API Quickstart

Make your first AVELIN API call in under five minutes. AVELIN is an AI Laboratory that builds Cross-Model MoE (Mixture-of-Experts) models — trained AI systems that fuse knowledge from multiple model architectures into single unified models. We don't just route; we build and fuse. The API serves these models through a single governed endpoint.

Enterprise licensing available: Deploy AVELIN models on your own GPUs with full data sovereignty. Request a POC for enterprise licensing options.

The API is a drop-in replacement: change only 2 lines of code (base URL and API key) to migrate from OpenAI or Anthropic.

1. Get an API key

Create a key from your AVELIN account. It will look like:

sk-ave...xxxx

Keep it secret. Set it as an environment variable so it never lands in code:

export AVELIN_API_KEY="sk-ave...xxxx"

2. Choose your track

Both tracks are backed by the same models — pick whichever matches your stack.

OpenAI track

Base URL: https://api.avelin.ai/v1
Endpoint: POST /v1/chat/completions

cURL

curl https://api.avelin.ai/v1/chat/completions \
  -H "Authorization: Bearer sk-avelin-YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "avelin-pro",
    "messages": [
      { "role": "user", "content": "Give me three productivity tips." }
    ]
  }'

Python (OpenAI SDK)

from openai import OpenAI

client = OpenAI(
    base_url="https://api.avelin.ai/v1",
    api_key="sk-ave...xxxx",  # or os.environ["AVELIN_API_KEY"]
)

resp = client.chat.completions.create(
    model="avelin-pro",
    messages=[{"role": "user", "content": "Give me three productivity tips."}],
)
print(resp.choices[0].message.content)

JavaScript / TypeScript (OpenAI SDK)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.avelin.ai/v1",
  apiKey: process.env.AVELIN_API_KEY,
});

const resp = await client.chat.completions.create({
  model: "avelin-pro",
  messages: [{ role: "user", content: "Give me three productivity tips." }],
});
console.log(resp.choices[0].message.content);

Response shape (OpenAI)

{
  "id": "chatcmpl-...",
  "object": "chat.completion",
  "model": "avelin-pro",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "1. Time-block your calendar...",
        "reasoning_content": "The user wants practical tips..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 180,
    "total_tokens": 192
  }
}

Streaming (OpenAI)

stream = client.chat.completions.create(
    model="avelin-pro",
    messages=[{"role": "user", "content": "Write a haiku about sovereignty."}],
    stream=True,
)
for chunk in stream:
    delta = chunk.choices[0].delta
    if delta.content:
        print(delta.content, end="", flush=True)

Anthropic track

Base URL: https://api.avelin.ai
Endpoint: POST /v1/messages
Header: anthropic-version: 2023-06-01

cURL

curl https://api.avelin.ai/v1/messages \
  -H "Authorization: Bearer sk-avelin-YOUR_KEY" \
  -H "Content-Type: application/json" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "avelin-pro",
    "max_tokens": 1024,
    "messages": [
      { "role": "user", "content": "Give me three productivity tips." }
    ]
  }'

Python (Anthropic SDK)

from anthropic import Anthropic

client = Anthropic(
    base_url="https://api.avelin.ai",
    api_key="sk-ave...xxxx",  # or os.environ["AVELIN_API_KEY"]
)

msg = client.messages.create(
    model="avelin-pro",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Give me three productivity tips."}],
)
print(msg.content[0].text)

JavaScript / TypeScript (Anthropic SDK)

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  baseURL: "https://api.avelin.ai",
  apiKey: process.env.AVELIN_API_KEY,
});

const msg = await client.messages.create({
  model: "avelin-pro",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Give me three productivity tips." }],
});
console.log(msg.content[0].text);

Response shape (Anthropic)

{
  "id": "resp_...",
  "type": "message",
  "role": "assistant",
  "model": "avelin-pro",
  "content": [
    { "type": "text", "text": "1. Time-block your calendar..." }
  ],
  "stop_reason": "end_turn",
  "usage": { "input_tokens": 12, "output_tokens": 180, "total_tokens": 192 }
}

System prompts (Anthropic)

The Anthropic surface uses a top-level system parameter instead of a system message in the messages array:

msg = client.messages.create(
    model="avelin-coding-fast",
    max_tokens=1024,
    system="You are a senior software engineer.",
    messages=[{"role": "user", "content": "Write a debounce decorator."}],
)

Thinking control (Anthropic)

Extended thinking is enabled by default on reasoning-capable models. To disable:

msg = client.messages.create(
    model="avelin-pro",
    max_tokens=1024,
    thinking={"type": "disabled"},
    messages=[{"role": "user", "content": "Summarize this document."}],
)

When thinking is enabled, the response includes a separate thinking content block before the final text block.

Streaming (Anthropic)

with client.messages.stream(
    model="avelin-pro",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Write a haiku about sovereignty."}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

3. Pick the right model

All models are built with AVELIN's Cross-Model MoE technology in our AI Lab.

Cost savings vs. direct providers:

• Intelligence family: up to 85% cheaper
• Coding family: up to 86% cheaper
• Agentic family: up to 87% cheaper

Full descriptions: Model Catalog.

Key differences between tracks

Next steps

• Full API Reference — every endpoint, parameter, and response shape
• SDKs & Tools — CLI and editor integration configuration
• Model Catalog — capabilities and selection guidance