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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 across multiple different benchmarks, like LongMemEval and LoCoMo. With supermemory, developers can provide perfect recall about their users to build AI agents that are more intelligent, more personalized, and more consistent. Additionally, supermemory has all the pieces of the context stack built in: All this, coming together, makes supermemory the best abstraction to provide to agents.

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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.

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Supermemory is context engineering.

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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:

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Memory API — Learned user context

memory graph Supermemory learns and builds the memory for the user. These are extracted facts about the user, that: This can then be provided to the LLM, to give more contextual, personalized responses.

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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
See the full API Reference for detailed endpoint documentation.
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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Next steps

Quickstart

Make your first API call in minutes

How it Works

Understand the knowledge graph architecture