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Agent Memory Made Simple: The Complete Visual Guide to Memory for AI Agents
From the creator of RAG Techniques and GenAI Agents - open-source AI curricula with more than 80,000 GitHub stars.Your agent is brilliant for one conversation, then forgets you exist.
Bigger context windows did not fix this, and they never will. Context is what an agent is looking at right now. Memory is what it can go and get. Agent Memory Made Simple is the complete map of the second one - 28 chapters, 184 custom illustrations, and not a single line of code.
Every technique is taught the same a familiar analogy makes the idea click, a custom diagram shows exactly how it works, and a short language-agnostic algorithm locks it in. By the end of a chapter you can explain the technique to a colleague at a whiteboard.
Inside this book you will master 30 agent memory techniques,
Short-term memory - conversation buffers, sliding windows, summary buffers, and budgeting in the unit that actually bills youLong-term stores - vector memory, entity records, knowledge graphs, episodic memory, semantic facts, and procedural skill librariesMemory architectures - working memory and salience, hierarchical tiers, consolidation, compaction, self-reflection, and routing between storesTime and forgetting - recency weighting, validity intervals, decay, reinforcement, and deliberate deletion as a featureRecall and coordination - hybrid retrieval and re-ranking, cross-session identity, shared memory across agents, and memory exposed as toolsSystems and proof - the MemGPT operating-system model, build versus buy, evaluation, LoCoMo benchmarks, and production patternsWho this book is AI engineers, agent builders, ML practitioners, and technical product managers who need agents that survive week three. If you have shipped a demo that fell apart the moment real users came back a second time, this book explains why and hands you the toolkit.
Why trust this book? Its companion open-source repository holds a runnable notebook for all 30 techniques, and its author maintains some of the most widely used AI engineering curricula on GitHub, followed by hundreds of thousands of developers. This is the second volume in the Super AI Engineering Series, after the Amazon bestseller RAG Made Simple. Free with Kindle Unlimited.
Bigger context windows did not fix this, and they never will. Context is what an agent is looking at right now. Memory is what it can go and get. Agent Memory Made Simple is the complete map of the second one - 28 chapters, 184 custom illustrations, and not a single line of code.
Every technique is taught the same a familiar analogy makes the idea click, a custom diagram shows exactly how it works, and a short language-agnostic algorithm locks it in. By the end of a chapter you can explain the technique to a colleague at a whiteboard.
Inside this book you will master 30 agent memory techniques,
Short-term memory - conversation buffers, sliding windows, summary buffers, and budgeting in the unit that actually bills youLong-term stores - vector memory, entity records, knowledge graphs, episodic memory, semantic facts, and procedural skill librariesMemory architectures - working memory and salience, hierarchical tiers, consolidation, compaction, self-reflection, and routing between storesTime and forgetting - recency weighting, validity intervals, decay, reinforcement, and deliberate deletion as a featureRecall and coordination - hybrid retrieval and re-ranking, cross-session identity, shared memory across agents, and memory exposed as toolsSystems and proof - the MemGPT operating-system model, build versus buy, evaluation, LoCoMo benchmarks, and production patternsWho this book is AI engineers, agent builders, ML practitioners, and technical product managers who need agents that survive week three. If you have shipped a demo that fell apart the moment real users came back a second time, this book explains why and hands you the toolkit.
Why trust this book? Its companion open-source repository holds a runnable notebook for all 30 techniques, and its author maintains some of the most widely used AI engineering curricula on GitHub, followed by hundreds of thousands of developers. This is the second volume in the Super AI Engineering Series, after the Amazon bestseller RAG Made Simple. Free with Kindle Unlimited.
561 pages, Kindle Edition
Published August 3, 2026
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