How I Built a Research Agent for Richer Blog Content (n8n + Vector Store)

Struggling to get Ai to write content that actually says something new? In this tutorial, I’ll show you how I built a research agent using n8n, OpenAI, Perplexity and a vector store setup that helps me gather, store and reuse deep web insights for every blog post. This agent doesn’t just automate research it creates a repeatable flow that pulls live context from trusted sources, extracts key takeaways and saves it all to Supabase for use in my RAG-powered content pipeline. Whether you're writing solo or leading a content team, this is how I’ve started producing richer, more insightful blog content without starting from scratch every time. Timestamps 0:00 - Intro 1:20 - Topic Researcher 1:43 - Deep Web Research with Perplexity 1:59 - Vector Store RAG System Explainer 3:15 - Citation Research 3:42 - Key Takeaways Agent 4:40 - Topic Analyst 6:18 - Summary ✅ What you'll learn in this video: How to automate deep web research using Perplexity and OpenAI How to store structured insights in a vector store (RAG-ready) How to extract citation-backed takeaways from source links How to build modular research flows with n8n + Supabase 🧠 This agent is part of a bigger AI SEO system I’ve built. Subscribe to follow the rest of the series. 💬 Questions on any part of the explainer I skimmed over? Drop them in the comments. #AISEO #n8ntutorial #researchagent #contentstrategy #openai #supabase #rag #seoautomation