lmstudio-python provides you a set APIs to interact with LLMs, embeddings models, and agentic flows.

Installing the SDK

lmstudio-python is available as a PyPI package. You can install it using pip.

pip install lmstudio

For the source code and open source contribution, visit lmstudio-python on GitHub.

Features

• Use LLMs to respond in chats or predict text completions
• Define functions as tools, and turn LLMs into autonomous agents that run completely locally
• Load, configure, and unload models from memory
• Generate embeddings for text, and more!

Quick Example: Chat with a Llama Model

import lmstudio as lms

model = lms.llm("llama-3.2-1b-instruct")
result = model.respond("What is the meaning of life?")

print(result)

Getting Local Models

The above code requires the Llama 3.2 1B model. If you don't have the model, run the following command in the terminal to download it.

lms get llama-3.2-1b-instruct

Read more about lms get in LM Studio's CLI here.

Interactive Convenience or Deterministic Resource Management?

As shown in the example above, there are two distinct approaches for working with the LM Studio Python SDK.

The first is the interactive convenience API (listed as "Python (convenience API)" in examples), which focuses on the use of a default LM Studio client instance for convenient interactions at a Python prompt, or when using Jupyter notebooks.

The second is a scoped resource API (listed as "Python (scoped resource API)" in examples), which uses context managers to ensure that allocated resources (such as network connections) are released deterministically, rather than potentially remaining open until the entire process is terminated.