General-purpose agentic browser automation. Completes real user outcomes on any website — search, extract, fill forms, compare prices, navigate complex UIs.

91.3% on WebVoyager (590 tasks, 15 sites) at $0.09/task. 100% on held-out competitive bench. 95.7% on WebbBench-50 (excl. DataDome sites). Default model: gpt-5.4.

Install

CLI (recommended)

curl -fsSL https://raw.githubusercontent.com/tangle-network/browser-agent-driver/main/scripts/install.sh | sh

Installs the bad command, downloads Chromium, adds PATH. Requires Node.js 20+.

Or via npm:

npm i -g @tangle-network/browser-agent-driver
npx playwright install chromium

As a library

pnpm add @tangle-network/browser-agent-driver
pnpm add -D playwright

Quick Start

CLI

# Run a task
bad run --goal "Find the cheapest flight from NYC to London on Jan 15" \
  --url https://www.google.com/travel/flights

# With vision (screenshot-based decisions)
bad run --goal "Compare MacBook Air prices" --url https://apple.com \
  --observation-mode hybrid

# Test suite from case file
bad run --cases ./my-tests.json --concurrency 4

# Authenticated session
bad run --goal "Check account settings" --url https://app.example.com \
  --storage-state .auth/session.json

# With proxy (residential or SOCKS5)
bad run --goal "Search hotels in Tokyo" --url https://booking.com \
  --proxy http://user:pass@proxy.example.com:port

SDK

import { chromium } from 'playwright'
import { PlaywrightDriver, BrowserAgent } from '@tangle-network/browser-agent-driver'

const browser = await chromium.launch({ channel: 'chrome' })
const page = await browser.newPage()
const driver = new PlaywrightDriver(page)

const agent = new BrowserAgent({
  driver,
  config: {
    model: 'gpt-5.4',
    observationMode: 'hybrid',    // vision + DOM
    plannerEnabled: true,          // plan-then-execute
  },
})

const result = await agent.run({
  goal: 'Find the top 3 trending repositories on GitHub',
  startUrl: 'https://github.com/trending',
})

console.log(result.success, result.reason)
await browser.close()

Config File

Create bad.config.ts (or .js, .mjs) in your project root:

import { defineConfig } from '@tangle-network/browser-agent-driver'

export default defineConfig({
  provider: 'openai',
  model: 'gpt-5.4',
  observationMode: 'hybrid',
  plannerEnabled: true,
  headless: true,
  concurrency: 4,
  maxTurns: 30,
  outputDir: './test-results',

  // Per-role model routing (Gen 28)
  models: {
    planner:    { model: 'claude-opus-4-6', provider: 'anthropic' },
    executor:   { model: 'gpt-4.1-mini' },
    verifier:   { model: 'gpt-4.1-mini' },
    supervisor: { model: 'gpt-5.4' },
  },

  // Parallel tabs for compound goals (Gen 21)
  parallelTabs: { enabled: true, maxTabs: 3 },

  // Proxy for anti-bot bypass
  proxy: 'http://user:pass@proxy:port',  // or set BAD_PROXY_URL env

  // Supervisor for stuck detection
  supervisor: { enabled: true, useVision: true },
})

Auto-detected by CLI and SDK. CLI flags override config values.

How It Works

Goal → DOM Planner (1 LLM call with screenshot)
  → N deterministic steps (click, type, fill, navigate)
  → If deviation: vision per-action loop
    → Observe (a11y tree + screenshot + SoM labels)
    → LLM decides action (ref-based, coordinate, or label)
    → Execute + verify expected effect
    → Recovery on failure (strategy shift, form reset retry, search fallback)
  → Goal verification (LLM or fast-path with script evidence)
  → Complete with structured result

Recovery is automatic: cookie consent, modal blockers, A-B-A-B oscillation loops, form field resets, date picker stalls, and CAPTCHA challenges are handled before the agent continues.

Features

Vision + DOM Hybrid

The agent sees BOTH a screenshot and the accessibility tree. Screenshots show visual layout, SoM labels mark interactive elements with numbered badges, and the a11y tree provides precise @ref IDs for targeting.

Three observation modes:

dom — a11y tree only (fastest, cheapest)
vision — screenshot + coordinates only
hybrid — both (default for benchmarks, most reliable)

Stealth & Anti-Bot

Built-in evasion for Cloudflare, Akamai, and most WAF systems:

System Chrome (channel: 'chrome') — real TLS/JA3/HTTP2 fingerprint, not bundled Chromium
Patchright — Playwright fork that patches CDP protocol leaks
Mouse humanization — Bezier curve movement (8-15 points), gaussian click offset
Browser fingerprint — navigator.webdriver, plugins, languages, WebGL, canvas noise, screenX/Y fix
GPU rendering — --use-gl=desktop for real WebGL fingerprint
Resource blocking — 99+ analytics/tracking domains blocked

Unblocks 9/13 previously-blocked sites on WebbBench-50 with zero configuration.

CAPTCHA Solving

Automatic CAPTCHA detection and solving during runs:

reCAPTCHA v2 — checkbox click + image grid solver (LLM vision-based)
Cloudflare Turnstile — checkbox behavioral click
Google "unusual traffic" — detected and solver attempted

// Enabled by default. To configure:
{ captcha: { enabled: true, maxAttempts: 5 } }

Parallel Tab Execution

For compound goals ("compare X vs Y", "find 5 items matching criteria"), the agent decomposes the goal and runs sub-tasks in parallel browser tabs.

{
  parallelTabs: { enabled: true, maxTabs: 3 },
}

The GoalDecomposer (1 cheap LLM call) classifies goals as simple or compound. Simple goals run as before. Compound goals get split into sub-goals, each running in its own tab via Promise.all, with results merged by the EvidenceMerger.

Multi-Model Orchestration

Different agent roles can use different models for optimal cost/quality:

{
  models: {
    planner:    { model: 'claude-opus-4-6', provider: 'anthropic' },  // best reasoning
    executor:   { model: 'gpt-4.1-mini' },                            // cheap, follows plans
    verifier:   { model: 'gpt-4.1-mini' },                            // structured yes/no
    supervisor: { model: 'gpt-5.4' },                                 // strategic recovery
  },
}

Each role falls back to the main model when not configured. The planner needs top-tier reasoning; the executor just follows instructions.

Design Audit

Vision-powered design quality analysis with closed-loop improvement:

# Audit any URL
bad design-audit --url https://your-app.com

# Multi-page crawl with cross-page systemic detection
bad design-audit --url https://your-app.com --pages 10

# Auto-fix: dispatch findings to a coding agent
bad design-audit --url http://localhost:3000 --evolve claude-code --project-dir ~/my-app

Reports rank Top Fixes by ROI — the highest-leverage changes scored by (impact * blast / effort). Multi-page systemic findings collapse automatically.

Wallet & DeFi Testing

Built-in MetaMask integration for DeFi app testing:

pnpm wallet:setup      # download MetaMask
pnpm wallet:onboard    # automate first-run wizard
pnpm wallet:anvil      # start Anvil mainnet fork (100 ETH + 10 WETH + 10k USDC)
pnpm wallet:validate   # run wallet test suite

Supports connect, swap, supply workflows across Uniswap, Aave, SushiSwap, 1inch.

Configuration

Option	Type	Default	Description
`provider`	string	`'openai'`	LLM provider
`model`	string	`'gpt-5.4'`	LLM model
`observationMode`	string	`'dom'`	`'dom'`, `'vision'`, or `'hybrid'`
`plannerEnabled`	boolean	`false`	Plan-then-execute mode
`headless`	boolean	`true`	Run browser headless
`maxTurns`	number	`30`	Max turns per task
`proxy`	string	—	Proxy URL (also `BAD_PROXY_URL` env)
`models`	object	—	Per-role model overrides
`parallelTabs`	object	—	`{ enabled, maxTabs }`
`supervisor`	object	—	`{ enabled, useVision, model }`
`captcha`	object	—	`{ enabled, maxAttempts }`
`goalVerification`	boolean	`true`	Verify goal before accepting
`vision`	boolean	`true`	Send screenshots to LLM
`concurrency`	number	`1`	Parallel test cases

See Configuration Reference for all options.

CLI Reference

bad run [options]              # Run tasks
bad design-audit [options]     # Design quality analysis
bad view <run-dir>             # Open session viewer
bad competitive [options]      # Head-to-head framework comparison

Run options

Flag	Description
`--goal "..."`	Natural language goal
`--url URL`	Starting URL
`--cases file.json`	Test case file
`--mode fast-explore\|full-evidence`	Run mode
`--model MODEL`	LLM model
`--provider openai\|anthropic\|google`	LLM provider
`--observation-mode dom\|vision\|hybrid`	Observation mode
`--headless`	Run headless (default)
`--proxy URL`	Residential/SOCKS5/HTTP proxy
`--profile default\|stealth\|benchmark-webvoyager`	Launch profile
`--concurrency N`	Parallel cases
`--show-cursor`	Animated cursor overlay in screenshots
`--live`	Real-time SSE viewer

SDK / Library Usage

import {
  // Core
  BrowserAgent,
  PlaywrightDriver,
  SteelDriver,
  TestRunner,

  // Config
  defineConfig,

  // Types
  type AgentConfig,
  type Scenario,
  type AgentResult,
  type Turn,

  // Multi-actor (parallel users)
  MultiActorSession,

  // Design audit
  runDesignAudit,

  // CAPTCHA
  detectCaptcha,
  solveCaptcha,
} from '@tangle-network/browser-agent-driver'

Multi-Actor Sessions

import { MultiActorSession } from '@tangle-network/browser-agent-driver'

const session = await MultiActorSession.create(browser, {
  actors: {
    admin:   { storageState: '.auth/admin.json' },
    user:    {},
  },
  agentConfig: { model: 'gpt-5.4', observationMode: 'hybrid' },
})

// Sequential
await session.actor('admin').run({ goal: 'Create project', startUrl: '/admin' })

// Parallel
await session.parallel(
  ['admin', { goal: 'Monitor dashboard', startUrl: '/admin' }],
  ['user',  { goal: 'Submit form', startUrl: '/app' }],
)

await session.close()

Test Suites

import { TestRunner } from '@tangle-network/browser-agent-driver'

const runner = new TestRunner({
  driver,
  config: { model: 'gpt-5.4', observationMode: 'hybrid' },
  concurrency: 4,
})

const results = await runner.runSuite([
  {
    id: 'login',
    goal: 'Log in with test@example.com / password123',
    startUrl: 'https://app.example.com/login',
  },
  {
    id: 'create-project',
    goal: 'Create a new project called "Test Project"',
    startUrl: 'https://app.example.com/projects',
  },
])

Drivers

The agent loop is decoupled from the browser via the Driver interface:

// Local Playwright (default)
const driver = new PlaywrightDriver(page)

// Steel cloud browser (anti-bot, residential proxies, CAPTCHA)
const driver = await SteelDriver.create({
  apiKey: process.env.STEEL_API_KEY,
  sessionOptions: { useProxy: true, solveCaptcha: true },
})

// Any Driver implementation works
const agent = new BrowserAgent({ driver, config })

Benchmarks

Benchmark	Score	Cost	Notes
WebVoyager (590 tasks, 15 sites)	91.3%	$0.09/task	Full run, Gen 25
Competitive (10 real-web sites)	100%	$0.03/task	Held-out, never optimized
WebbBench-50 (50 diverse sites)	88% raw, 95.7% excl. DataDome	—	Held-out generalization

Competitive position

Agent	WebVoyager	Cost/task
Surfer-2	97.1%	$1-5
Magnitude	93.9%	~$0.10
bad	91.3%	$0.09
OpenAI Operator	87%	ChatGPT Pro

Running benchmarks

# WebVoyager full 590
node scripts/run-scenario-track.mjs \
  --cases bench/external/webvoyager/cases.json \
  --config bench/scenarios/configs/vision-hybrid.mjs \
  --model gpt-5.4 --modes fast-explore --concurrency 5 \
  --out agent-results/webvoyager-full

# Multi-rep validation (≥3 reps required for any claim)
node scripts/run-multi-rep.mjs \
  --cases bench/scenarios/cases/my-cases.json \
  --config bench/scenarios/configs/vision-hybrid.mjs \
  --reps 3 --out agent-results/my-validation

GitHub Action

- uses: tangle-network/browser-agent-driver/.github/actions/design-audit@main
  with:
    url: ${{ steps.deploy.outputs.preview_url }}
    pages: 5
    fail-on-score-below: '6.5'
    evolve: claude-code
    openai-api-key: ${{ secrets.OPENAI_API_KEY }}

Posts Top Fixes as a PR comment, uploads full report as artifact, optionally fails on score regressions.

Session Viewer

bad view audit-results/stripe.com-1775502457141

Web UI with per-turn screenshots, action JSON, reasoning, element highlights. Pair with --show-cursor for animated cursor recordings.

Guides

Development

pnpm build              # TypeScript → dist/
pnpm test               # 993 tests
pnpm lint               # type-check
pnpm check:boundaries   # architecture boundaries

Publishing

Automated via Changesets + OIDC trusted publishing. Add a changeset with pnpm changeset, merge the auto-generated release PR, and npm publish fires automatically with provenance attestation.

License

Dual-licensed under MIT and Apache 2.0. See LICENSE.

Keywords

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Package last updated on 11 Apr 2026

@tangle-network/browser-agent-driver