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Label Studio

Freemium

Label any data. Evaluate any AI.

Label Studio is an open-source platform for data labeling, AI evaluation, and human-in-the-loop workflows. It supports annotation across images, text, audio, video, time series, and multi-modal data, with flexible XML-based templates that let teams define custom interfaces for virtually any labeling task. Teams can self-host the free Community Edition or upgrade to managed cloud plans for enterprise-grade security, role-based access control, and automated quality workflows.

data-labelingannotationmachine-learningmulti-modalcomputer-visionLLM-evaluationData AnalysisInformation ExtractionImage RecognitionSpeech-to-TextCharacter RecognitionSentiment AnalysisanalysisrecognitionData AnalysisImage & VisionAudio & VoiceVideo Creation

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What is it

Label Studio is a multi-type data labeling and annotation tool with a configurable interface, standardized output formats, and deep integration into machine learning pipelines. It is developed by HumanSignal and available as open-source software under the Apache 2.0 license.

What it can do

It provides pre-built templates for computer vision, NLP, audio, time series, and multi-modal tasks, while allowing full customization via XML configuration. Users can connect cloud storage, integrate ML backends for pre-labeling and active learning, and export annotated datasets to formats like COCO, YOLO, Pascal VOC, JSON, and CSV. The platform also supports LLM and agent evaluation, including RLHF preference collection, RAG retrieval QA, and agentic trace review.

Who is it for

Data scientists, machine learning engineers, AI researchers, and annotation team leads who need a flexible, secure, and scalable platform to prepare training data, evaluate model outputs, and manage internal labeling operations across diverse data types.

Key Features

Multi-Modal Data Labeling

Annotate images, text, audio, video, HTML, time series, and mixed data types within a single platform. Label Studio uses configurable XML templates to adapt the labeling interface to any dataset structure, making it one of the most flexible open-source annotation tools available for research and production ML pipelines.

LLM & Agent Evaluation

Evaluate large language models and agentic AI systems with human-in-the-loop workflows. Create custom benchmarks and rubrics, run side-by-side model comparisons, collect RLHF preferences and rankings, and grade RAG retrieval relevance against source documents.

AI-Assisted Pre-labeling

Accelerate annotation by connecting pre-trained ML models or LLMs to suggest labels automatically. Human annotators verify or refine AI-generated pre-labels, significantly reducing manual effort while maintaining high dataset quality for model training.

Computer Vision Annotation

Support for image classification, object detection with bounding boxes, polygons, circular shapes, and keypoints, object tracking frame-by-frame, and semantic segmentation. ML models can pre-label regions to optimize the segmentation process.

Use Cases

Computer vision dataset preparation

ML engineers label images and video frames for object detection, classification, and segmentation tasks, then export annotations to COCO, YOLO, or Pascal VOC formats for immediate use in model training pipelines.

NLP and document AI annotation

Data scientists annotate text for named entity recognition, sentiment analysis, and question answering, or process complex PDF documents with OCR to build and fine-tune language models.

Audio and speech data labeling

Teams transcribe audio recordings, perform speaker diarization, and tag emotions to create high-quality training datasets for speech recognition and voice AI systems.

LLM evaluation and RLHF data collection

AI researchers collect human preferences, corrections, and rankings to fine-tune large language models, evaluate retrieval-augmented generation systems, and benchmark model outputs against rubrics.