Your AI Agent Has a Backpack. It's Called Retrieval Memory.

The article explains retrieval memory as a way for AI agents to store useful information externally and fetch it only when relevant.

It compares retrieval memory with RAG and shows that it can hold user preferences, facts, experiences, procedures, and conversation summaries.

The main benefit is keeping long-term memory without crowding the active context window, enabling more personalized and efficient responses.

The piece also points to practical system design, including implementations like Project X and storage/query stacks such as PostgreSQL, SQLite, and Rust.