AWS SDK - Amazon Bedrock AgentCore

AWS SDK

Use the AWS SDK to directly interact with AgentCore Memory fine-grained control over memory operations. The following examples show how to access the AWS SDK with the SDK for Python (Boto3).

Install dependencies

pip install boto3

Add short-term memory

import boto3 from datetime import datetime # Initialize boto3 clients control_client = boto3.client('bedrock-agentcore-control', region_name='us-east-1') data_client = boto3.client('bedrock-agentcore', region_name='us-east-1') # Create short-term memory memory_response = control_client.create_memory( name="BasicMemory", description="Basic memory for short-term event storage", eventExpiryDuration=90 ) memory_id = memory_response['memory']['id'] actor_id = f"actor_{datetime.now().strftime('%Y%m%d%H%M%S')}" session_id = f"session_{datetime.now().strftime('%Y%m%d%H%M%S')}" # Create event with multiple conversation turns event = data_client.create_event( memoryId=memory_id, actorId=actor_id, sessionId=session_id, eventTimestamp=datetime.now(), payload=[ { 'conversational': { 'content': {'text': 'I like sushi with tuna'}, 'role': 'USER' } }, { 'conversational': { 'content': {'text': 'That sounds delicious! Tuna sushi is a great choice.'}, 'role': 'ASSISTANT' } }, { 'conversational': { 'content': {'text': 'I also like pizza'}, 'role': 'USER' } }, { 'conversational': { 'content': {'text': 'Pizza is another excellent choice! You have great taste in food.'}, 'role': 'ASSISTANT' } } ] )

Add long-term memory with strategies

import boto3 import time from datetime import datetime # Initialize boto3 clients control_client = boto3.client('bedrock-agentcore-control', region_name='us-east-1') data_client = boto3.client('bedrock-agentcore', region_name='us-east-1') # Create long-term memory memory_response = control_client.create_memory( name=f"ComprehensiveMemory", description="Memory with strategies for long-term memory extraction", eventExpiryDuration=90, memoryStrategies=[ { 'summaryMemoryStrategy': { 'name': 'SessionSummarizer', 'namespaces': ['/summaries/{actorId}/{sessionId}'] } }, { 'userPreferenceMemoryStrategy': { 'name': 'PreferenceLearner', 'namespaces': ['/preferences/{actorId}'] } }, { 'semanticMemoryStrategy': { 'name': 'FactExtractor', 'namespaces': ['/facts/{actorId}'] } } ] ) memory_id = memory_response['memory']['id'] actor_id = f"actor_{datetime.now().strftime('%Y%m%d%H%M%S')}" session_id = f"session_{datetime.now().strftime('%Y%m%d%H%M%S')}" ########## Wait for long-term memory to become active ########## # Create single event with all conversation turns event = data_client.create_event( memoryId=memory_id, actorId=actor_id, sessionId=session_id, eventTimestamp=datetime.now(), payload=[ { 'conversational': { 'content': {'text': 'I like sushi with tuna'}, 'role': 'USER' } }, { 'conversational': { 'content': {'text': 'That sounds delicious! Tuna sushi is a great choice.'}, 'role': 'ASSISTANT' } }, { 'conversational': { 'content': {'text': 'I also like pizza'}, 'role': 'USER' } }, { 'conversational': { 'content': {'text': 'Pizza is another excellent choice! You have great taste in food.'}, 'role': 'ASSISTANT' } } ] )

Full AWS SDK Amazon Bedrock AgentCore AgentCore Memory API reference can be found at: