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