AgentCore Memory Session Manager

{{ community_contribution_banner }}

The AgentCore Memory Session Manager leverages Amazon Bedrock AgentCore Memory to provide advanced memory capabilities with intelligent retrieval for Strands Agents. It supports both short-term memory (STM) for conversation persistence and long-term memory (LTM) with multiple strategies for learning user preferences, facts, and session summaries.

Installation

pip install 'bedrock-agentcore[strands-agents]'

Usage

Basic Setup (STM)

Short-term memory provides basic conversation persistence within a session. This is the simplest way to get started with AgentCore Memory.

Creating the Memory Resource

!!! note “One-time Setup” The memory resource creation shown below is typically done once, separately from your agent application. In production, you would create the memory resource through the AWS Console or a separate setup script, then use the memory ID in your agent application.

import os
from bedrock_agentcore.memory import MemoryClient

# This is typically done once, separately from your agent application
client = MemoryClient(region_name="us-east-1")
basic_memory = client.create_memory(
    name="BasicTestMemory",
    description="Basic memory for testing short-term functionality"
)

# Export the memory ID as an environment variable for reuse
memory_id = basic_memory.get('id')
print(f"Created memory with ID: {memory_id}")
os.environ['AGENTCORE_MEMORY_ID'] = memory_id

Using the Session Manager with Existing Memory

import uuid
import boto3
from datetime import datetime
from strands import Agent
from bedrock_agentcore.memory.integrations.strands.config import AgentCoreMemoryConfig
from bedrock_agentcore.memory.integrations.strands.session_manager import AgentCoreMemorySessionManager


MEM_ID = os.environ.get("AGENTCORE_MEMORY_ID", "your-existing-memory-id")
ACTOR_ID = "test_actor_id_%s" % datetime.now().strftime("%Y%m%d%H%M%S")
SESSION_ID = "test_session_id_%s" % datetime.now().strftime("%Y%m%d%H%M%S")

agentcore_memory_config = AgentCoreMemoryConfig(
    memory_id=MEM_ID,
    session_id=SESSION_ID,
    actor_id=ACTOR_ID
)

# Create session manager
session_manager = AgentCoreMemorySessionManager(
    agentcore_memory_config=agentcore_memory_config,
    region_name="us-east-1"
)

# Create agent with session manager
agent = Agent(
    system_prompt="You are a helpful assistant. Use all you know about the user to provide helpful responses.",
    session_manager=session_manager,
)

# Use the agent - conversations are automatically persisted
agent("I like sushi with tuna")
agent("What should I buy for lunch today?")

Long-Term Memory (LTM)

Long-term memory provides advanced capabilities with multiple strategies for learning and storing user preferences, facts, and session summaries across conversations.

Creating LTM Memory with Strategies

!!! note “One-time Setup” Similar to STM, the LTM memory resource creation is typically done once, separately from your agent application. In production, you would create the memory resource with strategies through the AWS Console or a separate setup script.

Bedrock AgentCore Memory supports three built-in memory strategies:

1. summaryMemoryStrategy: Summarizes conversation sessions
2. userPreferenceMemoryStrategy: Learns and stores user preferences
3. semanticMemoryStrategy: Extracts and stores factual information

import os
from bedrock_agentcore.memory import MemoryClient

# This is typically done once, separately from your agent application
client = MemoryClient(region_name="us-east-1")
comprehensive_memory = client.create_memory_and_wait(
    name="ComprehensiveAgentMemory",
    description="Full-featured memory with all built-in strategies",
    strategies=[
        {
            "summaryMemoryStrategy": {
                "name": "SessionSummarizer",
                "namespaces": ["/summaries/{actorId}/{sessionId}"]
            }
        },
        {
            "userPreferenceMemoryStrategy": {
                "name": "PreferenceLearner",
                "namespaces": ["/preferences/{actorId}"]
            }
        },
        {
            "semanticMemoryStrategy": {
                "name": "FactExtractor",
                "namespaces": ["/facts/{actorId}"]
            }
        }
    ]
)

# Export the LTM memory ID as an environment variable for reuse
ltm_memory_id = comprehensive_memory.get('id')
print(f"Created LTM memory with ID: {ltm_memory_id}")
os.environ['AGENTCORE_LTM_MEMORY_ID'] = ltm_memory_id

Configuring Retrieval

You can configure how the agent retrieves information from different memory namespaces:

Single Namespace Retrieval

from datetime import datetime
from bedrock_agentcore.memory.integrations.strands.config import AgentCoreMemoryConfig, RetrievalConfig
from bedrock_agentcore.memory.integrations.strands.session_manager import AgentCoreMemorySessionManager
from strands import Agent

MEM_ID = os.environ.get("AGENTCORE_LTM_MEMORY_ID", "your-existing-ltm-memory-id")
ACTOR_ID = "test_actor_id_%s" % datetime.now().strftime("%Y%m%d%H%M%S")
SESSION_ID = "test_session_id_%s" % datetime.now().strftime("%Y%m%d%H%M%S")

config = AgentCoreMemoryConfig(
    memory_id=MEM_ID,
    session_id=SESSION_ID,
    actor_id=ACTOR_ID,
    retrieval_config={
        "/preferences/{actorId}": RetrievalConfig(
            top_k=5,
            relevance_score=0.7
        )
    }
)

session_manager = AgentCoreMemorySessionManager(config, region_name='us-east-1')
ltm_agent = Agent(session_manager=session_manager)

Multiple Namespace Retrieval

config = AgentCoreMemoryConfig(
    memory_id=MEM_ID,
    session_id=SESSION_ID,
    actor_id=ACTOR_ID,
    retrieval_config={
        "/preferences/{actorId}": RetrievalConfig(
            top_k=5,
            relevance_score=0.7
        ),
        "/facts/{actorId}": RetrievalConfig(
            top_k=10,
            relevance_score=0.3
        ),
        "/summaries/{actorId}/{sessionId}": RetrievalConfig(
            top_k=5,
            relevance_score=0.5
        )
    }
)

session_manager = AgentCoreMemorySessionManager(config, region_name='us-east-1')
agent_with_multiple_namespaces = Agent(session_manager=session_manager)

Configuration Options

Memory Strategies

AgentCore Memory supports three built-in strategies:

1. summaryMemoryStrategy: Automatically summarizes conversation sessions for efficient context retrieval
2. userPreferenceMemoryStrategy: Learns and stores user preferences across sessions
3. semanticMemoryStrategy: Extracts and stores factual information from conversations

AgentCoreMemoryConfig Parameters

The AgentCoreMemoryConfig class accepts the following parameters:

Parameter	Type	Required	Description
memory_id	str	Yes	ID of the Bedrock AgentCore Memory resource
session_id	str	Yes	Unique identifier for the conversation session
actor_id	str	Yes	Unique identifier for the user/actor
retrieval_config	Dict[str, RetrievalConfig]	No	Dictionary mapping namespaces to retrieval configurations

RetrievalConfig Parameters

Configure retrieval behavior for each namespace:

Parameter	Type	Default	Description
top_k	int	10	Number of top-scoring records to return from semantic search (1-1000)
relevance_score	float	0.2	Minimum relevance threshold for filtering results (0.0-1.0)
strategy_id	Optional[str]	None	Optional parameter to filter memory strategies

Namespace Patterns

Namespaces follow specific patterns with variable substitution:

• /preferences/{actorId}: User-specific preferences across sessions
• /facts/{actorId}: User-specific facts across sessions
• /summaries/{actorId}/{sessionId}: Session-specific summaries

The {actorId} and {sessionId} placeholders are automatically replaced with the values from your configuration.

See the following docs for more on namespaces: Memory scoping with namespaces

Important Notes

!!! note “Session Limitations” Currently, only one agent per session is supported when using AgentCoreMemorySessionManager. Creating multiple agents with the same session will show a warning.

Resources

• GitHub: bedrock-agentcore-sdk-python
• Documentation: Strands Integration Examples
• Issues: Report bugs and feature requests in the bedrock-agentcore-sdk-python repository