+ P Observe for Vercel AI SDK agents
Capture every Vercel AI SDK run through its own telemetry integration interface — the same lifecycle hooks the SDK uses for its own instrumentation, not a bolted-on tracer.
Prefactor observes your Vercel AI SDK agents in real time — every LLM call, tool invocation, and custom span captured as structured trace data the moment it happens, with cost attributed per call and a tamper-evident audit trail of every run.
How to add observe to Vercel AI SDK
Install the package
Add @prefactor/ai to your project.
Create the middleware
Build it from your config — API URL, token, and agent.
Spans flow to Prefactor
LLM calls, tool invocations, and agent spans arrive as trace data.
// npm install @prefactor/ai
import { init } from "@prefactor/ai";
const prefactor = init({
transportType: "http",
httpConfig: {
apiUrl: process.env.PREFACTOR_API_URL!,
apiToken: process.env.PREFACTOR_API_TOKEN!,
agentIdentifier: "1.0.0",
},
});How the Vercel AI SDK integration actually works
- Registers as a Telemetry integration — via registerTelemetry() globally, or passed per-call through the telemetry option (the current name for what generateText/streamText still accept as the deprecated experimental_telemetry alias).
- The SDK's own executeTool and executeLanguageModelCall wrappers are the real enforcement point: each wrapper receives the actual tool or model call as a function before it runs, and decides whether to call it, throttle it, or block it — the same mechanism the SDK uses internally to compose multiple integrations, not a side channel.
- Beyond the auto-captured lifecycle events, use withSpan to record any custom step you define — an API call, a quality check, a business action.
What Prefactor captures for Vercel AI SDK agents
LLM calls
Model, prompt, completion, and token usage recorded for every call — not batched, not sampled after the fact.
Tool invocations
Arguments, results, and errors for each tool the agent calls.
Agent spans
The run captured as nested spans you can inspect step by step — plus custom spans for whatever's specific to your own domain.
Cost per call
Token usage rolled up into cost, attributed to the agent, team, and task that spent it — the same span data, not a separate metering system.
Immutable audit trail
Every span and run written once, tamper-evident, full-text searchable, and exportable for compliance review.
Trace anything — not just LLM calls
The SDK captures LLM, tool, and agent spans automatically. With withSpan you wrap any operation in your own span type — an API call, a database query, a quality check, a business action — each with its own payload and schema. It all flows into the same Observe, Evaluate, and cost views.
import { withSpan } from "@prefactor/core";
// Wrap ANY operation in a span you define — an API call, a quality
// check, a business action — with its own spanType, inputs and schema
await withSpan(
{
name: "research competitor",
spanType: "research_competitor",
inputs: { competitor },
},
async () => {
const results = await search(competitor); // your tool / API calls
return summarize(results); // captured as one span
},
);Nest spans to capture business-level actions, and start with permissive schemas you tighten over time. Instrumentation strategy →
An example run, span by span
Illustrative — a single Vercel AI SDK run as nested spans.
Illustrative example.
Manual logging vs DIY vs Prefactor
| Capability | Manual | DIY OpenTelemetry | Prefactor |
|---|---|---|---|
| LLM / tool / agent spans | Hand-rolled | ✓ build it | ✓ via the SDK |
| Token usage captured per call | — | Build it | ✓ |
| Configurable capture & sampling | — | Partial | ✓ |
| Hosted Admin UI (agents, instances) | — | — | ✓ |
| Risk profiles & audit trail | — | — | ✓ |
Vercel AI SDK observe — FAQ
What does Prefactor capture for Vercel AI SDK?+
LLM calls, tool invocations, and agent spans — with inputs, outputs, and token usage — sent to the Prefactor API as structured trace data the moment they happen, not reconstructed from a log afterward.
Is Prefactor in my Vercel AI SDK request path?+
No — the SDK instruments in-process and sends spans asynchronously. Your requests go straight to your model provider; Prefactor observes what happened alongside it.
How does cost tracking work for Vercel AI SDK?+
Cost is derived from the token usage captured on each LLM span and rolled up by agent, team, and task — a view over the same trace data, not a separate metering system, so a cost spike traces back to the run that caused it.
Can audit records for Vercel AI SDK runs be edited or deleted?+
No. Instances and spans are immutable once written, giving reviewers a real-time, queryable record of exactly what every agent did — the evidence a compliance review or an incident investigation actually needs.
Do I need a dedicated package for Vercel AI SDK?+
Vercel AI SDK has a native package (@prefactor/ai) that registers as a Telemetry integration; you can also instrument it today with the framework-agnostic prefactor-core SDK.
What does Prefactor capture from Vercel AI SDK?+
Prefactor hooks the SDK's own telemetry lifecycle — onStart/onEnd, onStepStart/onStepEnd, onLanguageModelCallStart/End, and onToolExecutionStart/End — as structured, timestamped spans, so every Vercel AI SDK run is captured as trace data you can reconstruct, search and export end to end.
Does Prefactor add latency or change how Vercel AI SDK runs?+
No. Observability capture is designed to stay off your agent's critical path, so it doesn't alter your Vercel AI SDK logic or your users' responses. The only part that acts inline is the optional runtime guardrails you enable per agent — by design, so a high-risk or low-confidence action can be held for human approval before it executes.
Can I evaluate agents built with Vercel AI SDK and catch regressions?+
Yes. Once runs are captured, eval suites score quality and groundedness on real traffic, drift detection flags behaviour changes after deployment, and versioned eval history catches regressions before they ship — the observe → evaluate → improve loop applied to your Vercel AI SDK agents.
Keep going
Observe for other frameworks
LangChain →CrewAI →LangGraph →OpenAI Agents SDK →Claude Agent SDK →Microsoft AutoGen →See it on your Vercel AI SDK agents
Book a 15-minute setup and our team gets you observe in production.