HKUDS는 GitHub에서 다양한 공개 리포지토리를 통해 데이터 지능 분야에 기여하고 있습니다. 주요 프로그래밍 언어로는 Python, Jupyter Notebook, TypeScript 및 Roff가 있으며, nanobot, CLI-Anything, LightRAG와 같은 널리 사용되는 프로젝트를 보유하고 있습니다. 이들 리포지토리는 오픈 소스 AI 에이전트와 개인화된 튜터링 시스템을 포함하여 여러 분야에 적용 가능합니다.
Lightweight, open-source AI agent for your tools, chats, and workflows.
"CLI-Anything: Making ALL Software Agent-Native" -- CLI-Hub: https://clianything.cc/
[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"
DeepTutor: Agent-native, Open-sourced Personalized Tutoring. https://deeptutor.info/.
"RAG-Anything: All-in-One RAG Framework"
"AI-Trader: 100% Fully-Automated Agent-Native Trading"
"DeepCode: Open Agentic Coding (Paper2Code & Text2Web & Text2Backend)"
"OpenHarness: Open Agent Harness with a Built-in Personal Agent--Ohmo!"
"Vibe-Trading: Your Personal Trading Agent"
"ViMax: Agentic Video Generation (Director, Screenwriter, Producer, and Video Generator All-in-One)"
"AutoAgent: Fully-Automated and Zero-Code LLM Agent Framework"
"ClawWork: OpenClaw as Your AI Coworker - 💰 $15K earned in 11 Hours"
"OpenSpace: Make Your Agents: Smarter, Low-Cost, Self-Evolving" -- Community: https://open-space.cloud/
[NeurIPS2025] "AI-Researcher: Autonomous Scientific Innovation" -- A production-ready version: https://novix.science/chat
"ClawTeam: Agent Swarm Intelligence" (One Command → Full Automation)
"Paper2Slides: From Paper to Presentation in One Click"
[KDD'2026] "VideoRAG: Chat with Your Videos"
"FastCode: Accelerating and Streamlining Your Code Understanding"
[ACL2026] "MiniRAG: Making RAG Simpler with Small and Open-Sourced Language Models"
"Your Fully-Automated Personal AI Assistant"
[SIGIR'2024] "GraphGPT: Graph Instruction Tuning for Large Language Models"
[ACL 2026] "OpenPhone: Mobile Agentic Foundation Models for AI Phone"
"VideoAgent: All-in-One Agentic Framework for Video Understanding, Editing, and Remaking"
"AnyTool: Universal Tool-Use Layer for AI Agents"
[ACL 2026 Oral] "LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?"
[ICML 2025] "SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator"
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
[WSDM'2024 Oral] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"
[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"
[KDD'2024] "UrbanGPT: Spatio-Temporal Large Language Models"
"CatchMe: Make Your AI Agents Truly Personal"
[KDD'2024] "LLM4Graph: A Survey of Large Language Models for Graphs"
[EMNLP2025] "GraphAgent: Agentic Graph Language Assistant"
[EMNLP'2024] "OpenGraph: Towards Open Graph Foundation Models"
"DeepInnovator: AI Research Assistant - Idea Spark & Scientific Discovery"
"AnyGraph: Graph Foundation Model in the Wild"
[WWW'2023] "MMSSL: Multi-Modal Self-Supervised Learning for Recommendation"
"MoChat: OpenClaw as Your Social Agent https://mochat.io"
[ICLR'2023] "LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation"
[EMNLP'2024] "XRec: Large Language Models for Explainable Recommendation"
"OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction"
"FutureShow: Can AI Predict the Future? Live Real-World Forecasting"
"Litewrite: Vibe Writing is Coming - Write Faster and Better! https://litewrite.ai"
[EMNLP'2025] "EasyRec: Simple yet Effective Language Model for Recommendation"
[KDD'2024] "HiGPT: Heterogenous Graph Language Models"
"GraphEdit: Large Language Models for Graph Structure Learning"
[WSDM'2024 Oral] "DiffKG: Knowledge Graph Diffusion Model for Recommendation"
[WSDM'2023] "HGCL: Heterogeneous Graph Contrastive Learning for Recommendation"
[ACM CSUR] "A Comprehensive Survey of Self-Supervised Learning for Recommendation"
[ACL2025] "RecLM: Recommendation Instruction Tuning"
[ACM MM'2024]"DiffMM: Multi-Modal Diffusion Model for Recommendation"
[NeurIPS'2023] "GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks"
[ICML'2024] "FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction"
[CIKM'2024] "RecDiff: Diffusion Model for Social Recommendation"
[KDD'2023] "KGRec: Knowledge Graph Self-Supervised Rationalization for Recommendation"
[WWW'2024] "GraphPro: Graph Pre-training and Prompt Learning for Recommendation"
[WSDM'2025] "DiffGraph: Heterogeneous Graph Diffusion Model"
[SIGIR'2024] "SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation"
[SIGIR'2023] "GFormer: Graph Transformer for Recommendation"
[KDD'2023] "AdaGCL: Adaptive Graph Contrastive Learning for Recommendation"
[WWW'2023] "AutoST: Automated Spatio-Temporal Graph Contrastive Learning"
[SIGIR'2023] "MAERec: Graph Masked Autoencoder for Sequential Recommendation"
[WWW'2023] "DCRec: Debiased Contrastive Learning for Sequential Recommendation"
[SIGIR'2023] "DCCF: Disentangled Contrastive Collaborative Filtering"
[CIKM'2023] "STExplainer: Explainable Spatio-Temporal Graph Neural Networks"
[WSDM'25] "LightGNN: Simple Graph Neural Network for Recommendation"
[ICML'2023] "GraphST: Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation"
[ACM TIST] "LLM4Urban: Urban Computing in the Era of Large Language Models"
[EMNLP2025] "RecGPT: A Foundation Model for Sequential Recommendation"
"FastAgent: Simple, Fast, and Strong LLM Agents"
[WWW'2024] "PromptMM: Multi-Modal Knowledge Distillation for Recommendation with Prompt-Tuning"
[WWW'23] "AutoCF: Automated Self-Supervised Learning for Recommendation"
"DeepResearch-Eval: An End-to-End Evaluation Framework for DeepResearch Systems"
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Memory Governance Protocol
[IJCAI'2023] "DSL: Denoised Self-Augmented Learning for Social Recommendation"
[WWW'23] "SimRec: Graph-less Collaborative Filtering"
[CIKM'2023] "CL4ST: Spatio-Temporal Meta Contrastive Learning"
[ICDE'23] "DGNN: Disentangled Graph Social Recommendation"
[ICDE'2024] "GraphAug: Graph Augmentation for Recommendation"
[WSDM'2025] "MixRec: Heterogeneous Graph Collaborative Filtering"
[CIKM'2024] "EasyST: A Simple Framework for Spatio-Temporal Prediction"
[CIKM'2023] "GTE: How Expressive are Graph Neural Networks for Recommendation?"
[Recsys'2023] "RCL: Multi-Relational Contrastive Learning for Recommendation"
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HKUDS는 데이터 지능과 관련된 여러 오픈 소스 프로젝트를 개발합니다. 주요 리포지토리로는 nanobot, CLI-Anything, DeepTutor 등이 있으며, 이들은 다양한 도구와 워크플로우에 통합될 수 있습니다.
HKUDS는 주로 Python, Jupyter Notebook, TypeScript 및 Roff와 같은 프로그래밍 언어를 사용하여 리포지토리를 개발합니다. 이러한 언어들은 데이터 처리 및 AI 개발에 적합합니다.
네, HKUDS의 모든 리포지토리는 공개적으로 이용 가능하며, 누구나 접근하고 활용할 수 있습니다. 이는 연구자와 개발자들이 이들의 작업을 활용할 수 있도록 합니다.
✨Data Intelligence Lab@HKU✨을 RepoGuard로 모니터링하고 새로운 공개 저장소가 나타나는 순간 알림을 받으세요.
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