Browser Use vs Stagehand vs Skyvern vs Computer Use: Best AI Browser Agent (2026)

Updated: August 30, 2026

For most production browser agents in 2026, pick Stagehand if you want deterministic TypeScript with an AI escape hatch, Browser Use if you want goal-level Python autonomy, Skyvern for vision-first form and portal work, and Anthropic Computer Use only when you have no DOM access. DOM-driven stacks (Stagehand, Browser Use) currently lead vision-driven stacks (Computer Use, OpenAI CUA) by 12–17 points on task success, so treat vision as a fallback, not a default. Pair whichever framework you pick with a managed browser runtime like Browserbase.

• DOM-driven frameworks (Stagehand, Browser Use) hit ~89–92% on WebVoyager in 2026; vision-driven stacks (Anthropic Computer Use, OpenAI CUA) sit around 75–78%.
• Browser Use gives every step to the LLM, which maximises autonomy but costs a token call per action and makes replays hard to trust.
• Stagehand v3 talks straight to the Chrome DevTools Protocol, runs ~44% faster than v2, and lets you mix deterministic Playwright with `act`/`extract`/`observe` primitives.
• Skyvern reads pages visually, handles 2FA and CAPTCHAs out of the box, and is the pick when selectors keep breaking across similar portals.
• Anthropic Computer Use and OpenAI CUA are the right choice when the target has no accessible DOM (Citrix, remote desktops, legacy Java apps), and the wrong choice for everything else.
• Framework and infrastructure are separate decisions: pair Browser Use, Stagehand, or Skyvern with Browserbase, Steel, or Kernel for session persistence, replay, and observability.

Why 2026 reshuffled the browser-agent landscape

I’ve been wiring browser agents into ops pipelines since the first Selenium-plus-GPT-4 hacks in late 2023, and 2026 is the year the category finally split into legible camps. The old “LLM in a while-loop poking at a Playwright page” pattern doesn’t survive contact with a real workflow. What survived and matured are four shapes: agent-owns-the-loop (Browser Use), you-own-the-loop-with-AI-help (Stagehand), vision-first form workhorses (Skyvern), and pure screen-pixel agents (Anthropic Computer Use, OpenAI CUA).

Three concrete shifts are worth naming. First, Stagehand v3 rewrote its runtime in February 2026 to talk directly to the Chrome DevTools Protocol, dropping the Playwright translation layer and gaining roughly 44% on run time. Second, Browser Use crossed 21k GitHub stars and began publishing an in-house ChatBrowserUse model tuned for the loop, which pushed WebVoyager scores to 89%. Third, both Anthropic and OpenAI now ship first-party computer-control endpoints, so vision-driven agents are no longer research demos. They’re SLAs.

So the practical consequence is that architecture, not accuracy, is now the decision. On WebVoyager, the top four stacks cluster inside four points. Where they diverge is on replay determinism, token spend per run, and what happens when a UI changes overnight. That’s the lens I’ll use through the rest of this comparison.

DOM-driven vs vision-driven: the real architectural split

Every AI browser agent is answering one question at every step: what can I see and click? The DOM-driven answer is “an accessibility tree serialised to text.” The vision-driven answer is “a screenshot plus x/y coordinates.” That single choice cascades into cost, reliability, and where the agent breaks.

DOM-driven agents (Stagehand, Browser Use, Playwright + Claude) extract the DOM, strip it to interactive elements, hand the LLM a compact tree, and receive back a structured action like click(id=42) or fill(id=17, "hello"). This is fast (no image tokens), grounded (element IDs disambiguate targets), and cheap. It also fails hard on canvas-heavy apps, PDF viewers, and anything rendered outside the DOM (think Figma, Google Sheets edit mode, or a Citrix session).

Vision-driven agents (Anthropic Computer Use, OpenAI CUA, Skyvern’s core loop) send a screenshot and receive back coordinates: click(x=482, y=310). They generalise to anything a human can see, including remote desktops, but pay two prices. First, image tokens on every step (5–10× the cost of a DOM tree turn). Second, worse grounding, because the model has to re-read pixels on every screenshot and can drift between similar-looking buttons. That’s the 12–17 point gap the 2026 WebVoyager numbers keep showing.

Skyvern is the interesting hybrid: it uses vision for grounding but keeps the DOM in scope for state-tracking and form validation, which is why it beats pure-vision stacks on form-heavy work while staying more portable than pure-DOM stacks across portals with weird custom controls.

Browser Use vs Stagehand vs Skyvern vs Computer Use at a glance

Dimension	Browser Use	Stagehand	Skyvern	Anthropic Computer Use
Primary language	Python (+ TS SDK)	TypeScript / Node	Python + hosted API	Any (API call)
Grounding approach	DOM tree + optional vision	DOM via CDP + `observe`	Vision-first + DOM state	Pure vision, coordinates
Who owns the loop	The agent	Your code, calls AI on demand	The framework (single API call)	The agent (or your wrapper)
WebVoyager 2026	~89%	~89%	~85.85% (v2.0)	~78%
LLM cost per step	1 call every step	Only when `act`/`extract` used	1 vision call per major step	1 vision call every step
Handles 2FA / CAPTCHA	Manual wiring	Manual wiring	Built-in	Manual wiring
Determinism / replay	Low (LLM every run)	High (cached action recording)	Medium	Low
Best for	Open-ended research, scraping	Product workflows, TS shops	Portals, forms, invoicing	No-DOM targets, generic GUIs

Browser Use: goal-level Python autonomy

Browser Use is the tool I reach for when the task is fuzzy, like “find me the cheapest same-day flight from LAX to JFK, allowing one layover.” The goal isn’t bounded, the pages are unknown, and I need the agent to plan. It hands the LLM a serialised page state on every step, asks for the next action, executes it in Playwright, and repeats until the task returns done. The 89% WebVoyager score isn’t marketing; it’s a fair reflection of how well the loop generalises.

Here’s the minimum viable production shape, with an explicit budget and a hook to persist trajectory events for later evaluation:

from browser_use import Agent, BrowserSession
from browser_use.llm import ChatAnthropic
import asyncio, json, uuid

async def run_task(goal: str, run_id: str) -> dict:
    session = BrowserSession(
        headless=True,
        keep_alive=False,
        storage_state="/var/agents/state/prod.json",
    )
    agent = Agent(
        task=goal,
        llm=ChatAnthropic(model="claude-sonnet-4-5"),
        browser_session=session,
        max_steps=25,               # hard budget: no more than 25 LLM turns
        max_failures=3,             # stop retrying if 3 in a row fail
        use_vision=False,           # DOM-only, cheaper + more deterministic
    )
    result = await agent.run()
    # Persist the trajectory for LLM-as-judge evaluation later.
    with open(f"/var/agents/traj/{run_id}.jsonl", "w") as f:
        for step in result.history:
            f.write(json.dumps(step.model_dump(), default=str) + "\n")
    return {"final": result.final_result(), "steps": len(result.history)}

if __name__ == "__main__":
    asyncio.run(run_task(
        goal="Find the current price of a MacBook Air M4 13-inch on apple.com and return it as JSON.",
        run_id=str(uuid.uuid4()),
    ))

The two production knobs that matter are max_steps and use_vision. Without a step budget, an agent that hits an infinite login loop will burn tokens for hours (I learned that one the hard way on a Bloomberg terminal proxy). Vision adds resilience on canvas-heavy pages but roughly doubles cost, so I leave it off by default and enable it per task. For evaluating whether the agent actually did what you asked, wire the persisted trajectory into an agent trajectory evaluation pipeline. Guessing whether a run succeeded from the final string is how you ship silent regressions.

Stagehand: TypeScript with deterministic escape hatches

Stagehand solves the biggest complaint I had about Browser Use in 2025: every run is a fresh LLM improvisation, so you can’t trust that yesterday’s green build means today’s run will work. Stagehand keeps you in Playwright-shaped code and reaches for the model only at the steps that genuinely need judgement. When you call stagehand.act("click the checkout button") and it works, Stagehand records the resolved action and can replay it on the next run without another LLM call.

Honestly, a real Stagehand workflow looks less like an agent and more like a script with three magic verbs sprinkled in: act, extract, and observe.

import { Stagehand } from "@browserbasehq/stagehand";
import { z } from "zod";

const stagehand = new Stagehand({
  env: "BROWSERBASE",           // or "LOCAL" for dev
  modelName: "claude-sonnet-4-5",
  enableCaching: true,          // reuse recorded actions across runs
  verbose: 1,
});

async function fetchInvoiceTotal(orderId: string) {
  await stagehand.init();
  const page = stagehand.page;

  await page.goto("https://portal.supplier.example/login");
  // First run learns the fields; later runs hit the cache and skip the LLM.
  await page.act("sign in with username OPS_USER and password from env");
  await page.act(`open order number ${orderId}`);

  const { total, currency } = await page.extract({
    instruction: "Read the invoice total and currency from the summary card.",
    schema: z.object({ total: z.number(), currency: z.string() }),
  });

  await stagehand.close();
  return { total, currency };
}

The pattern that actually matters here is the schema on extract. Stagehand uses Zod to force the model into structured output, so you never have to parse free-form text out of the DOM. If you’ve read our piece on structured outputs vs function calling, this is the same discipline applied to screen scraping. Combined with cached actions, a well-written Stagehand workflow tends to make zero LLM calls on cached happy paths. The model only fires when a page changes.

Skyvern: computer vision for forms and portals

Skyvern is the pick when the surface area is bounded but hostile: a hundred insurance portals, each with the same conceptual form, none with a shared markup. Its loop reads a screenshot, marks up interactive elements, and picks the next action based on both vision and a DOM projection. Because it doesn’t depend on selector stability, it survives the layout changes that break every other stack. The trade-off is that you’re paying vision tokens per step and running a heavier model.

Skyvern also ships the operational bits that Browser Use and Stagehand leave to you: 2FA hand-off, CAPTCHA solving, credential vaulting, session persistence, and a hosted workflow builder. That’s what makes it the right shape for “log into 40 vendor portals nightly and pull invoices” without building the harness yourself.

import os, requests

SKYVERN = "https://api.skyvern.com/api/v1"
HEADERS = {"x-api-key": os.environ["SKYVERN_API_KEY"]}

def scrape_invoice(portal_url: str, username: str, password_ref: str):
    task = {
        "url": portal_url,
        "navigation_goal": (
            "Log in, open the most recent invoice, and download the PDF. "
            "Return the invoice number, issue date, and total."
        ),
        "data_extraction_goal": (
            "Return JSON with keys invoice_number (string), "
            "issue_date (ISO 8601), total (number), currency (ISO 4217)."
        ),
        "navigation_payload": {"username": username, "password_ref": password_ref},
        "max_steps_per_run": 40,
        "webhook_callback_url": "https://ops.internal/agents/skyvern/webhook",
    }
    r = requests.post(f"{SKYVERN}/tasks", headers=HEADERS, json=task, timeout=30)
    r.raise_for_status()
    return r.json()["task_id"]

Notice the shape: you don’t drive Skyvern step-by-step. You hand it a goal, a payload, a step budget, and a webhook, and it comes back with a result. That’s a different mental model than Stagehand, and it’s the right one for workflows you want to schedule and forget. When runs need durable retries and backpressure, wrap Skyvern calls in a durable orchestrator like Temporal so a failed portal doesn’t block the batch.

Anthropic Computer Use and OpenAI CUA

Anthropic’s Computer Use tool and OpenAI’s Computer Using Agent (CUA) are the “pure vision” endpoints from the labs. You send a screenshot, the model returns an action like click(482, 310), type("hello"), or scroll(down), and you execute it in whatever runtime you like (a headless browser, a virtual display, a full VNC session). The pitch is generality: the model doesn’t need to know your app, and there’s no DOM assumption.

The 2026 numbers say to use them exactly when you have to, and not before. Computer Use lands around 78% on WebVoyager and CUA around 75%, while DOM-driven stacks sit 12–17 points higher. Costs are worse too. Every screenshot is thousands of image tokens, and multi-step tasks stack up quickly. The Anthropic Computer Use documentation is explicit that it’s beta and recommends sandboxing.

Where they’re irreplaceable:

• Remote-desktop targets: Citrix, VMware Horizon, RDP into a Windows box. There is no DOM to grab.
• Legacy Java Swing / .NET WinForms apps shoved inside a browser via ThinFrame. Same story, no DOM.
• Canvas-first web apps: Figma, tldraw, Miro, in-app data viewers rendered on a <canvas>.
• Generic OS operations: opening a file dialog, driving a native installer, poking at the taskbar.

Everywhere else, you’re paying vision-token prices to get worse grounding than a DOM agent. If you do run Computer Use in production, sandbox it hard. Run it inside a disposable microVM with no network egress except to the target and a scoped audit sink. Our agent sandbox comparison covers the runtime choices.

Do you need Browserbase to run browser agents in production?

Short answer: no, but you’ll rebuild it. Browserbase is managed browser infrastructure (not an agent framework itself) that gives you long-lived stealth Chromium instances, session recording, CDP-as-a-service, and a debugger that lets you scrub through what the agent actually saw. Steel is the open equivalent. Kernel wins when you need to keep authenticated sessions warm for weeks. Anchor Browser is the newer entrant with an aggressive free tier.

The three problems these platforms exist to solve are the ones that always show up on week two of shipping:

1. Session persistence. If every run starts from a clean profile, you re-do login, re-solve 2FA, and re-warm every consent banner. That kills throughput and gets you flagged as a bot.
2. Fingerprint and IP rotation. Sites like LinkedIn, Amazon, and most banking portals block headless Chromium by default. Managed platforms rotate residential IPs and patch fingerprints so your agent looks like a browser instead of a scraper.
3. Post-hoc debugging. When a run fails at step 34 of 40, you don’t want to re-run to reproduce, you want a scrubbable timeline of every screenshot, DOM state, and network call. Session replay is the difference between a two-hour debug and a two-day one.

You can absolutely self-host Playwright in a Kubernetes cluster and roll your own session storage. I’ve done it twice. Both times we ended up with 40% of a browser-runtime team’s work spread across four engineers who thought they were shipping a feature. Unless browser infra is your actual product, buy this layer. Pair it with your framework of choice: Browser Use + Browserbase and Stagehand + Browserbase are both first-class combos.

How to choose the right AI browser agent for your workflow

I ask five questions before writing a single line of agent code. In order:

1. Is the task bounded or open-ended? Bounded (“pull yesterday’s invoice from each vendor portal”) points to Stagehand or Skyvern. Open-ended (“research and summarise”) points to Browser Use.
2. Does the target have a usable DOM? If no (Citrix, canvas, remote desktop), Computer Use or CUA is the only option. If yes, DOM-driven wins on cost and accuracy.
3. How often does it run, and how tight is the budget? Nightly cron over 40 portals with a $50/day cap? Stagehand + action caching or Skyvern. Ad-hoc researcher tasks? Browser Use is fine at $0.10–$0.30 a run.
4. Who’s on the hook for reliability? If your on-call team has to page in when it breaks, deterministic replays matter and Stagehand pulls ahead. If it’s a background job with a queue and retries, Browser Use / Skyvern autonomy is fine.
5. What’s your language shop? Stagehand and its ecosystem are TypeScript-first. Browser Use and Skyvern are Python-first (both offer TS SDKs but the docs and community follow Python). Don’t pick a stack that fights your team.

Concretely: for a bounded, high-frequency TS workflow, Stagehand + Browserbase. For open-ended Python research, Browser Use + Browserbase. For portal-heavy Python ops work, Skyvern hosted. For no-DOM edge cases, Anthropic Computer Use inside an isolated sandbox. That covers 95% of what I ship.

Cost, latency, and reliability in production

Order-of-magnitude cost per task on real workflows I’ve run this year:

• Stagehand with cached actions: $0.00–$0.02 on the cached happy path, $0.05–$0.15 when the model has to re-think a step. Median under a penny.
• Browser Use, Claude Sonnet, DOM-only, ~15 steps: $0.10–$0.30 per task, dominated by input tokens on the serialised DOM.
• Skyvern hosted, single form-fill task: $0.20–$0.40 per task including 2FA hand-off.
• Anthropic Computer Use, vision every step, ~15 steps: $0.80–$2.00 per task. Image tokens add up fast.

Latency follows the same pattern. Cached Stagehand runs finish in 3–8 seconds. Uncached Stagehand or Browser Use runs sit at 20–60 seconds. Skyvern tasks average 45–90 seconds when there’s a login. Computer Use with vision every step routinely hits two minutes on a five-step task.

The reliability numbers matter more than the marketing scores. WebVoyager is a broad public benchmark, but your workflow is narrower. I run a private eval set of 30–50 tasks that represent our real load and re-run it before every framework or model bump. Wire the runs into your LLM observability stack so you can see per-task success rates over time. The single best predictor of production stability I’ve found is step-count variance. If the same task takes 8 steps one run and 24 the next, you have a reliability problem the aggregate success rate is hiding.

Frequently Asked Questions

What’s the difference between Browser Use and Stagehand?

Browser Use is a Python agent framework that owns the loop, so the LLM decides every action on every step. Stagehand is a TypeScript library where your code owns the loop and calls the LLM only at specific steps via act, extract, or observe. Stagehand caches successful actions and replays them without further LLM calls, which makes it far cheaper and more deterministic than Browser Use for repeated production workflows.

Is Anthropic Computer Use production-ready?

It’s a supported public feature but Anthropic still labels it beta and it hits about 78% on WebVoyager, well below DOM-driven stacks. Use it in production only when the target has no accessible DOM (remote desktops, Citrix, canvas apps), and always sandbox it in an isolated microVM with scoped network egress. For any target with a working DOM, Browser Use or Stagehand will be cheaper and more reliable.

Which AI browser agent has the highest success rate?

On the public WebVoyager benchmark for 2026, Playwright + Claude leads at ~92%, Browserbase and Stagehand cluster near 89–90%, Browser Use hits 89%, Skyvern 2.0 lands at 85.85%, and Anthropic Computer Use and OpenAI CUA sit at 78% and 75%. The scores are closer than they look once you hold the benchmark constant, so pick on architecture and cost rather than a percentage point.

Do you need Browserbase to run browser agents in production?

No, but you’ll rebuild what it does. Browserbase gives you managed Chromium, session persistence, fingerprint rotation, and post-hoc session replay, which are the three problems everyone hits on week two. Steel is the open alternative and Kernel is optimised for long-lived authenticated sessions. Self-hosting is viable if browser infrastructure is your actual product; otherwise pair a managed runtime with your chosen framework.

Can browser agents handle CAPTCHAs and 2FA?

Skyvern ships built-in 2FA hand-off and CAPTCHA solving as part of its hosted API. Browser Use and Stagehand leave that to you. The common patterns are pausing the run and posting to a human-in-the-loop queue for 2FA, or integrating a solver service like 2Captcha or CapSolver for image challenges. Session persistence via Browserbase or Kernel is the other half of the answer: authenticated sessions that stay warm for days don’t re-trigger 2FA.

How much does it cost to run a browser agent in production?

On real workloads in 2026, cached Stagehand runs cost fractions of a cent; Browser Use with Claude Sonnet averages $0.10–$0.30 per 15-step task; Skyvern hosted runs sit around $0.20–$0.40; and Anthropic Computer Use with vision every step routinely lands between $0.80 and $2.00 per task. Browser infrastructure (Browserbase, Steel) adds a session-minutes charge on top, roughly $0.05–$0.15 per typical run.