Neo4j

Vector RAG retrieves documents. Graph RAG retrieves relationships. When your agent needs to reason across entities, timelines, and decisions, the graph wins. Open Interactive Version → The Problem I Was Trying to Solve # My AI agent PostSingular, running on OpenClaw, talks to me every day. It helps me build Luminar, manages my YouTube channel, and tracks infrastructure decisions across sessions.

The default state of a language model is amnesia. Every session, it wakes up fresh with no memory of what happened before. I built a memory system that fixes this — and somewhere in the process, the agent got a name, a personality, and an opinion about font choices. The Problem # Every LLM session is stateless by design. You can inject previous conversation history, but:

Vector databases are fast and convenient. But they can’t answer “what did I decide about the auth system 3 weeks ago and why?” For that, you need relationships — and that means a knowledge graph. The Problem with Pure Vector Memory # Most AI memory systems work like this: embed text, store in ChromaDB, retrieve by cosine similarity. It works well for “find things similar to this query.”