AI Frameworks · browser-use

browser-use

Browser Use is a Python framework that enables AI agents to interact with websites by automating browser actions like clicking, typing, and form submission. It supports multiple LLM providers and offers both open-source and cloud-hosted deployment options for web automation tasks.

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Source: GitHub — github.com/browser-use/browser-use

103.4k

GitHub stars

11.4k

Forks

Python

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	browser-use/browser-use
Owner	browser-use
Primary language	Python
License	MIT — OSI-approved
Stars	103.4k
Forks	11.4k
Open issues	288
Latest release	0.13.3 (2026-07-01)
Last updated	2026-07-08
Source	https://github.com/browser-use/browser-use

What browser-use is

Browser Use provides an Agent class that orchestrates LLM reasoning with Playwright-based browser automation, supporting vision-based UI understanding and task execution. The framework integrates with OpenAI, Anthropic, Google, and proprietary Browser Use models, with optional cloud backend for stealth, proxy rotation, and scaling.

Quickstart

Get the browser-use source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/browser-use/browser-use.git2cd browser-use3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Automated Form Filling & Data Entry

Fill job applications, insurance forms, checkout flows, and multi-step registration processes with LLM-driven intent understanding and context awareness across page interactions.

E-Commerce & Shopping Automation

Add items to shopping carts, apply filters, compare products, and complete purchases on retail platforms (e.g., Instacart, custom PC builders) with natural language task descriptions.

Business Process Automation

Automate repetitive browser-based workflows in SaaS platforms (email, CRM, project management) without custom integrations, using direct web UI interaction guided by LLM reasoning.

Implementation considerations

Python 3.11+ required; async/await patterns used throughout; integration with external LLM APIs (OpenAI, Anthropic, Google) or proprietary Browser Use models; API key management and rate-limit handling.
Vision-based element detection and LLM reasoning introduce latency and cost per task; longer tasks accumulate token usage; requires monitoring and cost controls for production deployments.
Browser profiles, stealth options, and proxy support available via cloud backend; self-hosted deployments require manual Playwright configuration and scaling management.
Benchmark data shows accuracy varies by LLM provider and model; proprietary 'bu-*' models significantly outperform open-source alternatives; internal testing recommended before production rollout.
Open issues (288) and recent activity (pushed 2026-07-08) indicate active development; framework APIs may change; version lock and regression testing recommended.

When to avoid it — and what to weigh

Need High-Frequency API-Level Automation — Use dedicated REST/GraphQL APIs instead; browser automation is slower and more brittle than direct API calls for high-volume, latency-sensitive operations.
Require Guaranteed Accuracy on Complex Layouts — Vision-based UI understanding and LLM reasoning can fail on heavily obfuscated, dynamically-rendered, or non-standard UI patterns; test extensively before production.
Operating Under Strict Rate-Limiting or Bot Detection — Cloud deployment offers stealth and proxy rotation, but open-source deployment requires manual mitigation; some sites aggressively block browser automation regardless.
Require Offline or Air-Gapped Operation — Open-source agent works offline, but cloud features (stealth, captcha solving, integrations) require external service connectivity and API keys.

License & commercial use

MIT License permits free use, modification, and redistribution in commercial and proprietary projects, with no warranty or liability.

MIT license allows commercial use without explicit permission; however, cloud deployments incur API costs and require separate service agreements. Self-hosted deployments are free but require LLM provider API keys (paid). Requires review of Browser Use Cloud's terms if using managed service features.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Vision-based browser interaction sends screenshots to LLM provider APIs; sensitive data in web forms may be logged. Self-hosted deployments control data flow; cloud deployments transmit visual and interaction data externally. Bot detection and stealth features (cloud only) help avoid account flagging. No explicit security audit or vulnerability disclosure data provided; requires independent review before handling sensitive workflows.

Alternatives to consider

Selenium / Playwright (raw libraries)

Lower-level, language-agnostic browser automation; no LLM integration; requires manual task scripting and error handling; better for deterministic, pre-scripted workflows.

RPA Tools (UiPath, Automation Anywhere, Blue Prism)

Enterprise-grade, visual workflow builders; higher cost; better support for complex enterprise integrations; no LLM-driven reasoning; steeper learning curve.

API-first Automation (Zapier, Make, custom integrations)

Faster, more reliable for native API support; no browser latency; limited to pre-built integrations; requires dedicated connector development for custom use cases.

Software development agency

Build on browser-use with DEV.co software developers

Browser Use enables intelligent task automation without custom integrations. Explore open-source deployment or managed cloud service for production workloads.

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browser-use FAQ

Can I use Browser Use with my own LLM?

Yes. The Agent accepts any LLM via the `llm` parameter. ChatBrowserUse abstracts multiple providers (OpenAI, Anthropic, Google, Vertex). Custom LLM classes must implement the chat interface; see docs for extension patterns.

What's the difference between open-source and cloud deployment?

Open-source runs locally on your machine with full code control; cloud deployment provides stealth, proxy rotation, captcha solving, persistent filesystem, 1000+ integrations, and significantly higher accuracy on complex tasks (see benchmark). Cloud incurs per-task API costs.

How much does it cost to run tasks?

Open-source + self-hosted LLM (e.g., local Ollama) = LLM provider API costs only. Cloud agent and Browser Use LLM models incur additional per-task charges; exact pricing not provided in README; check cloud.browser-use.com for details.

Will this be blocked by websites?

Browser automation can trigger bot detection on some sites. Cloud deployment includes stealth and proxy rotation to mitigate. Self-hosted deployments are more vulnerable; use with caution on heavily-protected services. Test on target site before production.

Work with a software development agency

From first prototype to production, DEV.co delivers software development services around tools like browser-use. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across ai frameworks and beyond.

Need AI-Driven Web Automation for Your Business?

Browser Use enables intelligent task automation without custom integrations. Explore open-source deployment or managed cloud service for production workloads.

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