agent-browser

A hardened local browser for AI agents. Zero runtime dependencies. Drives the Chrome you already have over the DevTools Protocol, extracts token-efficient Markdown, and is not trivially flagged as automation.

Surface	Install	Use it for
MCP server	`npx -y @truenix/agent-browser mcp`	Claude Code, Cursor, Codex, any MCP client
CLI	`npx -y @truenix/agent-browser markdown <url>`	shells, scripts, CI
Library	`import { withBrowser } from '@truenix/agent-browser'`	your own Node code
DSH / Cordis plugin	composition row	native tools in a DSH harness

npx -y @truenix/agent-browser markdown https://news.ycombinator.com

Everything runs locally. No account, no API key, no remote service, no quota.

Why

Feeding an agent the full rendered DOM wastes most of its context. Measured on real pages:

And a browser that announces itself as automation gets blocked, degraded, or served different content — which quietly corrupts whatever the agent concluded.

Rendered-page benchmark (live DOM vs Markdown)

Each cell is UTF-8 bytes / exact o200k_base tokens. The first payload is the serialized DOM after JavaScript and the page load event, not a curl response. Reductions compare page-input tokens only.

site	rendered DOM	Markdown	`--links text`	exact token reduction
`en.wikipedia.org/wiki/WebAssembly`	861 kB / 282k tok	82.6 kB / 21.9k tok	48.0 kB / 12.2k tok	12.87× / 23.14×
`react.dev`	273 kB / 108k tok	11.3 kB / 3.1k tok	8.6 kB / 1.9k tok	34.73× / 57.23×
`nextjs.org`	337 kB / 124k tok	5.3 kB / 1.2k tok	4.2 kB / 0.9k tok	100× / 131.05×
`github.com/trending`	666 kB / 218k tok	7.7 kB / 2.2k tok	4.2 kB / 1.1k tok	100.96× / 200.63×
`apple.com`	362 kB / 143k tok	4.1 kB / 1.1k tok	3.1 kB / 0.9k tok	124.4× / 166.69×
`news.ycombinator.com`	34.5 kB / 11.7k tok	10.3 kB / 3.4k tok	3.2 kB / 1.1k tok	3.42× / 10.86×

From a source checkout, regenerate both charts and this table's underlying measurements with npm run charts — it measures live, then draws the PNGs using agent-browser itself, updates the table, and writes the full machine-readable snapshot to benchmark-results.json. The exact tokenizer is a development dependency; the published browser package remains zero-dependency.

Spending fewer page-input tokens

Links can dominate extracted output on navigation-heavy pages. When the agent is reading rather than navigating, drop their targets:

agent-browser markdown https://github.com/trending --links text
agent-browser markdown https://en.wikipedia.org/wiki/Rust --links relative

mode	renders	use it when
`inline` (default)	`[text](https://site/page)`	the agent will navigate next
`relative`	`[text](/page)`	same-site crawling; keeps targets, drops the origin
`text`	`text`	reading, summarising, question answering

Exact o200k_base page-input tokens for Hacker News + YC Blog. This excludes prompts, tool schemas, retries, caching, model output, and provider pricing; it is not an end-to-end cost estimate.

Pages that render after load

Navigation waits for the load event, driven by CDP lifecycle events rather than a polling loop and a fixed delay. On real pages that is 2–3× faster for byte-identical output:

page	`--wait load` (default)	old fixed 250 ms settle
`example.com`	7 ms	258 ms
`github.com/trending`	115 ms	340 ms
`nextjs.org`	160 ms	365 ms
`news.ycombinator.com`	218 ms	466 ms

A client-rendered app whose content arrives after load needs a real signal, not a bigger guess — the old 250 ms settle missed that content too:

agent-browser markdown https://some-spa.example --wait idle

mode	waits for	use it when
`domcontentloaded`	the DOM is parsed	you only need markup that shipped in the HTML
`load` (default)	the load event	almost always
`idle`	the network goes quiet	the result looks like an empty shell

Any other harness

If your framework speaks JSON Schema, it can drive agent-browser without this project knowing the framework exists. agent-browser tools prints the catalogue in whichever shape you need:

agent-browser tools                     # MCP:       {name, description, inputSchema}
agent-browser tools --format openai     # OpenAI:    {type:"function", function:{...}}
agent-browser tools --format anthropic  # Anthropic: {name, description, input_schema}

Each tool then runs either way, whichever your harness can do:

import { findTool, TOOLS } from '@truenix/agent-browser/tools';
import { withBrowser } from '@truenix/agent-browser';

// in-process: the harness can hold a CDP session
const tool = findTool('browser_markdown');
const text = await withBrowser({}, async (session) => {
  await session.navigate('https://example.com');
  return tool.run(session, { url: 'https://example.com', links: 'text' });
});

// out-of-process: the harness can only run a command (sandboxes, shells)
tool.cli({ url: 'https://example.com', links: 'text' });
// => ['markdown', 'https://example.com', '--links', 'text']

Install

As an MCP server

Claude Code

claude mcp add browser -- npx -y @truenix/agent-browser mcp

Cursor / Windsurf / generic mcpServers JSON

{
  "mcpServers": {
    "browser": {
      "command": "npx",
      "args": ["-y", "@truenix/agent-browser", "mcp"]
    }
  }
}

Tools: browser_markdown, browser_text, browser_html, browser_links, browser_screenshot, browser_evaluate, browser_accessibility_tree, browser_pdf, browser_probe.

Every surface exposes the same nine tools from one shared catalogue, so the CLI, the MCP server and the DSH plugin can never drift apart.

As a library

npm install @truenix/agent-browser

import { withBrowser } from '@truenix/agent-browser';

const md = await withBrowser({}, async (session) => {
  await session.navigate('https://example.com');
  return session.markdown();
});

As a DSH / Cordis plugin

One-liner (recommended — auto-wires when you mean DSH):

npx -y @truenix/agent-browser install          # adds the bundle to ~/.dsh/profiles/web/package.json, then pnpm install (the mount ships in the package's own cordis.patch.yml layer)
# npx -y @truenix/agent-browser install --profile web --dry-run  # preview
# npx -y @truenix/agent-browser uninstall      # remove again

Restart dsh — all nine browser_* tools appear as native tools. No handler runs on plain npm install; intentional install is required.

Manual (if you prefer to edit the composition yourself):

npm i -g @truenix/agent-browser
# or inside the harness checkout: pnpm add @truenix/agent-browser

Requires Node ≥ 18 and a Chrome/Chromium install. Then add to the host composition (tools registry lives on host, not per-agent):

# ~/.dsh/profiles/web/cordis.patch.yml  — persists for every web session
- insert:
  - id: agent-browser
    name: '@truenix/agent-browser/cordis'
    config:
      timeoutMs: 180000   # per-tool call budget; default respects AGENT_BROWSER_BIN / ENDPOINT
      # cli: 'npx -y @truenix/agent-browser'  # override only if needed

Short form (when the composition already wraps insert):

- '@truenix/agent-browser/cordis':
    timeoutMs: 180000

Env overrides: AGENT_BROWSER_BIN (Chrome binary), AGENT_BROWSER_ENDPOINT (attach to long-lived browser via --endpoint), or config.cli.

CLI

agent-browser <command> [options]

  markdown <url>     Extract the whole page as Markdown (main content by default)
  text <url>         Visible text only
  html <url>         Full serialized DOM after JavaScript runs
  links <url>        Every anchor as JSON
  screenshot <url>   PNG/JPEG   (-o file, --full)
  pdf <url>          PDF        (-o file)
  a11y <url>         Filtered accessibility tree
  eval <url> <expr>  Evaluate JS, return only its value (cheapest)
  probe              Browser, GPU and capability report
  mcp                Run as an MCP server on stdio
  tools              Print tool schemas (--format mcp|openai|anthropic)

Options: --headful, --no-stealth, --block-images, --gpu/--no-gpu, --width, --height, --viewport WxH, --main, --raw, --links, --max-rows, --limit, --wait, --settle, --world isolated|main, --full, --endpoint <ws>, --timeout, --json, -o.

`markdown` to read, `eval` to look something up

The most expensive mistake an agent makes with this tool is rendering a whole page to answer a one-line question. Three targeted questions across three heavy pages cost 235 bytes via eval, against 77 kB of Markdown:

agent-browser eval https://github.com/trending \
  "JSON.stringify(Array.from(document.querySelectorAll('article h2 a')).slice(0,3).map(a=>a.innerText.trim()))"
# ["openai / codex","mattpocock / skills","affaan-m / ECC"]   -> 58 bytes

Reach for innerText, not textContent. textContent hands back the raw source whitespace ("openai /\n\n codex"); innerText gives what is actually rendered ("openai / codex"). An agent that gets that wrong pays a whole retry round trip, which costs far more than the bytes it saved.

Use markdown when you actually need to read, summarise or search the page.

Each invocation launches its own browser (~1.4 s). For several lookups in a row, keep one browser alive and point --endpoint at it — the same three questions take 5.9 s across three cold starts, 3.2 s against a warm one.

--endpoint attaches to an already-running browser instead of launching one — useful for reusing a single long-lived browser across many calls.

Memory

Chrome's floor is about 420 MB PSS across 18 processes before it loads anything, and that floor is Chrome's, not this package's — flag tuning moves it ~5%, and the flag that moves it further (--enable-low-end-device-mode) reports navigator.deviceMemory: 2 next to 24 cores, an impossible machine and exactly the kind of inconsistency that gets a browser flagged. So the lever is fewer browsers, not smaller ones.

The MCP server keeps one browser and gives each tool call its own isolated context. Three heavy pages fetched concurrently:

	peak PSS	processes	wall clock
a browser per call	1289 MB	44	2114 ms
one browser, 3 contexts	654 MB	20	2049 ms

Isolation is unchanged — the per-call browser context was always what provided it. The browser shuts down after 30 s idle (AGENT_BROWSER_IDLE_MS, 0 to close immediately), so a long-lived server does not sit on 420 MB between conversations. A repeat call while it is warm skips the launch entirely and runs about twice as fast.

The trade: tasks share a process tree, so a browser-level crash takes out everything in flight rather than one call. A dead browser is detected and relaunched on the next call. If you need blast-radius isolation per task, use withBrowser, which still gives each call its own browser.

import { withPooledSession, shutdownPool } from '@truenix/agent-browser/pool';

await withPooledSession({}, async (session) => {
  await session.navigate('https://example.com');
  return session.markdown();
});
await shutdownPool();   // or let it idle out

For the CLI, each invocation is its own process, so reuse means pointing --endpoint at a browser you keep alive yourself.

The daemon: one browser for every CLI call

Each CLI invocation is its own process, so by default each one cold-starts its own Chrome — ten concurrent calls means ten browsers. --daemon shares one resident browser instead, each call still getting its own isolated context:

agent-browser markdown https://example.com --daemon
agent-browser daemon --status
agent-browser daemon --stop

	wall clock	peak Chrome processes
3 sequential lookups, no daemon	5330 ms	—
3 sequential lookups, `--daemon`	3254 ms	—
10 concurrent calls, no daemon	1443 ms	140
10 concurrent calls, `--daemon`	1144 ms	32

It is opt-in (--daemon, or AGENT_BROWSER_DAEMON=1) because starting a background process that outlives your command is a side effect worth asking for. It starts on demand and exits after five minutes idle (AGENT_BROWSER_DAEMON_IDLE_MS). Setting the env var also routes the MCP server at it, which is worth doing when several MCP clients share a machine.

--headful, --width, --height, --block-images and --gpu are fixed when a browser launches, so a shared one cannot honour them. Passing any of them wins: that call quietly gets its own private browser, and says so on stderr.

Four failure modes it is built around, each verified by test:

Exactly one daemon. Ten simultaneous first-callers all spawn one; nine lose the race to bind the socket and exit before launching anything. Binding the socket is the lock, so there is no lockfile to go stale.
The open socket is the refcount. A client holds its connection while it works, so a client killed with SIGKILL still releases — the kernel closes the socket. A "please release" message would have leaked a reference forever.
Idle exit, so it does not sit on ~420 MB between conversations.
A SIGKILLed daemon strands nothing: its profile marker names its pid, so the sweep below reclaims the browser on the next launch.

Cleaning up after itself

Every launch writes an owner marker into its temp profile and sweeps for profiles whose owner has died, killing the browser still attached to them. Combined with SIGINT/SIGTERM/SIGHUP handlers, that gives:

how the call ends	orphaned processes	after the next launch
normally	0	0
SIGTERM / SIGINT	0	0
SIGKILL (uncatchable)	1 browser	0

So a hard kill costs at most one stranded browser, not one per interrupted call. Ten concurrent CLI calls, repeated, leave nothing behind. A live browser is never swept — its owner process is still running, and an unattributable profile is left alone until nothing has it open and it has been idle 60 s.

Bot detection

Run it yourself: npm run test:bot. Latest result:

detector	result
`bot.sannysoft.com`	31 passed, 0 failed
`bot-detector.rebrowser.net`	8 green, 0 unsafe; active isolated probe stayed gray
`deviceandbrowserinfo.com`	`isBot: false`, 0 of 22 checks flagged
`arh.antoinevastel.com`	SKIP, detector returned HTTP 502

Plain headless Chrome fails four sannysoft rows (HEADCHR_UA, CHR_MEMORY, WebGL SwiftShader, old UA) and is reported as a bot.

A network failure or detector-side 5xx counts as SKIP, never as a pass. Loaded 4xx, challenge, incomplete, and unrecognized pages fail closed. The gate requires three reachable passes, so a day when the test sites are down cannot be mistaken for success.

Environment matters more than patching

Release 2.0.1, measured in two places:

	this workstation	GitHub Actions runner
IP	residential	datacenter
GPU	real (NVIDIA)	none → SwiftShader
`bot.sannysoft.com`	31 passed, 0 failed	30 passed, 1 failed (`WebGL Renderer`)
`bot-detector.rebrowser.net`	6 green, 0 red	6 green, 0 red
`deviceandbrowserinfo.com`	`isBot: false`	`isBot: true` (`hasSuspiciousWeakSignals`)

In that run, every CDP-level signal exercised by these detector suites stayed non-positive in both. What flipped the verdict was the environment: a datacenter ASN plus software rendering tripped a weak-signal composite that no amount of fingerprint patching addresses.

This is the honest shape of the problem. Hardening the browser removes the trivial tells. Where you run it decides the rest.

What the hardening does, and why

Every item came from a detector telling us we were wrong:

No automation-controlled Blink feature. Chrome's headless and --remote-debugging-port=0 paths normally expose navigator.webdriver = true. The launcher disables AutomationControlled and does not add --enable-automation, so the value stays false.
No Runtime.enable. It is the loudest CDP tell and powers the classic console/Error.stack detector. Runtime.evaluate works fine without it.
Isolated evaluation by default. Reads and Markdown extraction share the DOM but do not touch page-installed globals or prototype hooks. eval --world main is an explicit escape hatch for code that needs page-defined JavaScript.
Window and screen move together. --window-size without --ozone-override-screen-size gives outerWidth > screen.width, which is physically impossible — a stronger signal than plain headless.
No default device-metrics override. The usual 1280×720 is Playwright's default viewport and detectors flag it by name. Set --viewport only if you need it.
Real GPU when available, giving a genuine ANGLE (NVIDIA …) renderer instead of SwiftShader.
UA set at launch, not only over CDP. Emulation.setUserAgentOverride does not reach Web Workers, so a worker keeps reporting the headless UA while the page reports the clean one (hasInconsistentWorkerValues).
No acceptLanguage override. CDP derives navigator.languages by splitting that header, so "en-US,en;q=0.9" becomes ["en-US","en;q=0.9"] — a q-value where none can legally exist, and another page/worker mismatch. --lang does it correctly.
Client Hints derived from the binary's own version, so Sec-CH-UA cannot disagree with navigator.userAgent.
Isolated browser context per session, disposed on close — clean state per task without a second browser process.

The recurring lesson: consistency beats coverage. Four of those are cases where partial spoofing made detection easier, caught only by running real detectors.

Why WebGL spoofing is off by default

spoofWebgl exists and is implemented carefully — a Proxy around native getParameter, so Function.prototype.toString still reports [native code]. It is off, because measurement says it backfires. From test/webgl-spoof-experiment.mjs:

arm	renderer claimed	maxTexture	extensions	sannysoft	verdict
real GPU, no spoof	NVIDIA	32768	37	0 failed	`isBot: false` ✅
SwiftShader, honest	SwiftShader	8192	35	1 failed	`isBot: false` ✅
SwiftShader + spoof	NVIDIA	8192	35	0 failed	`isBot: true` ❌

Claiming hardware you do not have fixes one cosmetic row and fails the composite detector: the injected script does not reach Web Workers, so the worker still reports SwiftShader, and MAX_TEXTURE_SIZE stays at the software value while the renderer string claims a discrete GPU.

Honest SwiftShader passes. A convincing lie does not. Give the browser a real GPU instead — it is free.

What this does NOT do

Fingerprint-level detection is the entire scope. It does not defeat, and does not try to:

TLS/JA3-JA4 and HTTP/2 fingerprinting — decided before any JavaScript runs
IP reputation — datacenter vs residential ASN, often the real blocker
Behavioural analysis — mouse paths, timing, dwell

Commercial challenge products lean on those, so "passes the gate" means not trivially flagged as automation, never undetectable. Intended for your own sites, testing, accessibility work, and ordinary agent browsing.

Memory

A Chrome stack costs roughly 450 MB. The lever is architecture, not flags: run one browser and many isolated contexts rather than one browser per task. Start a browser once, then point every call at it with --endpoint / AGENT_BROWSER_ENDPOINT. --block-images helps for text work.

Zero dependencies

dependencies is empty, including the WebSocket transport.

Node's global WebSocket (WHATWG) cannot send request headers, which any authenticated or proxied CDP endpoint needs, and undici is not importable standalone. So src/ws.mjs implements RFC 6455 directly over node:http(s) — handshake, masking, continuation fragments, 64-bit lengths, ping/pong — which is everything CDP requires.

The Markdown converter walks the DOM with an explicit stack, keeping JS call depth at O(1) regardless of nesting, and uses native innerText for leaf-level inline nodes. That makes it both recursion-safe on deeply nested documents and markedly faster on large pages.

Environment


`AGENT_BROWSER_BIN`	path to a Chrome/Chromium binary
`AGENT_BROWSER_ENDPOINT`	attach to this CDP endpoint instead of launching

Requirements

Node ≥ 18 and a Chrome/Chromium install. No build step.

Credits

This project's hardening is almost entirely derived from other people's published detection research — see CREDITS.md. Particular thanks to rebrowser-bot-detector, bot.sannysoft.com, deviceandbrowserinfo.com, and Camoufox for showing how this is done properly.

License

MIT

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
cordis		cordis
scripts		scripts
src		src
test		test
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CREDITS.md		CREDITS.md
LICENSE		LICENSE
README.md		README.md
benchmark-results.json		benchmark-results.json
cordis.patch.yml		cordis.patch.yml
heavy-benchmark.png		heavy-benchmark.png
package-lock.json		package-lock.json
package.json		package.json
token-cost-graph.png		token-cost-graph.png

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agent-browser

Why

Rendered-page benchmark (live DOM vs Markdown)

Spending fewer page-input tokens

Pages that render after load

Any other harness

Install

As an MCP server

As a library

As a DSH / Cordis plugin

CLI

`markdown` to read, `eval` to look something up

Memory

The daemon: one browser for every CLI call

Cleaning up after itself

Bot detection

Environment matters more than patching

What the hardening does, and why

Why WebGL spoofing is off by default

What this does NOT do

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agent-browser

Why

Rendered-page benchmark (live DOM vs Markdown)

Spending fewer page-input tokens

Pages that render after load

Any other harness

Install

As an MCP server

As a library

As a DSH / Cordis plugin

CLI

markdown to read, eval to look something up

Memory

The daemon: one browser for every CLI call

Cleaning up after itself

Bot detection

Environment matters more than patching

What the hardening does, and why

Why WebGL spoofing is off by default

What this does NOT do

Memory

Zero dependencies

Environment

Requirements

Credits

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`markdown` to read, `eval` to look something up