{"product_id":"ai-agents-and-applications-with-langchain-langgraph-and-mcp","title":"AI Agents and Applications: With Langchain, Langgraph, and MCP","description":"\u003cb\u003eGet a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eAI-powered applications are rapidly becoming the new normal. Personal productivity assistants, coding agents, smarter search, and automated reporting tools are popping up everywhere. The LangChain ecosystem, and standards like MCP are driving this new gold rush. This book helps you claim your spot. \u003cp\u003e\u003c\/p\u003e This is your hands-on guide to creating real, production-ready language model solutions. With LangChain and LangGraph, you'll orchestrate powerful agentic workflows and build dynamic tool-based agents that can search, summarize, reason, and act. You'll move from essential prompt engineering to advanced Retrieval Augmented Generation (RAG), and finally to deploying multi-agent systems using modern integration standards like the Model Context Protocol (MCP). \u003cp\u003e\u003c\/p\u003e In \u003ci\u003eAI Agents and Applications: With LangChain, LangGraph and MCP\u003c\/i\u003e, you'll discover: \u003cp\u003e\u003c\/p\u003e - Prompt and context engineering for accurate, hallucination-resistant systems\u003cbr\u003e - Advanced RAG for summarization, semantic search, and reliable Q\u0026amp;A\u003cbr\u003e - Structured, multi-step agentic workflows with LangGraph\u003cbr\u003e - Tool-based agents that adapt in real time\u003cbr\u003e - Multi-agent systems for complex, real-world tasks\u003cbr\u003e - MCP integration to expose, compose, and consume plug-and-play tools \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e This book teaches you to design reliable LLM-powered systems by focusing on the concepts, architectures, and design patterns that will stay stable even as models and APIs change. You'll learn to structure prompts, compose modular chains, and build RAG pipelines that ingest documents, split them into chunks, embed them, retrieve the right context, and ground answers to elliminate (or vastly reduce) hallucinations. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the book\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e Along the way you'll build concrete applications--summarization and Q\u0026amp;A engines, context-aware chatbots with memory, and tool-using AI agents that orchestrate multi-step workflows with branching logic. For the examples, the book uses Python, LangChain, LangGraph, and LangSmith, but you'll be able to generalize to other frameworks. You'll understand with clarity and confidence how to keep integrations maintainable, manage context limits and cost\/latency tradeoffs, and evaluate, debug, and monitor behavior so your systems work in production. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eRoberto Infante\u003c\/b\u003e is an AI innovator with deep FinTech experience, working for a London-based hedge fund. He specializes in building agentic systems for both plain vanilla and exotic quantitative analysis. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e Part 1\u003cbr\u003e 1 Introduction to AI agents and applications\u003cbr\u003e 2 Executing prompts programmatically\u003cbr\u003e Part 2\u003cbr\u003e 3 Summarizing text using LangChain\u003cbr\u003e 4 Building a research summarization engine\u003cbr\u003e 5 Agentic workflows with LangGraph\u003cbr\u003e Part 3\u003cbr\u003e 6 RAG fundamentals with ChromaDB\u003cbr\u003e 7 Q\u0026amp;A chatbots with LangChain and LangSmith\u003cbr\u003e Part 4\u003cbr\u003e 8 Advanced indexing\u003cbr\u003e 9 Question transformations\u003cbr\u003e 10 Query generation, routing, and retrieval postprocessing\u003cbr\u003e Part 5\u003cbr\u003e 11 Building tool-based agents with LangGraph\u003cbr\u003e 12 Multi-agent systems\u003cbr\u003e 13 Building and consuming MCP servers\u003cbr\u003e 14 Productionizing AI agents: Memory, guardrails, and beyond\u003cbr\u003e Appendixes\u003cbr\u003e A: Trying out LangChain\u003cbr\u003e B: Setting up a Jupyter Notebook environment\u003cbr\u003e C: Choosing an LLM\u003cbr\u003e D: Installing SQLite on Windows\u003cbr\u003e E: Open source LLMs","brand":"Roberto Infante","offers":[{"title":"Paperback","offer_id":47606553346284,"sku":"9781633436541","price":79.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0684\/1791\/3068\/files\/9781633436541.jpg?v=1778154172","url":"https:\/\/allstora.com\/products\/ai-agents-and-applications-with-langchain-langgraph-and-mcp","provider":"Allstora","version":"1.0","type":"link"}