Build a typed browser agent with the Vercel AI SDK

Use Steel with the Vercel AI SDK v6 ToolLoopAgent for typed, tool-using browser agents.

Jun RyuUpdated Apr 24, 2026

Terminal

Scaffolds a starter project locally. Requires the Steel CLI.

The Vercel AI SDK v6 ships ToolLoopAgent, a typed agent that picks a tool, calls it, observes the result, and decides the next step. Give it tools that drive a Playwright page connected to a Steel session and you get a terminal browser agent with no UI scaffolding in the way.

const researchAgent = new ToolLoopAgent({
  model: anthropic("claude-haiku-4-5"),
  instructions: "You operate a Steel cloud browser via tools...",
  stopWhen: [stepCountIs(15), hasToolCall("reportFindings")],
  tools: { openSession, navigate, snapshot, extract, reportFindings },
  onStepFinish: async ({ stepNumber, toolCalls, usage }) => { ... },
});

const result = await researchAgent.generate({ prompt: "..." });

reportFindings is the terminator. Its inputSchema is a Zod object (a summary string and an array of repo records) and it has no execute. In AI SDK v6, a tool with no execute stops the loop the moment the model calls it, and the call's input is your final typed answer. This sidesteps the Anthropic-on-tools issue where forcing JSON response format disables tool calling.

Run it

cd examples/vercel-ai-sdk-ts
cp .env.example .env          # set STEEL_API_KEY and ANTHROPIC_API_KEY
npm install
npm start

Get keys at app.steel.dev/settings/api-keys and console.anthropic.com. The demo task is wired into main(): find the top 3 AI/ML repos on github.com/trending/python?since=daily and return name, URL, stars, and description.

Your output varies. Structure looks like this:

Steel + AI SDK v6 (ToolLoopAgent) Starter
============================================================
    openSession: session=842ms cdp=411ms
  step 1: openSession | 1183 tokens
    navigate: 1621ms
  step 2: navigate | 1402 tokens
    snapshot: 124ms (3892 chars, 48 links)
  step 3: snapshot | 5104 tokens
    extract: 98ms (10 rows)
  step 4: extract | 2881 tokens
  step 5: reportFindings | 3150 tokens

Agent finished.

Structured output:
{
  "summary": "The top trending Python repos today center on...",
  "repos": [
    { "name": "owner/repo", "url": "...", "stars": "1,204", "description": "..." },
    ...
  ]
}

Releasing Steel session...
Session released. Replay: https://app.steel.dev/sessions/ab12cd34...

A full run takes ~20 seconds and costs a few cents of Steel session time plus a small number of Anthropic tokens. The finally block in main calls steel.sessions.release().

Make it yours

Swap the task. Change the prompt in main() and the reportFindings schema. Everything else is task-agnostic.
Swap the model. claude-haiku-4-5 is the default. For harder tasks, try anthropic("claude-sonnet-4-6"), openai("gpt-5"), or google("gemini-2.5-pro"). You can also use the AI Gateway string form, like "anthropic/claude-haiku-4-5", to route through Vercel.
Add tools. A click tool wrapping page.click, a fill tool over page.fill, a screenshot tool that returns a base64 PNG for vision models.
Phase-gate steps. Use prepareStep to restrict which tools are callable on a given step. See the AI SDK's loop control page.
Turn on stealth. Pass useProxy, solveCaptcha, or sessionTimeout to steel.sessions.create({...}) inside openSession for sites with anti-bot.