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Documentation Index

Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt

Use this file to discover all available pages before exploring further.

Run Large Language Models locally with LM Studio LM Studio is a fantastic tool for running models locally. LM Studio supports multiple open-source models. See the library here. We recommend experimenting to find the best-suited model for your use-case. Here are some general recommendations:
  • llama3.3 models are good for most basic use-cases.
  • qwen models perform specifically well with tool use.
  • deepseek-r1 models have strong reasoning capabilities.
  • phi4 models are powerful, while being really small in size.

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Set up a model

Install LM Studio, download the model you want to use, and run it.

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Example

After you have the model locally, use the LM Studio model class to access it 
from agno.agent import Agent, RunResponse
from agno.models.lmstudio import LMStudio

agent = Agent(
    model=LMStudio(id="qwen2.5-7b-instruct-1m"),
    markdown=True
)

# Print the response in the terminal
agent.print_response("Share a 2 sentence horror story.")
View more examples here.

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Params

ParameterTypeDefaultDescription
idstr"qwen2.5-7b-instruct-1m"The id of the LM Studio model to use.
namestr"LM Studio "The name of this chat model instance.
providerstr"LM Studio " + idThe provider of the model.
base_urlstr"http://127.0.0.1:1234/v1"The base URL for API requests.
LM Studio also supports the params of OpenAI.