# I Replaced My AI Agent's Flat Fact Store with a Graph Database and It Runs in 85MBI've been building LocalClaw, a local-model-first AI agent framework running on personal hardware through Ollama. No cloud, no API costs. A few weeks ago I posted about the router/specialist architecture. A lot of people asked about the memory system so here's that.## The ProblemStarted with a JSONL fact store and embedding similarity retrieval. Simple enough until it wasn't. After a few weeks of real use I had 14 near-duplicate facts about the same topics from different sessions. Layered dedup on top of dedup and it still wasn't clean.The bigger problem was relationships. "Peter works at DevMesh" and "DevMesh is building an outreach platform" were two separate embeddings. You could retrieve each one but you couldn't traverse from one to the other. No multi-hop. No fact evolution. Old facts and new facts coexisted with no signal about which was current.Four iterations on the flat store later I accepted ...