Generate SEO-Optimized Blog Content with Gemini, Scrapeless and Pinecone RAG

This workflow contains community nodes that are only compatible with the self-hosted version of n8n.

How it works

This advanced automation builds a fully autonomous SEO blog writer using n8n, Scrapeless, LLMs, and Pinecone vector database. It’s powered by a Retrieval-Augmented Generation (RAG) system that collects high-performing blog content, stores it in a vector store, and then generates new blog posts based on that knowledge—endlessly.

Part 1: Build a Knowledge Base from Popular Blogs

Scrape existing articles** from a well-established writer (in this case, Mark Manson) using the Scrapeless node. Extract content from blog pages* and store it in Pinecone*, a powerful vector database that supports similarity search. Use Gemini Embedding 001** or any other supported embedding model to encode blog content into vectors. Result**: You’ll have a searchable vector store of expert-level content, ready to be used for content generation and intelligent search.

Part 2: SERP Analysis & AI Blog Generation

Use Scrapeless' SERP node to fetch search results based on your keyword and search intent. Send the results to an LLM (like Gemini, OpenRouter, or OpenAI) to generate a keyword analysis report in Markdown → then converted to HTML. Extract long-tail keywords, search intent insights, and content angles from this report. Feed everything into another LLM with access to your Pinecone-stored knowledge base, and generate a fully SEO-optimized blog post.

Set up steps

Prerequisites Scrapeless API key

Pinecone account and index setup An embedding model (Gemini, OpenAI, etc.) n8n instance with Community Node: n8n-nodes-scrapeless installed

Credential Configuration Add your Scrapeless and Pinecone credentials in n8n under the "Credentials" tab Choose embedding dimensions according to the model you use (e.g., 768 for Gemini Embedding 001)

Key Highlights

Clones a real content creator**: Replicates knowledge and writing style from top-performing blog authors. Auto-scrapes hundreds of blog posts** without being blocked. Stores expert content** in a vector DB to build a reusable knowledge base. Performs real-time SERP analysis** using Scrapeless to fetch and analyze search data. Generates SEO blog drafts** using RAG with detailed keyword intelligence. Output includes**: blog title, HTML summary report, long-tail keywords, and AI-written article body.

RAG + SEO: The Future of Content Creation

This template combines: AI reasoning** from large language models Reliable data scraping** from Scrapeless Scalable storage** via Pinecone vector DB Flexible orchestration** using n8n nodes

This is not just an automation—it’s a full-stack SEO content machine that enables you to: Build a domain-specific knowledge base Run intelligent keyword research Generate traffic-ready content on autopilot

💡 Use Cases

SaaS content teams cloning competitor success Affiliate marketers scaling high-traffic blog production Agencies offering automated SEO content services AI researchers building personal knowledge bots Writers automating first-draft generation with real-world tone