What is Hybrid Memory?
Hybrid memory is an architecture pattern that combines multiple memory storage and retrieval methods to leverage the strengths of different approaches. Typically, this involves integrating fast, capacity-limited memory (like conversation buffers for recent context) with slower but more scalable memory (like vector stores for historical information), and potentially specialized memory types (like entity stores or knowledge graphs) for structured information.
The architecture allows agents to benefit from complementary memory systems simultaneously. For example, a hybrid memory system might maintain recent conversation turns in a buffer for immediate access, use vector similarity search to retrieve relevant historical interactions, and query an entity store for structured facts about people or concepts mentioned in the conversation. The system orchestrates queries across these different memory types and combines their results to provide comprehensive context.
Implementing hybrid memory requires careful design decisions about when to query which memory types, how to prioritize or blend results from different sources, and how to manage the complexity of multiple storage backends. The approach offers significant advantages for sophisticated agent applications that need both recency-based and relevance-based recall, structured and unstructured information, and the ability to scale beyond context window limitations while maintaining fast access to recent interactions.