Vercel AI SDK

The AI SDK OpenAI provider accepts a custom API URL and credential.

Requirements

You need a running Archestra deployment, an OpenAI provider key, and a standard virtual key mapped to that provider. Use the virtual key as the client API key. Choose a model available through the mapped provider, such as gpt-4o.

The client must reach Archestra's API. The examples use http://localhost:9000/v1/openai; replace it with your deployment's URL. A client inside a separate Docker container needs a reachable hostname, such as host.docker.internal, rather than localhost.

Configure the Provider

Install the packages in your application:

bash

pnpm add ai @ai-sdk/openai

Set OPENAI_API_KEY in your application's environment to the virtual key. Keep the key in your server environment.

typescript

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

const openai = createOpenAI({
  baseURL: "http://localhost:9000/v1/openai",
  apiKey: process.env.OPENAI_API_KEY,
});

const result = await generateText({
  model: openai.chat("gpt-4o"),
  prompt: "Reply with connection verified.",
});
console.log(result.text);

The explicit .chat() selects Chat Completions. Archestra also supports the OpenAI Responses API. See the AI SDK OpenAI provider for model factory options. Keep this code on your server so the credential is not included in browser bundles.

Verify the Connection

Send Reply with connection verified. and check that the client returns a response. Open Logs → LLM Proxy in Archestra and find the request by its model and timestamp. Open the request to check its status and virtual key.

A 401 means the credential is missing or invalid. Check that the virtual key has an OpenAI mapping. A connection error means the client cannot reach the API URL. A model error means the selected model is unavailable through that provider.

To add remote tools, connect your application to an MCP Gateway.