Memory Architecture for Agents: Building Long-Term Memory for LLM Agents to Learn, Reason, and Evolve Over Time
In the age of intelligent agents, language models are no longer just tools for generating text—they're evolving into autonomous systems capable of learning from experience, making decisions, and adapting over time. But without memory, intelligence remains shallow and short-lived.
Memory Architecture for Agents is the definitive guide to equipping large language model (LLM) agents with persistent, structured, and actionable memory systems. This book presents a complete framework for building memory-enabled AI agents that can learn continuously, remember past interactions, reason over historical context, and evolve autonomously.
You’ll explore key concepts in memory design, including episodic and semantic storage, memory pruning, reinforcement through experience, and graph-based causal linking. You’ll also learn how to build and scale memory modules using modern frameworks like LangChain, CrewAI, and AutoGen. With detailed architectural blueprints, practical coding examples, and real-world case studies, this book bridges the gap between theory and implementation.
Inside, you’ll learn how to:
Whether you're an AI engineer, product architect, researcher, or developer, this book equips you with the tools, techniques, and insights needed to create memory-augmented agents that go beyond prompt-response behavior—and move toward true lifelong learning.
Build agents that don’t just respond—but remember, learn, and improve. Start mastering long-term memory for intelligent systems today.
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Paperback. Etat : new. Paperback. Memory Architecture for Agents: Building Long-Term Memory for LLM Agents to Learn, Reason, and Evolve Over TimeIn the age of intelligent agents, language models are no longer just tools for generating text-they're evolving into autonomous systems capable of learning from experience, making decisions, and adapting over time. But without memory, intelligence remains shallow and short-lived.Memory Architecture for Agents is the definitive guide to equipping large language model (LLM) agents with persistent, structured, and actionable memory systems. This book presents a complete framework for building memory-enabled AI agents that can learn continuously, remember past interactions, reason over historical context, and evolve autonomously.You'll explore key concepts in memory design, including episodic and semantic storage, memory pruning, reinforcement through experience, and graph-based causal linking. You'll also learn how to build and scale memory modules using modern frameworks like LangChain, CrewAI, and AutoGen. With detailed architectural blueprints, practical coding examples, and real-world case studies, this book bridges the gap between theory and implementation.Inside, you'll learn how to: Architect modular memory systems that persist and retrieve relevant knowledge across sessionsDesign and implement CRUD-based APIs for memory interaction and access controlIntegrate memory seamlessly into autonomous planning, reasoning, and coordinationBuild memory-aware agents for customer support, research automation, and multi-agent collaborationManage ethical, privacy, and safety issues including data ownership, secure access, and responsible forgettingApply proven patterns and reference architectures to scale memory across production environmentsWhether you're an AI engineer, product architect, researcher, or developer, this book equips you with the tools, techniques, and insights needed to create memory-augmented agents that go beyond prompt-response behavior-and move toward true lifelong learning.Build agents that don't just respond-but remember, learn, and improve. Start mastering long-term memory for intelligent systems today. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798285013518
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