KI-Agenten
yomo
Serverless AI agent framework running on geo-distributed edge infrastructure.
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Anleitung ansehen →mlx ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von ml-explore (ml-explore/mlx auf GitHub). Es hat 28.686 Sterne auf GitHub und 2.318 Forks, wird unter der Lizenz MIT veröffentlicht und wurde zuletzt am 8. Oktober 2026 auf GitHub aktualisiert.
Apple's array framework for running ML efficiently on Apple silicon.
Decide what you want the project to do before installing anything.
Grab the source and skim the README for setup requirements.
Install dependencies and add any API keys or settings it needs.
Launch it and follow the example workflow from the docs.
Built for the Mac GPU and unified memory.
Familiar array programming model.
Efficient computation graphs.
Use it from Python with pip install mlx.
Free for any use.
Maintained by Apple's ML research team.
Download the source and move into the project folder.
git clone https://github.com/ml-explore/mlx.git
cd mlxThe fastest way to try MLX is the pip package.
pip install mlxRun a tiny array computation to confirm it works.
python -c "import mlx.core as mx; print(mx.array([1, 2, 3]))"It is an open-source project at ml-explore/mlx with 2318 forks. Check the repo's contributors and recent commits to judge how active maintenance is.
Yes - it is MIT licensed, so you can use, modify, and distribute it freely, including in commercial projects.
No - this is infrastructure you run on your own hardware, so there is no per-request fee.
Usually not - agents call models, and MLX is the layer that runs those models fast on Macs. You need it if you are doing local inference on Apple silicon.
Clone the repo, read the README's quickstart, and run the provided example - most projects get you to a first result in minutes.
Serverless AI agent framework running on geo-distributed edge infrastructure.
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Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.