AI Engineering with LangChain and LangGraph

Artificial intelligence is transforming the way modern software is built. Large Language Models (LLMs) have made it possible to create intelligent applications that can reason, retrieve knowledge, interact with external tools, automate complex workflows, and collaborate as autonomous agents. But turning these capabilities into reliable, scalable, production-ready systems requires more than prompting a model it requires AI engineering.
AI Engineering with LangChain and LangGraph is a comprehensive, hands-on guide that teaches you how to design, build, deploy, and maintain modern AI applications using two of the most powerful frameworks in the LLM ecosystem: LangChain and LangGraph.
Whether you're a Python developer, software engineer, machine learning practitioner, or AI enthusiast, this book provides a structured learning path from the fundamentals of LLM application development to advanced agent architectures and enterprise deployment. Through practical examples, real-world projects, and industry best practices, you'll gain the skills needed to build intelligent systems that are robust, scalable, and ready for production.
Inside this book, you'll learn how to:

• Understand the foundations of AI engineering and modern LLM application architecture.
• Build intelligent applications with LangChain's modular components, prompts, models, chains, and runnables.
• Create Retrieval-Augmented Generation (RAG) systems that combine LLMs with external knowledge for more accurate and context-aware responses.
• Design powerful AI agents capable of reasoning, planning, and using external tools and APIs.
• Develop stateful workflows and orchestrate complex agent behavior using LangGraph.
• Build collaborative multi-agent systems for research, automation, and decision-making.
• Implement memory, conversation management, and long-term knowledge for personalized AI experiences.
• Evaluate, monitor, debug, and optimize AI applications using modern observability and testing techniques.
• Deploy secure, scalable AI services for real-world production environments.
• Apply proven design patterns, architecture principles, and best practices used in professional AI engineering.