An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models. This is a web UI for the deep-research project, with several improvements and fixes.
One-Minute Overview#
Deep Research Web UI is an AI-powered research tool that helps you conduct in-depth research on any topic. It combines search engines, web scraping, and large language models to build a comprehensive research process tree. Ideal for researchers, content creators, analysts, and anyone who needs systematic information collection and organization.
Core Value: A one-stop AI research platform with visual research process flow, ensuring data security while supporting multiple models and search engines.
Quick Start#
Installation Difficulty: Low - Complete Docker image available for one-click deployment
# Quick deployment with Docker
docker run -p 3000:3000 anotia/deep-research-web:latest
Is this suitable for me?
- ✅ Systematic research scenarios: Such as market research, academic research, content creation
- ✅ Users who want to visualize the research process: Tree structure shows the research process, making it easy to track and review
- ✅ Users who need to export research reports: Supports Markdown and PDF export formats
- ❌ Simple information lookup scenarios: May be overly complex for quick queries of simple questions
Core Capabilities#
1. Security & Privacy - Data Localization#
- All configurations, API requests, and data remain in the user's browser locally Actual Value: Protects user privacy without worrying about sensitive information leakage
2. Real-time Feedback - Streamed Responses#
- Streamed AI responses and real-time UI updates provide immediate feedback Actual Value: Enhances user experience with no need to wait for complete responses, allowing real-time monitoring of research progress
3. Research Visualization - Tree Structure Display#
- Shows the research process using a tree structure, supporting multi-language searches Actual Value: Visually presents research ideas and logical relationships, making it easy to understand and review the research process
4. Multi-Model Support - Wide Compatibility#
- Uses plain prompts instead of structured outputs to ensure compatibility with more providers Actual Value: Supports various AI models including OpenAI, DeepSeek, Ollama, and others, increasing tool flexibility
Technical Stack & Integration#
Development Languages: TypeScript, JavaScript Main Dependencies: Nuxt.js 4.0.3, Vue 3, Vue Router, @vue-flow/core, Pinia, TailwindCSS Integration Method: Web Application/API
Maintenance Status#
- Development Activity: Actively maintained with multiple commits per month
- Recent Updates: Updated in July 2025 with research history management functionality
- Community Response: Project provides comprehensive documentation and examples with multiple deployment options
Documentation & Learning Resources#
- Documentation Quality: Comprehensive
- Official Documentation: https://github.com/AnotiaWang/deep-research-web-ui
- Example Code: Complete environment variable configuration examples and Docker deployment commands provided