ИИ-агенты
Adala
Autonomous data-labeling agents: let AI do the annotation grunt work.
★ 1,6 тыс. звёздApache-2.0
Смотреть руководство →label-studio — проект с открытым исходным кодом из категории «ИИ-агенты» от HumanSignal (HumanSignal/label-studio на GitHub). У него 28 418 звёзд на GitHub и 3 734 форка, он распространяется по лицензии Apache-2.0 и последний раз обновлялся на GitHub 8 октября 2026.
The open-source studio for labeling data of every type.
Be clear about the problem you are solving before installing.
Clone the repository and skim the docs for hardware or key requirements.
Install dependencies, then add API keys, endpoints, or local-model settings.
Launch the example flow and verify the output matches expectations.
Text, image, audio, video, and time-series annotation.
Consistent export formats for training pipelines.
Bounding boxes and segmentation support.
Built for collaborative labeling teams.
Run it on your own infrastructure.
Permissive licensing.
Download the source and move into the project folder.
git clone https://github.com/HumanSignal/label-studio.git
cd label-studioInstall the TypeScript dependencies (see README for the full stack).
npm installStart the app per the install docs — Docker is the common path.
# see README for backend + Docker setup
npm run startIt is maintained by HumanSignal at HumanSignal/label-studio, with 3,734 forks. Check the releases for the current version.
The community edition is free under Apache-2.0; an enterprise offering also exists.
No — the self-hosted community edition is free to run on your own infrastructure.
Compare on supported data types, workflow features, and export formats for your pipeline.
Follow the install docs — the Docker route is the most common — then create your first labeling project.
Autonomous data-labeling agents: let AI do the annotation grunt work.
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