# Semantic memory strategy
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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](https://docs.aws.amazon.com/bedrock-agentcore/latest/APIReference/API_Conversational.html).

 **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 ( \$1XYZ-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}/` 

**Topics**
+ [System prompt for semantic memory strategy](memory-system-prompt.md)