Skip to main content
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.

​
How does it work? (at a glance)

  • You send Supermemory text, files, and chats.
  • Supermemory intelligently indexes them and builds a semantic understanding graph on top of an entity (e.g., a user, a document, a project, an organization).
  • At query time, we fetch only the most relevant context and pass it to your models.

​
Supermemory is context engineering.

​
Ingestion and Extraction

Supermemory handles all the extraction, for any data type that you have.
  • Text
  • Conversations
  • Files (PDF, Images, Docs)
  • Even videos!
… and then, We offer three ways to add context to your LLMs:

​
Memory API — Learned user context

memory graph Supermemory learns and builds the memory for the user. These are extracted facts about the user, that:
  • Evolve on top of existing context about the user, in real time
  • Handle knowledge updates, temporal changes, forgetfulness
  • Creates a user profile as the default context provider for the LLM.
This can then be provided to the LLM, to give more contextual, personalized responses.

​
User profiles

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 Developers can configure supermemory with what static and dynamic contents are, depending on their use case.
  • Static: Information that the agent should always know.
  • Dynamic: Episodic information, about last few conversations etc.
This leads to a much better retrieval system, and extremely personalized responses. 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
  • Full advanced metadata filtering
  • Contextual chunking
  • Works well with the memory engine
You can reference the full API reference for the Memory API here.
All three approaches share the same context pool when using the same user ID (containerTag). You can mix and match based on your needs.

​
Next steps

Head to the How it works guide to understand the underlying way of how supermemory represents and learns in data.