Deep Research
Deep Research leverages advanced "think" and "task" models, combined with Internet connectivity, to enable rapid and insightful analysis of a variety of topics. Your privacy is crucial-all data is processed and stored locally.
Features: ·Rapid in-depth research: Generate a comprehensive research report in about 2 minutes, greatly improving research efficiency. ·Multi-platform support: It can be quickly deployed to multiple platforms such as Vercel and Cloudflare. ·AI-driven: Leverage advanced AI models to provide accurate and insightful analysis. ·Focus on privacy: All data is stored locally in the browser to ensure data privacy and security. ·Large model support: Compatible with multiple mainstream large language models, including Gemini, OpenAI, Anthropic, Deepseek, Grok, Mistral, Azure OpenAI, OpenRouter, Olama, etc. ·Support web search: Integrate search engines such as Searxng, Tavily, Firecrawl, Exa, and Bocha to allow LLM that does not support search to easily use web search functions. ·Thinking and task model: Adopt complex "thinking" and "task" models that balance depth and speed, ensure high-quality output, and support switching research models. ·Support further research: Research content can be refined or adjusted at any stage of the project, and re-research from that stage is supported. ·Local knowledge base: Support uploading and processing text, Office, PDF and other resource files to generate a local knowledge base. ·Editing research results: Supports two editing modes (WYSIWYM and Markdown), which can adjust reading difficulty, article length and full-text translation. ·Knowledge map: Generate a knowledge map with one click to systematically understand the report content. ·Research history: Support the preservation of research history, review and conduct in-depth research at any time. ·Local and server API support: Flexible switching of local and server API calls to meet different needs. · SaaS and MCP support: It can be used as an in-depth research service (SaaS) through the SSE API, or it can be integrated into other AI services through MCP services. · PWA support: Using Progressive Web Application (PWA) technology, this project can be used like software. ·Multi-key support: Supports multi-key concurrency and improves API response efficiency. ·Multi-language support: Support English, Simplified Chinese and Spanish. ·Modern technology construction: Based on Next.js15 and Shadcn UI development, the interface is modern, performance is excellent, and experience is good. · MIT License: Open source, free for personal and commercial use.
