---
title: "LM Studio"
description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';"
type: skill
canonical_url: https://claudary.paisolsolutions.com/skills/lm-studio-2
source: "Claudary"
difficulty: intermediate
author: "Claude Code Knowledge Pack"
date: 2026-07-10T11:30:39.342Z
license: CC-BY-4.0
attribution: "LM Studio — Claudary (https://claudary.paisolsolutions.com/skills/lm-studio-2)"
---

# LM Studio
import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

## Overview

import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';

# LM Studio

https://lmstudio.ai/docs/basics/server

:::tip

**We support ALL LM Studio models, just set `model=lm_studio/<any-model-on-lmstudio>` as a prefix when sending litellm requests**

:::


| Property | Details |
|-------|-------|
| Description | Discover, download, and run local LLMs. |
| Provider Route on LiteLLM | `lm_studio/` |
| Provider Doc | [LM Studio ↗](https://lmstudio.ai/docs/api/openai-api) |
| Supported OpenAI Endpoints | `/chat/completions`, `/embeddings`, `/completions` |

## API Key
```python
# env variable
os.environ['LM_STUDIO_API_BASE']
os.environ['LM_STUDIO_API_KEY'] # optional, default is empty
```

## Sample Usage
```python
from litellm import completion
import os

os.environ['LM_STUDIO_API_BASE'] = ""

response = completion(
    model="lm_studio/llama-3-8b-instruct",
    messages=[
        {
            "role": "user",
            "content": "What's the weather like in Boston today in Fahrenheit?",
        }
    ]
)
print(response)
```

## Sample Usage - Streaming
```python
from litellm import completion
import os

os.environ['LM_STUDIO_API_KEY'] = ""
response = completion(
    model="lm_studio/llama-3-8b-instruct",
    messages=[
        {
            "role": "user",
            "content": "What's the weather like in Boston today in Fahrenheit?",
        }
    ],
    stream=True,
)

for chunk in response:
    print(chunk)
```


## Usage with LiteLLM Proxy Server

Here's how to call a LM Studio model with the LiteLLM Proxy Server

1. Modify the config.yaml 

  ```yaml
  model_list:
    - model_name: my-model
      litellm_params:
        model: lm_studio/<your-model-name>  # add lm_studio/ prefix to route as LM Studio provider
        api_key: api-key                 # api key to send your model
  ```


2. Start the proxy 

  ```bash
  $ litellm --config /path/to/config.yaml
  ```

3. Send Request to LiteLLM Proxy Server

  <Tabs>

  <TabItem value="openai" label="OpenAI Python v1.0.0+">

  ```python
  import openai
  client = openai.OpenAI(
      api_key="sk-1234",             # pass litellm proxy key, if you're using virtual keys
      base_url="http://0.0.0.0:4000" # litellm-proxy-base url
  )

  response = client.chat.completions.create(
      model="my-model",
      messages = [
          {
              "role": "user",
              "content": "what llm are you"
          }
      ],
  )

  print(response)
  ```
  </TabItem>

  <TabItem value="curl" label="curl">

  ```shell
  curl --location 'http://0.0.0.0:4000/chat/completions' \\
      --header 'Authorization: Bearer sk-1234' \\
      --header 'Content-Type: application/json' \\
      --data '{
      "model": "my-model",
      "messages": [
          {
          "role": "user",
          "content": "what llm are you"
          }
      ],
  }'
  ```
  </TabItem>

  </Tabs>


## Supported Parameters

See [Supported Parameters](../completion/input.md#translated-openai-params) for supported parameters.

## Embedding

```python
from litellm import embedding
import os 

os.environ['LM_STUDIO_API_BASE'] = "http://localhost:8000"
response = embedding(
    model="lm_studio/jina-embeddings-v3",
    input=["Hello world"],
)
print(response)
```


## Structured Output

LM Studio supports structured outputs via JSON Schema. You can pass a pydantic model or a raw schema using `response_format`.
LiteLLM sends the schema as `{ "type": "json_schema", "json_schema": {"schema": <your schema>} }`.

```python
from pydantic import BaseModel
from litellm import completion

class Book(BaseModel):
    title: str
    author: str
    year: int

response = completion(
    model="lm_studio/llama-3-8b-instruct",
    messages=[{"role": "user", "content": "Tell me about The Hobbit"}],
    response_format=Book,
)
print(response.choices[0].message.content)
```

---

Source: [Claudary](https://claudary.paisolsolutions.com/skills/lm-studio-2) · https://claudary.paisolsolutions.com