I Replaced My AI Agent's Flat Fact Store with a Graph Database

# I Replaced My AI Agent's Flat Fact Store with a Graph Database and It Runs in 85MB I'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 Problem Started 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 I was patching the wrong thing. ## Why FalkorDB Looked at Neo4j (Community Edition is intentionally crippled), Memgraph (no native vector search), and FalkorDB. FalkorDB runs in Docker, uses the Redis wire protocol, has native HNSW vector search, and the entire thing sits at 85MB at my current scale. Graph traversal, vector similarity, and hybrid keyword search in one container. No separate Qdrant, no sync issues between two stores. ## What the Graph Enables Every fact connects to the entities it references via ABOUT edges. Multi-hop traversal becomes natural - find everything connected to a project, find all entities mentioned alongside a technology. When a fact changes, the new fact gets a SUPERSEDES edge to the old one. Both persist with timestamps. Temporal queries now work. "What did the system know about this last month?" is a real query. The vector index runs inside FalkorDB on 4096-dimensional embeddings from qwen3-embedding:8b. O(log n) HNSW search. No external database.