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

# List Models

> List available LLM models for AI agents

Retrieve all LLM models available for your AI agents to use during conversations.

## Request Body

<ParamField body="provider" type="string">
  Filter by provider: `openai`.
</ParamField>

## Response

<ResponseField name="models" type="array">
  Array of model objects.
</ResponseField>

### Model Object

<ResponseField name="id" type="string">
  Model identifier to use in agent configuration.
</ResponseField>

<ResponseField name="name" type="string">
  Model display name.
</ResponseField>

<ResponseField name="provider" type="string">
  Model provider: `openai`.
</ResponseField>

<ResponseField name="description" type="string">
  Brief description of the model's characteristics.
</ResponseField>

<ResponseField name="latency" type="string">
  Expected latency level: `lowest`, `low`, or `medium`.
</ResponseField>

<ResponseField name="is_default" type="boolean">
  Whether this is the default model for new agents.
</ResponseField>

<RequestExample>
  ```bash cURL theme={null}
  curl -X POST https://api.magpipe.ai/functions/v1/list-models \
    -H "Authorization: Bearer YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{}'
  ```

  ```javascript Node.js theme={null}
  const response = await fetch(
    'https://api.magpipe.ai/functions/v1/list-models',
    {
      method: 'POST',
      headers: {
        'Authorization': 'Bearer YOUR_API_KEY',
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({}),
    }
  );

  const { models } = await response.json();
  console.log('Available models:', models.length);
  ```

  ```python Python theme={null}
  import requests

  response = requests.post(
      'https://api.magpipe.ai/functions/v1/list-models',
      headers={
          'Authorization': 'Bearer YOUR_API_KEY',
          'Content-Type': 'application/json',
      },
      json={}
  )

  data = response.json()
  for model in data['models']:
      print(f"{model['name']} - {model['description']}")
  ```
</RequestExample>

<ResponseExample>
  ```json Success Response theme={null}
  {
    "models": [
      {
        "id": "gpt-4.1-nano",
        "name": "GPT-4.1 Nano",
        "provider": "openai",
        "description": "Fastest response time, optimized for voice",
        "latency": "lowest",
        "is_default": true
      },
      {
        "id": "gpt-4o-mini",
        "name": "GPT-4o Mini",
        "provider": "openai",
        "description": "Fast and cost-effective",
        "latency": "low",
        "is_default": false
      },
      {
        "id": "gpt-4o",
        "name": "GPT-4o",
        "provider": "openai",
        "description": "Most capable, higher latency",
        "latency": "medium",
        "is_default": false
      }
    ]
  }
  ```
</ResponseExample>