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Keep docs asset URLs repo-root-relative and pass images as literal MDX children so Mintlify can compile them through OptimizedImage under the /docs mount. Mintlify's official guidance says: “Image paths are root-relative from your docs repository.” It also says relative paths such as `./screenshot.png` are unsupported. See [Image embeds](https://www.mintlify.com/docs/create/image-embeds). The existing `/images/...` paths were correct. The failure came from passing them through custom-component string props, which kept Mintlify from seeing those images during its MDX transform. - fix hero, building-block, and Slack avatar images - preserve Slack avatar clipping - normalize Hermes and Company Brain icon sizing Tested with Mintlify validation, broken-link checks, and browser checks across every changed route.
160 lines
7.5 KiB
Text
160 lines
7.5 KiB
Text
---
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title: "What is Supermemory?"
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description: "Supermemory is the long term and short term context and memory infrastructure for agents."
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sidebarTitle: "What is Supermemory?"
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icon: "book-open"
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mode: "wide"
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---
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export const BuildingBlock = ({ title, href, children }) => {
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return (
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<a
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href={href}
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className="group flex flex-row items-center gap-4 rounded-xl border border-gray-200 dark:border-zinc-800 bg-white dark:bg-zinc-900/40 p-4 no-underline transition-all duration-200 hover:border-gray-300 dark:hover:border-zinc-600 hover:shadow-sm"
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>
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{children}
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<span className="text-sm font-medium leading-snug text-gray-900 dark:text-zinc-100 group-hover:text-blue-600 dark:group-hover:text-blue-400">
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{title}
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</span>
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</a>
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)
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}
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Supermemory is **context infrastructure for AI agents**. We're one of the leading memory providers, with components to go beyond memory and configure it to be perfect for every usecase.
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It provides all the building blocks — Memory, Retrieval, Profiles, Connectors, Extractors, Evals, observability, and more.
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<div className="not-prose my-8 grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-3 gap-3">
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<BuildingBlock
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title="Memory & Continual Learning"
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href="/concepts/graph-memory"
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>
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<img src="/images/building-blocks/memory-router.svg" alt="" className="h-16 w-16 shrink-0 object-contain" />
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</BuildingBlock>
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<BuildingBlock
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title="SuperRAG (Retrieval)"
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href="/concepts/super-rag"
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>
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<img src="/images/building-blocks/document-retrieval.svg" alt="" className="h-16 w-16 shrink-0 object-contain" />
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</BuildingBlock>
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<BuildingBlock
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title="Filesystems"
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href="/smfs/overview"
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>
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<img src="/images/building-blocks/file-systems.svg" alt="" className="h-16 w-16 shrink-0 object-contain" />
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</BuildingBlock>
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<BuildingBlock
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title="Profiles"
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href="/concepts/user-profiles"
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>
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<img src="/images/building-blocks/user-profiles.svg" alt="" className="h-16 w-16 shrink-0 object-contain" />
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</BuildingBlock>
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<BuildingBlock
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title="Connectors"
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href="/connectors/overview"
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>
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<img src="/images/building-blocks/connectors.svg" alt="" className="h-16 w-16 shrink-0 object-contain" />
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</BuildingBlock>
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<BuildingBlock
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title="Extractors"
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href="/concepts/content-types"
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>
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<img src="/images/building-blocks/extractor.svg" alt="" className="h-16 w-16 shrink-0 object-contain" />
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</BuildingBlock>
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<BuildingBlock
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title="Qualitative Analysis"
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href="https://supermemory.ai/research"
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>
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<img src="/images/building-blocks/qualitative-analysis.svg" alt="" className="h-16 w-16 shrink-0 object-contain" />
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</BuildingBlock>
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<BuildingBlock
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title="Plugins"
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href="/supermemory-mcp/mcp"
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>
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<img
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src="/images/building-blocks/hermes.svg"
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alt=""
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className="h-16 w-16 shrink-0 object-contain"
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style={{ transform: "scale(0.85)" }}
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/>
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</BuildingBlock>
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<BuildingBlock
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title="Company Brain"
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href="/company-brain/overview"
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>
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<img
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src="/images/building-blocks/brain-head.png"
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alt=""
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className="h-16 w-16 shrink-0 object-contain"
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style={{ transform: "scale(1.3)" }}
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/>
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</BuildingBlock>
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</div>
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With supermemory, developers can provide perfect recall about their users to build AI agents that are more intelligent, more personalized, and more consistent.
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It is the [state of the art](https://supermemory.ai/research) across multiple different benchmarks, like LongMemEval and LoCoMo. It's also the best in a lot of independantly run benchmarks, like the [SWEContext](https://arxiv.org/pdf/2602.08316) bench.
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## How does it work? (at a glance)
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- You send Supermemory raw data in any format - text, files, and chats, or connect it to the data sources
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- Supermemory [intelligently indexes them](/concepts/how-it-works) using our user understanding model and builds a semantic understanding graph on top of an entity (e.g., a user, a document, a project, an organization). We call these entities `containerTag`
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- This knowledge is now traversed by the agent, and an automatic profile is built for it. The agent may now use it for memory operations or for retrieval.
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## Why add memory to your agent?
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Without memory, every session starts from zero. The model cannot know what the user preferred last week, which project they are on, or that a fact has changed since yesterday.
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**Memory** gives an agent durable understanding of *people and entities over time* — preferences, decisions, relationships, corrections. **Retrieval (RAG)** grounds answers in *documents and knowledge bases*. You usually want both.
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By adding memory to your agent, you can:
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- **Personalize** — remember preferences, roles, and history across sessions without stuffing the full chat log into every prompt
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- **Stay correct as facts change** — “I love Adidas” then “switching to Puma” should not leave both preferences equally true
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- **Ground answers** — pull the right policy, ticket, or doc when the question needs source material
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- **Ship multi-tenant products** — isolate each user or workspace so one customer’s memory never leaks into another’s
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You can think of memory as the always-on context a skilled teammate would carry — not a search box over raw logs.
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For the full category argument (vs DIY vectors, thin memory wrappers, pure RAG), see [Comparison](/overview/comparison) and [Memory vs RAG](/concepts/memory-vs-rag).
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## Why Supermemory?
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- **State of the art on long-horizon memory** — #1 on [LongMemEval](https://supermemory.ai/research), [LoCoMo](https://supermemory.ai/research), and [ConvoMem](https://supermemory.ai/research), plus independent benches like [SWEContext](https://arxiv.org/pdf/2602.08316)
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- **Memory is a graph, not a blob store** — facts [update, connect, and forget](/concepts/graph-memory) in real time; not nearest-neighbor chunks alone
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- **User profiles built in** — static + dynamic context the agent should [always know](/concepts/user-profiles), ~ready for the prompt
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- **Memory + SuperRAG in one engine** — personalize *and* ground on the same `containerTag` / context pool
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- **Every door, one store** — API, [MCP](/supermemory-mcp/mcp), plugins, [SMFS](/smfs/overview), connectors, and Company Brain share the same memories
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- **Multimodal by default** — text, chats, PDFs, images, video, code via [extractors](/concepts/content-types) and [connectors](/connectors/overview)
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- **Run it your way** — managed cloud or [self-host](/self-hosting/overview) as a single binary (including offline)
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<Note>
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Memory, profiles, and SuperRAG share the **same context pool** when you use the same isolation (`containerTag`). Mix and match for your product! A container can be anything - a user, a project, team, organization, etc.
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</Note>
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## Next steps
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<CardGroup cols={2}>
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<Card title="Quickstart" icon="play" href="/quickstart">
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Make your first API call in minutes
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</Card>
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<Card title="How it Works" icon="cpu" href="/concepts/how-it-works">
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Understand the knowledge graph architecture
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</Card>
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<Card title="Comparison" icon="scale" href="/overview/comparison">
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vs DIY vectors, thin memory layers, pure RAG
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</Card>
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<Card title="Self-host it" icon="server" href="/self-hosting/overview">
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One binary, zero config, fully offline
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</Card>
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<Card title="Billing & plans" icon="credit-card" href="/overview/billing">
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Credits, SM tokens, and how usage works
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</Card>
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<Card title="Security & compliance" icon="shield" href="/overview/security">
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SOC 2, GDPR, HIPAA BAA, encryption
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</Card>
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</CardGroup>
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