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EnglishWhat is supermemory and what does it do for AI agents?
Updated
supermemory is a memory and continual learning engine for AI agents. It sits outside the model itself: raw data (chats, URLs, files, raw data) goes in, a model called learner-1 extracts and organizes it into a vector-graph memory database, and relevant memories are injected back into your agent's context in real time. You reach it through an API, plugins, or MCP, and it is designed to work with any model and any harness. It is aimed at teams that want agents to keep learning across sessions and across model providers rather than starting fresh each time.
The core mechanism
The flow described on the site has three stages:
- Ingest — chats, URLs, files, and raw data enter supermemory.
- Process — learner-1 "updates, merges, infers and forgets" against the memory database. The site describes the stored learnings as dense, interconnected, and growing, with an understanding of time.
- Inject — prior memories are brought back to the model by injecting tokens into its context in real time, via hooks into your agent's context.
A concrete example: an agent handling support tickets for many tenants. Each conversation, document, and URL is ingested; learner-1 merges new facts with what it already knows about that tenant and drops stale ones; when the next ticket arrives, the relevant history is injected into the prompt so the model responds with context it was never trained on.
Why it lives outside the model
The site's argument is that model intelligence is no longer the bottleneck — pre-training and post-training keep improving models, but they don't let a model learn and improve in real time. So learning and memory should be separated from model providers and stay interoperable. That is what makes "any model, any harness" possible: switch models or tools and the memory layer carries over, and the memory stays yours.
Access and fit
| Dimension | What the site states |
|---|---|
| Access methods | API, plugins, and MCP |
| Model/harness scope | Any model, any harness; works across them |
| Deployment | Runs everywhere, including air-gapped environments |
| Track record | Serving 100k+ organizations and over a trillion tokens a month |
| Document handling | Advanced extraction for PDFs (OCR, descriptions of figures and diagrams) on Scale and Enterprise; Free, Pro, and Max keep standard PDF extraction |
Setup can start from a copyable setup prompt that opens in an assistant such as ChatGPT, Claude, Cursor, or Grok, or through "Talk to sales" for a direct conversation.
When it's a good fit — and when it isn't
Consider supermemory if you need memory that persists across sessions, tenants, or tasks; if you expect to change models or harnesses and don't want to rebuild memory each time; or if you need memory running in a restricted or air-gapped environment. If your use case is a single-session assistant with no need to recall prior interactions, a dedicated memory layer adds infrastructure you may not need.
Before committing, check the pricing page for current plan details and confirm which document-extraction tier your workload requires — the PDF capabilities differ between Free/Pro/Max and Scale/Enterprise.