This page describes the SDK¹s that can be used to call our API.

{% hint style="info" %} Comparing requests made with raw REST API and different SDKs, pay attention to the following common aspects:

• how the Authorization header and the AIML API key are provided,
• how the POST method and the endpoint URL are specified,
• how the input parameters are passed. {% endhint %}

{% hint style="success" %} Also take a look at the INTEGRATIONS section — it covers many third-party services and libraries (workflow platforms, coding assistants, etc.) that allow you to integrate our models in various ways. {% endhint %}

We use the REST API because it’s fast, simple, and easy to understand. Only in Python do you need to import a separate library (requests), while cURL and JavaScript (Node.js) already have built-in support for HTTP requests. Therefore, REST API is used in the documentation examples for all of our models.

In Python examples, you need to import the requests library. The Node.js and cURL examples do not require any additional imports.

Install the library first:

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

pip install requests

{% endtab %} {% endtabs %}

Import the library in every Python code snippet where you make calls to the REST API.

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

import requests

{% endtab %} {% endtabs %}

Our API authorization is based on a Bearer token. Include it in the Authorization HTTP header within the request. Example:

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

  --header 'Authorization: Bearer <YOUR_AIMLAPI_KEY>' \

{% endtab %}

{% tab title="JavaScript" %}

  headers: {
    Authorization: "Bearer <YOUR_AIMLAPI_KEY>",
  },

{% endtab %}

{% tab title="Python" %}

    headers={
        "Authorization": "Bearer <YOUR_AIMLAPI_KEY>",
    },

{% endtab %} {% endtabs %}

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

curl --request POST \
  --url https://api.aimlapi.com/chat/completions \
  --header 'Authorization: Bearer <YOUR_AIMLAPI_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "google/gemma-3-4b-it",
    "messages": [
        {
            "role": "user",
            "content": "What kind of model are you?"
        }
    ],
    "max_tokens": 512
}'

{% endtab %}

{% tab title="JavaScript" %}

fetch("https://api.aimlapi.com/chat/completions", {
  method: "POST",
  headers: {
    Authorization: "Bearer <YOUR_AIMLAPI_KEY>",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "google/gemma-3-4b-it",
    messages: [
      {
        role: "user",
        content: "What kind of model are you?",
      },
    ],
    max_tokens: 512,
  }),
})
  .then((res) => res.json())
  .then(console.log);

{% endtab %}

{% tab title="Python" %}

import requests
import json  # for getting a structured output with indentation

response = requests.post(
    url="https://api.aimlapi.com/chat/completions",
    headers={
        "Authorization": "Bearer <YOUR_AIMLAPI_KEY>",
        "Content-Type": "application/json",
    },
    data=json.dumps(
        {
            "model": "google/gemma-3-4b-it",
            "messages": [
                {
                    "role": "user",
                    "content": "What kind of model are you?",
                },
            ],
            "max_tokens": 512
        }
    ),
)

response.raise_for_status()
print(response.json())

{% endtab %} {% endtabs %}

The OpenAI SDK is a convenient library that simplifies working with our API. It automatically handles JSON responses, includes built-in error handling and retry logic, and provides simple, easy-to-use methods for all API features such as chat, embeddings, and completions.

pip install openai

import openai

npm install openai

import OpenAI from "openai";

{% tabs %} {% tab title="Python" %} {% code overflow="wrap" %}

from openai import OpenAI

# Insert your AIML API key in the quotation marks instead of <YOUR_AIMLAPI_KEY>:
api_key = "<YOUR_AIMLAPI_KEY>" 
base_url = "https://api.aimlapi.com/v1"
user_prompt = "Tell me about San Francisco"

api = OpenAI(api_key=api_key, base_url=base_url)


def main():
    completion = api.chat.completions.create(
        model="google/gemma-3-4b-it",
        messages=[
            {
              "role": "user", 
              "content": user_prompt
            },
        ],
        temperature=0.7,
        max_tokens=256,
    )

    response = completion.choices[0].message.content
    print("User:", user_prompt)
    print("AI:", response)


if __name__ == "__main__":
    main()

{% endcode %} {% endtab %}

{% tab title="JavaScript" %} {% code overflow="wrap" %}

#!/usr/bin/env node

const OpenAI = require("openai");
const baseURL = "https://api.aimlapi.com/v1";
const apiKey = "<YOUR_AIMLAPI_KEY>";
const systemPrompt = "You are a travel agent. Be descriptive and helpful.";
const userPrompt = "Tell me about San Francisco";

const api = new OpenAI({
  apiKey,
  baseURL,
});

const main = async () => {
  try {
    const completion = await api.chat.completions.create({
      model: "gpt-4o",
      messages: [
        {
          role: "system",
          content: systemPrompt,
        },
        {
          role: "user",
          content: userPrompt,
        },
      ],
      temperature: 0.7,
      max_tokens: 256,
    });

    const response = completion.choices[0].message.content;

    console.log("User:", userPrompt);
    console.log("AI:", response);
  } catch (error) {
      console.error("Error:", error.message);
  }
};

main();

{% endcode %} {% endtab %} {% endtabs %}

We have started developing our own SDK to simplify the use of our service.

{% hint style="success" %} If you’d like to contribute to expanding its functionality, feel free to reach out to us on Discord! {% endhint %}

After obtaining your AIML API key, create an .env file and copy the required contents into it.

touch .env

Copy the code below, paste it into your .env file, and set your API key in AIML_API_KEY="<YOUR_AIMLAPI_KEY>", replacing <YOUR_AIMLAPI_KEY> with your actual key:

AIML_API_KEY = "<YOUR_AIMLAPI_KEY>"
AIML_API_URL = "https://api.aimlapi.com/v1"

Install the package:

# install from PyPI
pip install aimlapi-sdk-python

{% tabs %} {% tab title="Python" %} {% code overflow="wrap" %}

from aiml_api import AIML_API

api = AIML_API()

completion = api.chat.completions.create(
    model = "mistralai/Mistral-7B-Instruct-v0.2",
    messages = [
        {"role": "user", "content": "Explain the importance of low-latency LLMs"},
    ],
    temperature = 0.7,
    max_tokens = 256,
)

response = completion.choices[0].message.content
print("AI:", response)

{% endcode %} {% endtab %} {% endtabs %}

To execute the script, use:

python3 <your_script_name>.py

• Check our full list of model IDs
• Browse and compare AI models, including GPT, Claude, and many others, using the Playground
• Learn more about special text model capabilities
• Join the community: get help and share your projects in our Discord