Build a Smart AI Agent with Supabase Memory and RAG
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This n8n workflow empowers you to create intelligent AI agents that remember past interactions and learn over time, leveraging Supabase as a robust backend for persistent memory and knowledge retrieval (RAG). Integrate powerful AI capabilities with reliable data management to build adaptive automation.
About This Workflow
Unlock the full potential of AI automation with this n8n workflow, designed to deploy sophisticated AI agents. By combining Langchain's advanced AI functionalities with Supabase's scalable database capabilities, your agent gains persistent memory, allowing it to remember interactions, manage tasks, and evolve its knowledge base. The workflow utilizes Retrieval Augmented Generation (RAG) by storing and retrieving contextual information from Supabase, powered by OpenAI embeddings, ensuring more accurate and relevant responses. Beyond conversation, the agent can perform specific actions within your Supabase database, transforming it into a truly actionable intelligence system.
Key Features
- Intelligent AI Agent Foundation: Built with Langchain for advanced reasoning and interaction capabilities.
- Persistent Memory with Supabase: Store and retrieve agent messages, tasks, status, and learned knowledge, allowing for continuous learning and context preservation.
- Retrieval Augmented Generation (RAG): Enhance agent intelligence by using Supabase as a vector store for context-aware data retrieval, powered by OpenAI embeddings.
- Actionable Data Management: Enable your AI agent to perform CRUD operations (Create, Read, Update, Delete) directly within your Supabase tables.
- Multi-Channel Platform Ready: Designed to integrate with a Multi-Channel Platform (MCP) trigger for versatile deployment.
How To Use
- Set up Supabase Credentials: Provide your Supabase API URL and Key in the 'Supabase account' credentials.
- Configure OpenAI Credentials: Input your OpenAI API key for 'OpenAi account' to enable embeddings and AI model interactions.
- Define Agent Trigger: Connect the 'MCP_SUPABASE' node to your desired Multi-Channel Platform event or another trigger to initiate the agent's workflow.
- Customize RAG Knowledge Base: Point the 'RAG' node to your specific Supabase table (e.g., 'documents') for storing and retrieving contextual knowledge.
- Adapt Supabase CRUD Operations: Adjust the 'Supabase Tool' nodes (e.g., 'CREATE_ROW_AGENT_MESSAGE', 'DELETE_ROW_INSCRICOES_CURSOS') to interact with your specific Supabase tables and define the data fields for agent memory and actions.
Apps Used
Workflow JSON
{
"id": "054a5bd4-bafa-48a7-a566-57c21a408fd1",
"name": "Build a Smart AI Agent with Supabase Memory and RAG",
"nodes": 10,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 054a5bd4-bafa...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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