Your journey into Generative AI has shown you what's possible with LLMs, but static text generation is just the beginning. The next leap forward is autonomous AI agents-systems that can perceive, plan, and act. This book provides the practical blueprint you need to build them.

LLM Agents with ReAct is the ultimate developer's guide to creating intelligent, tool-using agents that reason through problems. Focused on the core frameworks LangChain and AutoGen, you'll get hands-on experience building dynamic AI pipelines that can interact with the world.

Inside, you will find:

- A clear, code-driven guide to the ReAct reasoning loop.
- Practical projects to build and deploy agents with LangChain and AutoGen.
- Strategies for integrating external APIs and web tools.
- A dedicated section on building robust Retrieval Augmented Generation (RAG) systems.
- Expert insights into multi-agent systems and collaborative AI workflows.
- Best practices for observability, testing, and responsible AI design.
- A head-to-head comparison to help you choose the right framework for your project.

If you're a Python developer or engineer looking to build beyond the a-typical GenAI application, this book is your essential resource. Master the art of prompt engineering for dynamic action and unlock the full potential of OpenAI and other powerful LLMs in your projects.