# API Usage Examples

The only pre-requisite to start using Alpha API is to obtain a token. Learn about it in [Developer API](/developers/developer-api.md#authentification).

## Checking Account Status

***

The code sample below checks the account status of the API token owner: API Credits balance, User Tier, and $AIKEK balance. This API endpoint is free.

{% tabs %}
{% tab title="Python" %}

```python
import requests  # pip install requests

api_key = "YOUR_TOKEN_HERE"
url = "https://api.alphakek.ai/account"

response = requests.get(url, headers={"Authorization": f'Bearer {api_key}'})

user_data = response.json()

print(f"User credits: {user_data['credits']}")
print(f"User tier: {user_data['tier']}")
print(f"Amount of $AIKEK in USD: {user_data['tokens_usd']}")
```

{% endtab %}

{% tab title="JavaScript" %}

```javascript
const api_key = "YOUR_TOKEN_HERE";
const url = "https://api.alphakek.ai/account";

fetch(url, {
  method: 'GET',
  headers: {
    'Authorization': `Bearer ${api_key}`
  }
})
.then(response => {
  if (response.ok) {
    return response.json();
  }
  throw new Error('Network response was not ok.');
})
.then(user_data => {
  console.log(`User credits: ${user_data['credits']}`);
  console.log(`User tier: ${user_data['tier']}`);
  console.log(`Amount of $AIKEK in USD: ${user_data['tokens_usd']}`);
})
.catch(error => {
  console.error('There has been a problem with your fetch operation:', error);
});
```

{% endtab %}
{% endtabs %}

## Comparing Our AI Models

***

This code sample calls [Developer API](/developers/developer-api.md) for each of the three available [AI Playground](/research/ai-playground.md) [Universal Agents](/launch/universal-agents.md), asks them the same question, and returns a streaming response.

**Note**: The code sample below spends 6 API Credits upon running.

{% tabs %}
{% tab title="Python" %}

```python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_TOKEN_HERE",
    base_url="https://api.alphakek.ai/v1"
)
question = "What to expect from Ethereum ETFs?"
print("Question:", question)

models = ["versa", "nexus", "eclipse"]
for model in models:
    response = client.chat.completions.create(
        model=model,
        messages=[
            {
                'role': 'user',
                'content': question
            }
        ],
        stream=True,
    )

    print(f"\n\nResponse from the {model.capitalize()} model:")
    for chunk in response:
        if chunk.choices[0].delta.content is None:
            continue
        print(chunk.choices[0].delta.content, end="")

```

{% endtab %}

{% tab title="JavaScript" %}

```javascript
import OpenAI from 'openai';

const apiKey = "YOUR_TOKEN_HERE";
const client = new OpenAI({
    apiKey: apiKey,
    baseURL: 'https://api.alphakek.ai/v1'
});

const question = "What to expect from Ethereum ETFs?";
console.log("Question:", question);

const models = ["versa", "nexus", "eclipse"];
models.forEach(async model => {
    try {
        const stream = await client.chat.completions.create({
            model: model,
            messages: [{
                role: 'user',
                content: question
            }],
            stream: true,
        });

        console.log(`\n\nResponse from the ${model.charAt(0).toUpperCase() + model.slice(1)} model:`);
        for await (const chunk of stream) {
            if (chunk?.choices[0]?.delta?.content) {
                process.stdout.write(chunk.choices[0].delta.content);
            }
        }
    } catch (error) {
        console.error('Error handling:', error);
    }
});
```

{% endtab %}
{% endtabs %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.alphakek.ai/developers/api-usage-examples.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.