Supported SDKs [OLD]

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

Comparing requests made with different SDKs, pay attention to the following common aspects across all SDKs:

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.

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.

REST API

This SDK is simple to use, and the required requests library is commonly preinstalled in many environments. Therefore, this SDK is used in the documentation examples for all of our models.

Authorization

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

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

Request Example

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

import requests
import json

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

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

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

OpenAI

The OpenAI SDK is a nice module that allows us to use the AI/ML API without dealing with repetitive boilerplate code for handling cURL requests.

In the setting up article, we showed an example of how to use the OpenAI SDK with the AI/ML API. We configured the environment from the very beginning and executed our request to the AI/ML API.

The AI features that the OpenAI SDK supports

Streaming
Completions
Chat Completions
Audio
Beta Assistants
Beta Threads
Embeddings
Image Generation
File Uploads

Example Code

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()

#!/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();

AI/ML API Python library

We have started developing our own SDK to simplify the use of our service. Currently, it supports only chat completion and embedding models.

If you’d like to contribute to expanding its functionality, feel free to reach out to us on Discord!

Installation

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 aiml_api package:

# install from PyPI
pip install aiml_api

Request Example

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)

To execute the script, use:

python3 <your_script_name>.py

Next Steps

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REST API

Authorization

Request Example

OpenAI

Example Code

AI/ML API Python library

Installation

Request Example

Next Steps