Build persistent chat memory with GPT-4o-mini and Qdrant vector database
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 13 nodes and runs when a chat message arrives. Along the way it connects Embeddings OpenAI, Reranker Cohere, Qdrant Vector Store and OpenAI Chat Model.
How it runs
Trigger: When a chat message arrives.
- AI Agent: runs an AI agent, with model: GPT-4o-mini (Main); 1 tool (RAG_MEMORY); output parser: Structured Output Parser
- Store Conversation (Qdrant Vector Store): stores documents in the Qdrant vector store, with embeddings: Embeddings OpenAI; document loader: Default Data Loader
- Format Response (Edit Fields (Set)): sets 1 field: output
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (10)
- Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Recursive Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Reranker Cohere
@n8n/n8n-nodes-langchain.rerankerCohere - Qdrant Vector Store
@n8n/n8n-nodes-langchain.vectorStoreQdrant - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi - Structured Output Parser
@n8n/n8n-nodes-langchain.outputParserStructured - Edit Fields (Set)
n8n-nodes-base.set - AI Agent
@n8n/n8n-nodes-langchain.agent
From the author
🧠 Long-Term Memory System for AI Agents with Vector Database Transform your AI assistants into intelligent agents with persistent memory capabilities. This production-ready workflow implements a sophisticated long-term memory system using vector databases,…