LM Studio Developer Docs

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Overview ​

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) ​

npm install @lmstudio/sdk
import { LMStudioClient } from "@lmstudio/sdk";

const client = new LMStudioClient();
const model = await client.llm.model("openai/gpt-oss-20b");
const result = await model.respond("Who are you, and what can you do?");

console.info(result.content);

Full docs: lmstudio-js, Source: GitHub

Python (lmstudio-python) ​

pip install lmstudio
import lmstudio as lms

with lms.Client() as client:
    model = client.llm.model("openai/gpt-oss-20b")
    result = model.respond("Who are you, and what can you do?")
    print(result)

Full docs: lmstudio-python, Source: GitHub

HTTP (LM Studio REST API) ​

lms server start --port 1234
curl http://localhost:1234/api/v1/chat \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $LM_API_TOKEN" \
  -d '{
    "model": "openai/gpt-oss-20b",
    "input": "Who are you, and what can you do?"
  }'

Full docs: LM Studio REST API

[API Changelog

Updates and changes to the LM Studio API.](/api-changelog)