https://github.com/hrithikkoduri/WebRover
WebRover is an autonomous AI agent designed to interpret user input and execute actions by interacting with web elements to accomplish tasks or answer questions. It leverages advanced language models and web automation tools to navigate the web, gather information, and provide structured responses based on the user's needs.
https://github.com/hrithikkoduri/WebRover
Last synced: 10 days ago
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WebRover is an autonomous AI agent designed to interpret user input and execute actions by interacting with web elements to accomplish tasks or answer questions. It leverages advanced language models and web automation tools to navigate the web, gather information, and provide structured responses based on the user's needs.
- Host: GitHub
- URL: https://github.com/hrithikkoduri/WebRover
- Owner: hrithikkoduri
- License: mit
- Created: 2025-01-14T01:11:21.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-02-12T12:45:31.000Z (2 months ago)
- Last Synced: 2025-02-12T13:56:07.821Z (2 months ago)
- Language: Python
- Homepage:
- Size: 11.6 MB
- Stars: 470
- Watchers: 9
- Forks: 90
- Open Issues: 5
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
- awesome-LangGraph - WebRover
- awesome-generative-ai-data-scientist - WebRover - powered web agent that combines autonomous browsing with advanced research capabilities. | [GitHub](https://github.com/hrithikkoduri/WebRover) | (Browser Control Agents)
README
# 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
https://github.com/user-attachments/assets/325c6c55-9384-4939-a912-3b1d13635799
> Watch as the WebRover Deep Research Agent explores a topic, gathers information, and generates an academic paper.
## Demo Video - Task Agent
https://github.com/user-attachments/assets/95ae9afb-3fdf-47f8-857e-f6a1a0d94df5
> 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
```bash
git clone https://github.com/hrithikkoduri18/WebRover.git
cd WebRover
cd backend
```
2. Install Poetry (if not already installed)
Mac/Linux:
```bash
curl -sSL https://install.python-poetry.org | python3 -
```
Windows:
```bash
(Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | python -
```
3. Set Python version for Poetry
```bash
poetry env use python3.12
```
4. Activate the Poetry shell:
For Unix/Linux/MacOS:
```bash
poetry shell
# or manually
source $(poetry env info --path)/bin/activate
```
For Windows:
```bash
poetry shell
# or manually
& (poetry env info --path)\Scripts\activate
```
5. Install dependencies using Poetry:
```bash
poetry install
```
6. Set up environment variables in `.env`:
```bash
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
```bash
uvicorn app.main:app --reload --port 8000
```
For Windows User:
```bash
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:
```bash
cd frontend
```
2. Install dependencies:
```bash
npm install
```
3. Run the frontend:
```bash
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:
```bash
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](LICENSE) file for details.
---
Made with ❤️ by [@hrithikkoduri](https://github.com/hrithikkoduri)