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Quickstart

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  1. Create an API key
  2. Install an SDK
  3. Send a request
  4. Stream the reply
  5. Next steps

Mortar speaks the OpenAI chat-completions API, so you can use the official OpenAI SDKs pointed at Mortar. In a few minutes you'll create an API key, send one request and stream a reply.

Create an API key

  1. Sign in and open API keys in your settings.
  2. Create a key and give it a name you'll recognise later, such as quickstart. It expires in 30 days unless you choose otherwise.
  3. Copy the key. It starts with mk_live_, and Mortar shows it only once.

Creating keys needs the Admin or Owner role in your organization. Authentication explains how keys are stored, how they expire, and how to rotate and revoke them.

Keep the key out of your code. Export it in the shell you'll run the examples from:

Terminalbash
export MORTAR_API_KEY="mk_live_your_key_here"

Install an SDK

Skip this step if you'll use curl.

pip install openai

Send a request

Every example sends the same request: one user message to Qwen/Qwen2.5-7B-Instruct, the model this deployment serves. The base URL is https://api.mortar.one/v1.

import os

from openai import OpenAI

client = OpenAI(
    base_url="https://api.mortar.one/v1",
    api_key=os.environ["MORTAR_API_KEY"],
)

completion = client.chat.completions.create(
    model="Qwen/Qwen2.5-7B-Instruct",
    messages=[{"role": "user", "content": "Explain prefix caching in one sentence."}],
    stream=False,
)
print(completion.choices[0].message.content)
Important

Pass stream explicitly. When a request leaves it out, Mortar streams the reply, and the OpenAI SDKs leave it out unless you set it. Without stream=False (Python) or stream: false (TypeScript), the SDK receives an event stream where it expects JSON.

The reply is a chat completion object. The answer is in choices[0].message.content, and usage counts the tokens you're billed for:

Responsejson
{
  "id": "chatcmpl-5d0e8f6a2b4c4e1f9a3b7c6d",
  "object": "chat.completion",
  "created": 1767225600,
  "model": "Qwen/Qwen2.5-7B-Instruct",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Prefix caching keeps the attention state for a prompt's opening tokens, so a request that starts like an earlier one skips recomputing them."
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 19,
    "completion_tokens": 29,
    "total_tokens": 48
  }
}

Stream the reply

Set stream to true and Mortar sends the reply as server-sent events while it is generated, so you can show the first words before the last are written.

import os

from openai import OpenAI

client = OpenAI(
    base_url="https://api.mortar.one/v1",
    api_key=os.environ["MORTAR_API_KEY"],
)

stream = client.chat.completions.create(
    model="Qwen/Qwen2.5-7B-Instruct",
    messages=[{"role": "user", "content": "Explain prefix caching in one sentence."}],
    stream=True,
)
for chunk in stream:
    delta = chunk.choices[0].delta if chunk.choices else None
    if delta and delta.content:
        print(delta.content, end="", flush=True)
print()

With curl you see the raw stream: one chat.completion.chunk per event, ending with [DONE]. The SDKs parse these frames for you.

Response
data: {"id":"chatcmpl-5d0e8f6a2b4c4e1f9a3b7c6d","object":"chat.completion.chunk","created":1767225600,"model":"Qwen/Qwen2.5-7B-Instruct","choices":[{"index":0,"delta":{"role":"assistant"},"logprobs":null,"finish_reason":null}]}

data: {"id":"chatcmpl-5d0e8f6a2b4c4e1f9a3b7c6d","object":"chat.completion.chunk","created":1767225600,"model":"Qwen/Qwen2.5-7B-Instruct","choices":[{"index":0,"delta":{"content":"Prefix caching keeps"},"logprobs":null,"finish_reason":null}]}

data: {"id":"chatcmpl-5d0e8f6a2b4c4e1f9a3b7c6d","object":"chat.completion.chunk","created":1767225600,"model":"Qwen/Qwen2.5-7B-Instruct","choices":[{"index":0,"logprobs":null,"finish_reason":"stop"}]}

data: [DONE]

Stream responses covers the frame format in full, including usage reporting, cancellation and errors that arrive mid-stream.

Next steps