Enable long-term memory - Amazon Bedrock AgentCore
Creating a new memory with long-term strategiesAdding long-term strategies to an existing AgentCore Memory

Enable long-term memory

You can enable long-term memory in two ways: by adding strategies when you first create an AgentCore Memory, or by updating an existing resource to include them.

Creating a new memory with long-term strategies

The most direct method is to include strategies when you create a new AgentCore Memory. After calling create_memory, you must wait for the AgentCore Memory status to become ACTIVE before you can use it.

AgentCore CLI
agentcore add memory --name PersonalizedShoppingAgentMemory --strategies USER_PREFERENCE
agentcore deploy
Interactive

Run agentcore to open the TUI, then select add and choose Memory:

  1. Select the User preference strategy:

    Memory wizard: select USER_PREFERENCE strategy

  2. Review the configuration and press Enter to confirm:

    Memory wizard: confirm memory configuration

Then run agentcore deploy to provision the memory in AWS.

Adding long-term strategies to an existing AgentCore Memory

To add long-term capabilities to an existing AgentCore Memory, you use the update_memory operation. You can add, modify or delete strategies for an existing memory.

Note

The AgentCore CLI supports creating and managing memory resources. To update memory strategies on an existing memory, use the AWS SDK.

Example Add a Session Summary strategy to an existing AgentCore Memory
import boto3

# Initialize the Boto3 client for control plane operations
control_client = boto3.client('bedrock-agentcore-control', region_name='us-west-2')

# Assume 'memory_id' is the ID of an existing AgentCore Memory that has no strategies attached to it
memory_id = "your-existing-memory-id"

# Update the memory to add a summary strategy
response = control_client.update_memory(
    memoryId=memory_id,
    memoryStrategies=[
        {
            'summaryMemoryStrategy': {
                'name': 'SessionSummarizer',
                'description': 'Summarizes conversation sessions for context',
                'namespaceTemplates': ['/summaries/{actorId}/{sessionId}/']
            }
        }
    ]
)

print(f"Successfully submitted update for memory ID: {memory_id}")

# Validate strategy was added to the memory
memory_response = control_client.get_memory(memoryId=memory_id)
strategies = memory_response.get('memory', {}).get('strategies', [])
print(f"Memory strategies for memoryID: {memory_id} are: {strategies}")
Note

Long-term memory records will only be extracted from conversational events that are stored after a new strategy becomes ACTIVE. Conversations stored before a strategy is added will not be processed for long-term memory.