Memory Architecture Patterns
Learn common architectural patterns for designing scalable, efficient, and reliable agent memory systems.
Memory architecture is like designing the perfect bedroom for your brain! You need different places for different things: a desk for homework (working memory), a bookshelf for important books (long-term memory), and a bulletin board for reminders (short-term memory). AI agents need the same thing - different "rooms" in their brain-computer to store different types of memories in the best way possible!
Hierarchical Memory
Organizes memory in layers from fast, small caches to slower, larger storage. Similar to CPU cache hierarchies.
Federated Memory
Distributes memory across multiple specialized systems, each optimized for different data types or access patterns.
Hybrid Memory
Combines multiple memory approaches (RAG + long context, vector + graph databases) for optimal performance.
Storage Layer
Where memories are physically stored - databases, files, or cloud storage.
Retrieval Layer
How memories are found and retrieved when needed.
Processing Layer
How memories are encoded, compressed, and organized.
Management Layer
Policies for memory lifecycle, privacy, and maintenance.
Horizontal Partitioning
Split memory across multiple databases by user, time period, or data type. Each partition can be optimized independently.
Memory Tiering
Automatically move memories between storage tiers based on access patterns and age. Hot data stays fast, cold data moves to cheaper storage.
Distributed Caching
Use distributed cache layers to reduce latency and database load. Implement cache invalidation strategies for consistency.
- Design for the access patterns you expect
- Plan for data growth from day one
- Build in observability and monitoring
- Consider privacy and compliance early
- Over-engineering for scale you don't need
- Ignoring data consistency requirements
- Not planning for memory cleanup
- Mixing different data types inappropriately