LM Studio in Action: Building Safe, Private AI with LLMs, Function Calling and Agents with MCP

LM Studio in Action: Building Safe, Private AI with LLMs, Function Calling and Agents with MCP

Mark Watson
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Table of Contents

LM Studio in Action: Building Safe, Private AI with LLMs, Function Calling and Agents with MCP

  • Preface
    • Setup LM Studio
    • What’s New Since the First Edition of This Book
    • About the Author
    • Requests from the Author
    • Why Should We Care About Privacy?
  • Introduction to Using the LM Studio Application
  • Using the LM Studio Command Line Interface (CLI)
    • lms ls
    • lms load <model_key>
    • lms unload
    • lms get
    • lms chat
    • lms import
    • Server Status and Control
    • lms runtime
    • lms log stream
    • Headless Mode: lms daemon
    • LM Link: lms link
    • Developing and Publishing
  • Introduction to LM Studio’s Local Inference API
    • Using the Python OpenAI Compatibility APIs
    • Using the Python lmstudio Package
    • Three Python SDK API Styles
    • Streaming Responses
    • Structured Output
    • Embeddings
    • The Anthropic Compatibility API
    • The Responses API
    • API Authentication
    • Idle TTL and Auto-Evict
    • Optional Practice Problems
  • Tool Use / Function Calling
    • Native Tool Use via the OpenAI-Compatible API
    • The Python SDK .act() API
    • A Worked Example: DuckDuckGo Web Search
    • Manual Tool Use: Understanding the Basics
    • Optional Practice Problems
  • A Technical Introduction to Model Context Protocol and Experiments with LM Studio Using FastMCP
    • An Introduction to the Model Context Protocol
    • The Local AI Ecosystem: LM Studio as an MCP Host
    • Strategic Integration: A Blueprint for Local MCP-Powered Agents
    • Architectural Design for a Python-Based MCP Server
    • LM Studio Integration and Execution Guide
    • Advanced Considerations and Future Trajectories
    • MCP Wrap Up
    • Optional Practice Problems
  • Local Agents With LM Studio Bionic
    • Two Kinds of Projects: Work and Code
    • Choosing a Model: Local, Cloud, and Remote
    • Voice Input
    • Bionic and Your LM Studio Model Library
    • When to Use Bionic vs LM Studio Desktop
    • Wrap Up
  • Headless Deployment with llmster
    • Installing llmster on a Linux Server
    • Running as a systemd Service
    • JIT Loading for Multi-Model Serving
    • API Authentication for Network Deployments
    • Serving Models to Remote Clients with LM Link
    • Wrap Up
LM Studio in Action: Building Safe, Private AI with LLMs, Function Calling and Agents with MCP/Overview

LM Studio in Action: Building Safe, Private AI with LLMs, Function Calling and Agents with MCP

Course overview

8 chapters
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LM Studio in Action: Building Safe, Private AI with LLMs, Function Calling and Agents with MCP8 chapters

Begin ›
  1. Preface

  2. Introduction to Using the LM Studio Application

  3. Using the LM Studio Command Line Interface (CLI)

  4. Introduction to LM Studio’s Local Inference API

  5. Tool Use / Function Calling

  6. A Technical Introduction to Model Context Protocol and Experiments with LM Studio Using FastMCP

  7. Local Agents With LM Studio Bionic

  8. Headless Deployment with llmster