How Cobrainer built graph-based agent memory on one engine

Cobrainer, a Munich-based skills-intelligence company, replaced its S3-and-OpenSearch vector retrieval pipeline with a graph-based AI agent memory and agentic graph RAG system built on SurrealDB. The company sought to improve retrieval accuracy and reduce prompt-token usage by allowing its agent to traverse explicit relationships among people, roles, skills, and capabilities rather than relying only on broad semantic similarity matches. SurrealDB provided graph, vector, and full-text capabilities in a single managed engine queried through SurrealQL, while Cobrainer retained its existing RDS PostgreSQL deployment for other workloads. Using SurrealDB’s Rust SDK, the team built a Rust-native system that combines graph traversal and vector similarity in one query, and the agent automatically creates relationship links as it stores memories and session checkpoints. Cobrainer reports that it moved from evaluation to a customer-facing deployment in about three months, with improved response grounding, lower token costs, reduced infrastructure complexity, and EU-region data residency for its HR-related data.

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