How ChatGPT Dreaming V3 works (+ every other agent Memory Framework)

# TL;DR "Dreaming" is an asynchronous background process that synthesizes a single, coherent "memory state" for each user out of their raw sources (past chats, files, connected apps), instead of maintaining a hand-curated list of saved facts. The synthesis is continually re-run, which is how it stays fresh, reconciles contradictions, and re-dates stale facts as time passes. At chat time, ChatGPT injects the relevant slice of that synthesis and does fast on-demand search over past chats, gated by a "is personalization useful here?" decision and surfaced with per-source provenance. The single most important architectural fact, stated plainly in the FAQ: >"ChatGPT's memory is based on a continually updated synthesis of context from your past chats, which may be broader than what can be shown as individual items in a summary." Memory is a derived, regenerable artifact over the raw sources — not the source of truth itself. That one design choice explains nearly everything else (the staleness fixes, the "delete it everywhere" rule, the editable-but-not-authoritative summary). # Memory systems now cluster into 3 fundamentally different philosophies These are memory as stored objects, memory as compressed hierarchy, and memory as ongoing synthesis over raw sources. The last category contains only two frameworks: Karpathy knowledge bases and OpenAI Dreaming. In the rest of my post I breakdown how each of the open source memory frameworks are designed and how they compare to ChatGPT Dreaming * Knowledge Bases * mem0 * supermemory * Zep * Letta * Mastra * MemoryOS * A-MEM * LangMem * Memobase The link is in the comments, to be transparent this is to help support my original post by getting it some views, I hope this was helpful and check out the original post if you want to know more