Configure built-in strategies

AgentCore Memory provides three pre-configured, built-in memory strategies for common use cases.

User preferences

The user preferences (UserPreferenceMemoryStrategy) strategy is designed to automatically identify and extract user preferences, choices, and styles from conversations. This lets your agent build a persistent profile of each user, leading to more personalized and relevant interactions.

• Example use case: An e-commerce agent remembers a user's favorite brands and preferred size, letting it offer tailored product recommendations in future sessions.

Configuration example:

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="ECommerceAgentMemory",
    strategies=[
        UserPreferenceStrategy(
            name="UserPreferenceExtractor",
            namespaces=["/users/{actorId}/preferences"]
        )
    ]
)
            

Semantic

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

• Example use case: A customer support agent remembers that order #ABC-123 is related to a specific support ticket, so the user doesn't have to provide the order number again when following up.

Configuration example:

from bedrock_agentcore_starter_toolkit.operations.memory.manager import MemoryManager
from bedrock_agentcore_starter_toolkit.operations.memory.models.strategies import SemanticStrategy

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

# Create memory resource with semantic strategy
memory = memory_manager.get_or_create_memory(
    name="SupportAgentFactMemory",
    strategies=[
        SemanticStrategy(
            name="FactExtractor",
            namespaces=["/support_cases/{sessionId}/facts"]
        )
    ]
)
            

Session summaries

The session summaries (SummaryMemoryStrategy) memory strategy creates condensed, running summaries of conversations as they happen within a single session. This captures the key topics and decisions, letting an agent quickly recall the context of a long conversation without needing to re-process the entire history.

• Example use case: After a 30-minute troubleshooting session, the agent can access a summary like, "User reported issue with software v2.1, attempted a restart, and was provided a link to the knowledge base article."

Configuration example:

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")

# Create memory resource with summary strategy
memory = memory_manager.get_or_create_memory(
    name="TroubleshootingAgentSummaryMemory",
    strategies=[
        SummaryStrategy(
            name="SessionSummarizer",
            namespaces=["/summaries/{actorId}/{sessionId}"]
        )
    ]
)