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Definition

What is AI memory?

AI memory is AI memory is a feature that saves details from your past conversations, such as preferences and facts about you, and brings them back into later chats so you do not have to repeat yourself.

AI memory is the ability of a chatbot or agent to remember things between conversations. Without it, a model starts every chat blank: it knows only what is in the current context window. With memory, the product saves selected details and supplies them again when they are relevant.

What memory is, and what it is not

The model's learned knowledge, stored in its weights, does not change when you chat with it. Memory is usually a layer built around the model. The product stores notes, such as "prefers short answers" or "works in healthcare marketing," and inserts the relevant ones into the model's input at the start of a new conversation. The model then behaves as if it remembered, because the information is right there in front of it.

That makes memory a close cousin of context engineering: someone has to decide what to save, what to retrieve and what to leave out.

How it typically works

  1. Capture: the system notices something worth keeping, or you tell it to remember it.
  2. Store: the detail is written to a database tied to your account, outside the model.
  3. Retrieve: in a later chat, the system picks the entries that fit the task.
  4. Inject: those entries are added to the prompt, using up some of the window's tokens.

Because retrieval is a guess about what is relevant, memory can miss something you wanted or bring up something stale.

Why it matters

Memory saves repetition and makes an assistant feel personal. For an AI agent that works over days or weeks, it lets progress and decisions carry across sessions instead of vanishing when a chat ends.

It also raises questions worth asking before you trust it with sensitive material:

  • What exactly is stored, and for how long?
  • Can you view, edit and delete it?
  • Is it shared across devices, models or team members?
  • Is it used for anything beyond your own chats?

Read the provider's terms, because the answers differ from product to product.

An example

You tell an assistant once that you write for beginners and want American spelling. Weeks later you ask for a draft in a fresh chat. With memory, the draft arrives in that style. Without it, you would paste the same instructions again.

Practical tips

Keep memory entries short and factual. Review them occasionally, since an outdated note can quietly steer answers the wrong way. Turn memory off, or use a temporary chat, when you are working with information you would not want stored.

Questions people ask

Does AI memory change the model itself?

Generally no. Memory stores notes outside the model and adds the relevant ones to the prompt, so the model's weights stay the same.

Can I delete what an AI remembers about me?

Most products let you view and delete saved memories, but controls vary, so check the provider's settings and terms.

AI memory in the news

#04

Microsoft fences in Windows agents as Nvidia PCs ship

Microsoft made its agent containment layer generally available on Windows 11 and opened preorders for Nvidia RTX Spark PCs, from $2,599.99 for the Surface Laptop Ultra. The laptop gets the headlines, but the fence around your agents is the bigger change.

#01

Meta ships Muse Wonder with no benchmarks or prices

Meta released Muse Wonder and Muse Wonder Full on Oct. 12, calling them its most capable models, available now through the Meta Model API and Muse Code. The post gives no scores, no prices and no safety report yet, so the frontier claim is Meta's until you test it.

#02

Google launches Gemini agent, and it runs Claude too

Google Cloud unveiled the Gemini agent on Thursday, one agent for chat, long-running tasks and code that routes each job to Gemini or Anthropic's Claude models. Google gave no price and no general availability date, so plan for it but don't budget for it yet.

#03

Microsoft makes Maia its main AI inference chip

Microsoft says Maia 200 is in production and Maia 300 starts multi-gigawatt deployments in 2027, making its own chip the primary inference system for its products. GitHub Copilot and Foundry move to Maia by year end, so your requests will soon run on Microsoft silicon.