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The article is a technical primer on how AI agents store and retrieve information outside the model's context window, covering memory types, write and retrieval strategies, and how frameworks handle changing facts. For builders, the useful part is the design tradeoffs: reliability of retrieval, staleness of stored facts, and how memory is maintained over time. It treats memory as an engineering layer around the model, not a built-in capability.

Sources coveringMachine Learning Mastery, Latent Space, arXiv, NVIDIA

The AI Agent Memory Stack: Building Blocks, Patterns, and Frameworks

Machine Learning MasteryNovember 2, 20267 min read

In this article, you will learn how agent memory works, what types of memory exist, and how different frameworks approach storage, retrieval, and memory management...

In this article, you will learn how agent memory works, what types of memory exist, and how different frameworks approach storage, retrieval, and memory management at scale. The core memory types used in AI agents and what each one is designed to store. Practical design strategies for writing, retrieving, and maintaining agent memory reliably. How leading memory frameworks differ in their storage models, retrieval approaches, and handling of fact changes over time. A language model does not retain information between calls. Each time it runs, it works with the text provided in that request and does not automatically remember what happened before. This works well for simple, one-off questions, but AI agents need more when they have to track previous actions, remember user preferences, or continue a task across multiple steps. Memory solves this…

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