How to Set up Supabase and Postgres for RAG Agent with Memory in n8n (2025) - Advanced AI Agent

Overview

This professional n8n workflow is designed to automate How to Set up Supabase and Postgres for RAG Agent with Memory in n8n (2025) with maximum efficiency and minimal overhead.

Key Features

• Automated Execution: Fully asynchronous processing of tasks.
• Error Handling: Built-in retry logic and error notifications.
• Scalability: Designed to handle high-volume data streams seamlessly.
• Low Maintenance: Self-documenting nodes and clean architectural structure.

How it Works

This tutorial demonstrates a fully integrated workflow that sets up a Retrieval-Augmented Generation (RAG) agent using a relational database and a vector store. In the video, Nate Herk shows how to configure Postgres as chat memory and Supabase as a vector database using PG Vector for similarity search. The system is implemented within n8n by establishing credentials, configuring both database and tool nodes, and leveraging recursive text splitting for document ingestion. Detailed technical steps include setting up Supabase, connecting to Postgres, and embedding documents for AI-driven query responses.

What's Included?

• Exportable n8n JSON Workflow File
• Implementation Guide & Documentation
• API Configuration Template