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Getting Started

The Vercel AI SDK provides powerful React hooks and utilities for building AI-powered applications with built-in OpenTelemetry support. The SDK includes experimental telemetry features that automatically capture detailed traces of AI operations, making integration with Orq.ai straightforward for comprehensive observability.

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Prerequisites

Before you begin, ensure you have:
  • An Orq.ai account and an API Key.
  • Vercel AI SDK v3.1+ (with telemetry support).
  • Node.js 18+ and TypeScript support.
  • API keys for your LLM providers (OpenAI, Anthropic, etc.).

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Install Dependencies

# Core Vercel AI SDK with latest version
npm install ai@latest

# OpenTelemetry packages
npm install @opentelemetry/api @opentelemetry/sdk-node @opentelemetry/exporter-trace-otlp-http
npm install @opentelemetry/instrumentation @opentelemetry/resources
npm install @opentelemetry/semantic-conventions

# Provider SDKs (choose what you need)
npm install @ai-sdk/openai @ai-sdk/anthropic @ai-sdk/google

# Optional: For React applications
npm install @ai-sdk/react

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Configure Orq.ai

Set up your environment variables to connect to Orq.ai’s OpenTelemetry collector: Unix/Linux/macOS: 
export OTEL_EXPORTER_OTLP_ENDPOINT="https://api.orq.ai/v2/otel"
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer <ORQ_API_KEY>"
export OTEL_RESOURCE_ATTRIBUTES="service.name=vercel-ai-app,service.version=1.0.0"
export OPENAI_API_KEY="<YOUR_OPENAI_API_KEY>"
Windows (PowerShell): 
$env:OTEL_EXPORTER_OTLP_ENDPOINT = "https://api.orq.ai/v2/otel"
$env:OTEL_EXPORTER_OTLP_HEADERS = "Authorization=Bearer <ORQ_API_KEY>"
$env:OTEL_RESOURCE_ATTRIBUTES = "service.name=vercel-ai-app,service.version=1.0.0"
$env:OPENAI_API_KEY = "<YOUR_OPENAI_API_KEY>"
Using .env file: 
OTEL_EXPORTER_OTLP_ENDPOINT=https://api.orq.ai/v2/otel
OTEL_EXPORTER_OTLP_HEADERS=Authorization=Bearer <ORQ_API_KEY>
OTEL_RESOURCE_ATTRIBUTES=service.name=vercel-ai-app,service.version=1.0.0
OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>

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Integrations

The Vercel AI SDK has built-in OpenTelemetry support through the experimental_telemetry option. Here’s how to integrate it with Orq.ai: The simplest way to enable telemetry is using the SDK’s native support: 
// instrumentation.js
import { registerOTel, OTLPHttpJsonTraceExporter } from '@vercel/otel';
 
export function register() {
  registerOTel({
    serviceName: 'your-project-name',
    traceExporter: new OTLPHttpJsonTraceExporter({
      url: 'https://api.orq.ai/v2/otel/v1/traces',
      headers: {
        'Authorization': "Bearer $ORQ_API_KEY",
      },
    }),
  });
}

// index.js
import './instrumentation.js' 
import { generateText, streamText, generateObject } from "ai";
import { openai } from "@ai-sdk/openai";

// Simple usage with telemetry enabled
const result = await generateText({
  model: openai("gpt-4.1"),
  prompt: "Write a short story about a robot",
  experimental_telemetry: {
    isEnabled: true,
  },
});

// Advanced configuration with custom metadata
const resultWithMetadata = await generateText({
  model: openai("gpt-4.1"),
  prompt: "Explain quantum computing",
  experimental_telemetry: {
    isEnabled: true,
    functionId: "quantum-explanation",
    metadata: {
      userId: "user-123",
      requestId: "req-456",
      environment: "production",
    },
  },
});

// Control what data is recorded
const resultWithPrivacy = await generateText({
  model: openai("gpt-4.1"),
  prompt: userPrompt,
  experimental_telemetry: {
    isEnabled: true,
    recordInputs: false, // Don't record prompts
    recordOutputs: false, // Don't record responses
  },
});
Here the main factor to enable telemetry is to include the following payload when generating text. This can be used across the board. 
  experimental_telemetry: {
    isEnabled: true,
    recordInputs: false, // Don't record prompts
    recordOutputs: false, // Don't record responses
  },
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