You Can Learn AI Agent Memory Layers | Graph RAG, Vector DB, SQLite, Hermes, Waku

AI agent memory systems require a structured approach to ensure long-term context retention beyond standard LLM calls. Effective architectures rely on three pillars: procedural memory for skills, semantic memory for durable facts, and episodic memory for dated events. Storage strategies range from simple text files and relational databases to complex temporal graphs. Retrieval mechanisms, including keyword searching, vector-based RAG, and graph traversal, determine how agents access and connect information. Maintenance is equally critical, involving processes like superseding outdated data, attribute tracking, and periodic reflection to consolidate or prune information. Practical implementations like Waku Agent and ZEP demonstrate that while graph-based systems offer deep relational insights, they often incur significant latency compared to simpler, text-based or relational database approaches. Choosing the right memory layer depends on balancing the complexity of the agent's tasks with the need for speed and data accuracy.