[Farid-Khan]

English | 2026 | ISBN: 1806662272 | 438 pages | True PDF | 14.07 MB

Catergory: Computer Technology, Nonfiction

Learn to build composable and scalable LLM systems with the Model Context Protocol. Create context-rich, multi-agent AI apps with memory, orchestration, governance, and seamless LangChain or AutoGen integration.

Key Features
Build context-aware LLM systems using Model Context Protocol
Integrate resource providers, tool agents, and context gateways
Secure multi-agent orchestration across modular AI architectures
Optimize performance with caching, async tasks, and profiling
Connect MCP with Lang Chain, Auto Gen, and RAG framework

Book Description
AI developers face a growing challenge: building intelligent systems that retain long-term memory, reason over dynamic context, and integrate safely with external tools. Model Context Protocol for LLMs provides a modern solution-offering an open, modular architecture to construct scalable LLM agents with structured context exchange.

This book equips you with a complete hands-on journey to MCP. You'll implement the protocol's key components-resource providers, tool providers, and gateways-then use these to orchestrate agents, chain workflows, and add context-aware behavior. You'll also learn how MCP integrates seamlessly with LangChain, AutoGen, RAG systems, and multimodal applications.

Security and governance are covered in depth, helping you build privacy-compliant, threat-resistant AI apps. You'll explore caching, async tasks, load balancing, and scaling strategies for real-world readiness. With a continuous hands-on project, MCP becomes more than a standard-it becomes a blueprint for production-grade LLM development.

What you will learn
Understand why disconnected agents fail and how MCP solves it
Design standardized, context-aware interfaces with MCP
Implement MCP components with Python and cloud-native tools
Build LangChain and AutoGen workflows powered by MCP
Create scalable multi-agent systems that collaborate in real time
Secure agent interactions using authentication and access control
Optimize performance across client and server MCP deployments
Apply MCP to personalization, RAG, and multimodal AI systems

Who this book is for
AI/ML engineers, solution architects, MLOps and DevOps engineers, technical product managers, and data scientists who want to build real-world multi-agent systems with secure, standardized context management. Familiarity with Python, LLMs, and basic system design is recommended.

Contents of Download:

? Model Context Protocol For LLMs.pdf (Naveen Krishnan (2026) (14.07 MB)

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⭐️ Model Context Protocol For LLMs Build Secure Scalable And Context Aware AI Agents Using A Standardized Protocol ✅ (15.07 MB)
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