A living knowledge layer for your agents: SurrealDB + CocoIndex

SurrealDB combined with CocoIndex offers an innovative solution for maintaining a living knowledge layer for AI agents by integrating document storage, knowledge graphs, and vector indexing into one unified, multi-model database. This approach eliminates the need for separate databases and complex synchronization processes, allowing for a seamless, declarative pipeline that updates dynamically without the need for full re-embedding or manual data migration. By employing a Rust core for high-concurrency execution and a Python-defined logic, the system efficiently reconciles target states, ensuring that data remains fresh and interconnected through hybrid retrieval methods that merge vector similarity and graph traversal in a single query. The SurrealDB connector in CocoIndex v1 simplifies the creation of AI applications with durable agent memory and hybrid retrieval-augmented generation (RAG) capabilities, providing a streamlined, scalable solution for building complex data-driven applications.