# Quickstart

> Make your first request in two minutes: create a key, point an SDK at zurelay, send a message.

Source: https://zurelay.com/docs/quickstart

Three steps: a key, two settings, one request.

## 1. Create an account and a key

1. [Create an account](https://zurelay.com/signup) (no password: we email you a code) and add credit under [Billing](https://zurelay.com/app/billing).
2. Open [API keys](https://zurelay.com/app/keys), name a key and create it. Copy it right away: you see the full key once.

## 2. Set the key and base URL

Keep the key out of your code. In your shell, or in a .env file your app loads:

.env:

```
ZURELAY_API_KEY=zr_live_your_key_here
```

Then install an SDK if you don’t have one: `pip install openai` for Python, `npm install openai` for Node. Anthropic’s SDKs work too (see [Anthropic Messages](https://zurelay.com/docs/anthropic)).

## 3. Send a request

**Python** (`main.py`):

```python
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.zurelay.com/v1",
    api_key=os.environ["ZURELAY_API_KEY"],
)

response = client.chat.completions.create(
    model="gpt-6-sol",
    messages=[
      {
        "role": "system",
        "content": "You are a concise assistant."
      },
      {
        "role": "user",
        "content": "Give me three names for a coffee shop on the moon."
      }
    ],
)

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

**Node.js** (`index.mjs`):

```javascript
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.zurelay.com/v1",
  apiKey: process.env.ZURELAY_API_KEY,
});

const response = await client.chat.completions.create({
  "model": "gpt-6-sol",
  "messages": [
    {
      "role": "system",
      "content": "You are a concise assistant."
    },
    {
      "role": "user",
      "content": "Give me three names for a coffee shop on the moon."
    }
  ]
});

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

**cURL**:

```bash
curl https://api.zurelay.com/v1/chat/completions \
  -H "Authorization: Bearer $ZURELAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "gpt-6-sol",
  "messages": [
    {
      "role": "system",
      "content": "You are a concise assistant."
    },
    {
      "role": "user",
      "content": "Give me three names for a coffee shop on the moon."
    }
  ]
}'
```

The answer comes back in OpenAI’s format, with the model that answered and the tokens used:

Response:

```json
{
  "id": "chatcmpl-...",
  "object": "chat.completion",
  "created": 1790870400,
  "model": "gpt-6-sol",
  "choices": [
    {
      "index": 0,
      "message": { "role": "assistant", "content": "1. Lunar Grind\n2. ..." },
      "finish_reason": "stop"
    }
  ],
  "usage": { "prompt_tokens": 31, "completion_tokens": 24, "total_tokens": 55 }
}
```

## Stream it

Add `stream: true` to get the answer as it’s written, the same way OpenAI streams it.

**Python** (`main.py`):

```python
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.zurelay.com/v1",
    api_key=os.environ["ZURELAY_API_KEY"],
)

stream = client.chat.completions.create(
    model="gemini-3.7-flash",
    messages=[
      {
        "role": "user",
        "content": "Write a haiku about latency."
      }
    ],
    stream=True,
)

for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)
```

**Node.js** (`index.mjs`):

```javascript
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.zurelay.com/v1",
  apiKey: process.env.ZURELAY_API_KEY,
});

const stream = await client.chat.completions.create({
  "model": "gemini-3.7-flash",
  "messages": [
    {
      "role": "user",
      "content": "Write a haiku about latency."
    }
  ],
  "stream": true
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}
```

**cURL**:

```bash
curl https://api.zurelay.com/v1/chat/completions \
  -H "Authorization: Bearer $ZURELAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "gemini-3.7-flash",
  "messages": [
    {
      "role": "user",
      "content": "Write a haiku about latency."
    }
  ],
  "stream": true
}'
```

## Next

- [Pick a model](https://zurelay.com/docs/models): IDs, prices and what each model reads.
- [Send a picture or a PDF](https://zurelay.com/docs/multimodal): Files go in the message, as data or a link.
- [Make an image](https://zurelay.com/docs/images): One call to /v1/images/generations.
- [Use your tools](https://zurelay.com/docs/integrations): Claude Code, Cursor, LangChain and more.