Deep Research Web UI - An open-source AI research assistant that helps you delve deeper into research topics.

What is Deep Research Web UI?

Deep Research Web UI is an open-source AI research assistant tool that helps users conduct in-depth research efficiently. Through AI-driven iterative search, it progressively delves into a specific topic, visualizing the research process in a tree structure, allowing users to clearly understand the AI's reasoning and information gathering paths. The tool supports multilingual search to meet global research needs and allows research reports to be exported to Markdown or PDF formats for easy saving and sharing.

Main functions of Deep Research Web UI

• In-depth researchThrough multiple rounds of searching and reasoning, the system gradually delves deeper into the research topic, automatically expanding and refining questions to provide comprehensive research results. Based on the acquired information, the AI assistant adjusts the search direction in real time to ensure both depth and breadth of the research.
• Search visualizationThe research process is presented in the form of a tree diagram, clearly showing the search content and reasoning logic of each node, helping users track the research path of AI.
• Node Information ManagementUsers can view detailed information about each node, including search results, reference links, etc., and can also mark, delete, or re-search nodes.
• Multilingual supportIt supports searching and research in multiple languages, including but not limited to English, Chinese, Dutch, etc., to meet the needs of different users.
• Internet search and information retrievalDeep Research's Web UI can access the web in real time and retrieve relevant information from the internet, including text, images, and PDF files. It improves the efficiency and accuracy of information retrieval through intelligent search algorithms and supports various search services such as Tavily and Firecrawl.
• Data Analysis and ProcessingAfter retrieving information, the Deep Research Web UI analyzes and processes the data, extracting key information and data. It can handle multimodal data, including text, images, and tables, and uses custom algorithms to parse and understand data in tables and charts for structured processing.
• Report generation and visualizationBased on the analysis results, the Deep Research Web UI generates a detailed research report, including clear citations and a summary of the thought process. It contains text, images, tables, charts, and other formats.
• Running in browserAll configurations and API requests are completed on the browser side, and user data is not uploaded to the server, ensuring privacy and security.
• Supports multiple AI servicesIt is compatible with various AI services such as OpenAI, DeepSeek, OpenRouter, and Ollam, allowing users to select different models as needed.
• Custom deploymentIt supports rapid local deployment via Docker, and users can customize the configuration according to their needs.

The Technical Principles of Deep Research Web UI

• Natural Language Processing and Semantic UnderstandingDeep Research Web UI uses powerful Natural Language Processing (NLP) technology, based on OpenAI's o3 model, to perform semantic understanding and analysis of user-input research topics.
• Multi-step research planningThrough reinforcement learning technology, the Deep Research Web UI can autonomously plan multi-step research paths. Based on its understanding of the problem, it develops detailed research plans, including the types of information to be searched, potential information sources, and research priorities. It also has the ability to dynamically adjust its strategies based on real-time information.
• End-to-end reinforcement learningDeep Research's Web UI uses end-to-end reinforcement learning to train models, enabling them to perform reasoning and complex browsing tasks across various domains. At the heart of this approach is teaching models to autonomously plan and execute multi-step processes to find relevant data, including the ability to backtrack and adapt based on real-time information.

Project address for Deep Research Web UI

• Github repository:https://github.com/AnotiaWang/deep-research-web-ui

Application Scenarios of Deep Research Web UI

• literature reviewIt can quickly generate literature reviews, helping scholars and students understand the current status and development trends of a research field in a short period of time.
• Data AnalysisIt involves analyzing massive amounts of academic data, extracting key information, and assisting in thesis writing and research.
• Market researchBusinesses can use tools to conduct market research, understand market size, competitive landscape, consumer demand, and other factors, providing data support for business decisions.
• Financial Analysis: To analyze a company's financial data and assess its financial condition and investment value.
• Product ComparisonConsumers can use tools to compare the features and reviews of different products and make more informed purchasing decisions.