Browser.AI Documentation

Comprehensive technical documentation for the Browser.AI framework - a sophisticated browser automation system that makes websites accessible for AI agents.

Documentation Overview

This documentation provides detailed technical specifications, architecture diagrams, implementation guides, and workflow patterns for all Browser.AI components.

📋 Documentation Structure

Document	Description	Focus Area
Architecture Overview	High-level system design and component relationships	System Architecture
Agent Implementation	AI agent service, orchestration, and specializations	Agent System
DOM Processing	DOM analysis, element extraction, and processing workflows	DOM Service
Controller & Actions	Action system, registry patterns, and browser operations	Action Framework
Browser Management	Playwright integration, context management, and configurations	Browser Layer
Workflows & Integration	End-to-end workflows, integration patterns, and real-world scenarios	Implementation Patterns

🏗️ System Architecture

Browser.AI follows a modular architecture with four core components:

graph TB
    subgraph "Browser.AI Framework"
        Agent[🤖 AI Agent Service<br/>Task orchestration & LLM integration]
        Controller[🎮 Action Controller<br/>Browser action registry & execution]
        Browser[🌐 Browser Service<br/>Enhanced Playwright wrapper]
        DOM[📄 DOM Processing<br/>Element extraction & analysis]
    end
    
    subgraph "External Services"
        LLM[🧠 Language Models<br/>OpenAI, Anthropic, Ollama, etc.]
        PW[🎭 Playwright Engine<br/>Cross-browser automation]
    end
    
    Agent --> Controller
    Agent --> LLM
    Controller --> Browser
    Browser --> DOM
    Browser --> PW
    
    style Agent fill:#e1f5fe
    style Controller fill:#f3e5f5
    style Browser fill:#e8f5e8
    style DOM fill:#fff3e0

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🚀 Quick Start Guide

Basic Usage Example

import asyncio
from browser_ai import Agent, Browser
from langchain_openai import ChatOpenAI

async def main():
    # Initialize components
    llm = ChatOpenAI(model="gpt-4-turbo")
    browser = Browser()
    
    # Create agent
    agent = Agent(
        task="Search for Python tutorials on Google and extract top 5 results",
        llm=llm,
        browser=browser,
        use_vision=True
    )
    
    # Execute task
    result = await agent.run()
    print(f"Task completed: {result.is_done}")
    print(f"Results: {result.extracted_content}")

if __name__ == "__main__":
    asyncio.run(main())

Advanced Configuration

from browser_ai import Agent, BrowserConfig, Controller
from langchain_anthropic import ChatAnthropic

# Custom browser configuration
browser_config = BrowserConfig(
    headless=False,
    disable_security=True,
    extra_chromium_args=['--disable-blink-features=AutomationControlled']
)

# Custom controller with excluded actions
controller = Controller(exclude_actions=['search_google'])

# Claude-3 with custom settings
llm = ChatAnthropic(model="claude-3-sonnet-20240229", temperature=0)

agent = Agent(
    task="Complex web automation with custom actions",
    llm=llm,
    controller=controller,
    browser_config=browser_config,
    use_vision=True,
    max_failures=3
)

📖 Component Deep Dives

🤖 AI Agent Service

The Agent Service is the central orchestrator that combines intelligent planning with robust execution:

Key Features:

• Step-by-step task execution with planning
• Comprehensive conversation persistence
• Advanced prompt engineering with dynamic context

🎮 Action Controller

The Controller provides a flexible, extensible framework for browser actions:

• Registry Pattern: Decorator-based action registration
• Type Safety: Pydantic parameter validation
• Extensibility: Easy custom action development
• Built-in Actions: Navigation, interaction, tab management, and completion

Available Actions:

• Navigation: go_to_url, search_google, go_back
• Interaction: click_element, input_text, scroll, send_keys
• Tab Management: switch_tab, open_tab, close_tab
• Task Completion: done, custom output models

🌐 Browser Management

Enhanced Playwright wrapper with advanced capabilities:

• Multiple Connection Types: Local launch, CDP, WebSocket, existing instances
• Context Management: Isolated browsing contexts with state tracking
• Advanced Features: Download handling, file uploads, screenshot capture
• Performance Optimization: Resource management and memory cleanup

Connection Options:

• Local browser launch (default)
• Chrome DevTools Protocol (CDP)
• WebSocket connections (remote browsers)
• Existing Chrome instance reuse

📄 DOM Processing

Sophisticated DOM analysis combining JavaScript and Python processing:

• Interactive Element Detection: Smart identification of clickable elements
• Visual Highlighting: Color-coded element highlighting for AI analysis
• Coordinate Systems: Viewport and page coordinate mapping
• Element History: Cross-page element tracking and identity management

Processing Pipeline:

1. JavaScript injection for DOM analysis
2. Interactive element identification and filtering
3. Coordinate calculation and viewport analysis
4. Python DOM tree construction with relationships
5. Selector map generation for action targeting

🔄 Workflow Patterns

Complete Task Execution Flow

sequenceDiagram
    participant User
    participant Agent
    participant LLM
    participant Controller
    participant Browser
    participant DOM
    
    User->>Agent: Submit Task
    Agent->>LLM: Generate Plan
    
    loop Step Execution
        Agent->>Browser: Get Current State
        Browser->>DOM: Extract Elements
        DOM-->>Browser: DOM State
        Browser-->>Agent: Complete State
        
        Agent->>LLM: Decide Action
        LLM-->>Agent: Action Decision
        
        Agent->>Controller: Execute Action
        Controller->>Browser: Browser Operation
        Browser-->>Controller: Result
        Controller-->>Agent: Action Result
    end
    
    Agent-->>User: Task Complete

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Specialized Workflows

The documentation includes comprehensive examples for:

• Form Filling: Multi-step form completion with validation
• Data Extraction: Cross-site data mining with pagination handling
• E-Commerce Automation: Product research, cart management, checkout processes
• Social Media Management: Content posting, scheduling, engagement tracking
• Automated Testing: Regression testing, screenshot comparison, validation

🛠️ Advanced Features

🔧 Configuration Options

Environment Variables

# LLM API Keys
OPENAI_API_KEY=your_openai_key
ANTHROPIC_API_KEY=your_anthropic_key
GOOGLE_API_KEY=your_google_key

# Logging Configuration  
BROWSER_AI_LOGGING_LEVEL=info  # result | debug | info

Browser Configuration

config = BrowserConfig(
    headless=False,                    # Visual mode for development
    disable_security=True,             # Bypass web security for automation
    extra_chromium_args=[              # Custom Chrome arguments
        '--disable-blink-features=AutomationControlled',
        '--user-agent=Custom Browser AI Agent'
    ],
    proxy=ProxySettings(               # Proxy configuration
        server="http://proxy:8080",
        username="user",
        password="pass"
    )
)

📚 Learning Path

For developers new to Browser.AI, we recommend this learning progression:

1. Start with Architecture Overview

   • Understand the high-level system design
   • Learn component relationships and data flow

2. Explore Agent Implementation

   • Master the central orchestration system
   • Learn prompt engineering and conversation management

3. Study DOM Processing

   • Understand element extraction and analysis
   • Learn the JavaScript/Python integration

4. Examine Controller & Actions

   • Master the action system and registry patterns
   • Learn to create custom actions

5. Review Browser Management

   • Understand Playwright integration
   • Learn advanced configuration options

6. Practice with Workflows & Integration

   • Implement real-world automation scenarios
   • Learn advanced integration patterns

🤝 Contributing

This documentation is living and evolving. Areas for contribution:

• Additional Workflow Examples: Real-world automation scenarios
• Performance Optimization: Advanced tuning and scaling patterns
• Integration Guides: Third-party service integrations
• Troubleshooting: Common issues and solutions
• Best Practices: Lessons learned from production deployments

📋 API Reference

Each documentation section includes:

• Class Definitions: Complete API specifications
• Method Signatures: Parameter types and return values
• Configuration Options: All available settings
• Usage Examples: Practical implementation patterns
• Mermaid Diagrams: Visual architecture representations

🎯 Use Cases

Browser.AI excels in these automation scenarios:

• Web Scraping: Intelligent content extraction with AI-driven navigation
• E-Commerce Automation: Product research, price monitoring, inventory management
• Form Processing: Automated form filling with validation and error handling
• Testing & QA: Regression testing with visual validation
• Social Media Management: Content posting, engagement, analytics collection
• Research & Data Collection: Systematic information gathering across multiple sources
• Workflow Automation: Business process automation with decision-making capabilities