OpenAI Compatibility API

Send requests to Responses, Chat Completions (text and images), Completions, and Embeddings endpoints.

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OpenAI-like API endpoints

LM Studio accepts requests on several OpenAI endpoints and returns OpenAI-like response objects.

Supported endpoints

GET  /v1/models
POST /v1/responses
POST /v1/chat/completions
POST /v1/embeddings
POST /v1/completions

See below for more info about each endpoint

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Re-using an existing OpenAI client

Switching up the base url to point to LM Studio

Note: The following examples assume the server port is 1234

Python

from openai import OpenAI

client = OpenAI(
+    base_url="http://localhost:1234/v1"
)

# ... the rest of your code ...

Typescript

import OpenAI from 'openai';

const client = new OpenAI({
+  baseUrl: "http://localhost:1234/v1"
});

// ... the rest of your code ...

cURL

- curl https://api.openai.com/v1/chat/completions \
+ curl http://localhost:1234/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
-     "model": "gpt-4o-mini",
+     "model": "use the model identifier from LM Studio here",
     "messages": [{"role": "user", "content": "Say this is a test!"}],
     "temperature": 0.7
   }'

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Endpoints overview

/v1/models

• GET request
• Lists the currently loaded models.

cURL example

curl http://localhost:1234/v1/models

/v1/responses

• POST request
• Create responses with an input field. Supports streaming, tool calling, reasoning, and stateful interactions via previous_response_id.
• See OpenAI docs: https://platform.openai.com/docs/api-reference/responses

cURL example (non‑streaming)

curl http://localhost:1234/v1/responses \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-oss-20b",
    "input": "Provide a prime number less than 50",
    "reasoning": { "effort": "low" }
  }'

Stateful follow‑up

Use the id from the previous response as previous_response_id.

curl http://localhost:1234/v1/responses \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-oss-20b",
    "input": "Multiply it by 2",
    "previous_response_id": "resp_123"
  }'

Streaming

curl http://localhost:1234/v1/responses \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-oss-20b",
    "input": "Hello",
    "stream": true
  }'

You will receive SSE events like response.created, response.output_text.delta, and response.completed.

Tools and Remote MCP (opt‑in)

Enable remote MCP in the app (Developer → Settings). Example payload using an MCP server tool:

curl http://localhost:1234/v1/responses \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-oss-20b",
    "tools": [{
      "type": "mcp",
      "server_label": "tiktoken",
      "server_url": "https://gitmcp.io/openai/tiktoken",
      "allowed_tools": ["fetch_tiktoken_documentation"]
    }],
    "input": "What is the first sentence of the tiktoken documentation?"
  }'

/v1/chat/completions

• POST request
• Send a chat history and receive the assistant's response
• Prompt template is applied automatically
• You can provide inference parameters such as temperature in the payload. See supported parameters
• See OpenAI's documentation for more information
• As always, keep a terminal window open with lms log stream to see what input the model receives

Python example

# Example: reuse your existing OpenAI setup
from openai import OpenAI

# Point to the local server
client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio")

completion = client.chat.completions.create(
  model="model-identifier",
  messages=[
    {"role": "system", "content": "Always answer in rhymes."},
    {"role": "user", "content": "Introduce yourself."}
  ],
  temperature=0.7,
)

print(completion.choices[0].message)

/v1/embeddings

• POST request
• Send a string or array of strings and get an array of text embeddings (integer token IDs)
• See OpenAI's documentation for more information

Python example

# Make sure to `pip install openai` first
from openai import OpenAI
client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio")

def get_embedding(text, model="model-identifier"):
   text = text.replace("\n", " ")
   return client.embeddings.create(input = [text], model=model).data[0].embedding

print(get_embedding("Once upon a time, there was a cat."))

/v1/completions

• POST request
• Send a string and get the model's continuation of that string
• See supported payload parameters
• Prompt template will NOT be applied, even if the model has one
• See OpenAI's documentation for more information
• As always, keep a terminal window open with lms log stream to see what input the model receives

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Supported payload parameters

For an explanation for each parameter, see https://platform.openai.com/docs/api-reference/chat/create.

model
top_p
top_k
messages
temperature
max_tokens
stream
stop
presence_penalty
frequency_penalty
logit_bias
repeat_penalty
seed

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