LM StudioLM Studio

LM Studio Developer Docs

Build with LM Studio's local APIs and SDKs — TypeScript, Python, REST, and OpenAI and Anthropic-compatible endpoints.

Get to know the stack

TypeScript SDK: lmstudio-js

Use the TypeScript SDK to build apps, tools, and local AI workflows.

Python SDK: lmstudio-python

Work with local models from Python scripts, notebooks, and backend services.

LM Studio REST API

Use stateful chats, local server endpoints, and MCPs via HTTP.

OpenAI-compatible

Use chat, responses, embeddings, and other familiar OpenAI-style endpoints.

Anthropic-compatible

Use Claude-style Messages API flows against your local LM Studio server.

LM Studio CLI: lms

Download models, run the daemon, start the server, and script local workflows.

What you can build

Chat and text generation with streaming

Build local chat apps and text-generation flows with token streaming.

Tool calling and local agents with MCP

Connect tools, MCP servers, and agent-like workflows entirely on your machine.

Structured output (JSON schema)

Generate typed JSON outputs that validate against a schema.

Embeddings and tokenization

Create embeddings, inspect tokens, and build retrieval or indexing pipelines.

Model management (load, download, list)

Load models into memory, download new ones, and inspect what is available.

Install llmster for headless deployments

llmster is LM Studio's core, packaged as a daemon for headless deployment on servers, cloud instances, or CI. The daemon runs standalone, and it is not dependent on the LM Studio GUI.

Mac / Linux

curl -fsSL https://lmstudio.ai/install.sh | bash

Windows

irm https://lmstudio.ai/install.ps1 | iex

Basic usage

lms daemon up          # Start the daemon
lms get <model>        # Download a model
lms server start       # Start the local server
lms chat               # Open an interactive session

Learn more: Headless deployments

Super quick start

TypeScript (lmstudio-js)