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71 lines
2.7 KiB
Text
71 lines
2.7 KiB
Text
---
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title: "Overview — What is Supermemory?"
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sidebarTitle: "Overview"
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description = "Add long-term memory to your LLMs with three integration paths: AI SDK, Memory API, or Memory Router."
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---
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Supermemory gives your LLMs long-term memory. Instead of stateless text generation, they recall the right facts from your files, chats, and tools, so responses stay consistent, contextual, and personal.
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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](/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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## Supermemory is context engineering.
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#### Ingestion and Extraction
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Supermemory handles all the extraction, for any data type that you have.
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- Text
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- Conversations
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- Files (PDF, Images, Docs)
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- Even videos!
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... and then,
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We offer three ways to add context to your LLMs:
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#### Memory API — Learned user context
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Supermemory learns and builds the memory for the user. These are extracted facts about the user, that:
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- Evolve on top of existing context about the user, **in real time**
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- Handle **knowledge updates, temporal changes, forgetfulness**
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- Creates a **user profile** as the default context provider for the LLM.
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_This can then be provided to the LLM, to give more contextual, personalized responses._
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#### User profiles
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Having the latest, evolving context about the user allows us to also create a **User Profile**. This is a combination of static and dynamic facts about the user, that the agent should **always know**
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Developers can configure supermemory with what static and dynamic contents are, depending on their use case.
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- Static: Information that the agent should **always** know.
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- Dynamic: **Episodic** information, about last few conversations etc.
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This leads to a much better retrieval system, and extremely personalized responses.
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#### RAG - Advanced semantic search
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Along with the user context, developers can also choose to do a search on the raw context. We provide full RAG-as-a-service, along with
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- Full advanced metadata filtering
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- Contextual chunking
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- Works well with the memory engine
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<Info>
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You can reference the full API reference for the Memory API [here](/api-reference/manage-documents/add-document).
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</Info>
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<Note>
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All three approaches share the **same context pool** when using the same user ID (`containerTag`). You can mix and match based on your needs.
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</Note>
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## Next steps
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Head to the [**How it works**](/how-it-works) guide to understand the underlying way of how supermemory represents and learns in data.
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