Generate SEO-optimized blog content with Gemini, Scrapeless and Pinecone RAG
Needs changesWhat this workflow does
This AI workflow has 24 nodes and runs manually, when you click “Execute workflow” (plus 1 other trigger). Along the way it connects Pinecone Vector Store, Embeddings Google Gemini, Google Gemini Chat Model and Scrapeless (community).
How it runs
Trigger: Manually, when you click “Execute workflow” (plus 1 other trigger).
- Crawl all Blogs (Scrapeless): crawl (crawler)
- Edit Fields (Edit Fields (Set)): sets 2 fields: Keywords, Search Intent
- Pinecone Vector Store: searches the Pinecone vector store, with embeddings: Embeddings Google Gemini
- Parse content and extract information (Code): runs custom JavaScript code
- Analyze target keywords on Google SERP (Scrapeless): uses Scrapeless
- AI Agent: runs an AI agent, with model: Google Gemini Chat Model; memory: Window Buffer Memory
- Split Out: splits a list into separate items
- Basic LLM Chain: sends a prompt to an LLM, with model: Google Gemini Chat Model
- Loop Over Items (Loop Over Items (Split in Batches)): loops over the items in batches
- Markdown: converts between Markdown and HTML
…plus 5 more steps. See the full node list below.
Services it uses
Scraping pages by hand? Firecrawl is a simpler option (10% off for new users).
Before you upgrade to n8n 3.0
1 thing in this workflow is removed in n8n 3.0. Fix it before you upgrade:
- AI Agent v1 (old agent modes) in AI Agent1 (v1.7): replace with Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent.
Rather not rewrite it? Many of these flows can be rebuilt in Make (free plan).
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) AI Agent1 (v1.7) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (19)
- Manual Trigger
n8n-nodes-base.manualTrigger - Pinecone Vector Store
@n8n/n8n-nodes-langchain.vectorStorePinecone - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Recursive Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - AI Agent
@n8n/n8n-nodes-langchain.agent - Embeddings Google Gemini
@n8n/n8n-nodes-langchain.embeddingsGoogleGemini - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - Aggregate
n8n-nodes-base.aggregate - Convert to File
n8n-nodes-base.convertToFile - Edit Fields (Set)
n8n-nodes-base.set - Basic LLM Chain
@n8n/n8n-nodes-langchain.chainLlm - Markdown
n8n-nodes-base.markdown - HTML
n8n-nodes-base.html - Loop Over Items (Split in Batches)
n8n-nodes-base.splitInBatches - Scrapeless
n8n-nodes-scrapeless.scrapeless - Code
n8n-nodes-base.code - Split Out
n8n-nodes-base.splitOut
From the author
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…