AI Memory Systems (MemGPT and Beyond)
An LLM forgets everything when the chat ends. Memory systems fix that: a small "desk" of facts always in context, big filing cabinets outside it, and rules for what to write, update, and forget. MemGPT started it by treating the context window like RAM; the hard engineering turns out to be the write policy, not the storage.
01.The Problem: The Assistant That Forgets Everything by Tuesday
You tell your AI assistant on Monday: "I switched jobs, I work at Beta now. And please keep answers short."
On Tuesday you open a new chat and it asks where you work, then writes you a novel.
Why? Because a plain LLM call is stateless. It knows only what is inside its context window — the text sent along with your message — and when the session ends, the window is thrown away. Every session starts from zero.
So the question becomes
How do you give a stateless machine a durable past?
The honest first answer is: just paste the old conversations back in. But after a month that is 300,000 tokens of transcript — slow, expensive, and the model still misses the one fact buried at token 217,000.
Which points at the real design:
You cannot remember everything. So remember a small set of what matters, keep the full log searchable, and have rules for what earns a slot.
Carry this analogy through the topic: a busy manager with a small desk. The desk (context window) is tiny but everything on it is instantly readable. Around it: a filing cabinet (the full archive), an inbox (recent events), and a habit of tidying up at the end of the day. Memory engineering is desk management.
Memory as an OS: Tiers, Writes, and Consolidation 🗃️
Memory as an OS: Tiers, Writes, and Consolidation 🗃️
MemGPT's insight was architectural - treat the context window like RAM and everything else like disk. The hard engineering is in the write policy and consolidation, not the storage tier.
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