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Long-Term Memory Architectures for LLMs: Designing Scalable, Persistent Agent Workflows
Long-Term Memory Architectures for Designing Scalable, Persistent Agent Workflows
Ever been frustrated by an AI that forgets everything you’ve told it just moments ago? What if your agents could store, recall, and learn from every interaction—no resets, no context loss?
Summary
Long-Term Memory Architectures for LLMs shows you how to design scalable, persistent memory systems that turn one-off bots into lifelong learners. You’ll move beyond prompt hacks and short-term state to build robust workflows where your AI agents remember user preferences, project histories, compliance rules, and more.
What Sets This Book Apart?
Rather than abstract theory, you get hands-on code, patterns, and best practices across fifteen focused
Fundamentals of Agent Memory – Learn the cognitive roots and taxonomy of memory types.
Representing Memories – Master embeddings, metadata strategies, and hybrid graphs.
Storage Backends & Indexing – Compare FAISS, Pinecone, Neo4j, and relational fallbacks.
Retrieval Strategies – Build relevance-driven fetching, memory routers, and caching layers.
Core Architectures – Implement plugin patterns, retrieval-augmented pipelines, and streaming ingestion.
Frameworks & SDKs in Practice – Use LangChain, LangGraph, MCP, Haystack, LlamaIndex, and Chroma.
Building Your First Memory Router – Wire retrieval into FastAPI for real-time agents.
Memory-Augmented Agent Workflows – Combine tools, persistent personas, and stateful functions.
Multi-Agent Memory Sharing – Enforce namespaces, access controls, and conflict resolution.
Scaling & Performance – Load testing, geo-distributed stores, and cost trade-offs.
Evaluation & Benchmarking – Define Recall@K, LOCOMO metrics, and episodic reasoning tests.
Privacy, Security & Ethics – GDPR compliance, opt-in controls, bias mitigation, and hallucination checks.
Advanced Topics – Graph networks, adaptive pruning, meta-memory, and edge architectures.
Hands-On Case Studies – From support chatbots to research assistants and e-commerce collaborations.
Future Directions – Trends in lifelong learning, knowledge marketplaces, self-healing systems, and regulatory horizons.
Each chapter delivers clear examples and deployable code, empowering you to build agents that grow smarter every day.
Ready to give your AI agents a true memory? Grab your copy of Long-Term Memory Architectures for LLMs and start designing persistent, scalable workflows that learn from every interaction.
Ever been frustrated by an AI that forgets everything you’ve told it just moments ago? What if your agents could store, recall, and learn from every interaction—no resets, no context loss?
Summary
Long-Term Memory Architectures for LLMs shows you how to design scalable, persistent memory systems that turn one-off bots into lifelong learners. You’ll move beyond prompt hacks and short-term state to build robust workflows where your AI agents remember user preferences, project histories, compliance rules, and more.
What Sets This Book Apart?
Rather than abstract theory, you get hands-on code, patterns, and best practices across fifteen focused
Fundamentals of Agent Memory – Learn the cognitive roots and taxonomy of memory types.
Representing Memories – Master embeddings, metadata strategies, and hybrid graphs.
Storage Backends & Indexing – Compare FAISS, Pinecone, Neo4j, and relational fallbacks.
Retrieval Strategies – Build relevance-driven fetching, memory routers, and caching layers.
Core Architectures – Implement plugin patterns, retrieval-augmented pipelines, and streaming ingestion.
Frameworks & SDKs in Practice – Use LangChain, LangGraph, MCP, Haystack, LlamaIndex, and Chroma.
Building Your First Memory Router – Wire retrieval into FastAPI for real-time agents.
Memory-Augmented Agent Workflows – Combine tools, persistent personas, and stateful functions.
Multi-Agent Memory Sharing – Enforce namespaces, access controls, and conflict resolution.
Scaling & Performance – Load testing, geo-distributed stores, and cost trade-offs.
Evaluation & Benchmarking – Define Recall@K, LOCOMO metrics, and episodic reasoning tests.
Privacy, Security & Ethics – GDPR compliance, opt-in controls, bias mitigation, and hallucination checks.
Advanced Topics – Graph networks, adaptive pruning, meta-memory, and edge architectures.
Hands-On Case Studies – From support chatbots to research assistants and e-commerce collaborations.
Future Directions – Trends in lifelong learning, knowledge marketplaces, self-healing systems, and regulatory horizons.
Each chapter delivers clear examples and deployable code, empowering you to build agents that grow smarter every day.
Ready to give your AI agents a true memory? Grab your copy of Long-Term Memory Architectures for LLMs and start designing persistent, scalable workflows that learn from every interaction.
325 pages, Kindle Edition
Published May 20, 2025
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