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Every AI Node in n8n EXPLAINED | ONLY Guide You Need (Agents, RAG, Parsers & More)

N8N AI Mastery | Every AI Node in n8n EXPLAINED | ONLY Guide You Need (Agents, RAG, Parsers & More) 🔥 Our private AI + automation community is coming soon! 👉 Join the waitlist & lock your early-bird price → https://aiarchitects.futurminds.com/ 📌 Join my FREE Skool community to access all the resources ! 👇 https://www.skool.com/ai-architects For business enquiries: [email protected] Important Links & Interesting Videos: 👉Start creating n8n workflows: https://n8n.partnerlinks.io/futurminds 👉 N8N MCP Simplified -    • N8N MCP Simplified | 0% Hype | What, Why &...   👉 Stop Hallucinations! Best n8n AI Agent Settings Explained -    • Stop Hallucinations! Best n8n AI Agent Set...   👉Ultimate Guide to Creating a WhatsApp AI Agent with n8n -    • Ultimate Guide to Creating a WhatsApp AI A...   👉Build Your OWN RAG AI Voice Agent with n8n -    • Build Your OWN RAG AI Voice Agent with n8n...   👉100% Instagram Automation with GPT4o + MidJourney + N8N -    • 100% Instagram Automation with GPT4o + Mid...   👉Master Multi-AI Agent Workflows in N8N -    • Master Multi-AI Agent Workflows in N8N | U...   ⭐ Ready to master EVERY AI node in n8n? This deep-dive n8n tutorial walks through the entire AI toolbox—Agents, OpenAI, Output Parsers, Retrievers, Vector Stores, Embeddings, Memories and more. By the end you’ll know exactly which node to pick, how to wire it, and how to stop hallucinations in n8n workflows. 🔍 What we cover 👉Intro & why n8n AI nodes are different 👉Chat Models – GPT-4o, Claude-3, Gemini, Mistral, Ollama 👉 AI Agent vs Basic LLM Chain (when to choose each) 👉 Output Parsers – Structured vs Item-List vs Auto-Fixing (live JSON rescue) 👉 Document Loaders & Recursive Text Splitter – prepare data for RAG 👉 Embeddings & Vector Store RAG setup 👉 Retrievers: Vector, Multi-Query, Contextual Compression, Workflow 👉 Memory strategies – Redis, Supabase, Zep, Chat Memory Manager 👉 Build a full RAG chatbot in n8n (Question & Answer Chain demo) 👉 Cost-control checklist: temp/top-p, batch size, model router ⏱️ Timestamps: 00:00 Agenda 00:22 AI Nodes Overview 02:38 AI Agent Node, LLM Models, Memory, Tools, Parsers 09:14 OpenAI Node 09:31 Basic LLM Chain Node 12:02 Information Extractor Node 13:36 Sentiment Analysis Node 16:04 Summarization Chain Node, Text Splitters, Document Loaders 20:28 RAG 21:58 Question and Answer Chain Node, Retrievers 25:44 Mic Nodes, Chat Memory Manager, Langchain Code 27:23 Important Resources 🙋‍♂️ Have questions about the n8n AI agent settings, vector store errors, or structured output parser? Drop them below and I’ll help. 👉 Like, comment your biggest takeaway, and subscribe for weekly automation hacks!