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Using lmstudio-python in REPL

To enable interactive use, lmstudio-python offers a convenience API which manages its resources via atexit hooks, allowing a default synchronous client session to be used across multiple interactive commands.

This convenience API is shown in the examples throughout the documentation as the Python (convenience API) tab (alongside the Python (scoped resource API) examples, which use with statements to ensure deterministic cleanup of network communication resources).

The convenience API allows the standard Python REPL, or more flexible alternatives like Juypter Notebooks, to be used to interact with AI models loaded into LM Studio. For example:

>>> import lmstudio as lms
>>> loaded_models = lms.list_loaded_models()
>>> for idx, model in enumerate(loaded_models):
...     print(f"{idx:>3} {model}")
...
  0 LLM(identifier='qwen2.5-7b-instruct')
>>> model = loaded_models[0]
>>> chat = lms.Chat("You answer questions concisely")
>>> chat.add_user_message("Tell me three fruits")
ChatMessageDataUser(content=[ChatMessagePartTextData(text='Tell me three fruits')])
>>> print(model.respond(chat, on_message=chat.append))
Banana, apple, orange.
>>> chat.add_user_message("Tell me three more fruits")
ChatMessageDataUser(content=[ChatMessagePartTextData(text='Tell me three more fruits')])
>>> print(model.respond(chat, on_message=chat.append))
Pineapple, avocado, strawberry.
>>> chat.add_user_message("How many fruits have you told me?")
ChatMessageDataUser(content=[ChatMessagePartTextData(text='How many fruits have you told me?')])
>>> print(model.respond(chat, on_message=chat.append))
You asked for three fruits initially, then three more, so I've told you six fruits in total: banana, apple, orange, pineapple, avocado, and strawberry.