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Presets let you bundle a model, system prompt, and generation parameters into a single reusable configuration. Once created, you reference a preset exactly like a model ID — no extra code, no repeated config. Presets can be private (only your API key can use them) or public (shareable with anyone).

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Creating a Preset

Go to Dashboard → Presets → Create Preset and fill in the fields:
1

Name your preset

Give it a short, descriptive name. This is for your reference only.
2

Choose a model

Select any supported model, or choose Any Model to leave the model open at request time. With Any Model, callers must specify the model using the @ syntax — see Overriding the model below.
3

Write a system prompt

Enter the system prompt that should apply to every request using this preset. This is the main reason to use presets — ship a locked, versioned persona or instruction set without exposing the prompt in client code.
4

Set parameters (optional)

Configure defaults like temperature, max_tokens, top_p, and other generation parameters. These apply automatically to every request unless overridden.
5

Set visibility

Choose Private (default) or Public. Public presets can be used by anyone with the preset ID.

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Using a Preset

After creating a preset, copy its ID from the dashboard. It looks like: 
presets/0ede00c3-b202-4102-bda7-ec9a4b398767
Paste it into the model field of any Chat Completions request — everything else stays the same. 
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.routeway.ai/v1",
    api_key=os.getenv("ROUTEWAY_API_KEY")
)

response = client.chat.completions.create(
    model="presets/0ede00c3-b202-4102-bda7-ec9a4b398767",
    messages=[
        {"role": "user", "content": "hi"}
    ],
)

print(response.choices[0].message.content)
The preset’s system prompt and parameters are applied server-side — your request payload only needs model and messages.

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Overriding the Model

If a preset was created with Any Model, the model must be specified at request time using @ after the preset ID: 
presets/0ede00c3-b202-4102-bda7-ec9a4b398767@gpt-4o

response = client.chat.completions.create(
    model="presets/0ede00c3-b202-4102-bda7-ec9a4b398767@gpt-4o",
    messages=[
        {"role": "user", "content": "hi"}
    ],
)
This is useful when you want a shared system prompt and parameter set but need the caller to decide which underlying model to use — for example, letting users pick between gpt-4o and gpt-4o-mini while keeping the same persona.

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Streaming with Presets

Presets work with all standard Chat Completions parameters, including stream: 
stream = client.chat.completions.create(
    model="presets/0ede00c3-b202-4102-bda7-ec9a4b398767",
    messages=[
        {"role": "user", "content": "Tell me a short story."}
    ],
    stream=True,
)

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

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Public vs Private Presets

PrivatePublic
Who can use itOnly your API keyAnyone with the preset ID
Visible in dashboardYesYes
System prompt exposedNoYes
Use caseInternal apps, personal toolingShared assistants, published integrations
Public presets expose the system prompt to anyone who has the preset ID. Don’t include secrets, internal instructions, or sensitive information in the system prompt of a public preset.

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When to Use Presets

Ship locked personas

Define a customer support agent, coding assistant, or brand voice once and reference it by ID across all your apps.

Version your prompts

Create a new preset when you iterate on a system prompt instead of updating in-place, so you can roll back.

Share with teammates

Public presets let anyone on your team (or your users) call a consistent configuration without needing to manage the prompt themselves.

Keep clients simple

Offload model selection and prompt engineering to the dashboard. Client code only needs the preset ID.