What is Auto-Deep-Research
Auto-Deep-Research is an open-source fully automated personal AI assistant from Professor Chao Huang's lab at the University of Hong Kong, serving as an OpenAI Deep Research open-source alternative. Developed based on the AutoAgent framework, it focuses on deep research capabilities and adopts a modular multi-agent architecture, including a Web Agent, a Coding Agent, and a Local File Agent. The agents are respectively responsible for internet information search, programming implementation and debugging, and multi-format file parsing. It supports multiple large language models (LLMs), such as Anthropic, OpenAI, Mistral, Hugging Face, etc., and is built solely on Claude-3.5-Sonnet. Auto-Deep-Research supports importing browser cookies to better access specific websites.
Main Features of Auto-Deep-Research
- Deep Research Capabilities: Focused on automated handling of complex tasks, such as file parsing, web search, data analysis and visualization, and can generate detailed reports.
- Multi-Language Model Support: Compatible with multiple large language models (LLMs), such as Anthropic, OpenAI, Mistral, Hugging Face, etc.
- High Cost-Effectiveness: Built on Claude-3.5-Sonnet, with significant cost efficiency, making it the optimal solution among open-source options.
- Community-Driven Improvements: Based on community feedback, features such as one-click startup and enhanced LLM compatibility have been added.
- Easy to Deploy: Supports installation via Conda environment or Docker, and provides detailed startup configuration options.
Technical Principles of Auto-Deep-Research
- Multi-Agent Architecture: Includes a Web Agent (internet information search), a Coding Agent (programming implementation and debugging), and a Local File Agent (file parsing and understanding), working collaboratively through a core Orchestrator Agent.
- Web Agent: Focused on barrier-free access and deep search of internet information.
- Coding Agent: Responsible for programming implementation and debugging, with rigorous logical analysis capabilities.
- Local File Agent: Dedicated to parsing and content understanding of multi-format files.
Auto-Deep-Research Project Address
- Github Repository：https://github.com/HKUDS/Auto-Deep-Research
Application Scenarios of Auto-Deep-Research
- Scientific Research and Data Analysis: Researchers can use Auto-Deep-Research to quickly process and analyze data, and automatically generate high-quality analysis reports.
- Finance and Market Analysis: Financial analysts can use the tool to track industry trends, assess market trends, generate investment research reports, and assist data-driven decision-making.
- Education and Learning: Students and educators can use Auto-Deep-Research for literature reviews, organizing learning materials, and generating study reports.
- Corporate Strategy and Business Decision-Making: Enterprises can use the tool for industry analysis, competitor research, and business strategy evaluation, optimizing product planning and market expansion strategies.