Long-term memory
Long-term memory is the durable layer of an agent's memory store. Unlike per-conversation context, long-term memory survives across sessions, channels, and time, giving the agent a continuous sense of who you are and what matters to you.
Why it matters
A chat assistant without long-term memory is the same product reset every time you open it. You explain who you are; you re-state your preferences; you re-describe your work. The cumulative cost of that re-explanation is what makes generic chat products feel transactional rather than personal.
Long-term memory changes the relationship. The agent recalls last week's conversation, the unfinished task, the decision you made, the contact you mentioned. It stops asking you to repeat yourself. It starts feeling like an assistant who remembers, rather than a stranger you train every morning.
The engineering challenge is selective retention. Storing every conversation verbatim is expensive and useless; the right memory store remembers what is meaningful and forgets what is noise. Most useful designs are layered: an episodic memory for what was said and done, a semantic memory for stable facts, and a core memory for the small set of facts that should never be forgotten about the user.
How Ajeris handles this
Ajeris's long-term memory is layered and consolidated nightly. Conversations write to the layered store as they happen. A dreaming job at 3 a.m. UTC consolidates important new memories into long-term storage. A daily reflection at 11 p.m. UTC writes a 2-3 sentence summary of the day. The user can recall, list, or forget memories at any time. The structure means the agent stays useful over months and years without context bloat.
