What is Memory Index?
A memory index is a data structure that enables efficient lookup and retrieval of stored memories based on various query criteria. Rather than scanning through all memories linearly, indexes organize memories to support fast access based on attributes like content similarity (vector indexes), keywords (inverted indexes), temporal ordering (time-based indexes), entities (entity indexes), or importance scores. Multiple indexes may coexist to support different retrieval patterns.
The most common index type in modern agent memory systems is the vector index, which organizes memory embeddings to enable fast similarity search. When a query is issued, it's encoded into the same embedding space, and the index quickly identifies the most similar memories without computing distances to every stored memory. Other index types support complementary access patterns: time indexes for retrieving recent memories, keyword indexes for exact matching, and graph indexes for relationship traversal.
Effective memory indexing is crucial for agent performance at scale. As memory collections grow to thousands or millions of entries, linear search becomes impractically slow. Well-designed indexes enable sub-second retrieval even over large memory banks, making it feasible for agents to maintain extensive long-term memory without sacrificing responsiveness. The choice and configuration of indexes represents a key design decision that affects retrieval speed, accuracy, and memory system capabilities.