Professor Chao HUANG, Assistant Professor at the School of Computing and Data Science (CDS) and scholar at the Musketeers Foundation Institute of Data Science (IDS) at HKU, has led the development of "Auto-Deep-Research", an innovative automated personal AI assistant.
A research team from the School of Computing and Data Science (CDS) at The University of Hong Kong (HKU) has developed a fully automated AI-powered personal research assistant system. This solution excels in various domains, including web-based research, programming, document analysis, and comprehensive report generation. As an open-source tool, it demonstrates competitive performance in automated processing and reasoning, marking a significant advancement in AI-powered research solutions.
The global development of AI technology has witnessed remarkable advancement, particularly in research applications. Modern AI research assistants demonstrate sophisticated capabilities, autonomously conducting online searches, compiling comprehensive sources, performing data analysis, and generating structured reports. These innovations significantly enhance research productivity across diverse sectors, including finance, science, and engineering. However, the accessibility of such powerful tools often depends on paid subscriptions, potentially creating disparities in research efficiency and productivity among users.
Professor Chao HUANG, Assistant Professor at the School of Computing and Data Science (CDS) and scholar at the Musketeers Foundation Institute of Data Science (IDS) at HKU, has led the development of "Auto-Deep-Research", an innovative automated personal AI assistant. This groundbreaking tool seamlessly integrates with various Large Language Models (LLMs) while maintaining an open-source approach. Unlike existing commercial solutions that require expensive subscriptions, Auto-Deep-Research enables open-source deployment with users' own LLM Application Programming Interface (API) keys, making it both cost-effective and widely accessible.
Auto-Deep-Research demonstrates sophisticated capabilities in data analysis through a streamlined multi-step process. Upon receiving a user's request, the tool autonomously browses and synthesises massive amounts of text and PDFs from the web, systematically extracting key points and generating a structured report. Within approximately 10 minutes, users receive a comprehensive analysis featuring visualised findings and clear insights. The tool's exceptional efficiency in handling complex research tasks, combined with its consistently high-quality output, has established it as a leading solution among open-source alternatives.
One of the standout features of Auto-Deep-Research is its integration of AutoAgent, a groundbreaking framework that combines full automation with advanced self-development capabilities. Developed by Professor Huang and his research team, AutoAgent revolutionises the way users interact with LLM agents by enabling their creation and deployment through simple natural language commands. This innovative system empowers users to efficiently build and customise sophisticated tools, agents, and workflows without requiring any coding expertise. Beyond its user-friendly interface, AutoAgent serves as a versatile multi-agent framework for General AI Assistants, marking a significant advancement in accessible AI technology. This breakthrough initiative was developed collaboratively by Professor Huang and his PhD students, Jiabin Tang and Tianyu Fan from IDS and CDS, whose contributions were instrumental in creating both Auto-Deep-Research and AutoAgent.
"Auto-Deep-Research represents a significant milestone as the personal AI assistant powered by AutoAgent, demonstrating the remarkable efficiency and accessibility of creating sophisticated agent applications through our framework," said Professor Huang. "This breakthrough development holds immense potential for advancing practical AI applications. As technology continues to evolve, we envision future AI research assistants will become increasingly accessible and versatile, changing how we approach complex research tasks.
