Why AI forgets
Modern AI models have no persistent memory. A model seems to remember earlier turns only because the whole conversation is fed back into its context window with every message. The context window is the text the model can read at once. Think of it as a desk with limited space: new messages come in, older ones get pushed off the edge. The model isn’t forgetting in a human sense, it just can’t see what’s no longer on the desk. Start a new chat and the desk is wiped clean. MemoryPlugin closes that gap. It keeps your memories, chat history, and files outside the model, and pages the relevant pieces into the context window when they’re needed.
Storing memories
MemoryPlugin captures information two ways: you ask for it (“remember that I use TypeScript and Tailwind”), or the AI recognizes it and saves a detail worth keeping without you asking. How the save happens depends on the integration:- Browser Extension
- Custom GPT
- MCP Server
- TypingMind Plugin
On plain web chat surfaces (ChatGPT, Claude, Gemini, and others), no memory tool is wired into the page. So the extension pastes a short instruction asking the AI to emit a marker line, for example
to=memoryplugin&&memory=User prefers morning meetings. It watches the response, spots that line, and sends the memory to MemoryPlugin’s servers.Done well this is invisible. The tradeoff: it rides inside the conversation rather than a clean tool call, so it depends on the AI following the instruction.Only the memory text itself is sent to MemoryPlugin’s servers, never your full conversation. This holds for every integration.
Bringing memories back
When you start a new conversation, MemoryPlugin makes your stored memories available to the AI:- Browser Extension: pressing the MemoryPlugin button injects your memories into the message before it’s sent. You can also turn on automatic injection, which adds them to a new chat’s first message after a five-second countdown you can cancel.
- Custom GPT, MCP Server, TypingMind Plugin: the AI loads memories through a tool call, at the start of a chat or when you ask it to.
Chat history
Hold the two layers in your head this way: your memories are the notebook, short curated notes your AI keeps with it in every conversation. Chat History is the archive, the complete record of your past conversations, recalled when it’s relevant. The notebook is small and always present; the archive is large and consulted on demand. Chat History works in three steps:- Import brings in an export file from a platform (ChatGPT, Claude, TypingMind, and a few others) to backfill everything you already have.
- Sync captures new conversations automatically as you have them. Sync is browser-extension only and currently covers ChatGPT, Claude, Gemini, Grok, DeepSeek, and TypingMind.
- Recall is how your AI uses that archive. It decides when your past chats are relevant, writes its own search query, and pulls back a concise, relevant slice. It does not load your entire history into context.
What this costs in tokens
Every memory or piece of history that lands in the context window is text the model has to read, and text costs tokens. A handful of memories is nothing. A few hundred, injected into every message, adds up and can crowd the window. Two things keep this in check:- Smart Memory groups your memories into categories. Instead of loading everything every time, your AI loads only the category relevant to the current question. On large memory sets this cuts the tokens spent on memory by around 90% while keeping answers relevant.
- Chat History recall is bounded. A recall pulls a small, targeted amount of context (roughly 600 tokens by default, capped well below what a full history would cost), not the whole archive.
One memory. Every AI.
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