#🦜 HuBrowser Agent: Technical Details 🛠️

HuBrowser delivers robust, intelligent browser automation through a modular, extensible design. Below, we outline its architecture, state management, intelligence, and unique technical solutions.

#🚀 Overview & Architecture

• Flexible automation: Combine natural language, recorded actions, and code for adaptable control
• Advanced task decomposition: Breaks complex tasks into manageable subtasks with intelligent tool selection
• Comprehensive toolset: Integrates browser, file, command-line, API, and data analysis capabilities
• Real-time feedback: Track and modify intermediate results as tasks execute
• Modular, extensible design: Built for reliability, transparency, and easy extension

#Core Workflow

HuBrowser operates in a continuous loop:

1. Initialization: Agent is created with a task, LLM, and browser instance; all components are initialized with telemetry
2. Execution Loop: State retrieval → LLM analysis → action execution → state update
3. Completion: Task completion triggers event recording, history return, and optional GIF generation

#System Components

• Agent: Plans and executes tasks, manages state/history, handles LLM messaging and errors
• Controller: Registers browser actions, validates parameters, bridges LLM instructions to browser operations
• Browser: Manages browser instances, contexts, tabs, navigation, and DOM operations
• DomService: Builds DOM trees, identifies interactive elements, tracks history and viewport info
• Background Process (Virtual Sandbox): Executes automation tasks in an isolated environment, ensuring operations run smoothly without disrupting the user's browsing experience.

#🔍 Memory Management

• The agent operates with persistent memory of its previous actions. This gives it the ability to make informed decisions in subsequent steps by leveraging context from earlier interactions. For example, without persistent memory, if the agent clicks a button and navigates to a new page, it may not remember that it initiated the navigation when extracting data.
• Modeling historic data as "memory" significantly streamlines the development and robustness of automation scripts, compared to manually passing data between steps using explicit variables—a process that becomes cumbersome and error-prone for complex workflows.
  • Application State: Active tabs, form inputs, and authentication cookies
  • Personal Preferences: Learned through implicit signals
• This approach enables us to build more complex and dynamic automation scenarios where the agent can truly "understand" the context of its actions. Browsing History: Encoded via Sentence-BERT embeddings for semantic recall

#Basic Properties

• context_evaluation: Post-action outcome analysis and adjustment planning
• memory: Persistent storage of progress and key information
• short_term_goal: Immediate next steps
• long_term_goal: Overall objective guiding the sequence

#Compressed Memory

• Summarize each completed step: After every action (e.g., click, data extraction, navigation), the agent generates a concise summary of what just occurred. This summary could include details like:
  • "Navigate: amazon.com."
  • "Input text: 'parrot toy' into field 'Search Query'."
  • "Query: 'Product Name' as 'Parrot Toy', 'Price' as '$22'."
  • "Tap: on the 'Submit' button."
• Retain and utilize this memory for subsequent steps: The agent has access to these step summaries throughout the current automation run. This memory is accessible to inform decisions and actions in later steps.

HuBrowser manages state on two levels:

1. Multi-Tab Orchestration: Manages browser instances, contexts, and pages for complex workflows
2. Tab-Level Context: Parses and extracts page element information with precise targeting
   • Filters irrelevant nodes (empty text, scripts, SVG), assigns unique XPath identifiers
   • Detects interactive elements (ARIA roles, event listeners, content-editable)
   • Handles cookie banners/consent dialogs
   • Converts JavaScript flat maps to Python object networks

#📄 Page & DOM Processing

HuBrowser translates complex web structures into LLM-friendly descriptions using structured text and indexed screenshots. Page info is treated as one-time data, cleared after each request to maintain LLM context efficiency.

#DOM Tree Analysis

• Identifies visible, interactive, and top-layer elements
• Multi-layered interactivity checks (ARIA, events, content-editable)
• High-performance element highlighting overlays
• Focused on actionable types: buttons, text nodes, images, forms, containers
• Filtering: Ignores elements less than 5px (tracking pixels) or larger than screen size
• Extraction: Generates unified WebElementInfo with IDs, hashes, locators
• Tag logic: Uses parent tag for most elements, with special handling for forms
• Tree operations: Applies algorithms to transform into structured trees

Element Properties Example:

{
	"tagName": "a",
	"attributes": {},
	"xpath": "html/body/div/a[2]",
	"children": ["idx_459"],
	"isVisible": true,
	"isTopElement": true,
	"isInteractive": true,
	"isInViewport": true,
	"highlightIndex": 5,
	"elementId": "element_123",
	"indexId": "idx_456",
	"nodeHashId": "hash_789",
	"content": "HuBrowsing",
	"position": { "x": 100, "y": 200, "width": 60, "height": 30 },
	"centerCoordinates": { "x": 150, "y": 225 },
	"zoomLevel": 1.0,
	"screenDimensions": { "width": 1920, "height": 1080 }
}

#Screenshot Processing

• DPR (Device Pixel Ratio) adaptation for different screen resolutions: Adjusts screenshots for high-resolution screens (DPR > 1) to ensure pixel-accurate element positioning
• Element marking: Adds interactive element markers/borders for LLM identification and actionable UI cues

#User Intent Recognition

• Analyzes mouse and touch movement patterns to assess user interest, enabling agents to prioritize processing and tailor responses.
• Highlights the likelihood of interaction for each element based on semantic intent, and can optionally display color coding to users. This helps agents focus on the most relevant information or actions.

#🧠 Agent Intelligence & Tool Integration

#Tool Integration & Action Registry

• Structured function calling with JSON Schema validation for precise execution
• Flexible registration for sync/async operations

#🧅 Multi-Layer Context System

HuBrowser selects context layers based on task complexity and LLM needs, balancing performance and accuracy:

• Text Content: Visible text extraction
• HTML Metadata: Titles, meta tags
• Viewport Screenshot: Current visible area
• Page Thumbnail: Low-res previews
• Full Page Screenshot: Complete visual snapshot
• Flattened HTML Tree: Structured DOM with key properties
• Full HTML Tree: Complete DOM for deep inspection
• Web API Data: Structured data for detailed tasks

#🎯 Target Planning & Execution

#Planning Methodologies

1. Parameter Building: Creates detailed locating parameters with element context
2. Plan Creation: Generates comprehensive operation plans with fallback strategies
3. Task Execution: Converts plans into actionable tasks

AI Assist Response:

• Text Models: Element IDs with reasoning
• Visual Models: Bounding box coordinates

#Action Method & Context Management

• Retains single user message with summarized history
• Passes comprehensive page descriptions (DOM tree, element details) for general models
• Excludes DOM info for visual models to reduce overhead
• Generates detailed action plans with outcome predictions
• Maintains execution logs and error info
• Predicts results before execution for proactive error handling
• Preserves message history with up to four screenshots
• Integrates user instructions directly into system prompts
• Includes previous AI responses for contextual continuity
• Advanced:
  • Action decomposition: Breaks complex instructions into operations
  • Element locating optimization: Reuses located IDs/classes/coordinates to minimize model calls
  • Context efficiency: Balances history with performance

#🧩 Technical Challenges & Solutions

HuBrowser addresses real-world web automation challenges with advanced solutions:

• 🖼️ Iframe Processing: Recursive traversal of nested iframes, cross-origin handling
• 🛡️ CSP Restrictions: Multiple strategies to mitigate content security policy blocks
• 🌑 Shadow DOM: Advanced traversal for isolated components
• 🧩 Extension Automation: Native browser extension interaction
• 🔗 Inter-Context Messaging: Seamless communication across pages/extensions
• ⏱️ Script Injection Timing: Precise timing for reliable automation
• 📝 Non-Standard HTML: Robust extraction for poorly structured documents
• 🎯 Element Precision: Advanced filtering and locating for accurate targeting