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docs: rewrite Overview to sell the differentiation
The Overview didn't carry the positioning the product earns. Lead with proof and the technical edge instead of generic capability cards: - Open with the category + the #1-on-every-benchmark claim and the "reasons over a directed knowledge graph" thesis - Add a benchmarks table (LongMemEval 81.6%, LoCoMo, ConvoMem — all #1) - Replace the generic "what you can build" grid with "What makes Supermemory different": reasons (not retrieves), directed graph, whole context stack in one system, multi-modal, built to build on, run anywhere - Keep deployment callout, how-it-works, the three context approaches, and the start-building / ways-to-use cards Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Dhravya Shah
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@ -4,81 +4,82 @@ sidebarTitle: "Overview"
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icon: "book-open"
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---
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Supermemory is the long-term and short-term memory and context infrastructure for AI agents. It is the [state of the art](https://supermemory.ai/research) across multiple benchmarks, including LongMemEval and LoCoMo.
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Supermemory is the memory and context engine for AI agents — and it's the state of the art, ranked **#1 on every major memory benchmark**: [LongMemEval](https://github.com/xiaowu0162/LongMemEval), [LoCoMo](https://github.com/snap-research/locomo), and [ConvoMem](https://github.com/Salesforce/ConvoMem).
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With supermemory, you give your agents perfect recall about their users — so they're more intelligent, more personalized, and more consistent. Every piece of the context stack is built in, behind one API.
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Your AI forgets everything between conversations. Supermemory fixes that. It learns from every interaction, **reasons over a directed knowledge graph** to resolve contradictions and track how facts change over time, forgets what's expired, and serves the right context at the right moment — through one API.
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## State of the art, by the numbers
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Most memory systems claim to be better. Supermemory is measurably ahead — first place across all three independent benchmarks the field uses to grade AI memory.
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| Benchmark | What it measures | Supermemory |
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| [LongMemEval](https://github.com/xiaowu0162/LongMemEval) | Long-term memory across sessions, with knowledge updates | **81.6% — #1** |
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| [LoCoMo](https://github.com/snap-research/locomo) | Fact recall across long conversations (multi-hop, temporal, adversarial) | **#1** |
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| [ConvoMem](https://github.com/Salesforce/ConvoMem) | Personalization and preference learning | **#1** |
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Read the full methodology on the [research page](https://supermemory.ai/research), or reproduce it yourself with [MemoryBench](/memorybench/overview), our open-source benchmarking framework.
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## What makes Supermemory different
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Most "memory layers" are a vector store that retrieves the nearest chunk. Supermemory is an engine that *understands* — which is why it tops the benchmarks instead of just claiming to.
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<CardGroup cols={2}>
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<Card title="It reasons — not just retrieves" icon="brain" href="/concepts/how-it-works">
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Resolves contradictions, tracks temporal change, follows multi-hop relationships, and forgets expired facts automatically. Reasoning is why it wins every benchmark.
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</Card>
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<Card title="A directed knowledge graph" icon="git-fork" href="/concepts/graph-memory">
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One evolving directed graph and a single ontology across all your data — not a flat vector store, and not a raw graph you have to traverse yourself.
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</Card>
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<Card title="The whole context stack, one system" icon="layers" href="/concepts/super-rag">
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Memory, user profiles, hybrid search (RAG), connectors, and file processing — together, sharing one context pool. No stitching five tools together.
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</Card>
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<Card title="Multi-modal — everything in" icon="file-text" href="/concepts/content-types">
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Text, conversations, PDFs, images (OCR), video (transcription), and code (AST-aware chunking). Upload it and it just works.
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</Card>
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<Card title="Built to build on" icon="blocks" href="/integrations/supermemory-sdk">
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One API, plus SDKs, a CLI, a memory filesystem, and MCP. Infrastructure you ship products on — not a closed box.
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</Card>
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<Card title="Run it anywhere" icon="server" href="/self-hosting/overview">
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Hosted for zero-ops scale, or the full engine as one self-hosted binary — fully offline if you want, same API either way.
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</Card>
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</CardGroup>
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<Tip>
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**Hosted or self-hosted — same API.** Use the [managed platform](https://console.supermemory.ai) for zero-ops scale, or [run the entire engine on your own machine](/self-hosting/overview) — one binary, zero config, fully offline. Move between them by changing a single `baseURL`.
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</Tip>
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## What you can build with it
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<CardGroup cols={2}>
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<Card title="Agent memory" icon="brain" href="/concepts/graph-memory">
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Extract and evolve facts about each user over time — knowledge updates, temporal changes, automatic forgetting.
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</Card>
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<Card title="User profiles" icon="user-round" href="/concepts/user-profiles">
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A live blend of static and dynamic context your agent should always know, built automatically from memory.
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</Card>
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<Card title="Hybrid search (RAG)" icon="search" href="/search">
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Semantic search with metadata filtering, contextual chunking, and reranking — over memories and raw documents in one query.
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</Card>
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<Card title="Content extraction" icon="file-text" href="/concepts/content-types">
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Ingest text, conversations, PDFs, images, and even video — all turned into searchable, structured context.
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</Card>
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<Card title="Connectors and sync" icon="plug" href="/connectors/overview">
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Continuously pull from Google Drive, Notion, Gmail, OneDrive, and more, with no pipeline to maintain.
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</Card>
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<Card title="Managed RAG platform" icon="layers" href="/concepts/super-rag">
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Production-grade retrieval as a service, tuned to work alongside the memory engine.
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</Card>
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</CardGroup>
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All of it shares the **same context pool** for a given user (`containerTag`), so memory and search reinforce each other instead of living in separate silos.
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## How does it work? (at a glance)
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- You send Supermemory text, files, and chats.
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- Supermemory [intelligently indexes them](/concepts/how-it-works) and builds a semantic understanding graph on top of an entity (e.g., a user, a document, a project, an organization).
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- At query time, we fetch only the most relevant context and pass it to your models.
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- It [indexes them intelligently](/concepts/how-it-works) and builds a directed knowledge graph on top of an entity (a user, document, project, or organization).
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- At query time, it fetches only the most relevant context and passes it to your models.
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## Supermemory is context engineering
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## Three ways to add context
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We offer three ways to add context to your LLMs — mix and match them as your use case needs.
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Memory, profiles, and search all draw from the **same context pool** for a given user (`containerTag`) — so they reinforce each other instead of living in silos. Mix and match as your use case needs.
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#### Memory API — learned user context
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Supermemory learns and builds memory for each user. These are extracted facts that:
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Supermemory learns and builds memory for each user — extracted facts that:
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- [Evolve on top of existing context about the user](/concepts/graph-memory), **in real time**
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- Handle **knowledge updates, temporal changes, and forgetfulness**
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- Handle **knowledge updates, temporal changes, and contradictions**
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- Power a **user profile** that acts as the default context provider for the LLM
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_Provide this to your LLM for more contextual, personalized responses._
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#### User profiles
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The latest, evolving context about a user also produces a [**User Profile**](/concepts/user-profiles) — static and dynamic facts the agent should **always** know:
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The evolving context produces a [**User Profile**](/concepts/user-profiles) — the facts your agent should **always** know, in one ~50ms call:
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- **Static:** information the agent should **always** know.
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- **Dynamic:** **episodic** information about the last few conversations.
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Configure what counts as static vs. dynamic for your use case for extremely personalized retrieval.
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- **Static:** stable facts the agent should always know.
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- **Dynamic:** episodic context from the last few conversations.
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#### RAG — advanced semantic search
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Alongside user context, run search over the raw content. Full RAG-as-a-service, with:
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- Advanced metadata filtering
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- Contextual chunking
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- Tight integration with the memory engine
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<Note>
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All three approaches share the **same context pool** when using the same user ID (`containerTag`). Mix and match based on your needs.
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</Note>
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Run hybrid [search](/search) over the raw content too: advanced metadata filtering, contextual chunking, and reranking — tightly integrated with the memory engine, in a single query.
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## Start building
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