AI Agent Memory Engineering: Build AI Agents That Remember Across Sessions, Retrieve Accurate Facts, and Learn from Past Experience
English | October 10, 2026 | ASIN: B0HMLP2MJK | 218 pages | Epub | 1.03 MB
AI Agent Memory Engineering: Build AI Agents That Remember Across Sessions, Retrieve Accurate Facts, and Learn from Past ExperienceYour AI agent can solve complex problems. But can it remember what you told it yesterday?You build an AI agent that understands instructions, retrieves information, and completes tasks. Everything works-until the next conversation begins. Suddenly, the agent forgets important preferences, loses project context, retrieves outdated facts, or repeats mistakes it should have learned from.Sound familiar?The problem isn't always the language model. It's the memory system behind it.AI Agent Memory Engineering shows you how to build reliable long-term memory for AI agents using Python, persistent databases, intelligent retrieval, and practical evaluation techniques.Instead of relying on theory or disconnected examples, you'll progressively build MemoryLab, a working AI memory application that grows from basic storage into a memory-enabled agent capable of retrieving relevant knowledge, managing changing facts, and reusing lessons from previous tasks.What You'll Learn to BuildAI agents that remember across sessions: Preserve user preferences, project decisions, and important facts with SQLite and LangGraph.Accurate long-term memory retrieval: Use vector databases, PostgreSQL, pgvector, and hybrid search to find relevant information even when questions use different wording.Automatic memory management: Apply Mem0 to extract useful facts, reduce manual memory handling, and manage updates.Relationship-aware memory: Use Graphiti and Zep to connect entities, track changing information, and retrieve historical facts.Agents that learn from past experience: Apply LangMem to preserve task outcomes, extract reusable lessons, and improve future workflows.Faster, more efficient memory systems: Reduce unnecessary context, consolidate repeated information, and control retrieval overhead.Safer persistent memory: Implement user isolation, memory correction, deletion, retention controls, and defenses against untrusted information.Measurable reliability: Benchmark memory accuracy, retrieval latency, token consumption, and operating costs before deployment.You'll also bring these capabilities together in a FastAPI application, with executable Python examples, practical tests, and supporting project materials included in the book and its appendices.Whether you're a Python developer building your first memory-enabled assistant or an AI engineer improving production agent workflows, you'll gain practical techniques for creating systems that retain useful knowledge without accumulating unnecessary complexity.Stop rebuilding context every time your AI agent starts a new conversation.Get your copy of AI Agent Memory Engineering today and start building AI agents that remember what matters, retrieve what they need, and learn from experience.
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