Use an SDK

Point an SDK at your airmux inference endpoint, use a workspace inference key, and choose a model ID from the gateway catalog. OpenAI-compatible SDKs use /inf/v1; the Anthropic SDK uses /inf. airmux routes each request to the selected provider. Set these values for the examples:

export AIRMUX_URL='http://localhost:8080'
export AIRMUX_INFERENCE_KEY='sk-inf-your-key'
export AIRMUX_MODEL='openai/gpt-4o-mini'

import os
from openai import OpenAI

client = OpenAI(
    base_url=f"{os.environ['AIRMUX_URL'].rstrip('/')}/inf/v1",
    api_key=os.environ['AIRMUX_INFERENCE_KEY'],
)

response = client.responses.create(
    model=os.environ['AIRMUX_MODEL'],
    input="Explain request routing in one sentence.",
)
print(response.output_text)

The Messages endpoint uses a base URL ending in /inf; the SDK appends /v1/messages.

import os
from anthropic import Anthropic

client = Anthropic(
    base_url=f"{os.environ['AIRMUX_URL'].rstrip('/')}/inf",
    api_key=os.environ['AIRMUX_INFERENCE_KEY'],
)

message = client.messages.create(
    model="anthropic/claude-haiku-4-5-20251001",
    max_tokens=256,
    messages=[{"role": "user", "content": "Explain request routing in one sentence."}],
)
print(message.content[0].text)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: `${process.env.AIRMUX_URL}/inf/v1`,
  apiKey: process.env.AIRMUX_INFERENCE_KEY,
});

const response = await client.responses.create({
  model: process.env.AIRMUX_MODEL,
  input: "Explain request routing in one sentence.",
});
console.log(response.output_text);

package main

import (
	"context"
	"fmt"
	"os"

	"github.com/openai/openai-go/v3"
	"github.com/openai/openai-go/v3/option"
	"github.com/openai/openai-go/v3/responses"
)

func main() {
	client := openai.NewClient(
		option.WithBaseURL(os.Getenv("AIRMUX_URL") + "/inf/v1"),
		option.WithAPIKey(os.Getenv("AIRMUX_INFERENCE_KEY")),
	)
	response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
		Model: openai.ChatModel(os.Getenv("AIRMUX_MODEL")),
		Input: responses.ResponseNewParamsInputUnion{
			OfString: openai.String("Explain request routing in one sentence."),
		},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(response.OutputText())
}

Add the SDK to your Go module with go get github.com/openai/openai-go/v3.

Install ai and @ai-sdk/openai-compatible in your Node project:

npm install ai @ai-sdk/openai-compatible

import { generateText } from "ai";
import { createOpenAICompatible } from "@ai-sdk/openai-compatible";

const airmux = createOpenAICompatible({
  name: "airmux",
  baseURL: `${process.env.AIRMUX_URL}/inf/v1`,
  apiKey: process.env.AIRMUX_INFERENCE_KEY,
});

const { text } = await generateText({
  model: airmux(process.env.AIRMUX_MODEL!),
  prompt: "Explain request routing in one sentence.",
});

console.log(text);

Install the OpenAI integration with uv add langchain-openai, then invoke it with the airmux endpoint:

import os

from langchain_openai import ChatOpenAI

model = ChatOpenAI(
    model=os.environ["AIRMUX_MODEL"],
    base_url=f"{os.environ['AIRMUX_URL'].rstrip('/')}/inf/v1",
    api_key=os.environ["AIRMUX_INFERENCE_KEY"],
)

response = model.invoke("Explain request routing in one sentence.")
print(response.content)

Vercel AI SDK uses its OpenAI-compatible provider. LangChain uses the OpenAI integration with airmux’s Chat Completions endpoint. List models with the OpenAI SDK using client.models.list(). Results reflect the models visible to the key’s workspace policies and configured credential scopes. See the model discovery reference. Inference keys begin with sk-inf-. Management keys (sk-cp-) cannot be used for inference. The Messages reference, Chat Completions reference, and Responses reference describe supported request fields.

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