ZeroDOM

Suggest an edit

99.0%

selector resolution accuracy

98.9%

median token savings vs raw HTML

10-30ms

deterministic parse, no model call

Two workflows, one graph

The parse is identical either way, a flat list of every element the page can act on. What you pipe it into is the difference.

AI agent & automation engineers

Feed a token-dense graph into your LLM loop (LangChain, LlamaIndex, or a hand-rolled loop) as compact text or a JSONL node stream. Numbered handles stay stable across turns; the selector is resolved on your side, never in context.

• 98.9% fewer tokens than raw HTML
• JSONL straight into the loop: zerodom inspect --pipe
• MCP server for Claude / Cursor, no headless bot walls

Security operators & bug-bounty hunters

Drive your real, logged-in Chrome through the extension to reach authenticated surfaces, or spawn a zero-footprint throwaway Chrome over a raw CDP pipe. Match the parsed surface against deterministic rules that make no network calls of their own.

• Real session via the relay + extension (sidesteps bot walls)
• Zero-footprint recon: zerodom inspect --stealth
• Deterministic client-side rules: zerodom scan

From the terminal

Unix-native. Read a page, stream its nodes as JSONL, or scan a list of authorized targets against your ruleset, stdin in, structured lines out, ready for jq or an agent loop.

# Token-dense interaction graph + a savings report
zerodom inspect https://example.com

# Flat node array as JSONL, one node per line — straight into an agent loop
zerodom inspect --pipe https://example.com

# Scan targets you are authorized to test against surfaces.yaml; findings as JSONL
cat targets.txt | zerodom scan -

# Zero-footprint recon: throwaway-profile Chrome over a CDP pipe, no debug port
zerodom inspect --stealth https://example.com

--stealth spawns a fresh, throwaway-profile Chrome and speaks CDP over inherited file descriptors 3 and 4, no localhost debugging port, nothing left on disk. To drive your real, logged-in browser instead (authenticated pages, existing sessions), use the Chrome extension and relay.

The problem

An agent driving a browser gets two action spaces today. Both are bad. Pixels are slow and produce coordinates that go stale on scroll. The accessibility tree is cheaper but enormous, with no stable handles. ZeroDOM is a third option.

See it in action

Parse any page and get a flat graph of every clickable and fillable element. The model sees numbered handles, not CSS selectors, not raw HTML, not coordinates.

<form class="login-form" id="auth">
  <div class="field">
    <label for="email">Email</label>
    <input type="email" id="email" required>
  </div>
  <div class="field">
    <label for="pass">Password</label>
    <input type="password" id="pass">
  </div>
  <button class="btn-primary">Sign In</button>
  <a href="/forgot">Forgot password?</a>
</form>
<nav class="sidebar">
  <a href="/dashboard">Dashboard</a>
  <a href="/settings">Settings</a>
</nav>
<!-- 200+ more lines -->

[01] textbox 'Email'
[02] textbox 'Password'
[03] button 'Sign In'
[04] link 'Forgot password?'
[05] link 'Dashboard'
[06] link 'Settings'

The model sees [03] and clicks. ZeroDOM resolves button.btn-primary on your side, selector never enters context.

97%fewer tokens for the same six actions (~3,000 → ~80)

How it compares

How it works

The selector never leaves your process. A model only ever sees a node id like [03], the CSS path that actually addresses the element lives in selector_map(), on your side, and gets resolved right before the click.

1

Live page

a Playwright Page, or HTML you already have

2

ZeroDOMParser

one DFS pass: prune, collect, label

3

InteractionGraph

compact text or JSON, sent to the model

4

Model picks a node

e.g. click [03], never a selector

5

selector_map()[node]

resolved on your side, not the model's

6

Act

click/fill via Playwright, then re-read

Architecture

See Architecture for the full system design, data flow, and token economics.

Get started

Choose your path:

[

I want to try it

Parse a page and read the graph in three lines.

Quickstart](https://zerodom.vexralabs.com/docs/quickstart)[

I want to build an agent

Walkthrough](https://zerodom.vexralabs.com/docs/walkthrough)[

I want to integrate it

Wire ZeroDOM into Claude Desktop, Cursor, or your own agent loop.

MCP setup](https://zerodom.vexralabs.com/docs/mcp-setup)

Explore the docs

[

Architecture

System overview, data flow, parser internals, and token economics.

](https://zerodom.vexralabs.com/docs/architecture)[

Applications

QA testing, pen testing, bug bounty, automation, and data extraction.

](https://zerodom.vexralabs.com/docs/applications)[

Seeing the graph

Screenshots, HTML reports, and the on-page driving bar.

](https://zerodom.vexralabs.com/docs/seeing-the-graph)[

Label resolution

The nine-step priority order for naming controls.

](https://zerodom.vexralabs.com/docs/label-resolution)[

Selector audit

Check existing selectors against a live page.

](https://zerodom.vexralabs.com/docs/selector-audit)[

Integrations

LangChain, OpenAI, Anthropic, Vercel AI SDK, CrewAI.

](https://zerodom.vexralabs.com/docs/integrations)[

Bug bounty recon

Stealth Chrome pipe, surface scanning, and the isolation sandbox.

](https://zerodom.vexralabs.com/docs/bug-bounty)