Agent memory
The persistent state an agent maintains across turns and sessions — covers short-term context, long-term facts, episodic events, and procedural skills. Distinct from the LLM context window.
The LLM context window is what the model sees on one inference call. Agent memory is everything else — facts the agent learned in past sessions, episodes it remembers, procedures it has been taught, user preferences it inferred. Without memory, every conversation starts from scratch.
Production agents in 2026 manage memory in tiers: short-term (active conversation), working (current task scratchpad), episodic (past sessions and events), semantic (facts and entities), and procedural (learned skills). Frameworks like Letta, Mem0, and Zep specialize in this.
Memory is the single most underrated production concern. Agents without persistent memory feel forgetful and dumb. Agents with too-aggressive memory become biased by stale or wrong facts. Most teams iterate on memory policy more than on prompts.
Frequently asked
Do I need agent memory if I have a long context window?+
Yes. Even with a 1M-token context window, stuffing every past session is expensive and noisy. Memory lets the agent retrieve only what is relevant. Long context is the canvas; memory is the index.
What is the simplest agent memory I can ship?+
A vector store keyed on (user_id, conversation_id) with embedded summaries of each session. Retrieve the top-3 most relevant past summaries on each new conversation. Sufficient for 80% of personal-assistant use cases.
