Does this skill support vector search?

Yes, it enables the pgvector extension and creates optimized HNSW indexes for fast semantic vector retrieval within Supabase.

How does it handle data security?

It implements Supabase Row Level Security (RLS) policies to ensure users can only access their own memories or specific organization data based on your application's requirements.

Can I use this for production workloads?

Absolutely. It includes configurations for connection pooling (PgBouncer), indexing strategies for millions of records, and automated backup scripts designed for production.

Is there a migration path from Mem0 Platform?

Yes, the skill provides specific scripts to export your existing data from the Mem0 Platform API and import it directly into your Supabase instance.

Does it support graph memory?

Yes, it includes schema definitions for memory relationships to support graph-based memory structures alongside traditional vector storage.

Supabase Integration for Mem0

Name: Supabase Integration for Mem0
Author: vanman2024

byvanman2024

Database Management

Configures and optimizes Supabase as a production-ready vector and graph database backend for Mem0 OSS.

About

This skill provides a comprehensive framework for integrating Mem0 Open Source with Supabase, transforming it into a robust memory layer for AI agents. It automates the setup of PostgreSQL schemas with pgvector for semantic search, implements graph-based memory relationships, and enforces security through granular Row Level Security (RLS) policies. Whether you are migrating from the Mem0 Platform or building a self-hosted solution, this skill ensures your memory database is optimized with HNSW indexes, connection pooling via PgBouncer, and automated backup strategies.

Key Features

Advanced Row Level Security (RLS) for multi-tenant and user-level isolation
Optimized HNSW and GIN indexing for high-performance vector and text search
Comprehensive migration tools for moving data from Mem0 Platform to self-hosted Supabase
Automated PostgreSQL schema setup with pgvector extension support
Production-ready connection pooling and automated backup strategy configuration
0 GitHub stars

Use Cases

Building a secure, self-hosted long-term memory backend for AI agents
Implementing multi-tenant memory isolation in a production SaaS application
Scaling Mem0 vector storage using Supabase connection pooling and performance indexing

About

Key Features

Advanced Row Level Security (RLS) for multi-tenant and user-level isolation
Optimized HNSW and GIN indexing for high-performance vector and text search
Comprehensive migration tools for moving data from Mem0 Platform to self-hosted Supabase
Automated PostgreSQL schema setup with pgvector extension support
Production-ready connection pooling and automated backup strategy configuration
0 GitHub stars

Use Cases

Building a secure, self-hosted long-term memory backend for AI agents
Implementing multi-tenant memory isolation in a production SaaS application
Scaling Mem0 vector storage using Supabase connection pooling and performance indexing