Cobrainer builds self-growing graph agent memory using a single database engine

Munich-based skills-intelligence startup Cobrainer replaced its flat vector retrieval pipeline with a graph-based agent memory system to improve accuracy and reduce token costs. The company's previous setup relied on an S3 and OpenSearch pipeline, which returned semantically similar but contextually disconnected results. Rather than adding a separate graph database on top of existing infrastructure, Cobrainer consolidated graph, vector, and full-text capabilities into SurrealDB, a single multi-model engine queried through SurrealQL. The agent now automatically builds and traverses relationships between nodes as it works, grounding its answers in real entity connections instead of broad similarity matches. This approach allowed the team to avoid infrastructure fragmentation and deploy new storage patterns quickly without lengthy migrations.