KI-Agenten
TaskingAI
An open-source platform for AI-native application development — assistants, tools, RAG and models in one API.
★ 5,4 Tsd. SterneApache-2.0
Anleitung ansehen →RAG-Anything ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von HKUDS (HKUDS/RAG-Anything auf GitHub). Es hat 23.495 Sterne auf GitHub und 2.735 Forks, wird unter der Lizenz MIT veröffentlicht und wurde zuletzt am 6. Oktober 2026 auf GitHub aktualisiert.
One RAG framework for everything — including multimodal documents.
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.
Parsing through generation in one toolkit.
Text, images, tables — the messy reality.
From HKU with a published paper.
One interface across document types.
Fits the standard ML stack.
Free for commercial use.
Download the source and move into the project folder.
git clone https://github.com/HKUDS/RAG-Anything.git
cd RAG-AnythingIsolate dependencies, then install requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtLaunch using the entry point shown in the README.
python -m rag_anything # check README for the exact commandIt is an open-source project at HKUDS/RAG-Anything with 2735 forks. Check the contributors tab and recent commits to judge how active maintenance is.
Yes — it's MIT licensed: free to use, modify, and ship commercially. Just keep the license notice in copies.
Documents mixing text, images, tables, and more — the full messy reality of enterprise files. See the paper for details.
Yes — the design is described in an arXiv paper linked from the repo homepage field.
Clone the repo and follow the README quickstart — the README is the best starting point.
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Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.