Agent memory is the mechanism that lets an AI agent retain and recall information across steps and sessions instead of starting from a blank context window every time, split into short-term memory that holds the current task's working state and long-term memory that persists facts, preferences, and past outcomes between runs.
What is the difference between short-term and long-term agent memory?
Short-term memory is the working state inside the current run, the plan, recent tool results, and the conversation so far, and it disappears when the run ends. Long-term memory survives between runs, usually written to a database or vector store, so an agent recalls a preference or an earlier decision without replaying the whole history each time. Most production agents need both: short-term memory for the task at hand, long-term memory for anything that should carry over.
Why does a stateless model need agent memory at all?
A language model holds nothing between calls; every fact it appears to remember was fed back into its context on this call. Deep agents that run long, multi-step tasks lean especially hard on memory to survive a session without losing track of what already happened, and an AI agent without any memory layer re-plans from zero on every turn, which is slow and prone to repeating a mistake it already made once. Choosing what to write to memory and what to leave out is itself a discipline, one covered under context engineering; an agentic workflow that spans several steps usually cannot run reliably without it.
Frequently asked questions
Is agent memory the same as a longer context window?
No. A context window is the space available on a single call. Agent memory is a separate storage layer, often a database or vector store, that decides what gets written down and pulled back into that window on a later call, including calls in a different session entirely.
Do all AI agents need long-term memory?
No. A narrow, single-turn agent can run fine on short-term memory alone. Long-term memory earns its cost once an agent needs to recall a user's preferences, an earlier decision, or a past outcome across separate sessions rather than just within one run.
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