Build an AI research assistant that searches the live web, scrapes pages, and synthesizes answers in real time using NeuroAPI and a few dozen lines of code. Most "AI research assistants" are wrappers around a static retrieval index. Ask about something that happened yesterday and you get a confident hallucination or a polite shrug. If you're building tools for analysts, traders, journalists, or anyone whose questions change faster than your index refreshes, that's a dealbreaker. I wanted something different: a lightweight agent that can search the live web, grab the actual page content, extract structured data, and feed it to an LLM for synthesis — all in one loop, no pre-crawled corpus required. Turns out NeuroAPI's endpoint set makes this surprisingly straightforward. What we're building: A CLI-based AI research assistant that takes a natural-language question, searches the web for relevant sources, scrapes the top results to get clean content, and passes everything to an LLM for a synthesized answer with citations. No vector database, no embeddings pipeline, no RAG infrastructure. Just search → scrape → synthesize. If the answer isn't good enough, the agent can refine its query and try again. Architecture: The agent loop is simple. NeuroAPI does the heavy lifting on the data side. /v1/search returns web results plus scraped content for each hit, so you can skip the separate scrape call for quick queries. For deeper research, you hit /v1/scrape on individual URLs to get the full page as clean markdown. Step 1: Search the web. Step 2: Deep scrape for full context. Step 3: Synthesize with an LLM. Step 4: Wire it together. The whole thing runs in a few seconds.