# Messages

**Endpoint**

`POST https://llm.onerouter.pro/v1/messages`

### Basic message

Create a non-streaming message.

**Example request**

{% tabs %}
{% tab title="Python" %}

```python
import os
import anthropic
 
client = anthropic.Anthropic(
    api_key="<API_KEY>",
    base_url='https://llm.onerouter.pro'
)
 
message = client.messages.create(
    model='claude-sonnet-4-5@20250929',
    max_tokens=150,
    messages=[
        {
            'role': 'user',
            'content': 'Write a one-sentence bedtime story about a unicorn.'
        }
    ],
    temperature=0.7,
)
 
print('Response:', message.content[0].text)
print('Usage:', message.usage)
```

{% endtab %}

{% tab title="TypeScript" %}

```typescript
import Anthropic from '@anthropic-ai/sdk';
 
const anthropic = new Anthropic({
  "<API_KEY>",
  baseURL: 'https://llm.onerouter.pro',
});
 
const message = await anthropic.messages.create({
  model: 'claude-sonnet-4-5@20250929',
  max_tokens: 150,
  messages: [
    {
      role: 'user',
      content: 'Write a one-sentence bedtime story about a unicorn.',
    },
  ],
  temperature: 0.7,
});
 
console.log('Response:', message.content[0].text);
console.log('Usage:', message.usage);
```

{% endtab %}
{% endtabs %}

**Response format**

```json
{
  "id": "msg_bdrk_01DPGprdvqXyegS3qZVgD3mU",
  "content": [
    {
      "citations": null,
      "text": "A sleepy unicorn with a shimmering silver mane curled up beneath a rainbow, closed her eyes, and dreamed of dancing through clouds made of cotton candy.",
      "type": "text"
    }
  ],
  "model": "claude-sonnet-4-5@20250929",
  "role": "assistant",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "type": "message",
  "usage": {
    "cache_creation_input_tokens": 0,
    "cache_read_input_tokens": 0,
    "input_tokens": 20,
    "output_tokens": 39,
    "server_tool_use": null,
    "service_tier": null,
    "cache_creation": {
      "ephemeral_1h_input_tokens": 0,
      "ephemeral_5m_input_tokens": 0
    }
  }
}
```

### Streaming messages

Create a streaming message that delivers tokens as they are generated.

**Example request**

{% tabs %}
{% tab title="Python" %}

```python
import os
import anthropic
 
client = anthropic.Anthropic(
    api_key="<API_KEY>",
    base_url='https://llm.onerouter.pro'
)
 
with client.messages.stream(
    model='claude-sonnet-4-5@20250929',
    max_tokens=150,
    messages=[
        {
            'role': 'user',
            'content': 'Write a one-sentence bedtime story about a unicorn.'
        }
    ],
    temperature=0.7,
) as stream:
    for text in stream.text_stream:
        print(text, end='', flush=True)
```

{% endtab %}

{% tab title="TypeScript" %}

```typescript
import Anthropic from '@anthropic-ai/sdk';
 
const anthropic = new Anthropic({
  "<API_KEY>",
  baseURL: 'https://llm.onerouter.pro',
});
 
const stream = await anthropic.messages.create({
  model: 'claude-sonnet-4-5@20250929',
  max_tokens: 150,
  messages: [
    {
      role: 'user',
      content: 'Write a one-sentence bedtime story about a unicorn.',
    },
  ],
  temperature: 0.7,
  stream: true,
});
 
for await (const event of stream) {
  if (event.type === 'content_block_delta') {
    if (event.delta.type === 'text_delta') {
      process.stdout.write(event.delta.text);
    }
  }
}
```

{% endtab %}
{% endtabs %}

**Streaming event types**

Streaming responses use [Server-Sent Events (SSE)](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events). The key event types are:

* `message_start` - Initial message metadata
* `content_block_start` - Start of a content block (text, tool use, etc.)
* `content_block_delta` - Incremental content updates
* `content_block_stop` - End of a content block
* `message_delta` - Final message metadata (stop reason, usage)
* `message_stop` - End of the message


---

# 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://infronai.gitbook.io/docs/llm-apis/anthropic-compatible-api/messages.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.