Quickstart

Make your first API call to Oru-el in under 5 minutes.

Quickstart#

This guide walks you through making your first API call to Oru-el. You'll go from zero to a working LLM response in under 5 minutes.

1. Create an account#

2. Add funds to your wallet#

Navigate to Settings > Billing and add funds to your wallet. All API usage is billed against your pre-paid balance. You can start with as little as $1.

3. Create an API key#

Go to Settings > API Keys and click Create API Key. Give it a name (e.g., "development") and copy the key — it will only be shown once.

API keys start with oruel_ and look like this:

oruel_a1b2c3d4e5f6...

Store this key securely. You'll use it to authenticate all API requests.

4. Make your first API call#

Oru-el's inference API is fully compatible with the OpenAI SDK. Choose your preferred language below.

Python#

Install the OpenAI Python SDK:

pip install openai

Make a chat completion request:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.oru-el.com/v1/inference",
    api_key="oruel_your_api_key_here",
)

response = client.chat.completions.create(
    model="llama-4-maverick",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"},
    ],
    max_tokens=256,
)

print(response.choices[0].message.content)

JavaScript / TypeScript#

Install the OpenAI Node.js SDK:

npm install openai

Make a chat completion request:

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.oru-el.com/v1/inference",
  apiKey: "oruel_your_api_key_here",
});

const response = await client.chat.completions.create({
  model: "llama-4-maverick",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "What is the capital of France?" },
  ],
  max_tokens: 256,
});

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

cURL#

curl https://api.oru-el.com/v1/inference/chat/completions \
  -H "Authorization: Bearer oruel_your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-4-maverick",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What is the capital of France?"}
    ],
    "max_tokens": 256
  }'

Example response#

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1700000000,
  "model": "llama-4-maverick",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The capital of France is Paris."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 25,
    "completion_tokens": 8,
    "total_tokens": 33
  }
}

5. Try the Playground#

Don't want to write code yet? Use the Playground in the Oru-el dashboard to interact with models directly in your browser. You can:

Select any model from the catalog
Adjust parameters like temperature, max tokens, and top_p
Send messages and see responses in real time
Switch between models to compare outputs
View the equivalent API call for any conversation

Navigate to Playground in the sidebar to get started.

6. Explore further#

Now that you've made your first call, here's what to try next:

Streaming — get tokens as they're generated instead of waiting for the full response
Tool calling — let models call functions in your application
JSON mode — get structured JSON responses
Parameters — fine-tune model behavior with temperature, top_p, and more
Models — browse the full catalog of 100+ available models

Using environment variables#

For production use, never hardcode your API key. Use environment variables instead:

export ORUEL_API_KEY="oruel_your_api_key_here"

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.oru-el.com/v1/inference",
    api_key=os.environ["ORUEL_API_KEY"],
)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.oru-el.com/v1/inference",
  apiKey: process.env.ORUEL_API_KEY,
});