AIエージェント
MemOS
Self-evolving memory OS for LLMs and agents: persistent graph memory, hybrid retrieval, skill reuse.
★ 11.8k スターApache-2.0
ガイドを見る →LightRAGは、HKUDSが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: HKUDS/LightRAG）。GitHubスターは40,014、フォークは5,668で、MITライセンスの下で公開されています。GitHubでの最終更新は2026年10月3日です。
Simple, fast retrieval-augmented generation with graph smarts.
Pick one concrete task so you can judge whether the project fits.
Clone the repo and check the prerequisites listed in the README.
Install what it needs and configure credentials or model access.
Run a minimal example end to end before wiring it into your stack.
Uses knowledge-graph structure to organize retrieval.
Presented at EMNLP 2025 with an accompanying paper.
Positioned as a lean alternative to heavyweight RAG stacks.
Hackable Python codebase.
Permissive licensing.
Over 5,600 forks.
Download the source and move into the project folder.
git clone https://github.com/HKUDS/LightRAG.git
cd LightRAGSet up a virtual environment and install requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtConfigure your LLM/embedding keys and run the quickstart.
# see README for the exact example path
python examples/quickstart.pyIt is maintained by HKUDS at HKUDS/LightRAG, with 5,668 forks. The project page links to the EMNLP 2025 paper for background.
Yes. It is released under the MIT license, which permits commercial use, modification, and redistribution. Read the LICENSE file for the exact terms.
You will need an LLM and embedding provider for generation and embeddings; the framework itself is free.
Compare on retrieval quality and simplicity for your corpus — its selling point is lean, graph-enhanced retrieval.
Clone the repo, install dependencies, add your provider keys, and follow the README quickstart alongside the paper.
Self-evolving memory OS for LLMs and agents: persistent graph memory, hybrid retrieval, skill reuse.
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