Deep Research Agent

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Inspiration Research is scattered across search engines, academic papers, forums, and code platforms, forcing users to manually verify credibility and context. This fragmented workflow costs hours and still leaves gaps, contradictions, and uncertainty. Deep Research Agent was built to automate end-to-end research synthesis using only real, verifiable data.

What It Does The system searches Tavily, DuckDuckGo, Wikipedia, ArXiv, Hacker News, Reddit, and GitHub in parallel. Using Gemini 3, it analyzes, cross-references, scores source reliability, detects consensus, conflicts, and gaps. It generates a professional research report with clickable citations and suggested follow-up questions.

How It’s Built & What’s Next Built with React, Vite, Tailwind CSS, Framer Motion, Node.js, Express, and WebSockets for real-time streaming. A multi-agent architecture (Planner, Searcher, Analyzer, Synthesizer, Writer) ensures speed, depth, and zero hallucinations. Future plans include PDF/document analysis, citation exports (APA/MLA/BibTeX), collaboration, custom sources, history, and cloud sync.