Vercel AI SDK (v6 and earlier)

Vercel AI SDK (3.3+) provides high-level helpers — generateText, streamText, generateObject — for calling LLMs from TypeScript apps. Arize AX captures every AI SDK call by ingesting the SDK’s native OpenTelemetry spans through the @arizeai/openinference-vercel span processor.

Prerequisites

Node.js 18+
An Arize AX account (sign up)
An OPENAI_API_KEY from the OpenAI Platform
Vercel AI SDK 3.3 or higher

Launch Arize AX

Sign in to your Arize AX account.
From Space Settings, copy your Space ID and API Key. You will set them as ARIZE_SPACE_ID and ARIZE_API_KEY below.

Install

Pick the tab for your runtime — both install the same @arizeai/openinference-vercel processor and differ only in the OpenTelemetry registration helper.

This guide targets AI SDK v6 and earlier (ai@^6 with @ai-sdk/openai@^3). AI SDK v7 reworked how experimental_telemetry emits OpenTelemetry spans, so the wiring shown here does not produce spans on ai@7 — for v7, use the Vercel AI SDK v7 guide instead.Pin the processor to @arizeai/openinference-vercel@^2 as shown below. The latest major (@arizeai/openinference-vercel@3) targets AI SDK v7: it peer-depends on ai@^7 and @ai-sdk/otel, requires Node.js 22+, and ships ESM-only, so it does not work with this v6 setup.

Node.js
Next.js

npm install ai@^6 @ai-sdk/openai@^3 \
  @arizeai/openinference-vercel@^2 \
  @arizeai/openinference-semantic-conventions \
  @opentelemetry/api \
  @opentelemetry/exporter-trace-otlp-proto \
  @opentelemetry/resources \
  @opentelemetry/sdk-trace-node

npm install ai@^6 @ai-sdk/openai@^3 \
  @arizeai/openinference-vercel@^2 \
  @arizeai/openinference-semantic-conventions \
  @vercel/otel \
  @opentelemetry/api \
  @opentelemetry/exporter-trace-otlp-proto

Both runtimes wire the same OpenInferenceSimpleSpanProcessor from @arizeai/openinference-vercel — they differ only in how OpenTelemetry is registered, shown in the matching tab under Setup tracing. See Troubleshooting for the version-pinning rules.

Configure credentials

export ARIZE_SPACE_ID="<your-space-id>"
export ARIZE_API_KEY="<your-api-key>"
export ARIZE_PROJECT_NAME="vercel-ai-sdk-tracing-example"
export OPENAI_API_KEY="<your-openai-api-key>"

Setup tracing

Pick the tab that matches your runtime. The processor’s spanFilter and reparentOrphanedSpans options control which spans reach Arize and how each trace is rooted — see Span filter.

Node.js
Next.js

// instrumentation.ts
import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-proto";
import { resourceFromAttributes } from "@opentelemetry/resources";
import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";
import {
  isOpenInferenceSpan,
  OpenInferenceSimpleSpanProcessor,
} from "@arizeai/openinference-vercel";
import { SEMRESATTRS_PROJECT_NAME } from "@arizeai/openinference-semantic-conventions";

const projectName =
  process.env.ARIZE_PROJECT_NAME ?? "vercel-ai-sdk-tracing-example";

export const provider = new NodeTracerProvider({
  resource: resourceFromAttributes({
    [SEMRESATTRS_PROJECT_NAME]: projectName,
    model_version: "1.0.0",
  }),
  spanProcessors: [
    new OpenInferenceSimpleSpanProcessor({
      exporter: new OTLPTraceExporter({
        url: "https://otlp.arize.com/v1/traces",
        headers: {
          "arize-space-id": process.env.ARIZE_SPACE_ID ?? "",
          "arize-api-key": process.env.ARIZE_API_KEY ?? "",
        },
      }),
      spanFilter: isOpenInferenceSpan,
      reparentOrphanedSpans: true,
    }),
  ],
});

provider.register();

console.log("Arize AX tracing initialized for Vercel AI SDK.");

In Next.js, use @vercel/otel’s registerOTel from instrumentation.ts. Put the file where Next.js looks for it: src/instrumentation.ts if your app has a src/ directory (e.g. src/app/…), otherwise instrumentation.ts at the project root (next to package.json). A file in the wrong place is silently ignored — Next.js never calls register() and no spans are emitted. Next.js automatically calls the exported register() function once on the server, before any request — so you do not create a NodeTracerProvider or call forceFlush() yourself. Modern Next.js (13.4+) runs this file automatically; you no longer need experimental.instrumentationHook in next.config.js.Install @vercel/otel in place of @opentelemetry/sdk-trace-node:

npm install @vercel/otel \
  @arizeai/openinference-vercel@^2 \
  @arizeai/openinference-semantic-conventions \
  @opentelemetry/api \
  @opentelemetry/exporter-trace-otlp-proto

// instrumentation.ts (project root)
import { registerOTel } from "@vercel/otel";
import {
  isOpenInferenceSpan,
  OpenInferenceSimpleSpanProcessor,
} from "@arizeai/openinference-vercel";
import { SEMRESATTRS_PROJECT_NAME } from "@arizeai/openinference-semantic-conventions";
import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-proto";

export function register() {
  registerOTel({
    serviceName: process.env.ARIZE_PROJECT_NAME ?? "vercel-ai-sdk-tracing-example",
    // Arize AX routes spans to a project by the project-name resource attribute. Without it,
    // the app emits spans but they never appear under your project. `serviceName` alone is
    // not sufficient — set the project-name attribute here just as the Node.js example does.
    attributes: {
      [SEMRESATTRS_PROJECT_NAME]: process.env.ARIZE_PROJECT_NAME ?? "vercel-ai-sdk-tracing-example",
    },
    spanProcessors: [
      new OpenInferenceSimpleSpanProcessor({
        exporter: new OTLPTraceExporter({
          url: "https://otlp.arize.com/v1/traces",
          headers: {
            "arize-space-id": process.env.ARIZE_SPACE_ID ?? "",
            "arize-api-key": process.env.ARIZE_API_KEY ?? "",
          },
        }),
        spanFilter: isOpenInferenceSpan,
        reparentOrphanedSpans: true,
      }),
    ],
  });
}

There’s no provider/forceFlush() to import in your app code — once this file is in place, just enable telemetry at each AI SDK call site (see below).

Serverless flushing. OpenInferenceSimpleSpanProcessor exports each span synchronously as it ends, so it does not depend on a process-exit flush — this is what makes it safe on short-lived serverless routes (Vercel functions, edge) where you can’t call forceFlush(). Keep SimpleSpanProcessor rather than BatchSpanProcessor in those environments; batching can drop spans when the function freezes between invocations.

Run Vercel AI SDK

// example.ts

// Importing instrumentation first ensures tracing is set up before the
// AI SDK is used.
import { provider } from "./instrumentation";

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

// `experimental_telemetry: { isEnabled: true }` is the AI SDK opt-in
// flag — without it, no spans are emitted no matter how OTel is wired.
const { text } = await generateText({
  model: openai("gpt-5.5"),
  prompt: "Why is the ocean salty? Answer in two sentences.",
  experimental_telemetry: { isEnabled: true },
});

console.log(text);

// Flush any pending spans before the process exits.
await provider.forceFlush();

This is a standalone Node script. In Next.js, you don’t import provider or call forceFlush() — the root instrumentation.ts registers tracing automatically. Just add experimental_telemetry: { isEnabled: true } to the generateText/streamText calls inside your route handlers or server actions; that opt-in flag is the only app-code change needed.

Expected output

Arize AX tracing initialized for Vercel AI SDK.
The ocean is salty because rivers continuously dissolve mineral salts from rocks and soil and carry them to the sea, where they accumulate over millions of years. Water leaves the ocean through evaporation but the salts remain, steadily concentrating until reaching today's roughly 3.5% salinity.

Verify in Arize AX

Open your Arize AX space and select project vercel-ai-sdk-tracing-example.
You should see a new trace within ~30 seconds containing an ai.generateText parent span wrapping an ai.generateText.doGenerate LLM child span (with prompt, response, and token usage attached).
If no traces appear, see Troubleshooting.

Check from the skill, CLI, or SDK

Confirm spans are actually reaching your Arize AX project. Use whichever fits your workflow — the skill and CLI work for any framework; the SDK check is shown for each language.

Arize skill (agent)
AX CLI
SDK

Install the Arize Skills plugin and let your coding agent check for you:

npx skills add Arize-ai/arize-skills

Then prompt your agent:

Use the arize-trace skill to export and analyze recent traces from my project. Confirm spans are arriving, and summarize any errors or latency issues.

Export recent spans for your project — any rows mean traces are landing:

ax spans export "$ARIZE_PROJECT_NAME" --space "$ARIZE_SPACE_ID" \
  --limit 5 --stdout | jq 'length'

A non-zero count confirms spans reached Arize AX. Run ax auth login first if you have not authenticated. See the ax spans reference.

Query the project’s spans and check that at least one came back.

import os
from arize import ArizeClient

client = ArizeClient(api_key=os.environ["ARIZE_API_KEY"])
resp = client.spans.list(
    project=os.environ["ARIZE_PROJECT_NAME"],
    space=os.environ["ARIZE_SPACE_ID"],
    limit=5,
)
count = len(resp.spans)
print(
    f"{count} span(s) found" if count else "No spans yet — recheck setup"
)

// Reads ARIZE_API_KEY from the environment.
import { listSpans } from "@arizeai/ax-client";

const { data: spans } = await listSpans({
  project: process.env.ARIZE_PROJECT_NAME!,
  space: process.env.ARIZE_SPACE_ID!,
  limit: 5,
});
const count = spans.length;
console.log(
  count ? `${count} span(s) found` : "No spans yet — recheck setup",
);

client, err := arize.NewClient(
    arize.Config{APIKey: os.Getenv("ARIZE_API_KEY")},
)
if err != nil {
    log.Fatal(err)
}
resp, err := client.Spans.List(ctx, spans.ListRequest{
    Project: os.Getenv("ARIZE_PROJECT_NAME"),
    Space:   os.Getenv("ARIZE_SPACE_ID"),
    Limit:   5,
})
if err != nil {
    log.Fatal(err)
}
fmt.Printf("%d span(s) found\n", len(resp.Spans))

SDK span references: Python · TypeScript · Go.

Span filter

Other instrumentations registered alongside the AI SDK (@opentelemetry/instrumentation-http, @vercel/otel, Next.js’s built-in tracing) emit POST / GET spans for every fetch, and the AI SDK’s spans nest under those HTTP roots. Two options on the processor control which spans reach Arize and how they’re rooted:

spanFilter: isOpenInferenceSpan keeps only the AI spans and drops the rest — the raw HTTP/fetch spans those other instrumentations emit.
reparentOrphanedSpans: true re-roots any AI span left orphaned when the filter drops its parent, so the highest-level AI SDK span (ai.generateText, ai.streamText, …) becomes a clean trace root instead of pointing at a parent that was never exported.

new OpenInferenceSimpleSpanProcessor({
  exporter,
  spanFilter: isOpenInferenceSpan,
  reparentOrphanedSpans: true, // default: false
});

Troubleshooting

No traces in Arize AX. Every AI SDK call needs experimental_telemetry: { isEnabled: true } set on it — without that flag, the SDK never emits spans. Also confirm ARIZE_SPACE_ID and ARIZE_API_KEY are set in the same shell that runs example.ts. Enable OpenTelemetry debug logs with export OTEL_LOG_LEVEL=debug and re-run.
401 from OpenAI. Verify OPENAI_API_KEY is set and has access to gpt-5.5. Swap openai("gpt-5.5") for a model your key can call.
Process exits before spans flush. Always await provider.forceFlush() (or provider.shutdown()) before the process exits, otherwise trailing spans are dropped.
Next.js / Vercel runtime. Use @vercel/otel’s registerOTel(...) instead of NodeTracerProvider, and pin versions: @vercel/otel@1.x requires @opentelemetry/* 1.x (0.1.x for unstable APIs); @vercel/otel@2.x requires @opentelemetry/* 2.x (0.2.x). Mismatches surface as silent missing traces.
AI SDK spans orphaned on the Traces tab. Happens when isOpenInferenceSpan drops the HTTP root without re-rooting its children. Set reparentOrphanedSpans: true on the processor as shown in Span filter, and confirm @arizeai/openinference-vercel is 2.8.0 or later on the v2 line (@arizeai/openinference-vercel@^2). The v3 major targets AI SDK v7 and will not emit spans for this v6 setup.

Vercel AI SDK (v6 and earlier)

Prerequisites

Launch Arize AX

Install

Configure credentials

Setup tracing

Run Vercel AI SDK

Expected output

Verify in Arize AX

Check from the skill, CLI, or SDK

Span filter

Troubleshooting

Resources

Vercel AI SDK Documentation

OpenInference Vercel Span Processor

Vercel AI SDK GitHub

​Prerequisites

​Launch Arize AX

​Install

​Configure credentials

​Setup tracing

​Run Vercel AI SDK

​Expected output

​Verify in Arize AX

​Check from the skill, CLI, or SDK

​Span filter

​Troubleshooting

​Resources

Vercel AI SDK Documentation

OpenInference Vercel Span Processor

Vercel AI SDK GitHub

Prerequisites

Launch Arize AX

Install

Configure credentials

Setup tracing

Run Vercel AI SDK

Expected output

Verify in Arize AX

Check from the skill, CLI, or SDK

Span filter

Troubleshooting

Resources