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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.

Usá ego (lite) gratisGitHub
Con la confianza de desarrolladores de
GoogleAmazonShopifyTikTokHarvardStanfordUSCUCLA

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

Multitarea en paralelo, ejecución más rápida

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.

En ego lite no hay límite de Spaces: cada uno ejecuta su propia tarea a la vez, con su propio perfil de Chrome importado. Ninguno te roba la pestaña, y podés observar o tomar el control de cualquier Space en cualquier momento.

Se acabó el ida y vuelta Menos costo en 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.

Tu agente en ego lite agrupa varias acciones en pocas líneas de JavaScript por turno, en lugar de esperar una nueva llamada al modelo para seguir avanzando. Combinado con la entrada por Snapshot, la misma tarea se completa con muchos menos tokens en total.

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.

El mismo Chrome, nativo para agentes

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.

Construido sobre Chromium, ego lite importa toda tu configuración de Chrome con un clic, en vivo, sin tener que cerrar nada antes. Tus agentes heredan tus sesiones reales sin quedarse nunca trabados.

Importación del perfil de Chrome en ego lite: configuración con un clic y todas tus sesiones

ego lite vs Browser Harness

Feature comparison between ego lite and Browser Harness.
Funciónego liteBrowser Harness
Quién provee el modeloTu agente de código (Claude Code, Codex, Cursor)Vos, aportando una clave de API de LLM y pagando por token
ConfiguraciónInstalá la app y ejecutá /ego-browser en tu agenteProyecto en Python, pip install, configuración del modelo, código de la tarea
Sitios con sesión iniciada (SSO, 2FA)Importación del perfil de Chrome con un clic, con sesión iniciada por defectoChromium lanzado desde cero; la reutilización del perfil es manual y con inconvenientes
Cómo se ejecutan las accionesVarias acciones agrupadas por turno en JavaScriptEl bucle del agente llama al modelo en cada paso
Costo en tokens por tareaMenor: entrada por Snapshot más acciones agrupadas, medido por tareaMayor: cada paso reenvía el estado de la página a través del modelo
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
Tareas en paraleloLos Spaces aíslan las tareas dentro de un solo navegador visibleExperimental; múltiples instancias de agente, o la nube paga
También funciona como tu navegador de uso diarioSí, vos navegás en tu Space, los agentes trabajan en el suyoNo, es una librería de automatización y navegadores alojados
Habilidades reutilizables (próximamente)Convierte las ejecuciones exitosas en habilidades reutilizables; hasta 5 veces más rápido en tareas complejas a medida que el agente las repite (beta limitada)Sin equivalente incorporado
PrecioGratis, sin suscripciónCódigo abierto y gratuito; el uso de la nube y del LLM son pagos
Última actualización 20 ago 2026

Hacé de esto una transición sin fricciones

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. Descargar 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. Ejecuta tu primera tarea con /ego-browser

    Pega esto en tu agente

    /ego-browser Abrí ego.app y sumate a la lista de espera

    Ejecutá /ego-browser en Claude Code, Codex o Cursor. Sin entorno de Python, sin selección de modelo, sin clave de API.

  3. Míralo en acción
    Vista general de Spaces en ego lite con cuatro tareas de navegador corriendo en paralelo: Claude Code siguiendo la acción de Apple en Yahoo Finance, Codex filtrando autos por año en cars.com, Hermes terminando una tarea de back-office de SaaS, un usuario haciendo scraping en X, y una mano tocando + para abrir otro Space

    La tarea corre en su propio Space, no en una instancia de Chromium headless que solo podés intuir a partir de logs. Mirala en vivo o tomá el control en cualquier momento, y el resultado vuelve directo a la CLI de tu agente.

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.

Cuándo usar cada herramienta

Elegí ego (lite) cuando

  • Querés que un agente haga tu propio trabajo de navegador (investigación, formularios, tareas con sesión iniciada) sin tener que construir nada en Python.
  • Ya usás Claude Code, Codex o Cursor y no querés un segundo bucle de agente con su propia factura de clave de API.
  • Las tareas necesitan tus sesiones reales: la importación de perfil con un clic le gana a la configuración manual de perfil en Chromium.
  • Querés tareas corriendo en Spaces paralelos, en un navegador con interfaz visible que podés mirar o controlar.

Choose Browser Harness when

  • You're building a custom automation product or pipeline in Python. Browser Harness is a library designed to be embedded.
  • Querés tener el control total del bucle del agente en tu propio código: elegir el modelo, definir los prompts, controlar cada paso.
  • You need automations deployed to the cloud, running on Browser Use's hosted browsers when your laptop is closed.
  • Estás entregando automatización de navegador a otros usuarios. ego lite es un navegador para usuarios finales, no un SDK.

Dale a tu agente un navegador real

Gratis, funciona en tu Mac e importa tu perfil de Chrome con un clic. Funciona con Claude Code, Codex, Cursor y cualquier agente de CLI que escriba código.

Usá ego (lite) gratis

Seguí leyendo

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 extensionTodas las comparacionesSoluciones de automatización de navegador

Preguntas frecuentes

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. No hay un bucle de modelo aparte que pagar. La inteligencia viene del agente de código que ya usás, ya sea Claude Code, Codex, Cursor, Gemini CLI u OpenCode. ego lite en sí es gratis y sin suscripción.

Ambas son herramientas de código abierto para navegación impulsada por LLM, pero Browser Use es un framework de agentes en Python construido alrededor de un bucle autónomo, mientras que Stagehand es el SDK de Browserbase pensado ante todo para TypeScript, con primitivas act/extract/observe para automatización híbrida de código más IA. Las dos parten del código: armás un proyecto y proporcionás acceso al modelo. Mirá nuestra página de ego lite vs. Stagehand para ver esa comparación en detalle.

Resuelven capas distintas. Browser Use es el framework de agentes; Browserbase es la infraestructura de navegadores en la nube sobre la que corren esos frameworks. Los equipos suelen combinar una librería de agentes con navegadores alojados para escalar en producción, y eso es una ventaja real para flotas en la nube. ego lite no se ubica en ninguna de las dos capas: es un navegador local, con interfaz visible, para el trabajo de agentes que hacés en tu propia máquina, con tus propias cuentas.

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.

El shell de ego-browser que conecta a los agentes con el navegador es de código abierto con licencia MIT. El navegador ego lite en sí es una app gratuita para macOS, sin suscripción, y tus datos permanecen en tu dispositivo.