Supabase MCP Server

Developers building AI agents that need persistent memory and database interactions. This workflow creates a server for AI agents using Supabase. It lets agents store and retrieve messages, tasks, status, and knowledge. It uses RAG to pull relevant info from past interactions. The MCP trigger starts AI tool calls. Add your Supabase credentials. Set up tables: agent_messages, agent_tasks, agent_status, agent_knowledge, documents. Connect OpenAI API for embeddings. Activate the workflow and use the webhook path for triggers. n8n with Langchain nodes. Supabase project. OpenAI account. Tables in Supabase for agent data. Change table names or add fields. Adjust RAG topK or embedding model. Add more tools or modify MCP logic. Key Technologies: n8n Langchain Nodes,Supabase Database,OpenAI Embeddings,Vector Store RAG Automation Value: Stores AI agent state automatically, saving time on memory management. Uses RAG to give agents access to learned knowledge, improving responses. Handles CRUD for multiple agent components, making agents more reliable. Integrates AI with database via tools, enabling smart automations. Best Practices: Group similar operations with sticky notes for clarity. Use dynamic limits from AI to optimize queries. Integrate RAG for semantic search over exact matches. Ensure Supabase tables have proper indexes for performance. Test AI tools individually before full integration.