.
What kinds of tasks can it support?
Label Studio includes out-of-the-box templates for computer vision, NLP, audio, time series, conversational AI, LLM evaluation, RLHF, robotics, document AI, and other workflows.
Quick Facts
- Category
- Open source data labeling and AI evaluation
- Primary use
- Annotation, human-in-the-loop review, and AI evaluation
- Platform
- Web application with quick-start install options for pip, Brew, Git, and Docker
- Documentation
- Label Studio Documentation and template gallery
- Website
- labelstud.io
- Pricing
- No public pricing page was available at `/pricing` in the provided sources