Label Studio

Label Studio is an open source platform for labeling data and evaluating AI models. It supports many data types, from images and documents to audio and agent traces, and it includes humans in the review loop.

Label Studio gives teams labeling interfaces for images, text, audio, time series, documents and multimodal data. It can also be used for AI evaluation, such as side-by-side comparisons of model outputs, rubric-based scoring and human review of agent traces. Collected preferences, corrections and rankings can feed fine-tuning. The API, Python SDK and webhooks let you create projects and connect models and storage. The company also sells an enterprise edition.

• Labeling for images, text, audio, time series and documents
• Human review of agent traces and LLM outputs
• Collect preferences and rankings for fine-tuning
• Custom layouts and templates for each task
• API, Python SDK and webhooks for pipelines

Label Studio suits ML engineers, data labeling teams and evaluation teams who need humans in the loop. A team with a single small dataset and a spreadsheet may not need a platform.

The open source version is free to download and run under the Apache-2.0 license. The enterprise edition adds features for larger organizations and is sold separately. Labeling quality depends on clear guidelines and trained annotators, so the tool alone will not fix an unclear task.