name	lmstudio-python
description	LM Studio Python SDK (lmstudio package). Chat completions, streaming, structured output, agentic tool use (.act()), embeddings, tokenization, model management, VLM image input. Use when writing Python code that talks to LM Studio, local LLMs via lmstudio SDK, or building agents with local models. Also covers lms CLI, llmster headless daemon, LM Link remote access, and server configuration.

LM Studio Python SDK

pip install lmstudio — native SDK for LM Studio's local inference server.

Three API styles exist for every operation; pick one per project:

Convenience API — lms.llm(), lms.embedding_model() (sync, global client)
Scoped resource API — context managers for deterministic cleanup
Async API — structured concurrency (SDK ≥ 1.5.0)

All examples below use the convenience API. See references for scoped/async variants.

import lmstudio as lms

model = lms.llm("qwen2.5-7b-instruct")
print(model.respond("What is the meaning of life?"))

Streaming:

for fragment in model.respond_stream("What is the meaning of life?"):
    print(fragment.content, end="", flush=True)

Need	File
Chat, streaming, multi-turn, progress callbacks	chat-and-streaming.md
Structured output (Pydantic / JSON schema)	structured-output.md
Agentic tool use (`.act()`, tool definitions, error handling)	agentic-tools.md
Image input (VLMs)	image-input.md
Embeddings and tokenization	embeddings-tokenization.md
Model management (load, unload, list, TTL, config)	model-management.md
Inference & load parameters	parameters.md
Server, headless daemon, LM Link, CLI	server-and-cli.md

name	lmstudio-python
description	LM Studio Python SDK (lmstudio package). Chat completions, streaming, structured output, agentic tool use (.act()), embeddings, tokenization, model management, VLM image input. Use when writing Python code that talks to LM Studio, local LLMs via lmstudio SDK, or building agents with local models. Also covers lms CLI, llmster headless daemon, LM Link remote access, and server configuration.

pip install lmstudio — native SDK for LM Studio's local inference server.

Three API styles exist for every operation; pick one per project:

Convenience API — lms.llm(), lms.embedding_model() (sync, global client)
Scoped resource API — context managers for deterministic cleanup
Async API — structured concurrency (SDK ≥ 1.5.0)

All examples below use the convenience API. See references for scoped/async variants.

import lmstudio as lms

model = lms.llm("qwen2.5-7b-instruct")
print(model.respond("What is the meaning of life?"))

Streaming:

for fragment in model.respond_stream("What is the meaning of life?"):
    print(fragment.content, end="", flush=True)

Need	File
Chat, streaming, multi-turn, progress callbacks	chat-and-streaming.md
Structured output (Pydantic / JSON schema)	structured-output.md
Agentic tool use (`.act()`, tool definitions, error handling)	agentic-tools.md
Image input (VLMs)	image-input.md
Embeddings and tokenization	embeddings-tokenization.md
Model management (load, unload, list, TTL, config)	model-management.md
Inference & load parameters	parameters.md
Server, headless daemon, LM Link, CLI	server-and-cli.md

lmstudio-python