I got tired of repeating myself to every AI tool I use. My dietary restrictions, my tech stack, my family's names — every new agent starts from zero. So I built an open source personal database that any AI agent can read from and write to via MCP – Epitome.

It's five layers on top of Postgres: structured tables, a portable identity profile, semantic vector search (pgvector), a knowledge graph that auto-extracts entities and relationships, and a confidence-scored memory quality system that lets memories decay or get reinforced over time. Each user gets their own Postgres schema — not RLS, actual schema isolation.

Agents connect over Streamable HTTP with OAuth and granular consent controls. You decide which agent sees what. Append-only audit log for everything.

MIT licensed, self-hostable with docker compose up, or use the hosted version. Built with Hono, React, D3.js for graph viz.

GitHub: https://github.com/gunning4it/epitome

Would love feedback on the architecture and what's missing. Happy to go deep on any of the technical decisions or feel free to contribute.

Why PostgreSQL for the graph instead of a purpose-built graph database like Neo4j?

It's a personal knowledge graph, so expecting thousands of nodes, not billions.

At that scale, PostgreSQL with recursive CTEs is more than fast enough, and it means one database does everything.

Though I can imagine migrating to neo4j if necessary.