> ## Documentation Index
> Fetch the complete documentation index at: https://studio-docs.prem.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Client SDKs

> We provide client libraries in a number of popular languages that make it easier to work with the PremAI API.

<Note>
  We give you the option to use the [Prem AI API](/api-reference/introduction), the PremAI SDK, or the OpenAI SDK.
</Note>

<Note>
  Before using the SDKs, you'll need to create an API key. See our [API Key guide](/api-reference/api-key) for instructions.
</Note>

## Use with PremAI SDK

### Install the PremAI SDK

<CodeGroup>
  ```bash javascript/typescript theme={null}
  npm install premai
  ```

  ```bash python theme={null}
  pip install premai
  ```
</CodeGroup>

### List Models

<CodeGroup>
  ```javascript javascript/typescript theme={null}
  import PremAI from 'premai';

  const client = new PremAI({
    base_url: "https://studio.premai.io/",
    apiKey: process.env['PREMAI_API_KEY'], // This is the default and can be omitted
  });

  const response = await client.models.list();

  console.log(response.data);
  ```

  ```python python theme={null}
  import os
  from premai import PremAI

  client = PremAI(
      base_url="https://studio.premai.io/",
      api_key=os.environ.get("PREMAI_API_KEY"),  # This is the default and can be omitted
  )
  response = client.models.list()
  print(response.data)
  ```
</CodeGroup>

### Chat Completions

<CodeGroup>
  ```javascript javascript/typescript theme={null}
  import PremAI from 'premai';

  const client = new PremAI({
    base_url: "https://studio.premai.io/",
    apiKey: process.env['PREMAI_API_KEY'], // This is the default and can be omitted
  });

  const response = await client.chat.completions({
      messages: [{
          role: 'user',
          content: 'Write a one-sentence bedtime story about a unicorn.'
      }],
      model: 'llama3.2-3b'
  });

  console.log(response.choices[0].message.content);
  ```

  ```python python theme={null}
  import os
  from premai import PremAI

  client = PremAI(
      base_url="https://studio.premai.io/",
      api_key=os.environ.get("PREMAI_API_KEY"),  # This is the default and can be omitted
  )
  response = client.chat.completions(
      messages=[{
          "role": "user",
          "content": "Write a one-sentence bedtime story about a unicorn."
      }],
      model="llama3.2-3b",
  )
  print(response.choices[0].message.content)
  ```
</CodeGroup>

### Chat Completion with Streaming

<Warning>
  Streaming is not supported for the PremAI SDK. Will be supported in the future.
</Warning>

## Use with OpenAI SDK

### Install the OpenAI SDK

<CodeGroup>
  ```bash javascript/typescript theme={null}
  npm install openai
  ```

  ```bash python theme={null}
  pip install openai
  ```
</CodeGroup>

### List Models

<CodeGroup>
  ```javascript javascript/typescript theme={null}
  import OpenAI from 'openai';

  const client = new OpenAI({
    baseURL: "https://studio.premai.io/api/v1/",
    apiKey: process.env['PREMAI_API_KEY'], // This is the default and can be omitted
  });

  const response = await client.models.list();

  console.log(response.data);
  ```

  ```python python theme={null}
  import os
  from openai import OpenAI

  client = OpenAI(
      base_url="https://studio.premai.io/api/v1/",
      api_key=os.environ.get("PREMAI_API_KEY"),  # This is the default and can be omitted
  )
  response = client.models.list()
  print(response.data)
  ```
</CodeGroup>

### Chat Completions

<Note>
  The model name can be replaced with the name of your fine-tuned models as well.
</Note>

<CodeGroup>
  ```javascript javascript/typescript theme={null}
  import OpenAI from "openai";

  const client = new OpenAI({
      baseURL: "https://studio.premai.io/api/v1/",
      apiKey: process.env.PREMAI_API_KEY,
  });

  //Create a chat completion
  const response = await client.chat.completions.create({
      model: "llama3.2-3b", //Or any other model you want to use
      messages: [{ role: "user", content: "Write a one-sentence bedtime story about a unicorn." }]
  });

  console.log(response.choices[0].message.content);
  ```

  ```python python theme={null}
  import os
  from openai import OpenAI

  client = OpenAI(
      base_url="https://studio.premai.io/api/v1/",
      api_key=os.environ.get("PREMAI_API_KEY"),
  )

  # Create a chat completion
  response = client.chat.completions.create(
      messages=[{"role": "user", "content": "Who won the world series in 2020?"}],
      model="llama3.2-3b", # Or any other model you want to use
  )

  print(response.choices[0].message.content)
  ```
</CodeGroup>

### Chat Completion with Streaming

<CodeGroup>
  ```javascript javascript/typescript theme={null}
  import OpenAI from "openai";

  const client = new OpenAI({
      baseURL: "https://studio.premai.io/api/v1/",
      apiKey: process.env.PREMAI_API_KEY,
  });

  //Create a chat completion
  const response = await client.chat.completions.create({
      model: "llama3.2-3b", //Or any other model you want to use
      messages: [{ role: "user", content: "Write a one-sentence bedtime story about a unicorn." }],
      stream: true,
  });

  for await (const chunk of response) {
      process.stdout.write(chunk.choices[0].delta.content);
  }
  ```

  ```python python theme={null}
  import os
  from openai import OpenAI

  client = OpenAI(
      base_url="https://studio.premai.io/api/v1/",
      api_key=os.environ.get("PREMAI_API_KEY"),
  )

  # Create completion
  response = client.chat.completions.create(
      messages=[{"role": "user", "content": "Who won the world series in 2020?"}],
      model="llama3.2-3b", # Or any other model you want to use
      stream=True,
  )

  for chunk in response:
      print(chunk.choices[0].delta.content, end='', flush=True)
  ```
</CodeGroup>