AI Agent Framework Bundle

This book takes you from foundational concepts through advanced multi-agent architectures, teaching you to design, build, deploy, and maintain sophisticated AI agents using LlamaIndex in real-world production environments. Through three end-to-end projects of increasing complexity, you will learn data ingestion and retrieval at depth, master structured workflows and orchestration patterns, implement robust observability and evaluation, and design systems that handle failure gracefully. Every concept is paired with working code, architectural trade-offs are surfaced honestly, and the emphasis throughout is on what actually works in production.

This book teaches you how to design, build, and deploy production-ready multi-agent AI systems using the CrewAI framework. Starting from fundamental concepts and progressing through advanced patterns, you will learn to create autonomous agents that collaborate on complex tasks, integrate with external tools and data sources, and operate reliably at scale. Every concept is explained with clear prose, fully working code examples, and real-world project walkthroughs. Whether you are a beginner exploring AI agents for the first time or an experienced developer looking to master CrewAI's advanced capabilities, this book provides the practical knowledge and engineering discipline needed to ship robust agentic systems.

LangChain has become the dominant framework for building production-grade AI applications in Python, yet most developers only scratch its surface. This book takes you from foundational concepts to advanced patterns through detailed, production-ready code examples. You will learn to build agents that reason autonomously, construct retrieval-augmented generation pipelines that ground LLM responses in your own data, implement robust error handling and observability, and deploy your applications with confidence. Every chapter builds on the last, creating a complete mental model of how LangChain works under the hood and how to wield it effectively in real-world scenarios.

This book teaches you to build production-grade LLM applications using DSPy, the declarative programming framework from Stanford NLP. Through detailed tutorials, complete runnable code, and real-world projects, you will master DSPy's three core abstractions: signatures, modules, and optimizers. By the end, you will be able to design, build, optimize, evaluate, and deploy AI systems that improve with data rather than trial-and-error prompt iteration. No prior DSPy experience is required, but basic Python proficiency and familiarity with how LLM API calls work will help you get the most out of this book.

This book is a comprehensive guide to PydanticAI, the Python agent framework that brings the rigor and developer experience of the Pydantic ecosystem to building production-grade generative AI applications. Whether you are a Python developer new to LLMs or an experienced AI practitioner looking for a framework that takes type safety seriously, this book will take you from your first agent to complex multi-agent systems with durable execution. Every chapter includes working code examples, real-world patterns, and practical exercises.