Semantic memory strategy - Amazon Bedrock AgentCore

Semantic memory strategy

The semantic memory strategy is designed to identify and extract key pieces of factual information and contextual knowledge from conversational data. This lets your agent to build a persistent knowledge base about the entities, events, and key details discussed during an interaction.

Steps in the strategy

The semantic memory strategy includes the following steps:

  • Extraction – Identifies useful insights from short-term memory to place into long-term memory as memory records.

  • Consolidation – Determines whether to write useful information to a new record or an existing record.

Note

The semantic strategy processes only USER and ASSISTANT role messages during extraction. For more information about roles in agent conversations, see Conversational.

Strategy output

The semantic memory strategy returns facts as JSON objects, each representing a standalone personal fact about the user.

Example of facts captured by this strategy

  • An order number (#XYZ-123) is associated with a specific support case.

  • A project's deadline of October 25th.

  • The user is running version 2.1 of the software.

By referencing this stored knowledge, your agent can provide more accurate, context-aware responses, perform multi-step tasks that rely on previously stated information, and avoid asking users to repeat key details.

Default namespace

/strategy/{memoryStrategyId}/actors/{actorId}