ego (lite) 只是一款浏览器；ego 才是你跨设备的个人 Agent。
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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.

免费用 ego (lite)GitHub
开发者用户来自
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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

并行多任务，执行更快

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.

在 ego lite 中，Space 数量没有上限：每个 Space 都在自己导入的 Chrome 浏览器配置文件上同时运行各自的任务。它们都不会抢占你的标签页，你可以随时查看或接管任意一个 Space。

不再反复来回 消耗更少 Token

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.

在 ego lite 中，你的 Agent 每轮会用几行 JavaScript 批量执行多个操作，而不是每次都等待新的模型调用才能继续。叠加 Snapshot 输入之后，同一个任务整体消耗的 Token 会大幅减少。

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.

同一个 Chrome，为 Agent 而生

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.

ego lite 基于 Chromium 构建，一键实时导入你完整的 Chrome 配置，无需提前关闭任何东西。你的 Agent 可以直接继承真实的登录状态，不会卡住。

ego lite 的 Chrome 浏览器配置文件导入：一键设置，保留所有登录状态

ego lite vs Browser Harness

Feature comparison between ego lite and Browser Harness.
功能ego liteBrowser Harness
由谁提供模型你的编程 Agent（Claude Code、Codex、Cursor）你自己提供 LLM API 密钥，按 Token 付费
设置安装应用，在你的 Agent 中运行 /ego-browserPython 项目、pip install、模型配置、任务代码
已登录的网站（SSO、2FA）一键导入 Chrome 浏览器配置文件，默认已登录启动独立的 Chromium；浏览器配置文件需手动复用，且存在一些限制
操作如何执行每轮用 JavaScript 批量执行多个操作Agent 循环在每一步都调用模型
每个任务的 Token 消耗更低：Snapshot 输入加上批量操作，按整个任务计算更高：每一步都要通过模型重新发送页面状态
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
并行任务Space 在一个可见的浏览器内隔离各个任务实验性功能；需要多个 Agent 实例，或使用付费云服务
同时也是你的日常浏览器是的，你在自己的 Space 中浏览，Agent 在它们各自的 Space 中工作不是，它是一个自动化库以及托管浏览器服务
可复用技能（即将上线）将成功的运行过程提炼为可复用技能；Agent 重复执行复杂任务时最高可快 5 倍（限量测试中）没有内置的同类功能
价格免费，无需订阅开源免费；云服务和 LLM 使用需付费
最近更新 2026年8月20日

让切换无缝衔接

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. 下载 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. 用 /ego-browser 运行你的第一个任务

    粘贴到你的 Agent 里

    /ego-browser 打开 ego.app 加入等候名单

    在 Claude Code、Codex 或 Cursor 中运行 /ego-browser。无需 Python 环境，无需选择模型，也无需 API 密钥。

  3. 看它开始工作
    ego lite 的 Space 概览，四个浏览器任务并排运行：Claude Code 在 Yahoo Finance 上追踪苹果股价，Codex 在 cars.com 上按年份筛选车型，Hermes 在完成一项 SaaS 后台任务，一位用户在抓取 X 上的数据，还有一只手正在点击 + 打开另一个 Space

    任务在自己的 Space 中运行，而不是只能通过日志猜测状态的无头 Chromium 实例。你可以实时查看，也可以随时接管，结果会直接返回到你的 Agent 的 CLI 中。

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.

各工具的适用场景

以下情况选择 ego (lite)

  • 你希望有一个 Agent 帮你完成浏览器工作（调研、填表、需要登录的日常事务），而不用写任何 Python 代码。
  • 你已经在使用 Claude Code、Codex 或 Cursor，不想再维护一个需要单独付费 API 密钥的 Agent 循环。
  • 任务需要你的真实登录状态：一键导入浏览器配置文件比手动配置 Chromium 配置文件要简单得多。
  • 你希望任务在并行的 Space 中运行，并且是在一个可以查看或接管的有界面浏览器里。

Choose Browser Harness when

  • You're building a custom automation product or pipeline in Python. Browser Harness is a library designed to be embedded.
  • 你希望在自己的代码中掌控整个 Agent 循环：自己选择模型，设计提示词，控制每一步。
  • You need automations deployed to the cloud, running on Browser Use's hosted browsers when your laptop is closed.
  • 你要把浏览器自动化交付给别人使用。ego lite 是一款面向终端用户的浏览器，而不是一个 SDK。

为你的 Agent 配备真实浏览器

免费使用，运行在你的 Mac 上，一键导入 Chrome 浏览器配置文件。支持 Claude Code、Codex、Cursor，以及任何能写代码的 CLI Agent。

免费用 ego (lite)

继续阅读

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 extension所有对比浏览器自动化解决方案

常见问题

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.

不需要。没有另外的模型循环需要你付费维持。智能能力来自你已经在用的编程 Agent，无论是 Claude Code、Codex、Cursor、Gemini CLI 还是 OpenCode。ego lite 本身完全免费，无需订阅。

两者都是用于 LLM 驱动浏览的开源工具，但 Browser Use 是围绕自主循环构建的 Python Agent 框架，而 Stagehand 是 Browserbase 推出的以 TypeScript 为主的 SDK，提供 act/extract/observe 等原语，用于代码与 AI 结合的混合自动化。两者都是代码优先的方案：你需要搭建项目并提供模型访问方式。这方面的对比可以参阅我们的 ego lite 对比 Stagehand 页面。

它们解决的是不同层面的问题。Browser Use 是 Agent 框架；Browserbase 是供框架运行的云端浏览器基础设施。团队常常会把 Agent 库和托管浏览器结合起来用于生产环境规模化部署，这对云端浏览器集群来说确实很有价值。ego lite 不属于这两个层面：它是一款本地、有界面的浏览器，用于你在自己的机器上、用自己的账号完成的 Agent 工作。

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.

连接 Agent 与浏览器的 ego-browser 外壳采用 MIT 许可证开源。ego lite 浏览器本身是一款免费的 macOS 应用，无需订阅，你的数据也始终保存在本地。