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DataFreemiumOpen Source
LABEL STUDIO
Label data and evaluate AI systems with human-in-the-loop workflows
Apache-2.0
ABOUT
Training and evaluating AI systems requires high-quality labeled data, review workflows, and human feedback, but teams often end up juggling spreadsheets, ad hoc annotation tools, and custom review UIs. Label Studio gives teams one platform to label multimodal datasets, review model outputs, and capture human judgments for workflows such as model training, agent evaluation, and RAG quality assessment.
INSTALL
pip install label-studioINTEGRATION GUIDE
1. Label images, text, audio, and documents to create datasets for machine learning workflows
2. Review LLM, agent, or RAG outputs with human feedback before shipping to production
3. Build RLHF and preference datasets from structured annotation and review tasks
4. Run quality assurance workflows for document extraction, OCR, and classification systems
5. Coordinate annotation projects across internal teams and external reviewers
TAGS
data-labelingannotationai-evaluationhuman-in-the-loopmultimodalrlhfrag-evaluationopen-source