WebRover

Your AI Co-pilot for Web Navigation 🚀

Autonomous Web Agent | Task Automation | Information Retrieval | Deep Research

Overview

WebRover is an AI-powered web agent that combines autonomous browsing with advanced research capabilities. While maintaining its core ability to automate web tasks, version 2.0 introduces sophisticated research workflows including multi-source analysis, academic paper generation, and deep topic exploration. The system intelligently routes queries between task automation and research modes, providing a versatile tool for both quick actions and comprehensive research.

Motivation

While traditional web automation tools excel at task execution, and search engines help with information retrieval, there's a growing need for tools that can handle both while specializing in deep research workflows. WebRover bridges this gap by offering task automation alongside intelligent research capabilities, with a particular focus on comprehensive information gathering, analysis, and synthesis. This dual-purpose approach aims to transform how we interact with web content, making both task execution and research more efficient and thorough.

Demo Video - Deep Research Agent

deep_research_agent_demo.mp4

Watch as the WebRover Deep Research Agent explores a topic, gathers information, and generates an academic paper.

Demo Video - Task Agent

demo_edit_149.mp4

Watch as the WebRover Task Agent navigates a website and performs a task.

Key Features

Agent Capabilities

• Three specialized agents for different use cases (Task, Research, Deep Research)
• Dynamic agent selection based on task complexity
• Real-time agent state visualization
• Streaming agent actions and thoughts

Browser Integration

• Local browser instance for privacy and control
• Multi-tab management
• PDF document handling
• Secure browsing sessions

User Interface

• Modern chat interface with real-time updates
• Interactive agent selection
• Action streaming with visual feedback
• Real-time page annotations and highlights

Output Options

• Direct chat responses
• One-click Google Docs export
• PDF download functionality
• Copy to clipboard support

Research Tools

• Vector store for information retention
• Multi-source verification
• Academic paper generation
• Reference management

Technical Features

• State-of-the-art LLM integration (GPT-4o, o3-mini-high, Claude-3.5 sonnet)
• RAG pipeline for enhanced responses
• LangGraph for state management
• Playwright for reliable web automation

Agent Types

1. Task Agent

A specialized automation agent for executing web-based tasks and workflows.

• Custom action planning for multi-step tasks
• Dynamic element interaction based on context
• Real-time task progress monitoring

2. Research Agent

An information gathering specialist with smart content processing.

• Intelligent source selection and validation
• Adaptive search refinement
• Single-pass comprehensive information gathering

3. Deep Research Agent (New! 🎉)

An advanced research agent that produces academic-quality content through systematic topic exploration.

• Automatic topic decomposition and structured research
• Independent subtopic exploration
• Academic paper generation with proper citations
• Cross-referenced bibliography compilation

Agent Architecture Diagrams

Deep Research Agent Flow

Deep Research Agent's workflow for comprehensive research and content generation

Research Agent Flow

Research Agent's workflow for information gathering and synthesis

Task Agent Flow

Task Agent's workflow for automating web interactions

Architecture

The system is built on a modern tech stack with three distinct agent types, each powered by:

1. State Management

   • LangGraph for maintaining agent state
   • Handles complex navigation flows and decision making
   • Structured workflow management

2. Browser Automation

   • Playwright for reliable web interaction
   • Custom element detection and interaction system
   • Automated navigation and content extraction

3. Content Processing

   • RAG (Retrieval Augmented Generation) pipeline
   • Vector store integration for efficient information storage
   • PDF and webpage content extraction
   • Automatic content structuring and organization

4. AI Decision Making

   • Multiple LLM integration (GPT-4, Claude)
   • Context-aware navigation
   • Self-review mechanisms
   • Structured output generation

Setup Instructions

Backend Setup

1. Clone the repository

   git clone https://github.com/hrithikkoduri18/WebRover.git
   cd WebRover
   cd backend

2. Install Poetry (if not already installed)

   Mac/Linux:

   curl -sSL https://install.python-poetry.org | python3 -

   Windows:

   (Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | python -

3. Set Python version for Poetry

   poetry env use python3.12

4. Activate the Poetry shell: For Unix/Linux/MacOS:

   poetry shell
   # or manually
   source $(poetry env info --path)/bin/activate

   For Windows:

   poetry shell
   # or manually
   & (poetry env info --path)\Scripts\activate

5. Install dependencies using Poetry:

   poetry install

6. Set up environment variables in .env:

   OPENAI_API_KEY="your_openai_api_key"
   LANGCHAIN_API_KEY="your_langchain_api_key"
   LANGCHAIN_TRACING_V2="true"
   LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
   LANGCHAIN_PROJECT="your_project_name"
   ANTHROPIC_API_KEY="your_anthropic_api_key"

7. Run the backend:

   Make sure you are in the backend folder

   uvicorn app.main:app --reload --port 8000 

   For Windows User:

   uvicorn app.main:app --port 8000

8. Access the API at http://localhost:8000

Frontend Setup

1. Open a new terminal and make sure you are in the WebRover folder:

   cd frontend

2. Install dependencies:

   npm install

3. Run the frontend:

   npm run dev

4. Access the frontend at http://localhost:3000

For mac users:

Try running http://localhost:3000 on Safari browser. If you face any with connecting to browser, open terminal and run:

pkill -9 "Chrome"

and try again.

If you still face issues, try changing the websocket port from 9222 to 9223 in the webrover_browser.py file in the backend/Browser folder.

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Made with ❤️ by @hrithikkoduri