Agentic Rag System With Mcp And Langchain
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Agentic Rag System With Mcp And Langchain
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Author : Rowan Creed
language : en
Publisher: Independently Published
Release Date : 2025-06-28
Agentic Rag System With Mcp And Langchain written by Rowan Creed and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-28 with Computers categories.
The future of AI isn't just about retrieval-it's about reasoning. Agentic RAG (Retrieval-Augmented Generation) combines powerful large language models with structured tool use, dynamic memory, and feedback-driven adaptation. When paired with frameworks like LangChain, LangGraph, and Modular Cognitive Protocol (MCP), you unlock scalable, explainable, and intelligent agent systems capable of handling complex real-world tasks. This book focuses on how agentic intelligence, RAG pipelines, multi-agent orchestration, and modular memory architectures converge to build smarter, more reliable AI applications for production. Written by a seasoned practitioner in the field of AI automation, agent design, and applied LangChain systems, this guide blends real-world engineering expertise with practical, deployable insights. The book reflects up-to-date knowledge based on current tools, open-source best practices, and real use cases-ideal for ML engineers, AI developers, architects, and CTOs navigating the cutting edge of LLM systems. Agentic RAG System with MCP and LangChain is the definitive guide to building robust, modular, and intelligent AI agents using retrieval-augmented generation pipelines. Going beyond simple retrieval, it introduces a layered design system-Modular Cognitive Protocol (MCP)-that enables agents to plan, observe, act, revise, and collaborate with tool interfaces, vector stores, long-term memory, and feedback loops. From foundational concepts to advanced production deployment patterns, this book helps you design, build, and scale trustworthy and performant agentic systems. Architecture deep dives into LangChain, LangGraph, and AutoGen Full walkthrough of the MCP framework and modular agent design Best practices for memory (short/long-term), planning, feedback loops Advanced agent behavior patterns: multi-hop reasoning, critic agents, query refinement Vector store tuning, reranking strategies, latency mitigation, and tool drift handling Production-ready orchestration: serverless deployments, CI workflows, observability Real-world case studies in enterprise search, customer support, research assistants, and industry-specific agents (finance, healthcare, education) This book is written for machine learning engineers, AI product developers, full-stack engineers, data scientists, and technical founders who want to go beyond plug-and-play LLMs and build modular, goal-driven AI agents using the most reliable and extensible frameworks available today. Whether you're transitioning from traditional RAG to agentic intelligence, or leading the architecture of your company's AI stack-this guide gives you the strategic depth and technical clarity you need. You don't need months of trial and error to build scalable, agentic AI systems. In just a few focused weeks, you'll go from foundational understanding to implementing full-stack agent pipelines, complete with memory, toolchains, and orchestration. Accelerate your AI roadmap without starting from scratch. Unlock the future of AI automation. Grab your copy of Agentic RAG System with MCP and LangChain today and start building advanced LLM-powered agents that reason, remember, and act with purpose. Whether you're launching next-gen AI products or optimizing internal enterprise systems, this book is your blueprint for building trustworthy, modular, and production-grade AI agents.
Building Ai Agents With Llms Rag And Knowledge Graphs
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Author : Salvatore Raieli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-07-11
Building Ai Agents With Llms Rag And Knowledge Graphs written by Salvatore Raieli and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-11 with Computers categories.
Master LLM fundamentals to advanced techniques like RAG, reinforcement learning, and knowledge graphs to build, deploy, and scale intelligent AI agents that reason, retrieve, and act autonomously Key Features Implement RAG and knowledge graphs for advanced problem-solving Leverage innovative approaches like LangChain to create real-world intelligent systems Integrate large language models, graph databases, and tool use for next-gen AI solutions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis AI agents book addresses the challenge of building AI that not only generates text but also grounds its responses in real data and takes action. Authored by AI specialists with deep expertise in drug discovery and systems optimization, this guide empowers you to leverage retrieval-augmented generation (RAG), knowledge graphs, and agent-based architectures to engineer truly intelligent behavior. By combining large language models (LLMs) with up-to-date information retrieval and structured knowledge, you'll create AI agents capable of deeper reasoning and more reliable problem-solving. Inside, you'll find a practical roadmap from concept to implementation. You’ll discover how to connect language models with external data via RAG pipelines for increasing factual accuracy and incorporate knowledge graphs for context-rich reasoning. The chapters will help you build and orchestrate autonomous agents that combine planning, tool use, and knowledge retrieval to achieve complex goals. Concrete Python examples built on popular libraries, along with real-world case studies, reinforce each concept and show you how these techniques come together. By the end of this book, you’ll be well-equipped to build intelligent AI agents that reason, retrieve, and interact dynamically, empowering you to deploy powerful AI solutions across industries.What you will learn Learn how LLMs work, their structure, uses, and limits, and design RAG pipelines to link them to external data Build and query knowledge graphs for structured context and factual grounding Develop AI agents that plan, reason, and use tools to complete tasks Integrate LLMs with external APIs and databases to incorporate live data Apply techniques to minimize hallucinations and ensure accurate outputs Orchestrate multiple agents to solve complex, multi-step problems Optimize prompts, memory, and context handling for long-running tasks Deploy and monitor AI agents in production environments Who this book is for If you are a data scientist or researcher who wants to learn how to create and deploy an AI agent to solve limitless tasks, this book is for you. To get the most out of this book, you should have basic knowledge of Python and Gen AI. This book is also excellent for experienced data scientists who want to explore state-of-the-art developments in LLM and LLM-based applications.
Agentic Ai Systems
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Author : Roberto Pizzlo
language : en
Publisher: Independently Published
Release Date : 2025-06-15
Agentic Ai Systems written by Roberto Pizzlo and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-15 with Computers categories.
Agentic AI Systems: Build Multi-Agent Workflows with LangChain, MCP, RAG & Ollama (A Practical Guide to Local LLM Orchestration, Retrieval-Augmented Generation, and Autonomous Agents) Unlock the power of local LLMs, agentic AI architectures, and multi-agent orchestration with this hands-on guide designed for developers, AI engineers, and system architects building intelligent applications beyond the cloud. In an era where data privacy, autonomous workflows, and cost-effective deployments are critical, this book offers a production-ready blueprint using LangChain, Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and Ollama. Whether you're designing AI copilots, deploying autonomous agents, or developing secure on-premise AI systems, this guide helps you go from concept to execution with confidence. What You'll Learn: Set up a complete agentic AI stack with LangChain, LangGraph, MCP, and Ollama Run private LLMs like Llama 3 and Mixtral with full control using Ollama Fine-tune models with LoRA/QLoRA for domain-specific applications Design and orchestrate multi-agent systems using LangGraph and graph-based coordination Build robust Retrieval-Augmented Generation pipelines using FAISS and Chroma Implement secure message-passing and streaming using MCP Handle authentication, observability, and compliance (GDPR, HIPAA, SOC 2) Deploy agents with Docker, Kubernetes, and scalable CI/CD pipelines Who This Book Is For: AI engineers and backend developers working with LLMs and LangChain Security-conscious teams needing private and auditable AI workflows DevOps and MLOps professionals deploying containerized AI systems Researchers and tech leads building autonomous agent systems Anyone interested in real-world agentic AI with local deployment capabilities Unlike cloud-reliant AI books or overly academic texts, Agentic AI Systems delivers actionable blueprints for building and deploying real systems on local infrastructure. You'll explore hands-on code, architecture diagrams, and reusable patterns that scale from laptops to clusters. No fluff-just proven strategies and reproducible workflows grounded in current LLM capabilities. Roberto Pizzlo is an AI infrastructure engineer and systems architect specializing in agentic orchestration and secure LLM deployments. Known for translating cutting-edge AI concepts into practical engineering, he brings a wealth of expertise in LangChain, LangGraph, RAG architectures, and edge AI systems. His experience bridges research, enterprise, and open-source ecosystems-making this book an essential guide for professionals navigating the fast-evolving world of autonomous AI. This guide reflects 2025 technologies and best practices, including the latest versions of LangChain, Ollama (v0.2.16+), CUDA 12.9, and RAG toolchains. It ensures your understanding remains relevant in a rapidly changing AI landscap
Ai Agents In Practice
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Author : Valentina Alto
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-08-28
Ai Agents In Practice written by Valentina Alto and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-28 with Computers categories.
Master the art of building AI agents with this hands-on guide to orchestration, multi-agent systems, real-world case studies, and ethical insights to drive immediate business impact Key Features Build production-ready AI agents with hands-on tutorials for diverse industry applications Explore multi-agent system architectures with practical frameworks for orchestrator comparison Future-proof your AI development with ethical implementation strategies and security patterns Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionAs AI agents evolve to take on complex tasks and operate autonomously, you need to learn how to build these next-generation systems. Author Valentina Alto brings practical, industry-grounded expertise in AI Agents in Practice to help you go beyond simple chatbots and create AI agents that plan, reason, collaborate, and solve real-world problems using large language models (LLMs) and the latest open-source frameworks. In this book, you'll get a comparative tour of leading AI agent frameworks such as LangChain and LangGraph, covering each tool's strengths, ideal use cases, and how to apply them in real-world projects. Through step-by-step examples, you’ll learn how to construct single-agent and multi-agent architectures using proven design patterns to orchestrate AI agents working together. Case studies across industries will show you how AI agents drive value in real-world scenarios, while guidance on responsible AI will help you implement ethical guardrails from day one. The chapters also set the stage with a brief history of AI agents, from early rule-based systems to today's LLM-driven autonomous agents, so you understand how we got here and where the field is headed. By the end of this book, you'll have the practical skills, design insights, and ethical foresight to build and deploy AI agents that truly make an impact.What you will learn Build core agent components such as LLMs, memory systems, tool integration, and context management Develop production-ready AI agents using frameworks such as LangChain with code Create effective multi-agent systems using orchestration patterns for problem-solving Implement industry-specific agents for e-commerce, customer support, and more Design robust memory architectures for agents with short- and long-term recall Apply responsible AI practices with monitoring, guardrails, and human oversight Optimize AI agent performance and cost for production environments Who this book is for This book is ideal for AI engineers and data scientists looking to move beyond basic LLM implementations to build sophisticated autonomous agents. Software developers and system architects will find practical guidelines for integrating agents into existing tech stacks. Product managers and technical entrepreneurs will gain strategic insights into how AI agents can solve business problems across industries. A basic understanding of machine learning concepts and working knowledge of Python are required to make the most of this book and implement production-ready AI agent systems.
Generative Ai With Langchain
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Author : Ben Auffarth
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-05-23
Generative Ai With Langchain written by Ben Auffarth and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-23 with Computers categories.
Go beyond foundational LangChain documentation with detailed coverage of LangGraph interfaces, design patterns for building AI agents, and scalable architectures used in production—ideal for Python developers building GenAI applications Key Features Bridge the gap between prototype and production with robust LangGraph agent architectures Apply enterprise-grade practices for testing, observability, and monitoring Build specialized agents for software development and data analysis Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis second edition tackles the biggest challenge facing companies in AI today: moving from prototypes to production. Fully updated to reflect the latest developments in the LangChain ecosystem, it captures how modern AI systems are developed, deployed, and scaled in enterprise environments. This edition places a strong focus on multi-agent architectures, robust LangGraph workflows, and advanced retrieval-augmented generation (RAG) pipelines. You'll explore design patterns for building agentic systems, with practical implementations of multi-agent setups for complex tasks. The book guides you through reasoning techniques such as Tree-of -Thoughts, structured generation, and agent handoffs—complete with error handling examples. Expanded chapters on testing, evaluation, and deployment address the demands of modern LLM applications, showing you how to design secure, compliant AI systems with built-in safeguards and responsible development principles. This edition also expands RAG coverage with guidance on hybrid search, re-ranking, and fact-checking pipelines to enhance output accuracy. Whether you're extending existing workflows or architecting multi-agent systems from scratch, this book provides the technical depth and practical instruction needed to design LLM applications ready for success in production environments.What you will learn Design and implement multi-agent systems using LangGraph Implement testing strategies that identify issues before deployment Deploy observability and monitoring solutions for production environments Build agentic RAG systems with re-ranking capabilities Architect scalable, production-ready AI agents using LangGraph and MCP Work with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI's o3-mini Design secure, compliant AI systems aligned with modern ethical practices Who this book is for This book is for developers, researchers, and anyone looking to learn more about LangChain and LangGraph. With a strong emphasis on enterprise deployment patterns, it’s especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this updated edition expands its reach to support engineering teams and decision-makers working on enterprise-scale LLM strategies. A basic understanding of Python is required, and familiarity with machine learning will help you get the most out of this book.
Model Context Protocol Mcp
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Author : Marcel Hirsch
language : en
Publisher: Independently Published
Release Date : 2025-06-06
Model Context Protocol Mcp written by Marcel Hirsch and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-06 with Computers categories.
What if your AI could think with memory, act with autonomy, and collaborate across systems like a team of experts? Welcome to Model Context Protocol (MCP): The Definitive Developer's Reference for Agentic RAG-the groundbreaking guide that unlocks the full power of intelligent agents through structured, context-aware design. This is not another vague AI hype manual. It's a deeply technical, hands-on blueprint for developers building the next generation of AI systems that are context-native, memory-augmented, and agentically capable. Inside, you'll learn how to design and implement AI workflows that remember, reason, and react in dynamic environments. From setting up memory architectures and secure context-sharing, to orchestrating multi-agent research pipelines and embedding your agents within real-world applications using tools like LangChain, CrewAI, FastAPI, and more-this book takes you from theory to deployment with clarity and purpose. What's in it for you? Master context modeling with Pydantic and modern Python workflows Build scalable, secure multi-agent systems with isolated and scoped context Trace, visualize, and debug context state across complex AI pipelines Explore real-world projects like federated assistants, autonomous RAG pipelines, and decentralized AI ecosystems Whether you're an AI engineer, a systems architect, or a developer chasing the bleeding edge of generative intelligence-this book is your tactical field guide. The unique value? A fully modular, open-standard protocol approach to context-built to integrate, adapt, and evolve with any AI framework or model. Don't just build agents. Build intelligent systems that think, remember, and collaborate. Grab your copy now and lead the future of agentic AI development.
Autonomous Ai With Mcp And Rag
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Author : Nathaniel Crossfield
language : en
Publisher: Independently Published
Release Date : 2025-06-08
Autonomous Ai With Mcp And Rag written by Nathaniel Crossfield and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-08 with Computers categories.
Autonomous AI with MCP and RAG: Principles, Architecture, and Building Blocks Unlock the future of AI systems with Autonomous AI with MCP and RAG-a practical, hands-on guide for developers, engineers, and AI architects who want to design intelligent agents using cutting-edge technologies like Model-Context-Protocol (MCP) frameworks and Retrieval-Augmented Generation (RAG). This book is your complete blueprint for building real-world AI agents that think, plan, and act independently. Learn how to architect and deploy agentic systems using LangChain, OpenAI, AutoGen, CrewAI, ChromaDB, and LlamaIndex. Whether you're developing advanced LLM-based assistants, RAG-enhanced search agents, or collaborative multi-agent workflows, this guide offers clear explanations, code-rich examples, and step-by-step tutorials aligned with 2025's most current practices. What You'll Learn: The fundamentals of autonomous agents and multi-agent systems How to design core agent loops with LangChain and OpenAI Integrating RAG with vector databases like ChromaDB and Weaviate Building and deploying FastAPI-based agent backends using Docker Applying ethical, auditable, and secure practices for AI operations Real-world architecture diagrams, templates, and deployment patterns Whether you're a machine learning engineer, Python developer, startup builder, or enterprise architect, this book equips you with the tools and knowledge to create scalable, explainable, and production-ready AI systems.