Arize AX Observability

Arize AX is an enterprise-grade observability, evaluation, and experimentation platform purpose-built for agents and complex AI systems. It empowers teams to rigorously develop and improve real-world AI applications.

<Note> You can also find this guide in the [Arize AX docs](https://arize.com/docs/ax/integrations/ts-js-agent-frameworks/vercel). </Note>

Setup

Arize AX offers first-class OpenTelemetry integration and works directly with the AI SDK in both Next.js and Node.js environments.

<Note> Arize AX has an [OpenInferenceSimpleSpanProcessor](https://github.com/Arize-ai/openinference/blob/main/js/packages/openinference-vercel/src/OpenInferenceSpanProcessor.ts#L32) and an [OpenInferenceBatchSpanProcessor](https://github.com/Arize-ai/openinference/blob/main/js/packages/openinference-vercel/src/OpenInferenceSpanProcessor.ts#L86). All of the examples below can be used with either the simple or the batch processor. For more information on simple / batch span processors see our [documentation](https://arize.com/docs/ax/observe/tracing/configure/batch-vs-simple-span-processor#batch-vs-simple-span-processor). </Note>

Next.js

In Next.js applications, use one of the OpenInference span processors with registerOtel from @vercel/otel.

First, install the required dependencies for the AI SDK, OpenTelemetry and OpenInference.

bash

npm install ai @ai-sdk/openai @vercel/otel @arizeai/openinference-vercel @opentelemetry/exporter-trace-otlp-proto

Then, in your instrumentation.ts file add the following:

typescript

import { registerOTel } from '@vercel/otel';
import {
  isOpenInferenceSpan,
  OpenInferenceSimpleSpanProcessor,
} from '@arizeai/openinference-vercel';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-proto';

export function register() {
  registerOTel({
    attributes: {
      model_id: 'my-ai-app',
      model_version: '1.0.0',
    },
    spanProcessors: [
      new OpenInferenceSimpleSpanProcessor({
        exporter: new OTLPTraceExporter({
          url: 'https://otlp.arize.com/v1/traces',
          headers: {
            space_id: process.env.ARIZE_SPACE_ID,
            api_key: process.env.ARIZE_API_KEY,
          },
        }),
        // Optionally add a span filter to only include AI related spans
        spanFilter: isOpenInferenceSpan,
      }),
    ],
  });
}

Spans will show up in Arize AX under the project specified in the model_id field above.

You must set the experimental_telemetry flag to true in all calls using the AI SDK.

typescript

const result = await generateText({
  model: openai('gpt-5-mini'),
  prompt: 'Please write a haiku.',
  experimental_telemetry: {
    isEnabled: true,
  },
});

Node.js

In Node.js you can use the NodeSDK or the NodeTraceProvider.

NodeSDK

First, install the required dependencies for the AI SDK, OpenTelemetry and OpenInference.

bash

npm install ai @ai-sdk/openai @opentelemetry/sdk-node @arizeai/openinference-vercel @opentelemetry/exporter-trace-otlp-proto @opentelemetry/resources

Then, in your instrumentation.ts file add the following:

typescript

import {
  isOpenInferenceSpan,
  OpenInferenceSimpleSpanProcessor,
} from '@arizeai/openinference-vercel';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-proto';
import { resourceFromAttributes } from '@opentelemetry/resources';
import { NodeSDK } from '@opentelemetry/sdk-node';

const sdk = new NodeSDK({
  resource: resourceFromAttributes({
    model_id: 'my-ai-app',
    model_version: '1.0.0',
  }),
  spanProcessors: [
    new OpenInferenceSimpleSpanProcessor({
      exporter: new OTLPTraceExporter({
        url: 'https://otlp.arize.com/v1/traces',
        headers: {
          space_id: process.env.ARIZE_SPACE_ID,
          api_key: process.env.ARIZE_API_KEY,
        },
      }),
      spanFilter: isOpenInferenceSpan,
    }),
  ],
});

sdk.start();

Spans will show up in Arize AX under the project specified in the model_id field above.

You must set the experimental_telemetry flag to true in all calls using the AI SDK.

typescript

const result = await generateText({
  model: openai('gpt-5-mini'),
  prompt: 'Please write a haiku.',
  experimental_telemetry: {
    isEnabled: true,
  },
});

NodeTraceProvider

First, install the required dependencies for the AI SDK, OpenTelemetry and OpenInference.

bash

npm install ai @ai-sdk/openai @opentelemetry/sdk-trace-node @arizeai/openinference-vercel @opentelemetry/exporter-trace-otlp-proto @opentelemetry/resources

Then, in your instrumentation.ts file add the following:

typescript

import {
  isOpenInferenceSpan,
  OpenInferenceSimpleSpanProcessor,
} from '@arizeai/openinference-vercel';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-proto';
import { resourceFromAttributes } from '@opentelemetry/resources';
import { NodeTracerProvider } from '@opentelemetry/sdk-trace-node';

const provider = new NodeTracerProvider({
  resource: resourceFromAttributes({
    model_id: 'my-ai-app',
    model_version: '1.0.0',
  }),
  spanProcessors: [
    new OpenInferenceSimpleSpanProcessor({
      exporter: new OTLPTraceExporter({
        url: 'https://otlp.arize.com/v1/traces',
        headers: {
          space_id: process.env.ARIZE_SPACE_ID,
          api_key: process.env.ARIZE_API_KEY,
        },
      }),
      spanFilter: isOpenInferenceSpan,
    }),
  ],
});
provider.register();

Spans will show up in Arize AX under the project specified in the model_id field above.

You must set the experimental_telemetry flag to true in all calls using the AI SDK.

typescript

const result = await generateText({
  model: openai('gpt-5-mini'),
  prompt: 'Please write a haiku.',
  experimental_telemetry: {
    isEnabled: true,
  },
});

Resources

After sending spans to your Arize AX project check out other features:

Rerunning spans in the prompt playground to iterate and compare prompts and parameters
Add spans to datasets for evaluation and development workflows
Continuously run online evaluations on your incoming spans to understand application performance

AX has a TypeScript client for managing your datasets and evaluations.