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ego lite vs Browser Harness

The Best Browser Harness Alternative

Browser Harness is Browser Use's local version: a Python agent loop you run on your own machine with your own LLM API key, launching its own browser and calling the model again at every step to plan the next move.

ego lite needs no framework: the coding agent you already run drives your real, logged-in browser directly, batching actions instead of narrating them one at a time. Across 31 live-site jobs it finishes 96.8% of tasks with a perfect score to Browser Harness's 83.9%, on 41% fewer model round trips.

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Browser automation benchmark: ego lite vs Browser Harness

This is live browser automation, not a sandbox demo. ego lite and Browser Harness, Browser Use's local version, ran the same 31 multi-step tasks on real websites: several pages, several decisions, often a login you already have. The hosted cloud product was not part of this run. Local against local, same jobs, scored the same way.

agent: pimodel: ChatGPT 5.6 Solthinking: max31 tasks

Task completion rate

How often the agent finished the job. Stuck at login, skipped a step, or returned the wrong result: that task is a fail. Higher is better.

ego lite96.8%30 of 31 tasks perfect
Browser Harness (Browser Use local version)83.9%26 of 31 tasks perfect

Cost per completed task

What one finished job costs in model spend. Failures still get billed, so they push this number up. Lower is better.

ego lite$1.98
Browser Harness (Browser Use local version)$3.04

Model turns per task

How many times the model had to look at the page and choose the next action. Extra looks mean extra tokens, extra waiting, and extra places to stall. Lower is better.

ego lite30.3
Browser Harness (Browser Use local version)51.2

Average task time

How long a job took from start to finish, on average, including time spent waiting on the model. Lower is better.

ego lite8m 38s
Browser Harness (Browser Use local version)9m 58s

We ran every tool twice and kept the better score. A failed task still counts against it.

Check the numbers, or rerun the tasks yourselfThe 31 tasks, the grading checklists, and the raw results are open source. If a number looks off, open the repo.ego-browser-benchmark-framework

Why ego lite is better than Browser Harness

With Browser Harness, the browser agent is something you build: a Python project, an LLM API key, a loop that calls the model every step. With ego lite, it's something you already have. Run /ego-browser inside Claude Code, Codex, or Cursor and your agent gets your actual browser, logins and all. (Browser Use's hosted cloud product is a different category; the local-versus-local matchup is this page.)

96.8% of tasks done perfectly. No other tool tops 84%.

Browser Harness's agent loop calls the LLM at every step: read the page, decide the next batch of actions, wait, repeat. It turned in the most careful runs of any competitor on those 31 jobs, and paid for the care in round trips: 51.2 model calls per task, the most of the five tools measured, each one re-sending page state just to plan the next few clicks.

In ego lite, every page reaches your agent as a compressed Snapshot it can act on immediately, several actions per JavaScript turn, no separate model loop deciding a step at a time. The result on the same 31 tasks: 96.8% perfect to Browser Harness's 83.9%, on 41% fewer round trips and 35% less model spend per completed task.

Time to finish a task, shorter is better
518 sego lite
598 sbrowser harness
Data source - Task: finish an average live-site job

Parallel multitasking, execute faster

Browser Use's own docs mark parallel Browser Harness runs experimental: developers note agents can conflict when they share resources, and real isolation means standing up a separate browser instance per task yourself.

In ego lite there's no cap on Spaces: each runs its own task at once, on its own imported Chrome profile. None of them steal your tab, and you can watch or take over any Space anytime.

No more back and forth Cost fewer tokens

Browser Harness's loop calls the model at every step and carries the page state along, and on longer tasks that cost compounds fast enough that users ask the Browser Use maintainers how to avoid burning tokens on a task that runs the same way every time.

Your agent in ego lite batches several actions in a few lines of JavaScript per turn instead of waiting on a fresh model call to keep going. Stacked with Snapshot input, the same task completes on far fewer tokens overall.

Claude Code · ego lite
>/ego-browser Add the first product to the cart, then check checkout loads
●Bash(
ego-browser nodejs <<'EOF' await openOrReuseTab('localhost:3000') await click('.product-card') await click('.add-to-cart') await click('a[href="/checkout"]') cliLog('checkout loaded') EOF
)
⎿Output:
[t=4480ms] checkout loaded
Done. Both steps work.
─── 1 tool call · ↑ 1.1k tokens ───
Browser Harness · agent.run()
>$ python run_task.py 🎯 Task: Add the first product to the cart, then check checkout loads
📍 Step 1:
👍 Eval: Page loaded successfully
🧠 Memory: On demo store homepage, need to find a product
🎯 Next goal: Open first product and add it to cart
📍 Step 1: Ran 3 actions in 2.1s: ✅ 3
↑ 3.4k tokens sent so far
📍 Step 2:
👍 Eval: Product added to cart
🧠 Memory: Item in cart, now check checkout
🎯 Next goal: Navigate to checkout and confirm it loads
📍 Step 2: Ran 2 actions in 1.9s: ✅ 2
↑ 7.1k tokens sent so far
📍 Step 3:
👍 Eval: Checkout page did not load on first click
🧠 Memory: Link required a second click, retrying
🎯 Next goal: Retry the checkout link and wait for the form
📍 Step 3: Ran 1 action in 2.3s: ✅ 1
↑ 10.8k tokens sent so far
📍 Step 4:
👍 Eval: Payment form is present
🧠 Memory: Task complete
🎯 Next goal: done
📍 Step 4: Ran 1 action in 1.4s: ✅ 1
↑ 13.6k tokens sent so far
─── 4 model calls · ↑ 13.6k tokens ───
The same task, the same model. Left: one batched JavaScript call in ego lite, 1.1k tokens total. Right: Browser Harness's agent loop, a fresh model call per step, tokens climb to 13.6k.

Same Chrome, agent-native

Browser Harness launches its own Chromium over the DevTools protocol, and the Browser Use GitHub issues are full of developers hitting profile-lock errors pointing it at their real Chrome: the --profile flag copies the live profile into a temp directory, and on Windows that copy fails outright while Chrome holds the file locks.

Built on Chromium, ego lite imports your entire Chrome setup in one click, live, with nothing to close first. Your agents inherit real logins without ever getting stuck.

ego lite Chrome profile import: one-click setup with all your logins

ego lite vs Browser Harness

Feature comparison between ego lite and Browser Harness.
Featureego liteBrowser Harness
Who supplies the modelYour coding agent (Claude Code, Codex, Cursor)You, bring an LLM API key and pay per token
SetupInstall the app, run /ego-browser in your agentPython project, pip install, model config, task code
Logged-in sites (SSO, 2FA)One-click Chrome profile import, signed in by defaultLaunched Chromium; profile reuse is manual, with caveats
How actions executeSeveral actions batched per turn in JavaScriptAgent loop calls the model every step
Token cost per taskLower: Snapshot input plus batched actions, measured per taskHigher: every step re-sends page state through the model
Browser automation on live sites (31 tasks, same model)96.8% perfect, $1.98 per completed task, 30.3 round trips83.9% perfect, $3.04 per completed task, 51.2 round trips
Parallel tasksSpaces isolate tasks inside one visible browserExperimental; multiple agent instances, or the paid cloud
Doubles as your daily browserYes, you browse in your Space, agents work in theirsNo, an automation library and hosted browsers
Reusable skills (coming soon)Distills successful runs into reusable skills; up to 5x faster on complex tasks as the agent repeats them (limited beta)No built-in equivalent
PriceFree, no subscriptionOpen source free; cloud and LLM usage are paid
Last updated Aug 20, 2026

Make it a seamless transition

If you set up Browser Harness to automate your own browsing (research, form filling, logged-in chores) rather than to ship a product, the switch removes the whole project layer.

  1. Download ego (lite)

    Download ego lite and import your Chrome profile in one click. The logins you were configuring Browser Harness to reach come along automatically.

  2. Run your first task with /ego-browser

    Paste into your agent

    /ego-browser Open ego.app and join the waitlist

    Run /ego-browser in Claude Code, Codex, or Cursor. No Python environment, no model selection, no API key.

  3. Watch it work
    ego lite Spaces overview with four browser tasks running side by side: Claude Code tracking Apple stock on Yahoo Finance, Codex filtering cars by year on cars.com, Hermes finishing a SaaS back-office task, a user scraping X, and a hand tapping + to open another Space

    The task runs in its own Space, not a headless Chromium instance you can only guess at from logs. Watch it live or take over anytime, and the result lands back in your agent's CLI.

Keep Browser Harness where it belongs: inside Python products and pipelines you're building for others. ego lite covers the agent browsing you do yourself.

When to use each tool

Choose ego (lite) when

  • You want an agent doing your own browser work (research, forms, logged-in chores) without building anything in Python.
  • You already run Claude Code, Codex, or Cursor and don't want a second agent loop with its own API-key bill.
  • The tasks need your real logins: one-click profile import beats manual Chromium profile configuration.
  • You want tasks running in parallel Spaces, in a headful browser you can watch or take over.

Choose Browser Harness when

  • You're building a custom automation product or pipeline in Python. Browser Harness is a library designed to be embedded.
  • You want to own the agent loop in your own code: pick the model, shape the prompts, control each step.
  • You need automations deployed to the cloud, running on Browser Use's hosted browsers when your laptop is closed.
  • You're shipping browser automation to others. ego lite is an end-user browser, not an SDK.

Give your agent a real browser

Free, runs on your Mac, imports your Chrome profile in one click. Works with Claude Code, Codex, Cursor, and any CLI agent that writes code.

Try ego (lite)

Keep reading

Still weighing your options? See how Browser Harness compares with the other tools in the same space.

ego (lite) vs Puppeteerego (lite) vs Playwright MCPego (lite) vs Claude Chrome extensionego (lite) vs Browserbaseego (lite) vs Stagehandego (lite) vs agent-browserego (lite) vs Playwrightego (lite) vs Seleniumego (lite) vs Codex browserego (lite) vs Kimi Chrome extensionego (lite) vs Codex Chrome extensionAll comparisonsBrowser automation solutions

FAQ

Browser Harness is Browser Use's local version. Browser Use is one of the most popular open-source AI browser agent projects: an MIT-licensed Python agent loop that lets an LLM control a browser, launching its own Chromium over the DevTools protocol and deciding actions step by step against the page state. You bring your own model API key and run it on your own machine. Browser Use's paid cloud product runs hosted browser agents with stealth and captcha-solving features, which is a different category from a local browser, so this page compares the harness: local against local. For developers building custom automation products and pipelines in Python, it's a strong, actively developed foundation, and of the five local tools we measured it posted the best completion rate of any competitor.

If you want an agent to do browser work for you, yes. ego lite plugs your existing coding agent into your real logged-in browser with no Python project and no API-key billing, and across 31 live-site jobs it finished 96.8% of tasks perfectly to Browser Harness's 83.9%, at $1.98 per completed task against $3.04. If you're building a browser-automation product or pipeline in Python, Browser Use is the better fit: it's a library designed to be embedded, and ego lite isn't an SDK.

Browser Use is the project; Browser Harness is its local version, the open-source Python agent loop you run on your own machine with your own LLM API key. Browser Use also sells a cloud product that runs hosted browser agents on their infrastructure. ego lite is a local browser, so the apples-to-apples comparison on this page is against Browser Harness; if you're weighing hosted browser infrastructure, see our Browserbase page instead.

We ran both tools on the same 31 browser automation tasks on live websites, driven by the same pi agent with the same model (gpt-5.6-sol, thinking effort max) and graded by the same written checklist. Each tool keeps its better of two full runs, so a failed task still counts. ego lite finished 30 of 31 tasks perfectly (96.8%) at $1.98 per completed task on 30.3 model turns; Browser Harness finished 26 of 31 (83.9%) at $3.04 on 51.2 turns, the best completion rate of any competitor measured. Browser Use's hosted cloud product was not part of the run; the comparison is local against local. The tasks, the checklists, and the raw results are open source in the ego-browser-benchmark-framework repository on GitHub, so every number can be audited or reproduced.

Browser Harness can, with manual configuration, and it's a common source of friction. The --profile flag copies your live Chrome profile into a temp directory, and on Windows that copy fails outright while Chrome is running and holding file locks, so the workaround is closing Chrome first. Connecting to an already-running Chrome over CDP works too, but Chrome 136 and later blocks CDP on your default profile, so most setups end up on a separate, non-default one. In ego lite, the real profile is the starting point: one-click import of logins, cookies, sessions, and extensions, shared safely with your own browsing through separate Spaces.

No. There's no separate model loop to fund. The intelligence comes from the coding agent you already run, whether that's Claude Code, Codex, Cursor, Gemini CLI, or Opencode. ego lite itself is free with no subscription.

Both are open-source tools for LLM-driven browsing, but Browser Use is a Python agent framework built around an autonomous loop, while Stagehand is Browserbase's TypeScript-first SDK with act/extract/observe primitives for hybrid code-plus-AI automation. Both are code-first: you scaffold a project and supply model access. See our ego lite vs Stagehand page for that side of the comparison.

They solve different layers. Browser Use is the agent framework; Browserbase is cloud browser infrastructure that frameworks run on. Teams often combine an agent library with hosted browsers for production scale, and that's a real win for cloud fleets. ego lite sits at neither layer: it's a local, headful browser for the agent work you do on your own machine, with your own accounts.

Browser Harness's agent loop calls the model at every step, and each call carries a fresh serialized snapshot of the page just to plan the next batch of clicks. Users running recurring or long-horizon tasks have asked the maintainers how to cut that cost, since the same task run the same way still pays full model price every time. On those 31 jobs that loop averaged 51.2 model round trips per task, the most of the five tools measured, against ego lite's 30.3. ego lite reduces the round trips themselves: the agent batches several actions in one JavaScript execution and reads pages as compressed Snapshots, so the savings compound over a whole task.

The ego-browser shell that connects agents to the browser is MIT-licensed open source. The ego lite browser itself is a free app for macOS, no subscription, and your data stays local.