Agent memory

Agent memory is what turns a stateless model call into a system that remembers. Short-term memory stores the messages of a conversation, scoped by a user identifier and a session identifier, and replays the relevant history at the start of the next turn. Long-term memory is a separate, durable store of facts about a user that an agent can write to and read from across every session, not just one conversation thread.

In a regulated enterprise, memory is a data governance question as much as a product feature. Conversation history can contain account numbers, case details or health information, so where it lives, how long it is kept, and whether one caller's history can ever reach another caller matters as much as whether the agent sounds coherent across turns.

A support agent that remembers a caller mentioned an order number earlier in the same call, but never carries that number into a different caller's session, is agent memory working correctly. Two sessions of the same user do not see each other's history either, by design.

In Dynamiq, memory is configured on the Agent node with a choice of 8 memory backends, from the managed platform store to Postgres, DynamoDB, Qdrant, Pinecone and Weaviate, a save mode that controls how much of a run is persisted, and a retrieval strategy of the most recent messages, the most relevant ones, or both. A separate long-term memory store holds durable, user-scoped facts across sessions, which the agent updates rather than duplicates.