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 get_or_create_memory, you must wait for the AgentCore Memory status to become ACTIVE before you can use it.

Example Create a new AgentCore Memory
Starter toolkit CLI
agentcore memory create PersonalizedShoppingAgentMemory \
  --region us-west-2 \
  --strategies '[{"userPreferenceMemoryStrategy": {"name": "UserShoppingPreferences", "namespaces": ["/users/{actorId}/preferences"]}}]' \
  --wait
Starter toolkit
from bedrock_agentcore_starter_toolkit.operations.memory.manager import MemoryManager
from bedrock_agentcore_starter_toolkit.operations.memory.models.strategies import UserPreferenceStrategy

# Create memory manager
memory_manager = MemoryManager(region_name="us-west-2")

# Create memory resource with user preference strategy
memory = memory_manager.get_or_create_memory(
    name="PersonalizedShoppingAgentMemory",
    strategies=[
        UserPreferenceStrategy(
            name="UserShoppingPreferences",
            namespaces=["/users/{actorId}/preferences"]
        )
    ]
)

memory_id = memory.get('id')
print(f"Memory resource is now ACTIVE with ID: {memory_id}")

Adding long-term strategies to an existing AgentCore Memory

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

Note

The starter toolkit CLI currently supports creating and managing memory resources. To update memory strategies on an existing memory, use the starter toolkit Python API or AWS SDK.

Example Add a Session Summary strategy to an existing AgentCore Memory
from bedrock_agentcore_starter_toolkit.operations.memory.manager import MemoryManager
from bedrock_agentcore_starter_toolkit.operations.memory.models.strategies import SummaryStrategy

# Create memory manager
memory_manager = MemoryManager(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"

summaryStrategy = SummaryStrategy(
    name="SessionSummarizer",
    description="Summarizes conversation sessions for context",
    namespaces=["/summaries/{actorId}/{sessionId}"]
) 
                  
memory = memory_manager.update_memory_strategies(
    memory_id=memory_id,
    add_strategies=[summaryStrategy]
)

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

# Validate startegy was added to the memory
memory_strategies = memory_manager.get_memory_strategies(memoryId=memory_id)
print(f"Memory strategies for memoryID: {memory_id} are: {memory_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.