Vercel AI SDK Tracing: Debug Your Next.js AI Apps in 2026

Vercel AI SDK Tracing: The Missing Piece

Vercel AI SDK tracing is the capability most Next.js developers wish they had configured before their first production incident. The Vercel AI SDK has quietly become the default way to build AI features in Next.js apps. It gives you streamText, generateText, streamObject, tool calling, and a clean abstraction over OpenAI, Anthropic, Google, and a dozen other providers. You ship a chatbot in an afternoon. You ship a research agent in a weekend. Then it breaks in production and you have no idea why.

That is the gap. The Vercel AI SDK is excellent at letting you build fast. It is not designed to give you visibility into what is actually happening when a user sends a prompt, when a tool call fires, when a multi-step agent loops three times before giving up. You get a stream of tokens and a finish reason and whatever you remembered to console.log. When a request is slow, when an output is wrong, when a tool returns garbage, you are flying blind.

Vercel AI SDK debugging in 2026 is no longer optional. Users expect AI features to work. Investors expect you to know your cost per user. Support expects you to answer why a specific conversation went off the rails. Without Nextjs AI tracing, every question becomes a guessing game and every fix becomes a rewrite.

This guide walks through what Vercel AI SDK observability actually needs to cover, the three realistic ways to get it, and how to wire up Glassbrain with the Vercel AI SDK in about sixty seconds. Glassbrain is free for a thousand traces a month with no card required, works with the OpenAI and Anthropic clients the Vercel AI SDK already uses under the hood, and gives you a visual trace tree, replay, and AI fix suggestions without any of the setup effort of a full observability stack.

Why Vercel AI SDK Apps Are Hard to Debug

The Vercel AI SDK hides a lot of complexity behind a pleasant API. That is the point. But when something goes wrong, all that hidden complexity comes back to bite you, and standard debugging tools do not help.

Start with streaming responses. When you call streamText, you are not getting a single request and a single response. You are getting a long-lived HTTP connection that dribbles tokens back over seconds or minutes. If a user reports that the response was slow, was it slow because the model was slow, or because your edge function cold-started, or because a tool call in the middle stalled? Traditional request logs show you a single line with a single duration. They cannot tell you where the time went.

Then there is the edge runtime. A lot of Vercel AI SDK code runs on Vercel Edge Functions, which use a stripped-down V8 environment. Many Node APIs are missing. Many observability SDKs assume they have file system access, long-lived processes, or background threads that edge simply does not provide. If you pick the wrong tracing tool, you find out at deploy time when your build fails.

Server actions add another layer. A form submit triggers a server action, which calls generateText, which calls a tool, which reads from your database, which returns a result, which goes back into the model. The user sees a spinner and then an answer. You see a single request in your Vercel logs. The intermediate steps, the ones that actually broke, are invisible without proper Vercel AI SDK tracing.

Tool calls are the most failure-prone part of any modern AI app. The model decides to call a tool with arguments. Your code runs the tool. The result goes back to the model, which decides whether to call another tool or finish. Any step can fail silently. The model can hallucinate tool names. Arguments can be malformed. Your tool can throw. The model can loop forever. Without Vercel AI SDK debugging, you will not know which of these happened.

Multi-step generateText flows, where maxSteps is set above one, turn a single call into an autonomous agent. Now you have loops, state, and branching. Console logs will not save you.

What You Need to Trace

Good Vercel AI SDK observability covers five distinct surfaces. Miss any of them and you will be left guessing at exactly the moments you cannot afford to guess.

streamText calls

Every streamText invocation should produce a trace that captures the prompt, the system message, the tool definitions, the full streamed completion, token counts, cost, finish reason, and total duration. Streaming makes the duration interesting because you care about both time-to-first-token and total time. Good Nextjs AI tracing shows both.

generateText calls

generateText is the non-streaming cousin. Traces should capture the same fields as streamText, plus any tool calls made during the generation. If maxSteps is greater than one, each step should be its own span inside the parent generateText trace so you can see how the model iterated.

Tool calls and results

Each tool call should appear as a child span under the parent generation. The span should include the tool name, the arguments the model produced, how long your tool took to execute, whether it succeeded, and the result that was returned to the model. This is where most Vercel AI SDK debugging questions actually get answered.

Multi-step agent calls

When the Vercel AI SDK runs an agent loop with multiple steps, you want each step to be visible. Step one: model asks for tool X. Step two: tool X runs. Step three: model asks for tool Y. Step four: tool Y runs. Step five: model produces final answer. Without this structure, a failed multi-step call just looks like a slow single call.

Streaming latency per token

For user-facing chat, time-to-first-token matters more than total duration. A good Vercel AI SDK tracing tool records the timestamp of the first token and the cadence of subsequent tokens. When a user complains that the chatbot felt sluggish, you can look at the trace and see whether the model was slow to start or the stream stalled halfway through.

Tracing Options for Vercel AI SDK

There are three realistic paths to Vercel AI SDK observability in 2026. Each has trade-offs.

OpenTelemetry (Vercel AI SDK has built-in support)

The Vercel AI SDK ships with experimental OpenTelemetry support. You enable it with experimental_telemetry in your generateText or streamText call, configure an OTel exporter, and send traces to a compatible backend. This is the most powerful option and the most painful to set up. You will spend a day wiring up the OTel SDK, picking a backend (Honeycomb, Datadog, Tempo, SigNoz), configuring sampling, and debugging why spans are not appearing. The payoff is a unified view across your whole stack. The cost is that you are now maintaining an OTel pipeline.

Glassbrain SDK Wrapping

Because the Vercel AI SDK is a thin wrapper over provider clients like OpenAI and Anthropic, anything that traces those clients traces the Vercel AI SDK. Glassbrain works by wrapping the OpenAI or Anthropic client you pass to the Vercel AI SDK. One npm install, one wrapOpenAI or wrapAnthropic call, zero OTel configuration. Every generateText, streamText, and streamObject call runs through the wrapped client and gets traced automatically. Free for a thousand traces a month with no card. This is the simplest path for teams that want visibility without owning an observability stack.

Proxy-based tools

Some tools work by proxying all OpenAI or Anthropic calls through their own service. You change the baseURL of your provider client and traffic flows through their proxy, which logs everything. This works, and it does not require any SDK changes, but it adds a network hop to every request and requires you to trust the proxy with your raw API keys and all your prompts. For latency-sensitive streaming apps, the extra hop is a real cost. For privacy-sensitive apps, the key handling is a real concern.

For most Next.js teams using the Vercel AI SDK, the Glassbrain SDK wrapping path is the fastest to working tracing. OpenTelemetry is better if you already have an OTel pipeline. Proxies are a last resort if you cannot modify code at all.

Setting Up Glassbrain with Vercel AI SDK

Getting Vercel AI SDK tracing running with Glassbrain takes about sixty seconds. Here is the full walkthrough.

Step one: install the package. In your Next.js project root, run npm install glassbrain-js. The package is small, has no heavy dependencies, and works in both Node and Edge runtimes.

Step two: get your Glassbrain API key. Sign up at Glassbrain. The free tier gives you a thousand traces a month with no card required, which is enough for most early-stage apps to run in production without upgrading. Copy your API key from the dashboard and add it to your environment as GLASSBRAIN_API_KEY.

Step three: wrap your provider client. The Vercel AI SDK takes a model parameter, which is built from a provider client like openai or anthropic. Instead of passing the raw client, pass the Glassbrain-wrapped version.

For OpenAI-based models, wrap the OpenAI client with wrapOpenAI before passing it to the Vercel AI SDK. For Anthropic-based models, wrap the Anthropic client with wrapAnthropic. The wrapping is a single function call. The API of the wrapped client is identical to the unwrapped client, so the Vercel AI SDK does not know or care that Glassbrain is there.

Step four: deploy. Push to Vercel as you normally would. Glassbrain traces are exported asynchronously and do not block your response, so streaming latency is unaffected. The async export is important for streamText in particular because any added latency is visible to the user as a slower first token.

Step five: use your app. Every call to generateText, streamText, streamObject, or generateObject that routes through a wrapped client will produce a trace in the Glassbrain dashboard. Tool calls show up as child spans. Multi-step agent loops show up as sequences of spans under a parent generation. Streaming responses show up with token-by-token timing.

That is the entire setup for Vercel AI SDK debugging with Glassbrain. There is no OTel config, no exporter config, no sampling config. If you decide you want to stop tracing a particular call, you can use the unwrapped client for that call. Everything else continues to work.

Edge Runtime Considerations

One of the most common questions about Vercel AI SDK tracing is whether a given tool works in the Edge runtime. The Edge runtime is a restricted V8 environment that does not have file system access, Node.js streams, or many common APIs. A lot of observability SDKs assume they have those APIs and break at build time or request time when deployed to the edge.

Glassbrain works in both the Node runtime and the Edge runtime. The glassbrain-js SDK is built with edge compatibility as a first-class concern. No file system dependencies, no Node-only APIs, no native modules. If your Next.js route handler, server action, or API route is marked with export const runtime = 'edge', Glassbrain works there just like it works in Node.

The async export design is particularly important in the edge context. Edge functions have short execution budgets and are billed by duration. If tracing added synchronous network calls, it would both slow down responses and increase your edge function bill. Glassbrain exports traces asynchronously, which means the trace is queued and sent in the background while your response streams to the user. The user sees no added latency. Your edge function duration is unchanged.

For streaming responses in particular, this matters. The entire point of streamText is to get tokens to the user as fast as possible. Any observability layer that blocks the stream defeats the purpose. Vercel AI SDK observability done right is invisible to the user and visible to you.

What You See in the Trace

Once Glassbrain is wired up, every Vercel AI SDK call produces a visual trace tree in your dashboard. Here is what that tree looks like for a typical streaming chat endpoint.

The root span is your route handler or server action. It captures the incoming request, the total duration, and any metadata you attach. Below the root, the first child is the streamText call itself. The streamText span captures the model name, the temperature, the system prompt, the user messages, the tool definitions, the full streamed completion, the token counts, the cost, and the finish reason.

If the model called tools, each tool call appears as a child span under streamText. The tool span captures the tool name, the arguments the model generated, the duration of your tool implementation, and the result that was returned. If your tool called other services, like a database or another API, and those calls were also traced, they appear as grandchildren. You can see the full causal chain from user prompt to final token.

For streamText specifically, the trace includes a token-by-token view. You can see the timestamp of the first token, which is the most important latency metric for chat UX, and the cadence of subsequent tokens. If a stream stalled, you can see exactly where. The finish reason, whether stop, length, tool_calls, or content_filter, is displayed prominently.

Multi-step agent calls, where maxSteps is greater than one, show each step as its own span. Step one: reasoning and tool call. Step two: tool execution. Step three: reasoning and final answer. The structure makes it obvious when an agent is looping, stuck, or taking an inefficient path.

Replay lets you re-run any trace against the current version of your code without needing the user's API keys. AI fix suggestions highlight likely causes for failed or unusual traces.

Frequently Asked Questions

Does Glassbrain work with Vercel AI SDK?

Yes. The Vercel AI SDK is a wrapper over provider clients like OpenAI and Anthropic. Glassbrain wraps those clients with wrapOpenAI and wrapAnthropic. Any Vercel AI SDK function that uses a wrapped client, including generateText, streamText, streamObject, and generateObject, is traced automatically. No special Vercel AI SDK integration is required.

Does it work in Edge runtime?

Yes. The glassbrain-js SDK is built to run in both Node and Edge runtimes. There are no Node-only dependencies, no file system access, and no native modules. If your route is marked as edge, Glassbrain works there without modification.

Can I trace streaming responses?

Yes. streamText and streamObject calls are fully traced, including time-to-first-token and token-by-token timing. Traces are exported asynchronously so they do not block the stream or add user-visible latency.

What about server actions?

Server actions are just server-side functions. If a server action calls generateText or streamText through a wrapped client, the call is traced. You can also attach metadata to link multiple calls from the same server action into a single trace if you want to see them grouped.

Does it affect streaming latency?

No measurable effect. Trace export is asynchronous and does not block the response. Your time-to-first-token and total stream duration are unchanged. This is especially important for user-facing chat interfaces where any added latency is immediately visible.

Can I use it with multi-step agents?

Yes. When you call generateText or streamText with maxSteps greater than one, each step is traced as its own span under the parent generation. Tool calls made during each step are visible as children. You can see exactly how the agent reasoned, what tools it called, and where it finished.