Skip to main content
Go to documentation:
⌘U
Weaviate Database

Develop AI applications using Weaviate's APIs and tools

Deploy

Deploy, configure, and maintain Weaviate Database

Query Agent

Run agentic search over your Weaviate Cloud collections

Weaviate Cloud

Manage and scale Weaviate in the cloud

Engram

Persistent memory for LLM agents and applications

Additional resources

Integrations
Contributor guide
Events & Workshops
Weaviate Academy

Need help?

Weaviate LogoAsk AI Assistant⌘K
Community Forum

Concepts

Engram organizes and processes memories for your AI applications. Here's how the core concepts work together.

ConceptDescription
MemoriesDiscrete pieces of information stored in Engram, automatically embedded as vectors for semantic search.
GroupsA named configuration bundle. Each group contains topics (what to remember) and a pipeline (how to process). Most projects start with a single group.
TopicsA category of memory within a group. Each topic defines what kind of information to extract, like UserKnowledge or ConversationSummary.
ScopesControls memory visibility. Every memory belongs to a project. Topics can additionally require a user_id and custom properties (e.g. conversation_id) for isolation.
Input data typesThe three content formats Engram accepts: string, pre-extracted, and conversation.
PipelinesThe processing flow that turns raw input into stored memories. Steps include extracting facts, transforming with context, and committing to storage. Configurable pipelines are available on enterprise plans.
SearchSearch retrieval strategies for finding memories: vector, BM25, and hybrid.

How concepts relate

Below is an overview of Engram's key concepts and how they relate to each other:

Weaviate Engram Concepts

  • You send input data (text, a conversation, or pre-extracted facts) along with scope parameters (user_id and any properties the target topic requires) that control how the memories are isolated.
  • The input is routed to a group, which bundles topics with a pipeline — one group per use case.
  • Topics tell the pipeline what kinds of information to extract (e.g. UserKnowledge, ConversationSummary) and which scopes are required.
  • The pipeline extracts facts from the input, deduplicates and merges them with existing data, and commits the results to storage.
  • The output is a set of memories — vector-embedded, categorized by topic, and isolated by scope so each user's data stays separate.

Questions and feedback

If you have any questions or feedback, let us know in the user forum.