Return finished web results inside Vercel AI SDK.

Why Vercel AI SDK + Tabstack

Adding web access to an AI SDK app usually means writing your own fetch and parse code. @tabstack/ai replaces that with a hosted API exposed as typed tool() definitions: define the shape with Zod or JSON Schema and get that shape back, with JS-heavy pages rendered server-side. No Playwright, no binaries. One key covers extraction, research, generation, and automation.

Quickstart

Install the adapter and set your key:

npm install @tabstack/ai ai zod

export TABSTACK_API_KEY="your-key-here"

Pass the tool set straight to generateText (or streamText):

import { openai } from "@ai-sdk/openai";
import { generateText, stepCountIs } from "ai";
import { tabstackTools } from "@tabstack/ai";

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools: tabstackTools,
  stopWhen: stepCountIs(5), // let the model call a tool, then use the result
  prompt: "What are Vercel's pricing plans and how do they compare?",
});

console.log(text);

tabstackTools reads TABSTACK_API_KEY from the environment and is a named object keyed by tool name. Pass all of them, or pick a subset:

import { streamText, stepCountIs } from "ai";
import { tabstackTools } from "@tabstack/ai";

const result = streamText({
  model: openai("gpt-4o"),
  tools: {
    research_question: tabstackTools.research_question,
    extract_page_content: tabstackTools.extract_page_content,
  },
  stopWhen: stepCountIs(5),
  prompt: "Summarize the latest on quantum error correction, with sources.",
});

for await (const chunk of result.textStream) process.stdout.write(chunk);

The tools

Common use cases

• Add live web research with citations to a chat app.
• Stream a model answer that pulls typed data from a URL mid-response.
• Gate expensive automation behind a stepCountIs budget.
• Ship only the tools a given surface needs by passing a subset.

Next steps

• Adapter on GitHub
• @tabstack/ai on npm
• Get an API key