Browser Use

Purpose-built LLMs trained on browser automation patterns. BU Mini handles routine tasks cost-efficiently at approximately $0.72 per 1M input tokens and $4.20 per 1M output tokens, while BU Max tackles complex multi-step workflows at approximately $3.60/$18.00 per 1M tokens. Browser Use reports that these models complete browser tasks in roughly 40% fewer steps than GPT-4o on their internal evaluation suite, though independent benchmarks are not yet available. Both models generate tighter action sequences by understanding browser-specific patterns like form fields, navigation menus, and authentication flows.

Combines screenshot analysis with DOM tree extraction to identify page elements through two complementary methods. Unlike pure selector-based tools that break when layouts change, the hybrid approach adapts to website redesigns automatically. The agent sees the page visually and structurally, choosing the most reliable identification method per element. This dual approach is especially effective on dynamic single-page applications (React, Vue, Angular) where DOM structure alone can be ambiguous and visual context resolves which element to target.

Record a browser workflow once and expose it as a callable API endpoint. Each skill costs $2.00 to create and $0.02 per execution — compared to $0.15–$0.50+ in LLM token costs for a full agent run of the same workflow. Eliminates per-step LLM costs for repetitive tasks, providing API-level reliability with browser automation flexibility. Pay-as-you-go plans support up to 5 active Skills; Startup plans support up to 100. Available only on cloud plans.

Cloud-hosted browsers with fingerprint randomization, human-like mouse movements and typing patterns, CAPTCHA auto-solving, and premium proxy pools covering 195+ countries. Basic stealth is included on pay-as-you-go plans. Advanced stealth on Startup ($40/month) and above adds agent-level behavioral mimicry that simulates realistic browsing patterns — scroll behavior, dwell time, and interaction cadence — to evade sophisticated bot detection systems used by major e-commerce and financial platforms.

The complete agent framework is open source on GitHub with 55,000+ stars as of early 2026 and an active contributor community. Run locally for development, testing, or production without any licensing costs. Same codebase works with local browsers or cloud infrastructure — toggle use_cloud=True to switch. The MIT license imposes no restrictions on commercial use, modification, or distribution, making it safe for enterprise adoption without legal review concerns.

Works with ChatBrowserUse models, OpenAI GPT-4, Anthropic Claude, Google Gemini, and any LangChain-compatible LLM. Switch models per task to optimize cost and capability — use cheaper models like BU Mini (~$0.72/1M input tokens) for simple navigation and premium models like BU Max or GPT-4o for complex reasoning-heavy workflows. Also integrates with multi-agent frameworks like CrewAI for orchestrating browser agents alongside other AI tools in larger automation pipelines.