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EnglishHow to Get Started with supermemory Using the Setup Prompt
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The fastest way to start is to copy the setup prompt from supermemory.ai and open it in an assistant you already use — ChatGPT, Claude, Cursor, or Grok are listed as supported targets. This works if you want to try memory in an existing chat or coding environment without building an integration first. If you need programmatic control or enterprise deployment, the site also offers an API, plugins, and MCP, with a sales contact for enterprise needs.
What the setup prompt does
supermemory describes itself as a memory and continual-learning engine for agents. Raw data — chats, URLs, files — enters the system; its learner-1 model extracts, merges, infers, and forgets against a vector-graph memory database; and the result is injected back into your agent's context as tokens in real time.
The setup prompt is the entry point that wires this loop into an assistant. The site presents it as a copyable block with two actions: Copy setup prompt and Open in ChatGPT / Claude / Cursor / Grok.
Step-by-step: start with the setup prompt
- Open supermemory.ai and locate the setup prompt block near the "Build with supermemory" call to action.
- Choose your path:
- Click Copy setup prompt if you want to paste it into an assistant manually.
- Click Open in ChatGPT, Claude, Cursor, or Grok to hand the prompt directly to that tool.
- Run the prompt in your assistant. The assistant receives the setup instructions and configures the connection to supermemory's memory layer.
- Verify it works by giving the assistant something worth remembering — for example, a preference or a project detail — then starting a new session and asking it to recall that detail. If the memory layer is connected, the context should carry over.
- Continue in your normal workflow. From here, memory updates happen as you use the assistant: learner-1 processes new context and injects relevant prior memories via hooks into your agent's context.
If you prefer a different entry point
| Entry point | Best for | What to expect |
|---|---|---|
| Setup prompt (copy or open in assistant) | Fastest trial in ChatGPT, Claude, Cursor, or Grok | Prompt-driven setup inside a tool you already use |
| API | Developers building memory into their own agents | Programmatic access to the memory engine |
| Plugins | Teams using supported platforms | Memory added through existing plugin systems |
| MCP | Agents and harnesses that speak MCP | Memory exposed as an MCP integration |
| Sales contact | Enterprise and air-gapped deployments | Direct conversation about deployment needs |
Common sticking points
- Model and harness portability: supermemory states that learning and memory live outside model providers, so they carry across models and harnesses. If you switch assistants later, your memory is designed to follow — but confirm the new harness supports the injection method (hooks or MCP) you rely on.
- Air-gapped environments: the site says supermemory runs everywhere, including air-gapped environments. That path is not self-serve through the setup prompt; it goes through sales.
- PDF extraction depth varies by plan: advanced document extraction — OCR plus descriptions of figures and diagrams — is listed under Scale and Enterprise. Free, Pro, and Max keep standard PDF extraction. If your use case depends on reading diagrams, check the plan comparison before committing.
- Plan details: the site links a pricing page, but the provided material does not list prices or limits for Free, Pro, Max, or Enterprise. Treat plan specifics as something to confirm on the pricing page rather than assume.
When the setup prompt is the right choice
Use it if your goal is to see memory working inside an assistant within minutes and you already have a ChatGPT, Claude, Cursor, or Grok workflow. Move to the API or MCP if you need memory embedded in a custom agent, and to sales if you need enterprise scale or an air-gapped deployment. The site reports the underlying infrastructure already serves 100k+ organizations and over a trillion tokens a month, which is a signal of production readiness rather than a substitute for testing your own use case.