$ show browser-use/browser-use
browser-use is a comprehensive Python framework that enables AI agents to automate web browser interactions. It provides a unified interface for controlling browsers through Playwright/CDP, integrates with multiple LLM providers for decision-making, and includes sophisticated features like structured data extraction, security controls, token usage tracking, and sandboxed execution. The architecture is modular with clear separation between agent reasoning, browser management, tool execution, and extensibility mechanisms. Key innovations include a flexible action registry system with special parameter injection, domain-aware security watchdogs, automatic LLM cost tracking, and MCP integration for extending agent capabilities. The project demonstrates strong engineering practices with extensive testing, CI/CD pipelines, and comprehensive documentation through examples.
The LLM identified 5 files worth highlighting for reuse:
Implements comprehensive token usage tracking and cost calculation across multiple LLM providers. Features automatic pricing data fetching from LiteLLM repository with caching, support for custom pricing models, and detailed usage analytics. Demonstrates non-obvious skill in handling the complexity of various LLM provider pricing schemes while providing a unified interface for cost monitoring.
Provides robust domain-based URL filtering and IP address blocking capabilities for browser automation security. Implements security-conscious glob pattern validation to prevent bypass attempts, supports both IPv4 and IPv6 address blocking, and includes special handling for localhost and private IP ranges. Shows thoughtful security engineering with defense-in-depth approach to protecting against malicious web interactions.
Sophisticated action registration and execution system that enables extensible tool frameworks. Features special parameter injection (automatically providing browser_session, page_extraction_llm, etc. to actions), action-to-action calling capabilities, and comprehensive parameter normalization. Demonstrates advanced plugin architecture patterns with clean separation between action definition and execution.
Secure sandbox execution environment for running untrusted code with automatic dependency management. Uses cloudpickle for parameter and result serialization, implements SSE streaming for real-time output, and provides isolated execution with proper error handling. Shows expertise in building safe execution environments while maintaining usability for agent workflows.
Converts complex DOM structures into LLM-friendly text representations optimized for context limits. Implements multiple serialization strategies (HTML, markdown, clickable elements), processes shadow DOM content, and uses paint order information to prioritize visible elements. Demonstrates clever optimization techniques for making web content consumable by LLMs while preserving important interactive elements.