Your AI prototype works once, then breaks. Ready for production stability? Tired of hallucinations ruining demos and stakeholder trust? Need agent workflows that survive real-world edge cases? Wondering how early adopters ship multi-agent systems today? Stop guessing, start delivering value with proven, open-source tools. Build confident, future-proof AI products your users will rely on.

• Prompt and context engineering: Produce accurate, hallucination-free outputs your business can trust.
• Advanced RAG techniques: Summarize documents, power semantic search, and answer questions with verifiable sources.
• LangGraph agent workflows: Orchestrate multi-step processes for complex, real-world tasks.
• MCP tool integration: Plug, compose, and reuse capabilities across projects for faster releases.
• End-to-end code examples: Copy, adapt, and deploy without time-consuming trial and error.

AI Agents and Applications with LangChain, LangGraph, and MCP by seasoned developer Roberto Infante delivers a pragmatic, code-first roadmap in a clear softcover format. Infante shows exactly how to move from idea to production-grade AI.

Each chapter builds a working solution, starting with solid prompt engineering, layering in advanced RAG, then evolving into structured agents and multi-agent systems. Visual explanations, annotated Python listings, and architecture diagrams speed learning and reduce guesswork.

By the final page you will confidently design, test, and deploy LLM applications that delight users and impress stakeholders. The tools and patterns scale with your data, your team, and tomorrow’s models.

Ideal for Python developers who grasp LLM basics and crave real production wins. Perfect for engineers, data scientists, and tech leads modernizing products with generative AI.

Table of Contents

PART 1 GETTING STARTED WITH LLMS

1 ■ Introduction to AI agents and applications

2 ■ Executing prompts programmatically

PART 2 SUMMARIZATION

3 ■ Summarizing text using LangChain

4 ■ Building a research summarization engine

5 ■ Agentic workflows with LangGraph

PART 3 Q&A CHATBOTS

6 ■ RAG fundamentals with ChromaDB

7 ■ Q&A chatbots with LangChain and LangSmith

PART 4 ADVANCED RAG

8 ■ Advanced indexing

9 ■ Question transformations

10 ■ Query generation, routing, and retrieval postprocessing

PART 5 AI AGENTS

11 ■ Building tool-based agents with LangGraph

12 ■ Multi-agent systems

13 ■ Building and consuming MCP servers

14 ■ Productionizing AI agents: Memory, guardrails, and beyond

appendix A Trying out LangChain

appendix B Setting up a Jupyter Notebook environment

appendix C Choosing an LLM

appendix D Installing SQLite on Windows

appendix E Open source LLMs

Review

• The book explains the topic really well. It's clear and does not go into irrelevant details. I really enjoyed it.

Piotr Jastrzebski

• This is a great introduction for any software engineer looking for practical explanation of how LangChain and LLM's can be used to develop AI based applications.

Borko Djurkovic

About the Author

Roberto Infante is a veteran software engineer and quant leader known for turning complex ideas into reliable systems. With decades in high-stakes finance and emerging AI projects, Roberto brings clarity, rigor, and real-world wisdom to every page. He distills deep expertise into practical examples that help developers ship AI features with confidence.