Direct answer

Add Memory to Cursor

To add memory to Cursor that survives the editor session, connect Cursor to the Zambo MCP at https://zambo.dev/mcp and save project facts with memory_remember: the architecture decision, the naming rule, the deploy gotcha. Cursor recalls them next week with memory_recall, and so do Claude, ChatGPT, and Grok on the same account, because the store lives outside the editor. Project memory stops being something one session knew and becomes something your whole toolchain knows.

Check the record layer before you trust it: follow recorded work at zambo.dev/today and verify any receipt free at the public verifier. Memory and receipts sit on the same account.

Updated October 20, 2026.

Why Cursor sessions forget your project

Cursor knows your codebase remarkably well while a session is open. It reads the files, follows the thread, and builds real understanding of how your project fits together. Then the session ends, the context closes, and the next session starts by re-deriving what it can from the code alone. Code shows what the project is. It does not show why: why this library and not the obvious one, why that endpoint is shaped oddly on purpose, why a tempting refactor was tried in March and reverted.

Rules files and notes help, and many teams lean on them hard. Their limit is reach. A rule written for the editor lives in the editor. The same decision also matters to the Claude chat where you plan the feature, the ChatGPT session where you draft the docs, and the agent run that ships at night, and none of them read your editor files. Searching for a way to add memory to Cursor is usually a search for project memory that lives above the editor, where every tool on the account can reach it.

The Zambo route, step by step

Zambo, the verifiable work layer for AI, serves its memory tools from one MCP endpoint that Cursor connects to like any other MCP server. Open the MCP or connectors settings in Cursor, add the server with the URL https://zambo.dev/mcp, and save. No API key is needed. Open a fresh session, confirm the Zambo tools show up, and run one small save before you rely on the setup for a real project fact.

Save the facts code cannot tell the next session. After a decision, say it in one sentence and ask Cursor to save it with memory_remember: we chose the queue over cron because jobs must survive restarts, or never rename the legacy billing table, three integrations read it directly. Next session, before touching code, ask Cursor to recall the project facts with memory_recall. The session starts informed instead of guessing, and the same facts are waiting in Claude or ChatGPT when the work moves there.

Keep the store factual and small. Zambo memory holds only what you explicitly save; it does not import your chat history or scan your repo for lessons. Every fact in it was a decision you made to write something down, which is why recalling it feels like reading your own notebook instead of interrogating a black box.

Project memory shared with the other AIs on the account

The quiet payoff arrives a week later, outside the editor entirely. You are in Claude, planning the next feature, and you ask what constraints the billing table carries. Claude, connected to the same Zambo account, recalls the fact Cursor saved. You are in ChatGPT drafting release notes and the deploy gotcha Cursor recorded shapes the checklist. The project memory belongs to the account, so whichever AI is holding the work at that moment holds the memory too.

Continuity extends the same idea from facts to work state. Save where a job stands, the steps done, the output so far, the open item, and pick it up in another connected AI without a briefing document. For a solo developer this ends the daily ritual of re-explaining the project to a fresh session. For a small team it means the facts one person saved at their keyboard are available to everyone else connected work, in the tool they already use.

Open source questions, answered honestly

Some searchers add open source to this query, and they usually mean one of two things: can I inspect what holds my memory, or can I self-host it. The honest answer splits in two. AER-1, the receipt format under Zambo saved results, is an open IETF Internet-Draft, draft-zambo-aer1-24, so the proof layer is a public specification anyone can read and implement, not a private format you have to trust blind. The memory store itself is a hosted service on your Zambo account, not a self-hosted package.

If your hard requirement is a fully self-hosted, open source memory you run on your own machines, this is not that, and a page that pretended otherwise would waste your time. If your requirement is memory your editor and your other AIs actually share this week, with an open format under the receipts and full control over what is saved and deleted, the hosted store is the practical route. Many developers run both postures at once: local notes for deep internals, shared Zambo facts for the decisions every tool needs.

Memory is not logs: the vs logs question

Logs and memory answer different questions, and confusing them produces bloated stores and bad recalls. A log records what happened, in order, at full volume: every tool call, every output, every error. Its job is audit and debugging. Memory records what is worth knowing next time: a distilled fact, saved on purpose, small enough to recall whole. Saving your logs as memory would flood every future session with noise; keeping only memory and no record would leave you unable to prove what a past session actually did.

Zambo keeps the two layers separate and connected. Work run through its tools produces a verifiable receipt for each saved result, checkable free at https://zambo.dev/verify/, and those receipts are the log-grade record: they confirm the saved result was not changed after the fact, without claiming the work was right. Memory, through memory_remember and memory_recall, is the thin layer of facts on top. Use logs to answer what happened, memory to answer what should we remember, and never force one layer to do the other job.

Choosing a memory tool for Cursor

Judge any memory tool for Cursor on four tests. Reach: does the memory work outside this editor, in the other AIs you use, or is it trapped here? Control: do you decide what is saved, and can you read and delete the whole store? Size discipline: does it save short facts, or does it stuff transcripts into every future session? Proof: when a saved result matters, can anyone check it was not altered later? A tool that passes reach and control but fails size discipline will still slow you down, just more politely.

Run the same four tests on the Zambo route and it holds up in the open. Reach: every client connected to https://zambo.dev/mcp reads the same store. Control: only explicit saves, reviewable and deletable. Size: one sentence facts by habit. Proof: receipts under saved results, verifiable by anyone free. And the cost of entry is a paste: a Day Pass at $0.99 covers 24 hours of full access to wire memory into a real project day, with the Zambo Pass at $29 a month, $290 a year, or $19 a month for $ZAMBO holders after that. Humans pay by PayPal or card through PayPal; agents pay USDC on Base through x402.

What to save for a project, and what never to save

Save: architecture decisions with a five word reason, naming and formatting rules the code does not enforce, deploy and environment gotchas, known traps and reverted experiments, and the current state of long running work.

Keep in the repo instead: anything the code already says clearly. Memory earns its place by holding what files cannot show: intent, history, and warnings.

Never save: passwords, tokens, private keys, connection strings with credentials, or customer data. Project memory is shared across your AIs by design, so treat every fact in it as widely read inside your account.

Review the store when a project phase ends. Decisions get reversed, gotchas get fixed, and a recalled warning about a bug that no longer exists will cost a future session real time. Because the same store serves Claude, ChatGPT, and Grok, one ten minute cleanup in Cursor improves every tool session that follows, which is the compounding return that editor-only memory never pays.

Honest limits

Three limits, plainly. First, Zambo memory is explicit only: it saves what you tell it to save, so a decision you never recorded will not greet the next session. Build the one sentence habit at decision time. Second, recall happens when a connected client calls memory_recall, so start sessions by asking for the project facts, rather than assuming they are silently loaded. Third, memory complements your repo documentation, it does not replace it: deep design belongs in the project, and the store holds the short facts every AI needs before it reads the project.

Frequently asked questions

How do I add memory to Cursor?

Connect Cursor to the Zambo MCP and save project facts on purpose. Open the MCP or connectors settings in Cursor, add the server with the URL https://zambo.dev/mcp, and save, with no API key. In a session, state a durable project fact in one sentence and ask Cursor to save it with memory_remember. In the next session, ask for memory_recall before you start work. The fact lives on your Zambo account, so it survives closed sessions, restarts, and switching to another editor or AI.

Is this memory shared with Claude and ChatGPT?

Yes. The store sits on your Zambo account, not inside Cursor. Connect Claude, ChatGPT, and Grok to the same endpoint, https://zambo.dev/mcp, and each of them can recall the project facts Cursor saved, using memory_recall. That sharing is the main reason to keep memory at the account level: the architecture decision you record while coding is the same fact the planning chat and the docs draft need, and none of them should have to be re-briefed by you.

What is the difference between Cursor memory and logs?

Logs record what happened, in order and at full volume, for audit and debugging. Memory records what is worth knowing next time, as short facts saved on purpose. Zambo keeps both layers: work run through its tools produces a verifiable receipt for each saved result, checkable free at https://zambo.dev/verify/, while memory_remember holds the distilled facts. Stuffing logs into memory floods future sessions with noise, and keeping memory without receipts leaves past work unprovable, so use each layer for its own question.

Is there an open source way to add memory to Cursor?

Partly, and the honest split matters. AER-1, the receipt format under Zambo saved results, is an open IETF Internet-Draft, draft-zambo-aer1-24, a public specification anyone can read and implement. The Zambo memory store itself is a hosted service on your account, not a self-hosted open source package. If you strictly need to run memory on your own machines, choose a self-hosted tool. If you need memory shared across your editor and your other AIs now, with an open format under its receipts, this route fits.

How much does it cost to add memory to Cursor this way?

Connecting the MCP and testing a save and recall cycle costs nothing to set up, and checking a receipt is free at https://zambo.dev/verify/ with no signup. For $0.99, a Day Pass covers 24 hours of full access, enough to wire memory into a real project and use it across a full day of sessions. Ongoing use is the Zambo Pass at $29 a month or $290 a year, and $19 a month for $ZAMBO holders. Humans pay by PayPal or card through PayPal; agents pay USDC on Base through x402.