Vercel AI SDK - Observability & Analytics
Telemetry is an experimental feature of the AI SDK and might change in the future.
The Vercel AI SDK is the TypeScript toolkit designed to help developers build AI-powered applications with React, Next.js, Vue, Svelte, Node.js, and more.
The SDK supports tracing via OpenTelemetry. With the LangfuseExporter
you can collect these traces in Langfuse.
Full Demo
Get Started
You need to be on "ai": "^3.3.0"
to use the telemetry feature as it was
recently added. In case of any issues, please update to the latest version as
this feature is under active development.
Enable Telemetry
While telemetry is experimental (docs), you can enable it by setting experimental_telemetry
on each request that you want to trace.
const result = await generateText({
model: openai("gpt-4-turbo"),
prompt: "Write a short story about a cat.",
experimental_telemetry: { isEnabled: true },
});
Collect Traces With LangfuseExporter
To collect the traces in Langfuse, you need to add the LangfuseExporter
to your application.
You can set the Langfuse credentials via environment variables or directly to the LangfuseExporter
constructor. Create a project in the Langfuse dashboard to get your secretKey
and publicKey
.
LANGFUSE_SECRET_KEY="sk-lf-..."
LANGFUSE_PUBLIC_KEY="pk-lf-..."
LANGFUSE_BASEURL="https://cloud.langfuse.com" # 🇪🇺 EU region
# LANGFUSE_BASEURL="https://us.cloud.langfuse.com" # 🇺🇸 US region
Now you need to register this exporter via the OpenTelemetry SDK.
NextJS has experimental support for OpenTelemetry instrumentation on the framework level. Learn more about it in the Next.js OpenTelemetry guide.
Install dependencies:
npm install @vercel/otel langfuse-vercel @opentelemetry/api-logs @opentelemetry/instrumentation @opentelemetry/sdk-logs
Enable the instrumentationHook
in your next.config.js
:
/** @type {import('next').NextConfig} */
const nextConfig = {
experimental: {
instrumentationHook: true,
},
};
module.exports = nextConfig;
Add LangfuseExporter
to your instrumentation:
import { registerOTel } from "@vercel/otel";
import { LangfuseExporter } from "langfuse-vercel";
export function register() {
registerOTel({
serviceName: "langfuse-vercel-ai-nextjs-example",
traceExporter: new LangfuseExporter(),
});
}
Done! All traces that contain AI SDK spans are automatically captured in Langfuse.
Example Application
We created a sample repository (langfuse/langfuse-vercel-ai-nextjs-example) based on the next-openai template to showcase the integration of Langfuse with Next.js and Vercel AI SDK.
Customization
Group multiple executions in one trace
You can open a Langfuse trace and pass the trace ID to AI SDK calls to group multiple execution spans under one trace. The passed name in functionId will be the root span name of the respective execution.
import { randomUUID } from "crypto";
import { Langfuse } from "langfuse";
const langfuse = new Langfuse();
const parentTraceId = randomUUID();
langfuse.trace({
id: parentTraceId,
name: "holiday-traditions",
});
for (let i = 0; i < 3; i++) {
const result = await generateText({
model: openai("gpt-3.5-turbo"),
maxTokens: 50,
prompt: "Invent a new holiday and describe its traditions.",
experimental_telemetry: {
isEnabled: true,
functionId: `holiday-tradition-${i}`,
metadata: {
langfuseTraceId: parentTraceId,
langfuseUpdateParent: false, // Do not update the parent trace with execution results
},
},
});
console.log(result.text);
}
await langfuse.flushAsync();
await sdk.shutdown();
The resulting trace hierarchy will be:
Disable Tracking of Input/Output
By default, the exporter captures the input and output of each request. You can disable this behavior by setting the recordInputs
and recordOutputs
options to false
.
Link Langfuse prompts to traces
You can link Langfuse prompts to Vercel AI SDK generations by setting the langfusePrompt
property in the metadata
field:
import { generateText } from "ai";
import { Langfuse } from "langfuse";
const langfuse = new Langfuse();
const fetchedPrompt = await langfuse.getPrompt("my-prompt");
const result = await generateText({
model: openai("gpt-4o"),
prompt: fetchedPrompt.prompt,
experimental_telemetry: {
isEnabled: true,
metadata: {
langfusePrompt: fetchedPrompt.toJSON(),
},
},
});
The resulting generation will have the prompt linked to the trace in Langfuse. Learn more about prompts in Langfuse here.
Pass Custom Attributes
All of the metadata
fields are automatically captured by the exporter. You can also pass custom trace attributes to e.g. track users or sessions.
const result = await generateText({
model: openai("gpt-4-turbo"),
prompt: "Write a short story about a cat.",
experimental_telemetry: {
isEnabled: true,
functionId: "my-awesome-function", // Trace name
metadata: {
langfuseTraceId: "trace-123", // Langfuse trace
tags: ["story", "cat"], // Custom tags
userId: "user-123", // Langfuse user
sessionId: "session-456", // Langfuse session
foo: "bar", // Any custom attribute recorded in metadata
},
},
});
Debugging
Enable the debug
option to see the logs of the exporter.
new LangfuseExporter({ debug: true });
Troubleshooting
- If you deploy on Vercel, Vercel’s OpenTelemetry Collector is only available on Pro and Enterprise Plans (docs).
- You need to be on
"ai": "^3.3.0"
to use the telemetry feature as it was recently added. In case of any issues, please update to the latest version as this feature is under active development. - On NextJS, make sure that you only have a single instrumentation file.
- If you use Sentry, make sure to either:
- set
skipOpenTelemetrySetup: true
in Sentry.init - follow Sentry’s docs on how to manually set up Sentry with OTEL
- set
Short-lived environments
In short-lived environments such as Vercel Cloud Functions, AWS Lambdas etc. you may force an export and flushing of spans after function execution and prior to environment freeze or shutdown by awaiting a call to the forceFlush
method on the LangfuseExporter instance.
Learn more
See the telemetry documentation of the Vercel AI SDK for more information.