label-studio

Label Studio addresses the critical need for efficient data labeling in machine learning and AI projects. It provides a versatile solution for ML/AI teams, enabling them to annotate various data types with a standardised output format. With a steady growth of 1,053 stars over 96 days, it demonstrates strong community interest and is considered a production-ready solution. However, it may not be ideal for teams requiring highly specific or custom annotation workflows that exceed its capabilities.

Label Studio is a strong choice when teams need a robust, open source tool for engineering teams that can handle multiple data types and formats. Consider alternatives if your project demands highly specialised annotation features or if you require extensive integrations with other systems.

This tool is particularly beneficial for data scientists, machine learning engineers, and AI researchers who need to manage large datasets efficiently. It is commonly integrated into products and systems focused on computer vision, natural language processing, and other data-intensive applications.

Latest commit: 2026-02-13. Over the past 97 days, this repository gained 1.1k stars (+4.1% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.