Agentic Rag System With Mcp And Langchain
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Agentic RAG System with MCP and LangChain
Author: Rowan Creed
language: en
Publisher: Independently Published
Release Date: 2025-06-28
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
Author: Salvatore Raieli
language: en
Publisher: Packt Publishing Ltd
Release Date: 2025-07-11
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
Author: Roberto Pizzlo
language: en
Publisher: Independently Published
Release Date: 2025-06-15
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