Save and retrieve insights
Once you have configured an AgentCore Memory with at least one long-term memory strategy and the strategy is ACTIVE, the service will automatically begin processing conversational data to extract and store insights. This process involves two distinct steps: saving the raw conversation and then retrieving the structured insights after they have been processed.
Step 1: Save conversational events to trigger extraction
The entire long-term memory process is triggered when you save conversational data
to short-term memory using the create_event operation. Each time you
record an event, you are providing new raw material for your active memory
strategies to analyze.
Important
Only events that are created after a memory
strategy's status becomes ACTIVE will be processed for long-term
memory extraction. Any conversations stored before the strategy was added and
activated will not be included.
The following example shows how to save a multi-turn conversation to a memory resource.
Example Save a conversation as a series of events
#'memory_id' is the ID of your memory resource with an active summary strategy. from bedrock_agentcore.memory.session import MemorySessionManager from bedrock_agentcore.memory.constants import ConversationalMessage, MessageRole import time actor_id = "User84" session_id = "OrderSupportSession1" # Create session manager session_manager = MemorySessionManager( memory_id=memory_id, region_name="us-west-2" ) # Create a session session = session_manager.create_memory_session( actor_id=actor_id, session_id=session_id ) print("Capturing conversational events...") # Add all conversation turns session.add_turns( messages=[ ConversationalMessage("Hi, I'm having trouble with my order #12345", MessageRole.USER), ConversationalMessage("I am sorry to hear that. Let me look up your order.", MessageRole.ASSISTANT), ConversationalMessage("lookup_order(order_id='12345')", MessageRole.TOOL), ConversationalMessage("I see your order was shipped 3 days ago. What specific issue are you experiencing?", MessageRole.ASSISTANT), ConversationalMessage("The package arrived damaged", MessageRole.USER), ] ) print("Conversation turns added successfully!")
Step 2: Retrieve extracted insights
The extraction and consolidation of long-term memories is an asynchronous process that runs in the background. It may take a minute or more for insights from a new conversation to become available for retrieval. Your application logic should account for this delay.
To retrieve the structured insights, you use the
retrieve_memory_records operation. This operation performs a
powerful semantic search against the long-term memory store. You must provide the
correct namespace that you defined in your strategy and a
searchQuery that describes the information you are looking
for.
The following example demonstrates how to wait for processing and then retrieve a summary of the conversation saved in the previous step.
Example Wait and retrieve a session summary
# 'session' is an existing session object that you created when adding the coversation turns # session should be created on a memory resource with an active summary strategy. # --- Example 1: Retrieve the user’s shipping preference --- memories = session.search_long_term_memories( namespace_prefix=f"/summaries/{actor_id}/{session_id}", query="What problem did the user report with their order?", top_k=5 ) print(f"Found {len(memories)} memories:") for memory_record in memories: print(f"Retrieved Issue Detail: {memory_record}") print("--------------------------------------------------------------------") # Example Output: # Retrieved Issue Detail: The user reported that their package for order #12345 arrived damaged.
