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

Описание: N8N AI Mastery | Every AI Node in n8n EXPLAINED | ONLY Guide You Need (Agents, RAG, Parsers & More)

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⭐ 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.

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