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Full Description
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.
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 CHROMA DB
7 Q&A CHATBOTS WITH LANGCHAIN AND LANGSMITH
PART 4: ADVANCED RAG
8 ADVANCED INDEXING
9 QUESTION TRANSFORMATIONS
10 QUERY GENERATION, ROUTING AND RETRIEVAL POST-PROCESSING
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
APPENDICES
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



