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

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

  variants:
    pip:
      language: bash
      code: |
        pip install lmstudio

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

• 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!

  variants:
    "Python (convenience API)":
      language: python
      code: |
        import lmstudio as lms

        model = lms.llm("qwen/qwen3-4b-2507")
        result = model.respond("What is the meaning of life?")

        print(result)

    "Python (scoped resource API)":
      language: python
      code: |
        import lmstudio as lms

        with lms.Client() as client:
            model = client.llm.model("qwen/qwen3-4b-2507")
            result = model.respond("What is the meaning of life?")

            print(result)

    "Python (asynchronous API)":
      language: python
      code: |
        # Note: assumes use of an async function or the "python -m asyncio" asynchronous REPL
        # Requires Python SDK version 1.5.0 or later
        import lmstudio as lms

        async with lms.AsyncClient() as client:
            model = await client.llm.model("qwen/qwen3-4b-2507")
            result = await model.respond("What is the meaning of life?")

            print(result)

The above code requires the qwen3-4b-2507 model. If you don't have the model, run the following command in the terminal to download it.

lms get qwen/qwen3-4b-2507

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

As shown in the example above, there are three 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 synchronous Python prompt, or when using Jupyter notebooks.

The second is a synchronous 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.

The last is an asynchronous structured concurrency API (listed as "Python (asynchronous API)" in examples), which is designed for use in asynchronous programs that follow the design principles of "structured concurrency" in order to ensure the background tasks handling the SDK's connections to the API server host are managed correctly. Asynchronous applications which do not adhere to those design principles will need to rely on threaded access to the synchronous scoped resource API rather than attempting to use the SDK's native asynchronous API. Python SDK version 1.5.0 is the first version to fully support the asynchronous API.

Some examples are common between the interactive convenience API and the synchronous scoped resource API. These examples are listed as "Python (synchronous API)".

Required Python SDK version: 1.5.0

Starting in Python SDK version 1.5.0, the synchronous API defaults to timing out after 60 seconds with no activity when waiting for a response or streaming event notification from the API server.

The number of seconds to wait for responses and event notifications can be adjusted using the lmstudio.set_sync_api_timeout() function. Setting the timeout to None disables the timeout entirely (restoring the behaviour of previous SDK versions).

The current synchronous API timeout can be queried using the lmstudio.get_sync_api_timeout() function.

Required Python SDK version: 1.5.0

As asynchronous coroutines support cancellation, there is no specific timeout support implemented in the asynchronous API. Instead, general purpose async timeout mechanisms, such as asyncio.wait_for() or anyio.move_on_after(), should be used.