Choose the model your agent needs

The problem

An agent set up to use one AI service cannot automatically use another. Connecting each service separately takes extra work and often means managing another account. That makes it harder to choose a different AI service for a particular task.

How NativePort helps

NativePort connects to different AI providers, such as OpenAI, Anthropic, and xAI, through one account. Your agent can use the available services for different tasks or compare their answers. This gives it more choices without requiring a separate connection to each provider.

Let your agent build it

You don’t need to write this code yourself. Copy the prompt and paste it into your agent. It will follow NativePort’s setup guide, help you use an existing account or create one, read this page, and ask what you’d like to do next.

View prompt

Help me use NativePort for this use case.

1. Read https://nativeport.ai/SKILL.md and follow its instructions to learn how to use NativePort. Save or update the skill if your environment supports it.

2. Check whether I already have a working NativePort connection or account. Reuse it if available. Otherwise, help me sign in or create an account by following the skill's setup instructions. Guide me through any steps that require me to use a browser, then help connect my agent. Keep credentials private.

3. Follow the skill to learn how to find the right tools, check their prices and instructions, and use them through NativePort.

4. Read this entire use-case page, including the instructions for agents inside the accordion: https://nativeport.ai/use-cases/chat-across-models/

Once setup is complete and you have read the page, briefly explain what you can help me do and ask me what I would like to do next. Wait for my answer before implementing the example or running paid tools.

Instructions for the agents - You don't need to read this

Technical implementation

Use Python 3 with requests installed (python -m pip install requests). Set NATIVEPORT_API_KEY in your environment to your NativePort key. Run the snippets on your server, where your key stays private.

Choose an available model with GET /inference/v1/models and set its full provider/model ID as NATIVEPORT_MODEL. The examples use the common text chat endpoint; they do not assume access to a particular model.

python

import os
import requests

BASE = "https://api.nativeport.ai"
HEADERS = {"Authorization": f"Bearer {os.environ['NATIVEPORT_API_KEY']}"}

catalog = requests.get(BASE + "/inference/v1/models", headers=HEADERS, timeout=30)
catalog.raise_for_status()
for model in catalog.json()["data"]:
    print(model["id"])

def chat(messages):
    response = requests.post(BASE + "/inference/v1/chat/completions",
        headers=HEADERS,
        json={"model": os.environ["NATIVEPORT_MODEL"], "messages": messages,
              "max_tokens": 800}, timeout=120)
    response.raise_for_status()
    return response.json()["choices"][0]["message"]["content"]

answer = chat([{"role": "user", "content": "Explain how a heat pump works in three sentences."}])
print(answer)

Set NATIVEPORT_MODEL to another returned ID to try that model. Hugging Face entries use IDs beginning with huggingface/; availability depends on your account. NativePort routes the chosen model explicitly, without automatic fallback. Keep optional parameters within the common inference contract.

Tool costs

Tool used in the example	Price per call
NativePort Inference — chat	Varies by selected model and token usage

Prices in USD. Usage-based tools have no fixed per-call price. View pricing.

The problem

How NativePort helps

Let your agent build it

Technical implementation

Tool costs

Keep exploring

Check what a model can do before choosing it

Get AI answers in a consistent structured format

Give Claude more room to reason through a problem

Explore all use cases