Label Studio

Label Studio is an open-source platform designed for data labeling and artificial intelligence evaluation, enabling users to create, manage, and review labeled datasets across a wide range of data modalities. It supports computer vision tasks such as image segmentation, bounding boxes, and keypoint annotation, as well as document and natural language processing tasks including text classification, named entity recognition, relation extraction, and question answering. For audio and speech data, the platform offers transcription, speaker diarization, and emotion recognition labels. Time series data can be annotated for anomaly detection, classification, and forecasting. Additionally, Label Studio handles multi-modal data, allowing simultaneous annotation of images with text captions or audio with transcripts. The platform is particularly useful for training and evaluating large language models (LLMs) and agent-based AI systems. It provides tools for reinforcement learning from human feedback (RLHF), where human annotators rank, correct, or provide preferences on model outputs, enabling fine-tuning of generative models. For agent traces—sequences of steps taken by AI agents—Label Studio integrates with observability tools to allow human-in-the-loop review, debugging, and labeling of intermediate actions, tool usage, and reasoning paths. This capability is essential for building reliable AI agents that perform complex multi-step tasks. Users can set up custom labeling configurations using a flexible XML-based interface, defining visual tools like polygons, brush masks, keypoints, and text spans. The platform supports collaborative workflows where multiple annotators work on the same dataset, with conflict resolution and consensus scoring. Built-in quality control features i