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Amazon Bedrock AgentCore Memory
Overview
Memory is a critical component of intelligence. While Large Language Models (LLMs) have impressive capabilities, they lack persistent memory across conversations. Amazon Bedrock AgentCore Memory addresses this limitation by providing a managed service that enables AI agents to maintain context over time, remember important facts, and deliver consistent, personalized experiences.
Key Capabilities
AgentCore Memory provides:
- Core Infrastructure: Serverless setup with built-in encryption and observability
- Event Storage: Raw event storage (conversation history/checkpointing) with branching support
- Strategy Management: Configurable extraction strategies (SEMANTIC, SUMMARY, USER_PREFERENCES, CUSTOM)
- Memory Records Extraction: Automatic extraction of facts, preferences, and summaries based on configured strategies
- Semantic Search: Vector-based retrieval of relevant memories using natural language queries
How AgentCore Memory Works
AgentCore Memory operates on two levels:
Short-Term Memory
Immediate conversation context and session-based information that provides continuity within a single interaction or closely related sessions.
Long-Term Memory
Persistent information extracted and stored across multiple conversations, including facts, preferences, and summaries that enable personalized experiences over time.
Memory Architecture
- Conversation Storage: Complete conversations are saved in raw form for immediate access
- Strategy Processing: Configured strategies automatically analyze conversations in the background
- Information Extraction: Important data is extracted based on strategy types (typically takes ~1 minute)
- Organized Storage: Extracted information is stored in structured namespaces for efficient retrieval
- Semantic Retrieval: Natural language queries can retrieve relevant memories using vector similarity
Memory Strategy Types
AgentCore Memory supports four strategy types:
- Semantic Memory: Stores factual information using vector embeddings for similarity search
- Summary Memory: Creates and maintains conversation summaries for context preservation
- User Preference Memory: Tracks user-specific preferences and settings
- Custom Memory: Allows customization of extraction and consolidation logic
Getting Started
Explore the memory capabilities through our organized tutorials:
- Short-Term Memory: Learn about session-based memory and immediate context management
- Long-Term Memory: Understand persistent memory strategies and cross-conversation continuity
Sample Notebooks Overview
| Memory Type | Framework | Use Case | Notebook |
|---|---|---|---|
| Short-Term | Strands Agent | Personal Agent | personal-agent.ipynb |
| Short-Term | LangGraph | Fitness Coach | personal-fitness-coach.ipynb |
| Short-Term | Strands Agent | Travel Planning | travel-planning-agent.ipynb |
| Long-Term | Strands Hooks | Customer Support | customer-support.ipynb |
| Long-Term | Strands Hooks | Math Assistant | math-assistant.ipynb |
| Long-Term | Strands Tool | Culinary Assistant | culinary-assistant.ipynb |
| Long-Term | Strands Multi-Agent | Travel Booking | travel-booking-assistant.ipynb |
Prerequisites
- Python 3.10 or higher
- AWS account with Amazon Bedrock access
- Jupyter Notebook environment
- Required Python packages (see individual sample requirements.txt files)