Documentation

General
Running LLMs Locally
API
Configuration
Advanced
CLI
SDK (TypeScript)

OpenAI Compatibility API

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

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 / chat / completions;
POST / v1 / embeddings;
POST / v1 / completions;
See below for more info about each endpoint

Re-using an existing OpenAI client

Pro Tip

You can reuse existing OpenAI clients (in Python, JS, C#, etc) by switching up the "base URL" property to point to your LM Studio instead of OpenAI's servers.

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
   }'

Endpoints overview

/v1/models

  • GET request
  • Lists the currently loaded models.
cURL example
curl http://localhost:1234/v1/models

/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

Heads Up

This OpenAI-like endpoint is no longer supported by OpenAI. LM Studio continues to support it.

Using this endpoint with chat-tuned models might result in unexpected behavior such as extraneous role tokens being emitted by the model.

For best results, utilize a base model.

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

Community

Chat with other LM Studio developers, discuss LLMs, hardware, and more on the LM Studio Discord server.