> **Experimental**
>
> `@aws-cdk/aws-bedrock-agentcore-alpha` is an [**experimental**](/stability) (alpha) package: its API may change in any release, without a major version bump.
# MemoryStrategy

Factory class for creating memory strategies If you need long-term memory for context recall across sessions, you can setup memory extraction strategies to extract the relevant memory from the raw events.

Use built-in strategies for quick setup, use built-in strategies with override to specify models and prompt templates.

- Source: [`lib/memory/memory-strategy.ts`](https://github.com/aws/aws-cdk/blob/v2.274.0-alpha.0/packages/@aws-cdk/aws-bedrock-agentcore-alpha/lib/memory/memory-strategy.ts#L128)

## Example

```ts
// The code below shows an example of how to instantiate this type.
// The values are placeholders you should change.
import * as bedrock_agentcore_alpha from '@aws-cdk/aws-bedrock-agentcore-alpha';
const memoryStrategy = new bedrock_agentcore_alpha.MemoryStrategy();
```

## Initializer

```ts
new MemoryStrategy()
```

## Methods

- `usingBuiltInEpisodic(): ManagedMemoryStrategy` (static) — Default strategies for organizing and extracting memory data, each optimized for specific use cases.

  Captures meaningful slices of user and system interactions, preserve them into compact records after summarizing.
  Extracted memory example: User first asked about pricing on Monday, then requested feature comparison on Tuesday, finally made purchase decision on Wednesday.

  **Returns:** A ManagedMemoryStrategy.

- `usingBuiltInSemantic(): ManagedMemoryStrategy` (static) — Default strategies for organizing and extracting memory data, each optimized for specific use cases.

  Distills general facts, concepts, and underlying meanings from raw conversational data, presenting the information in a context-independent format.
  Extracted memory example: In-context learning = task-solving via examples, no training needed.

  **Returns:** A ManagedMemoryStrategy.

- `usingBuiltInSummarization(): ManagedMemoryStrategy` (static) — Default strategies for organizing and extracting memory data, each optimized for specific use cases.

  This strategy compresses conversations into concise overviews, preserving essential context and key insights for quick recall.
  Extracted memory example: Users confused by cloud setup during onboarding.

  **Returns:** A ManagedMemoryStrategy.

- `usingBuiltInUserPreference(): ManagedMemoryStrategy` (static) — Default strategies for organizing and extracting memory data, each optimized for specific use cases.

  Captures individual preferences, interaction patterns, and personalized settings to enhance future experiences.
  Extracted memory example: User needs clear guidance on cloud storage account connection during onboarding.

  **Returns:** A ManagedMemoryStrategy.

- `usingEpisodic(config: ManagedStrategyProps): ManagedMemoryStrategy` (static) — Creates an episodic memory strategy with custom configuration.

  Captures meaningful slices of user and system interactions, preserve them into compact records after summarizing.
  Extracted memory example: User first asked about pricing on Monday, then requested feature comparison on Tuesday, finally made purchase decision on Wednesday.

  **Parameters**

  - `config` — - The configuration for the episodic memory strategy.

  **Returns:** A ManagedMemoryStrategy.

- `usingSelfManaged(config: SelfManagedStrategyProps): SelfManagedMemoryStrategy` (static) — Creates a self-managed memory strategy.

  A self-managed strategy gives you complete control over your memory extraction and consolidation pipelines.

  **Parameters**

  - `config` — - The configuration for the self-managed memory strategy.

  **Returns:** A SelfManagedMemoryStrategy.

- `usingSemantic(config: ManagedStrategyProps): ManagedMemoryStrategy` (static) — Creates a semantic memory strategy with custom configuration.

  Distills general facts, concepts, and underlying meanings from raw conversational data, presenting the information in a context-independent format.
  Extracted memory example: In-context learning = task-solving via examples, no training needed.

  **Parameters**

  - `config` — - The configuration for the semantic memory strategy.

  **Returns:** A ManagedMemoryStrategy.

- `usingSummarization(config: ManagedStrategyProps): ManagedMemoryStrategy` (static) — Creates a summarization memory strategy with custom configuration.

  This strategy compresses conversations into concise overviews, preserving essential context and key insights for quick recall.
  Extracted memory example: Users confused by cloud setup during onboarding.

  **Parameters**

  - `config` — - The configuration for the summarization memory strategy.

  **Returns:** A ManagedMemoryStrategy.

- `usingUserPreference(config: ManagedStrategyProps): ManagedMemoryStrategy` (static) — Creates a user preference memory strategy with custom configuration.

  Captures individual preferences, interaction patterns, and personalized settings to enhance future experiences.
  Extracted memory example: User needs clear guidance on cloud storage account connection during onboarding.

  **Parameters**

  - `config` — - The configuration for the user preference memory strategy.

  **Returns:** A ManagedMemoryStrategy.