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Semantic memory

Semantic memory is long-term memory for meanings, concepts, and general knowledge organized by meaning rather than by specific personal episodes.

How 2 Manning books explain it

AI Agents in Action, Second Edition

Semantic memory is memory retrieved according to the meaning of stored information rather than only its explicit graph relationships or literal terms.

How it is used
Deploys semantic memory as long-term agent storage for retrieving facts, concepts, and contextual information by meaning.
How deep it goes
Explains encoding, indexing, retrieval, augmentation, backend selection, and hybrid search, while deferring production operations to separate work.
Examples
Uses vector-store retrieval, generated retrieval questions, and a ChromaDB-plus-knowledge-graph hybrid agent.
What you can do after
Operate/configure semantic retrieval and combine it with graph search for richer agent memory, while recognizing ephemeral-demo limitations.

Read how AI Agents in Action, Second Edition explains semantic memory →

AI Agents in Action

Semantic memory is a memory form organized around meanings, including conceptual information and associated feelings or emotions.

How it is used
Supports agent recall by storing and retrieving meaning-based facts, concepts, preferences, and other long-term information.
How deep it goes
Explains semantic categorization, query extraction, retrieval, and configuration, but does not specify customizable memory prompts.
Examples
Uses facts and preferences, semantic memory stores, and comparisons of identical statements represented as different memory types.
What you can do after
Operate/configure semantic memory stores and understand how semantic augmentation improves relevant agent recall.

Read how AI Agents in Action explains semantic memory →

Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.