Browser Use

What is Browser Use?

Browser Use is an open source browser automation framework for AI agents. It was created in 2024 by Magnus Müller and Gregor Zunic to solve a problem many teams had run into with Selenium-style automation, brittle scripts break whenever a site changes a button, a class name, or a page layout. Browser Use takes a different path. Instead of asking developers to handcraft selectors for every step, it gives agents a way to understand pages through both the DOM and the visual interface, then act on natural language instructions.

We researched Browser Use as both a developer tool and a company. The project has grown unusually fast, with more than 79,000 GitHub stars and a $17 million seed round led by Felicis, with backing tied to Y Combinator and Andreessen Horowitz. That momentum matters because Browser Use is not only a library anymore. It now spans open source tooling, a managed cloud product, custom models for browser tasks, stealth browsing, and proxy infrastructure. The company says it is used by Fortune 500 teams including Airbnb, Amazon, and Anthropic.

In practice, Browser Use is for teams that need an AI system to actually use the web, not just read static pages. Developers use it for scraping sites without APIs, automating forms and back office work, running QA flows, monitoring websites for changes, and building agents that can log in, navigate, click, extract, and recover when the interface shifts. The appeal is simple, web tasks described in plain language are often easier to maintain than traditional automation code. The tradeoff is also simple, it is still browser automation, so speed, reliability, and anti-bot friction are real parts of the story.

Key Features

• Natural language browser agents: Browser Use lets developers describe tasks in plain language instead of scripting every click and selector. That matters because websites change constantly, and semantic instructions tend to survive small UI shifts better than brittle CSS rules.

• DOM plus visual understanding: The system combines HTML structure with what the page looks like on screen. We found this is one of the reasons Browser Use performs well on web navigation benchmarks, because an agent can cross-check visual cues against page structure instead of relying on only one source of truth.

• Built on Playwright: Browser Use runs on top of Playwright, which gives it access to modern browser control and support across major browsers. For developers, this means it inherits a mature browser automation base instead of reinventing low-level browser control from scratch.

• Open source and self-hosted option: Teams can run Browser Use on their own infrastructure rather than sending everything to a hosted black box. This matters for companies with stricter data handling rules, or teams that want full control over debugging, deployment, and model choice.

• Broad LLM support: The open source version works with LangChain-compatible models, including OpenAI, Anthropic, Gemini, DeepSeek, and Qwen. That flexibility is a real differentiator versus tools tied to a single model provider, especially for teams trying to manage cost or avoid lock-in.

• Custom browser models: Browser Use has trained its own models, including ChatBrowserUse-2, for browser automation. In the company’s published numbers, ChatBrowserUse-2 reached 63.3% on Browser Use’s benchmark and completed tasks in about 68 seconds, compared with 225 to 330 seconds for several general-purpose models.

• Cloud agent performance: Browser Use Cloud, listed as bu-ultra in benchmarks, reached 78% on the company’s internal benchmark and 89% on WebVoyager. Those are strong numbers for a browser agent product, and they put it close to or ahead of well-known alternatives depending on the benchmark.

• Stealth browsing: Browser Use offers anti-detection tooling, CAPTCHA solving, and support for 195+ country-specific proxies. This matters because many practical automation projects fail less from browser control issues and more from being blocked.

• Proxy infrastructure: The platform includes residential and datacenter proxies priced at $5/GB. For teams doing high-volume extraction or geographically distributed browsing, built-in proxy access removes a lot of operational work.

• Workflow recording and replay: Browser Use’s ecosystem includes tools for recording deterministic workflows, then replaying them quickly. This is useful when a task is repetitive and stable, because teams can mix agent reasoning for discovery with fixed execution for speed.

• Session persistence and login handling: Browser Use can maintain browser state and support authenticated workflows, including some 2FA retrieval scenarios. That opens up internal tools, SaaS dashboards, and account-based workflows that are usually harder than public-page scraping.

Use Cases

One of the clearest Browser Use stories is web data extraction where no API exists, or the API is incomplete. Teams use it to pull product data, listings, prices, and page content from sites that would be painful to maintain with selector-heavy scripts. The advantage is not just that it can scrape, many tools do that. The advantage is that Browser Use often keeps working after minor site redesigns because the agent understands page intent, not just exact element paths.

Another strong use case is business process automation across websites and SaaS tools. The research pointed to workflows like prospecting on LinkedIn and pushing records into a CRM, though teams need to think carefully about platform rules before doing that. More broadly, Browser Use fits repetitive browser work such as form submissions, internal portal updates, invoice handling, and moving data between systems that were never designed to talk to each other.

QA and testing is another area where Browser Use feels different from older tools. Instead of writing detailed test scripts, teams can describe flows like logging in, creating an order, checking confirmation, canceling it, and verifying the result. That does not mean it replaces every Playwright or Selenium test suite, but it gives non-specialist QA teams a way to build browser checks with less scripting, and it tends to be more forgiving when a UI shifts.

Browser Use is also being used for monitoring and research. A team can ask an agent to watch a competitor’s pricing page, check inventory changes, or gather findings from multiple sites and synthesize them into a report. These are the kinds of jobs that humans often do manually because websites are dynamic and inconsistent. Browser Use turns them into repeatable agent tasks, though success still depends on the complexity of the site and how aggressively it blocks automation.

On the adoption side, the company says Browser Use is trusted by Airbnb, Amazon, and Anthropic. Those names do not tell us the exact workflows, but they do suggest the tool is being evaluated or used in serious environments, not just weekend demos.

Strengths and Weaknesses

Strengths:

• Browser Use is unusually strong at web-specific automation compared with more general computer-use tools. In published benchmark results, it reached 89% on WebVoyager, compared with 56% for Anthropic Computer Use and 87% for OpenAI Operator. For teams focused on browsers, that specialization shows up in results.

• The model flexibility is a real practical advantage. Teams can use Claude, GPT, Gemini, DeepSeek, or Browser Use’s own models instead of being forced into one vendor. We think this matters more than feature checklists suggest, because browser automation costs can swing dramatically depending on token usage and task length.

• It is faster than many general-purpose model setups when using the company’s own browser models. Browser Use reports about 68 seconds per task for ChatBrowserUse-2, versus 225 to 330 seconds for several frontier models in the same benchmark context. If you are running hundreds or thousands of tasks, that difference becomes budget and throughput, not just convenience.

• The open source foundation gives technical teams a lot of confidence. You can self-host it, inspect how it works, and build around it. Compared with fully managed products that hide the internals, Browser Use is easier to trust for teams that need control.

• It handles minor UI drift better than selector-based scripts. This is one of the most repeated reasons teams move away from older automation stacks. A checkout button moving slightly or changing class names is less likely to break an agent that understands the page semantically.

Weaknesses:

• It is still not fully reliable on complex, messy workflows. Browser Use’s own benchmark puts its cloud offering at 78%, which is good for this category, but it also means failures are normal. For anything mission-critical, teams still need retries, fallbacks, and human review.

• Browser automation is resource-heavy. Research cited roughly 250 MB of RAM per browser instance, which adds up fast. If you want 100 concurrent sessions, you are not thinking only about agent quality, you are thinking about infrastructure too.

• The best results often depend on Browser Use’s own models or cloud service. The open source setup with external models can perform well, but success rates and costs vary a lot by model. That means teams need to test rather than assume they will get the benchmark numbers out of the box.

• Anti-bot systems are still an arms race. Browser Use offers stealth features, CAPTCHA solving, and proxies, but determined sites can still detect automation. This is not a Browser Use-specific flaw, it is part of the category, but buyers should go in with realistic expectations.

• Enterprise controls are still catching up. Browser Use has strong momentum and funding, but some governance, compliance, and observability needs are still more mature in older enterprise automation categories. Regulated teams should check those details early.

Pricing

• Free: $0 Includes 3 concurrent sessions and a 4-hour session timeout. This is enough to test ideas, build a proof of concept, and see if your workflows are even a fit before spending money.

• Professional: $40/month Includes up to 50 concurrent sessions. This looks like the entry point for small teams that want real usage without jumping into enterprise pricing.

• Growth: $500+/month Includes up to 500 concurrent sessions. This is where Browser Use starts to look like infrastructure for production workloads rather than a developer tool.

• Usage pricing: from $0.002 per step Browser Use also uses consumption-based pricing for task execution, with some materials citing about $0.01 as a starting point for a simple task. Actual spend depends on how many steps your agent takes and which model powers it.

• Proxies: $5/GB Proxy traffic is billed separately. If your use case depends on stealth browsing, geo-routing, or high-volume extraction, this can become a meaningful line item.

The big pricing story is not the subscription fee, it is total task cost. Browser Use argues its custom models are much cheaper than frontier LLMs for browser jobs, and the research backs that up. In one comparison, 100 tasks with Claude Opus 4 could cost close to $100 in model fees alone, while Browser Use’s own stack was far lower. So if you are comparing Browser Use with DIY Playwright plus a general LLM, the right question is not “what is the monthly plan,” it is “what does one successful task actually cost me at my volume?”

Alternatives

OpenAI Operator Operator is the polished, consumer-friendly take on AI browser use. It is easier to approach if you want a managed experience and do not care much about deep customization yet. Browser Use is a better fit for teams building products or internal systems around browser automation, while Operator is more appealing if you want a simpler end-user experience and are comfortable with OpenAI’s product boundaries.

Anthropic Computer Use Computer Use is broader than Browser Use because it is designed for interacting with computers, not just browsers. That makes it interesting for desktop workflows and software environments outside the web. Browser Use tends to be stronger for web-native tasks, and its benchmark lead on WebVoyager reflects that, but Computer Use may be the better choice if your automation needs to move between browser windows, local apps, and desktop interfaces.

Playwright Playwright is the lower-level foundation many developers already trust. If your workflows are deterministic, your team is comfortable maintaining scripts, and you do not need natural language control, Playwright can be cheaper and more predictable. Browser Use becomes more attractive when interfaces change often, tasks are less structured, or you want agents to reason through steps instead of hardcoding every action.

Selenium Selenium is still common in enterprise QA and legacy automation stacks, mostly because it has been around for years and fits existing testing workflows. Compared with Browser Use, Selenium asks much more from developers in setup and maintenance. Teams usually choose Browser Use when they are tired of brittle selectors and want an agentic approach, but Selenium can still make sense in organizations deeply invested in established test infrastructure.

DIY browser agents with LangChain or custom orchestration Some teams stitch together Playwright, a frontier model, prompts, and custom retry logic themselves. That gives maximum flexibility, but it also means taking on the hard parts Browser Use is trying to package, browser state, action planning, benchmark tuning, model selection, and anti-bot handling. Browser Use is often the faster route unless your team has a very specific architecture or wants to own every layer.

FAQ

What is Browser Use used for?

It is used to build AI agents that can operate websites. Common jobs include scraping data, filling forms, running QA flows, logging into tools, monitoring page changes, and automating repetitive browser work.

Is Browser Use open source?

Yes. The core framework is open source, and there is also a managed cloud product with additional services and infrastructure.

Who built Browser Use?

Browser Use was founded in 2024 by Magnus Müller and Gregor Zunic. The company has since raised $17 million in seed funding.

How does Browser Use work?

It combines browser control with AI reasoning. The system looks at both the page structure and the visual interface, then uses an LLM to decide what actions to take.

Which models does Browser Use support?

The open source version supports LangChain-compatible models, including OpenAI, Anthropic, Gemini, DeepSeek, Qwen, and others. Browser Use also offers its own custom models for browser tasks.

How accurate is Browser Use?

In published results, Browser Use Cloud scored 89% on WebVoyager and 78% on the company’s own benchmark. Those are strong numbers, but they still mean some tasks fail and need recovery logic.

How do I get started?

The simplest path is to try the free tier or install the open source package, connect a model, and run a small task. We suggest starting with one workflow you already do manually so you can compare cost, speed, and reliability directly.

How long does it take to set up?

For a basic proof of concept, setup can take under 15 minutes if you are comfortable in Python. Production setups take longer because you will need to test models, retries, proxies, and monitoring.

Does Browser Use work with login flows and 2FA?

It can handle authenticated sessions and some 2FA retrieval flows. Whether it works well depends on the site and the exact authentication method.

Can Browser Use avoid bot detection?

It includes stealth browsing, CAPTCHA solving, and proxy support, which helps. But no browser automation tool can promise that it will never be detected.

Is Browser Use better than Playwright?

Not in every case. If your workflow is fixed and predictable, plain Playwright may be simpler and more reliable. Browser Use is more compelling when tasks are variable, websites change often, or you want an agent to reason through the flow.

Is Browser Use good for enterprise teams?

It can be, especially for teams that want self-hosting and model flexibility. But enterprise buyers should still review governance, observability, and compliance features carefully, because those areas are still maturing.