# Getting Started

This guide gets you from zero to first successful request in a couple of minutes using the OpenAI-compatible endpoints.

***

### 1. Create an API Key

1. Log in to your AlphaNeural Dashboard
2. Navigate to **API Keys**
3. Click **Create Key**
4. Copy your key (starts with `sk-...`)

> Treat your API key like a password. Never expose it publicly.

***

### 2. Set your environment variables

```bash
export ALPHANEURAL_API_KEY="YOUR_API_KEY"
export ALPHANEURAL_BASE_URL="https://proxy.alfnrl.io/v1"
```

### 3. Make your first request (Chat Completions)

Chat Completions is the quickest end-to-end smoke test.

### &#x20;<sup>cURL Example</sup>

```bash
curl "$ALPHANEURAL_BASE_URL/chat/completions" \
  -H "Authorization: Bearer $ALPHANEURAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3",
    "messages": [{"role":"user","content":"Hello AlphaNeural"}]
  }'
```

This endpoint follows the OpenAI Chat Completions shape.

* ### Use the OpenAI SDK (Python)

AlphaNeural is OpenAI-compatible, so you can point the OpenAI SDK at AlphaNeural by setting the base URL.

```python
from openai import OpenAI
import os

client = OpenAI(
  api_key=os.environ["ALPHANEURAL_API_KEY"],
  base_url=os.environ["ALPHANEURAL_BASE_URL"],
)

resp = client.chat.completions.create(
  model="qwen3",
  messages=[{"role":"user","content":"Write a haiku about routers"}],
)
print(resp.choices[0].message.content)

```

* ### <sup>Discover available models</sup>

List models exposed by the proxy:

```bash
curl "$ALPHANEURAL_BASE_URL/models" \
  -H "Authorization: Bearer $ALPHANEURAL_API_KEY"
```

Model listing is available at `GET /v1/models`


---

# 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.alphaneural.io/getting-started.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.