KI-Agenten
Adala
Autonomous data-labeling agents: let AI do the annotation grunt work.
★ 1,6 Tsd. SterneApache-2.0
Anleitung ansehen →label-studio ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von HumanSignal (HumanSignal/label-studio auf GitHub). Es hat 28.418 Sterne auf GitHub und 3.734 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 8. Oktober 2026 auf GitHub aktualisiert.
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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Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.