HKUDS, operating under the name ✨Data Intelligence Lab@HKU✨, has a notable public GitHub presence with a wide range of repositories. The organization primarily utilizes programming languages such as Python, Jupyter Notebook, and TypeScript. Notable projects include nanobot, CLI-Anything, and LightRAG, which focus on AI and agent-based solutions.
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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No description provided for this repository.
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HKUDS builds a variety of projects primarily centered around artificial intelligence and agent-based systems. Their repositories include applications like nanobot and CLI-Anything, which aim to enhance user workflows through automation and intelligent assistance.
HKUDS predominantly uses Python, alongside Jupyter Notebook and TypeScript. These languages support their focus on developing advanced AI tools and frameworks, facilitating efficient coding and data manipulation.
Yes, all of HKUDS's repositories are public on GitHub. This transparency allows users and developers to access and contribute to their projects, fostering collaboration within the open-source community.
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