Dreaming: Better memory for a more helpful ChatGPT
OpenAI is rolling out a more capable and scalable memory system for ChatGPT, built on top of a background curation method called dreaming. The update aims to address challenges with memory staleness, correctness, and scalability across hundreds of millions of users. It is currently available to Plus and Pro users in the US and is expanding to Free and Go users in the coming weeks.
The Core Development
Today we’re beginning to roll out a more capable and scalable system for synthesizing memory, developed to tackle the staleness, correctness, and scalability challenges that we observe when memory is applied to the hundreds of millions of users and multi-year time horizons in ChatGPT.
Memory is what helps ChatGPT learn your preferences, projects, and constraints, allowing future conversations to start from shared context rather than from scratch.
Over the last two years, memory has grown into a critical part of the ChatGPT experience, helping ChatGPT better understand your context so it can help you accomplish meaningful goals over time. This is central to making ChatGPT more useful: knowing you, helping you, and doing more for you.
How It Works & Technical Architecture
Today, we are launching a significantly more capable and compute-efficient memory architecture built on top of dreaming.
Strategic Perspective & Ecosystem Impact
This update is available to Plus and Pro users in the US today, and will roll out to additional countries and Free and Go users over the coming weeks.
Memory first launched in April 2024 (also known as saved memories). The feature let you ask ChatGPT to remember information and carry it forward into future chats.
Saved memories were only written during the conversation and relied on strong cues to decide when to trigger memory, such as an instruction to "remember I’m traveling to Singapore in July." In practice, interacting with this system could feel like talking to someone who took a few notes, but still forgot everything that wasn’t written down. Saved memories also tend to go stale over time and eventually become incorrect or irrelevant.
Why it matters
This update demonstrates how AI providers are scaling personalized memory features across hundreds of millions of users by improving compute efficiency. By automating background memory synthesis, platforms can maintain context across time horizons while reducing user friction.