AI Agent Memory Stack: Core Components, Design Patterns, and Mainstream Framework Comparisons
The AI Agent Memory Stack: Building Blocks, Patterns, and Frameworks
The article systematically reviews the design and architecture of AI agent memory, categorizing it into four core types: working, episodic, semantic, and procedural memory. Addressing production engineering, it highlights common failure modes such as indiscriminately vectorizing all raw data and allowing unmaintained, unbounded memory growth. It also compares mainstream frameworks—including Letta, Mem0, Graphiti, and Cognee—across storage paradigms such as text chunks, entity vectors, and bi-temporal knowledge graphs.
The article outlines the four fundamental types of agent memory alongside design anti-patterns, offering a systematic comparison of architectural trade-offs in fact updates and relational modeling across mainstream memory frameworks.
Source:Hacker News · AI · machinelearningmastery.com