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The difference is implementation comes down to business goals more than anything.

There is a clear directionality for ChatGPT. At some point they will monetize by ads and affiliate links. Their memory implementation is aimed at creating a user profile.

Claude's memory implementation feels more oriented towards the long term goal of accessing abstractions and past interactions. It's very close to how humans access memories, albeit with a search feature. (they have not implemented it yet afaik), there is a clear path where they leverage their current implementation w RL posttraining such that claude "remembers" the mistakes you pointed out last time. It can in future iterations derive abstractions from a given conversation (eg: "user asked me to make xyz changes on this task last time, maybe the agent can proactively do it or this was the process last time the agent did it").

At the most basic level, ChatGPT wants to remember you as a person, while Claude cares about how your previous interactions were.

The link to the breakdown of ChatGPT's memory implementation is broken, the correct link is: https://www.shloked.com/writing/chatgpt-memory-bitter-lesson

This is really cool, I was wondering how memory had been implemented in ChatGPT. Very interesting to see the completely different approaches. It seems to me like Claude's is better suited for solving technical tasks while ChatGPT's is more suited to improving casual conversation (and, as pointed out, future ads integration).

I think it probably won't be too long before these language-based memories look antiquated. Someone is going to figure out how to store and retrieve memories in an encoded form that skips the language representation. It may actually be the final breakthrough we need for AGI.

I love Claude's memory implementation, but I turned memory off in ChatGPT. I use ChatGPT for too many disparate things and it was weird when it was making associations across things that aren't actually associated in my life.

They are changing the way memory works soon, too: https://x.com/btibor91/status/1965906564692541621

Edit: They apparently just announced this as well: https://www.anthropic.com/news/memory

I wrote about how ChatGPT memory and also the chat history work a while ago.

Figured to share since it also includes prompts on how to dump the info yourself

https://embracethered.com/blog/posts/2025/chatgpt-how-does-c...

> Most of this was uncovered by simply asking ChatGPT directly.

Is the result reliable and not just hallucination? Why would ChatGPT know how itself works and why would it be fed with these kind of learning material?

I generally turn off memory completely. I want to have exact control over the inputs.

To be honest, I would strip all the system prompts, training, etc, in favor of one I wrote myself.

"Claude recalls by only referring to your raw conversation history. There are no AI-generated summaries or compressed profiles—just real-time searches through your actual past chats."

AKA, Claude is doing vector search. Instead of asking it about "Chandni Chowk", ask it about "my coworker I was having issues with" and it will miss. Hard. No summaries or built up profiles, no knowledge graphs. This isn't an expert feature, this means it just doesn't work very well.

What are the barriers to external memory stores (assuming similar implementations), used via tool calling or MCP? Are the providers RL’ing their way into making their memory implementations better, cementing their usage, similar to what I understand is done wrt tool calling? (“training in” specific tool impls)

I am coming from a data privacy perspective; while I know the LLM is getting it anyway, during inference, I’d prefer to not just spell it out for them. “Interests: MacOS, bondage, discipline, Baseball”

ChatGPT is quickly approaching (perhaps bypassing?) the same concerns that parents, teachers, psychologists had with traditional social media. It's only going to get worse, but trying to stop the technological process will never work. I'm not sure what the answer is. That they're clearly optimizing for people's attention is more worrisome.

This post was great, very clear and well illustrated with examples.

Note that Anthropic announced a new variation on memory just yesterday (only for team accounts so far) which works more like the OpenAI one: https://www.anthropic.com/news/memory

Regarding https://www.shloked.com/writing/chatgpt-memory-bitter-lesson I am very confused if the author thinks the ChatGPT is injecting those prompts when the memory is not enabled. If your memory is not enabled, its pretty clear at least in my instance, there is no metadata of recent conversations or personal preferences injected. The conversation stays stand-alone for that conversation only. If he was turning memory on and off for the experiment, maybe something got confused, or maybe I just didn't read the article properly?

ChatGPT is designed to be addictive, with secondary potential for economic utility. Claude is designed to be economically useful, with secondary potential for addiction. That’s why.

In either case, I’ve turned off memory features in any LLM product I use. Memory features are more corrosive and damaging than useful. With a bit of effort, you can simply maintain a personal library of prompt contexts that you can just manually grab and paste in when needed. This ensures you’re in control and maintains accuracy without context rot or falling back on the extreme distortions that things like ChatGPT memory introduce.

This is awesome! It seems to line up with the idea of agentic exploration versus RAG which I think Anthropic leans on the agentic exploration side of.

It will be very interesting to see which approach is deemed to "win out" in the future

I've been using LLMs for a long time, and I've thus far avoided memory features due to a fear of context rot.

So many times my solution when stuck with an LLM is to wipe the context and start fresh. I would be afraid the hallucinations, dead-ends, and rabbit holes would be stored in memory and not easy to dislodge.

Is this an actual problem? Does the usefulness of the memory feature outweigh this risk?

Interesting article! I keep second guessing whether it’s worth it to point out mistakes to the LLM for it to improve in the future.

If I remember correctly, Gemini also have this feature? Is it more like Claude or ChatGPT?

I am often surprised how Claude Code make efficient and transparent! use of memory in form of "to do lists" in agent mode. Sometimes miss this in web/desktop app in long conversations.

ChatGPT memory seems weird to me. It knows the company I work at and pretty much our entire stack - but when I go to view it's stored memories none of that is written anywhere.

memory is the biggest moat, do we really want to live in the future where one or two corporations know us better than we know ourselves?

> Anthropic's more technical users inherently understand how LLMs work.

good (if superficial) post in general, but on this point specifically, emphatically: no, they do not -- no shade, nobody does, at least not in any meaningful sense

Why is the scroll so unnatural on this page?

Switched off memory (in Claude) immediately, not even tempted to try.

Curious about the interaction between this memory behavior and fine-tuning. If the base model has these emergent memory patterns, how do they transfer or adapt when we fine-tune for specific domains?

Has anyone experimented with deliberately structuring prompts to take advantage of these memory patterns?

> Anthropic's more technical users inherently understand how LLMs work.

Yes, I too imagine these "more technical users" spamming rocketship and confetti emojis absolutely _celebrating_ the most toxic code contributions imaginable to some of the most important software out there in the world. Claude is the exact kind of engineer (by default) you don't want in your company. Whatever little reinforcement learning system/simulation they used to fine-tune their model is a mockery of what real software engineering is.