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Label Studio
Label multimodal data and evaluate AI models with Label Studio, an open-source annotation platform.
About Label Studio
Label Studio is a platform for data labeling, AI evaluation, and human-in-the-loop workflows. It handles image, text, document, audio, time-series, multimodal data, and LLM and agent outputs.
It supports tasks such as object detection, segmentation and tracking, named entity recognition, question answering, audio transcription and speaker diarization, time-series event labeling, RLHF preference collection, RAG retrieval and answer grading, and side-by-side model comparison. It offers an API, Python SDK and webhooks, data storage sync and model connections for assisted labeling, and local installation via Pip, Brew, Git and Docker. The DICOM and LiDAR interfaces are described as Label Studio Enterprise.
Key features
- Image detection with boxes, polygons, circles and keypoints
- Side-by-side LLM comparison with custom benchmarks and rubrics
- RLHF collection for preferences, corrections and rankings
- RAG evaluation for retrieval relevance and answer grading
- Audio transcription with waveform display and speaker diarization
- API, Python SDK and webhooks for pipeline workflows
Best for: Teams that need to label image, text, audio and time-series data and review AI outputs.
Good for
- Covers image, text, audio, time-series and LLM evaluation
- Offers local installation via Pip, Brew, Git and Docker
- Provides API, Python SDK and webhooks for pipeline integration
Watch out for
- DICOM and LiDAR interfaces are described as Enterprise
Label Studio at a glance
- Website
- labelstud.io
- Type
- Website
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Label Studio FAQ
How do I run Label Studio locally?
Install with pip install -U label-studio and run label-studio, or run the Docker image, then visit http://localhost:8080/.
What LLM evaluation tasks does it support?
Custom benchmarks and rubrics, side-by-side comparison, collection of preferences and rankings for RLHF, and retrieval and answer grading for RAG.
What image labeling tasks are supported?
Image classification, object detection, object tracking frame-by-frame, and semantic segmentation.
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