Why the OpenAI Agents SDK + Tabstack

Wiring web access into an agent usually means hand-rolled fetches, HTML parsing, and prompt gymnastics to coax structured data out of messy text. @tabstack/openai-agents replaces that with a hosted API exposed as native Agents SDK tools: schema-enforced output, server-side rendering of JS-heavy pages, and one key for extraction, research, generation, and automation. Tool names stay in lockstep with the @tabstack/langchain, @tabstack/ai, @tabstack/eve, and Python langchain-tabstack packages.

Quickstart

Install the adapter and set your key:

npm install @tabstack/openai-agents @openai/agents zod
 
export TABSTACK_API_KEY="your-key-here"

@openai/agents (v0.13 or later) and zod (v4) are peer dependencies. tabstackTools is an array of ready-to-use tools, so hand it to an Agent and run it:

import { Agent, run } from "@openai/agents";
import { tabstackTools } from "@tabstack/openai-agents";
 
const agent = new Agent({
  name: "Research assistant",
  instructions:
    "You are a research assistant with web intelligence tools. Use research_question for open " +
    "questions that need multiple sources, extract_page_content to read a specific URL as " +
    "markdown, and the extract tools to pull structured fields from a page. Always cite sources.",
  tools: tabstackTools,
});
 
const result = await run(agent, "What are Vercel's pricing plans, with sources?");
console.log(result.finalOutput);

The tools resolve TABSTACK_API_KEY lazily on first call, so importing the package never requires a key.

For a custom key, base URL, or a shared client, build the tools explicitly:

import { createTabstackOpenAIAgentsTools } from "@tabstack/openai-agents";
 
const tools = createTabstackOpenAIAgentsTools({ apiKey: process.env.MY_KEY });
// or pass an SDK client you already have: createTabstackOpenAIAgentsTools({ client })

The tools

Tool name	Export	What it does
`extract_structured_data`	`extractStructuredDataTool`	Pull specific fields from a URL into a JSON shape you define.
`extract_page_content`	`extractPageContentTool`	Fetch a page as clean markdown.
`research_question`	`researchQuestionTool`	Synthesized answer with cited sources across multiple pages.
`generate_structured_data`	`generateStructuredDataTool`	Fetch a page, then AI-transform it into derived or reshaped JSON.
`automate_browser_task`	`automateBrowserTaskTool`	Run a multi-step, natural-language browser task.

Strict mode and schemas

The Agents SDK forces strict JSON Schema mode whenever a tool's parameters is a Zod schema, and passing strict: false alongside a Zod schema throws. Strict mode cannot represent two constructs the shared core schemas rely on: .optional() fields, since strict mode requires every property to appear in required, and automate_browser_task's open data object, which needs a schema-valued additionalProperties that strict mode forbids.

So the adapter converts each core Zod schema to a JSON Schema and registers the tools with strict: false. The model still sees full field descriptions, and every call is validated against the core Zod schema inside execute before the request runs, so malformed model output fails fast with a clear error.

Good to know

automate_browser_task runs non-interactively. It does not pause for human-in-the-loop form input, so it never blocks. It returns the final answer plus the data it extracted and the pages it visited.
Failed calls throw TabstackToolError, a normalized message plus an HTTP status for API errors. The Agents SDK surfaces tool errors to the model as a tool result, so a failing call does not abort the run by default.
Requires Zod 4. The SDK depends on it, and the adapter uses Zod 4's native z.toJSONSchema to advertise each tool's parameters.

Common use cases

Give a research agent cited, multi-source answers instead of a single fetch.
Pull structured fields off a page without writing parsing code.
Let an agent drive a multi-step browser task in natural language.
Keep tool names consistent across your TypeScript and Python agents.

OpenAI Agents