Local Ai Agents with Mcp

Build private AI agents that can do more than chat.

Local AI Agents with MCP is a practical, project-driven guide to building capable AI agents that run on infrastructure you control.

Follow the evolving Lantern system as you move from local model inference to real-world agent engineering with Ollama, llama.cpp, Model Context Protocol (MCP), RAG, memory, tool calling, durable workflows, and multi-agent systems.

You'll learn how to:

• Run and compare local LLMs with Ollama and llama.cpp
• Connect AI agents to tools and data using MCP servers
• Build private RAG over local knowledge
• Add useful, controlled agent memory
• Design reliable tool-use and multi-step agent workflows
• Build and coordinate multi-agent systems
• Defend against prompt injection and unsafe tool execution
• Add permissions, approvals, tracing, evaluation, and regression tests
• Deploy a private AI agent stack you can observe, secure, and recover

Instead of hiding complexity behind a framework, this book shows you how an agent actually works-from model decisions and tool calls to permissions, verification, and production deployment.

Perfect for Python developers, AI engineers, technical learners, and anyone building private or local AI systems.

Build agents that are local, capable, observable, and under your control.