Beyond foundational LangChain documentation and LangGraph interfaces, learn enterprise patterns, key design pattern to build AI agents, battle-tested strategies, and proven architectures used in production. Ideal for Python developers building generative AI at scale. Key Features Get to grips with building AI agents with LangGraph Learn about enterprise-grade testing, observability, and LLM evaluation frameworks Cover RAG implementation with cutting-edge retrieval strategies and new reliability techniques Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis revised edition builds a foundation in agentic AI, LLM fundamentals, LangChain, & LangGraph for developers at all levels. Fully updated to cover the latest in LangChain and production LLM applications, it captures the evolving ecosystem and enterprise deployment landscape. New coverage includes multi-agent architectures, LangGraph interfaces, robust RAG techniques with hybrid search, re-rankers, and advanced fact-checking mechanisms, plus enterprise-grade testing frameworks. It provides coverage of key design patterns behind agentic systems, practical implementations of multi-agent systems for complex tasks. Explore cutting-edge agent strategies such as Tree of Thought, multi-agent orchestration, detailed error handling, and structured output generation. Coverage dedicated to evaluation, testing, and production deployment reflect the maturing LLM application landscape. Design secure, compliant AI systems with built-in production safeguards, responsible development practices, and a perspective on future research directions.The enhanced RAG coverage features techniques like hybrid search, re-rankers, and fact-checking mechanisms. Whether upgrading existing LLM applications or building new enterprise-scale solutions, by the end of the book, you will have updated knowledge on the practical patterns needed for production successWhat you will learn Design and implement refined multi-agent systems using LangGraph Enterprise-grade testing and evaluation frameworks for LLM applications Deploy production-ready observability and monitoring solutions Build RAG systems with hybrid search and re-ranking capabilities Implement agents for software development and data analysis Work with latest LLMs and providers Google Gemini, Anthropic and Mistral, DeepSeek, and OpenAI o3-mini Optimize cost and performance across different deployment types Design secure, compliant AI systems with current best practices Who this book is forThe book is for developers, researchers, and anyone interested in learning more about LangChain and LangGraph, wanting to build production-ready LLM applications. This book emphasizes on enterprise deployment patterns, making it especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this version also caters to engineering teams and decision-makers implementing enterprise-wide LLM strategies. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.

Table of Contents:
Table of Contents Introduction to Large Language Models Development Environment and Setup Building Workflows with LangChain Retrieval Augmented Generation Building Intelligent Agents Advanced Applications and Multi-Agent Systems Software Development and Data Analysis Agents Evaluation and Testing Observability and Production Deployment Future Directions and Advanced Topics