ChatGPT introduces a new memory system to better remember preferences, keeping context fresh and relevant across conversations.
How ChatGPT Remembers You: A Deep Dive into Its Memory and History Features
Deep-Dive on how ChatGPT profiles your account and how it can reference it during conversations
OpenAI on Twitter / X
We’re starting to roll out a lightweight version of memory improvements to Free users. In addition to existing saved memories, ChatGPT now references your recent conversations to provide more personalized responses. https://t.co/RZFYUC9JAT https://t.co/O8lubCBNZO— OpenAI (@OpenAI) June 3, 2025
ChatGPT will now use its 'memory' to personalize web searches | TechCrunch
ChatGPT will now use its 'memory' to personalize web searches, thanks to a new feature called Memory with Search.
ChatGPT is smarter now that it's learned to forget - a huge memory upgrade is coming
The AI chatbot will remember what matters most and quietly let the rest fade
ChatGPT Memory and the Bitter Lesson
I reverse-engineered ChatGPT's memory system to understand what it stores, how it works (spoiler: not RAG!), and why OpenAI built it this way. Complete with prompts you can try yourself to explore your own ChatGPT memory.
ChatGPT acts as a "cognitive crutch" that weakens memory, new research suggests
A recent experiment provides evidence that relying on artificial intelligence to study tends to reduce how much information students remember weeks later.
I Reverse Engineered ChatGPT's Memory System, and Here's What I Found!
When I asked ChatGPT what it remembered about me, it listed 33 facts from my name and career goals to my current fitness routine. But how does it actually store and retrieve this information? And why does it feel so seamless? After extensive experimentation, I discovered that ChatGPT’s memory system is far simpler than I expected. No vector databases. No RAG over conversation history. Instead, it uses four distinct layers: session metadata that adapts to your environment, explicit facts stored long-term, lightweight summaries of recent chats, and a sliding window of your current conversation.
Use Claude’s chat search and memory to build on previous context | Anthropic Help Center
You can prompt Claude to search through your previous conversations to find and reference relevant information in new chats. Claude can also remember context from your chats and carry it into new conversations and Cowork tasks. This article explains how chat search and memory work, what Claude does and doesn't remember, how to review and edit what's saved, and how to turn these features on or off.
OpenAI Developers on Twitter / X
Last week, we released a preview of memories in Codex.Today, we’re expanding the experiment with Chronicle, which improves memories using recent screen context.Now, Codex can help with what you’ve been working on without you restating context. pic.twitter.com/b3p8I5eXOy— OpenAI Developers (@OpenAIDevs) April 20, 2026
Shlok Khemani on Twitter / X
Super interesting that eve doesn't ship with any long-term or cross-session memory (yet). The main context-management lever out of the box is conversation compaction. This is unlike OpenClaw and Hermes, each of which comes with a default opinionated memory implementation. https://t.co/DDIyc4rFtW— Shlok Khemani (@shloked) June 17, 2026
Comparing the memory implementations of Claude and ChatGPT
Shlok Khemani has been doing excellent work reverse-engineering LLM systems and documenting his discoveries. Last week he wrote about ChatGPT memory. This week it's Claude. Claude's memory system has two …
Basic Memory
AI conversations that actually remember. Never re-explain your project to your AI again. Join our Discord: https://discord.gg/tyvKNccgqN
I Benchmarked OpenAI Memory vs LangMem vs Letta (MemGPT) vs Mem0 for Long-Term Memory: Here’s How They Stacked Up
145 votes, 53 comments. Lately, I’ve been testing memory systems to handle long conversations in agent setups, optimizing for: Factual consistency…
DREAM — Dynamic Retention Episodic Architecture for Memory
Modern AI systems lack persistent, user-specific episodic memory. Existing approaches rely on short-term context windows, shallow preference storage, or static conversation logs that do not scale and cannot preserve meaningful long-term continuity. This paper introduces DREAM (Dynamic Retention Episodic Architecture for Memory), a scalable, opt-in, episodic memory framework designed to work with current LLM and agent architectures. DREAM integrates episodic summarization, user-controlled opt-in memory, semantic retrieval via per-user vector indexes, an adaptive retention mechanism that expands TTL based on user engagement, and horizontal sharding of orchestrators and storage for large-scale deployments. The paper details the architecture, components, data flows, and implementation examples, and argues that DREAM provides a practical path toward AI systems capable of consistent, privacy-aligned long-term reasoning. This project has been extended with a conceptual analysis and simulation of a "DREAM-as-a-Support" (DaaS) hybrid layer. This extension demonstrates DREAM's architectural extensibility, reframing it from a standalone framework into a foundational platform component. The DaaS model provides core memory governance such as adaptive retention (ARM) and user-centric opt-in as an on-demand service to complementary cognitive systems, validating the original four-pillar design through a scalable, internal API. Reference Implementation A reference implementation of the DREAM architecture is available as an open-source Python framework:Official Reference Implementation This implementation is intended for experimentation and architectural validation and does not represent a production-ready system DREAM Architecture — Official GitHub Repository
MemGPT: Towards LLMs as Operating Systems
Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversations and document analysis. To enable using context beyond limited context windows, we propose virtual context management, a technique drawing inspiration from hierarchical memory systems in traditional operating systems that provide the appearance of large memory resources through data movement between fast and slow memory. Using this technique, we introduce MemGPT (Memory-GPT), a system that intelligently manages different memory tiers in order to effectively provide extended context within the LLM's limited context window, and utilizes interrupts to manage control flow between itself and the user. We evaluate our OS-inspired design in two domains where the limited context windows of modern LLMs severely handicaps their performance: document analysis, where MemGPT is able to analyze large documents that far exceed the underlying LLM's context window, and multi-session chat, where MemGPT can create conversational agents that remember, reflect, and evolve dynamically through long-term interactions with their users. We release MemGPT code and data for our experiments at https://memgpt.ai.
