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WebSage

AI agent that can scrape websites, crawl pages, and extract structured data in real-time to answer your complex queries

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Runs on your computer. Pick your agent and operating system.

This project does not publish an install method we can turn into a config. Follow the README instead.

README

synced Oct 23, 2026

🕷️ WebSage

Python 3.13+ OpenAI License: MIT Firecrawl MCP

An intelligent conversational agent that combines OpenAI's GPT models with Firecrawl's powerful web scraping capabilities through the Model Context Protocol (MCP). Chat naturally with an AI that can scrape websites, crawl pages, and extract structured data in real-time.

🖼️ Snapshot

🔧 Prerequisites

📥 Installation

  1. Clone the project
git clone https://github.com/smaranjitghose/websage
  1. Navigate inside the directory
cd websage
  1. Create a .env file and store the API keys from OPENAI and FIRECRAWL as:
OPENAI_API_KEY=your-openai-api-key-here
FIRECRAWL_API_KEY=your-firecrawl-api-key-here
  1. Run the agent using uv (one the first run it will create a virtual environment and install the dependencies in it)
uv run main.py
  1. Start chatting with examples:
🗣️  You: Scrape https://news.ycombinator.com and get the top 5 story titles

🗣️  You: Extract all product prices from https://store.example.com

🗣️  You: Crawl https://blog.company.com and summarize the latest 3 articles
  1. Exit when done:
🗣️  You: quit

💡 Usage Examples

Basic Web Scraping

Scrape https://example.com for main content and headings

E-commerce Data Extraction

Get all product names and prices from https://shop.example.com/category/electronics

News and Content Analysis

Crawl https://techblog.com and extract all article titles published this month

Research and Data Collection

Scrape https://research-site.edu/publications and extract paper titles, authors, and abstracts

SEO and Competitive Analysis

Analyze the main headings and meta descriptions from https://competitor.com

🔍 How It Works

  1. MCP Integration - Uses Model Context Protocol to connect GPT with Firecrawl
  2. Tool Loading - Dynamically loads available Firecrawl scraping tools
  3. ReAct Agent - Employs reasoning and acting pattern for intelligent tool use
  4. Conversation Loop - Maintains context across multiple scraping requests
  5. Error Recovery - Handles network issues and API errors gracefully

🛡️ Best Practices

Ethical Scraping

  • Always respect robots.txt files
  • Be mindful of rate limiting and server load
  • Only scrape publicly available content
  • Consider reaching out to site owners for large-scale scraping

API Usage

  • Monitor your Firecrawl and OpenAI API usage
  • Set appropriate rate limits in production
  • Cache results when appropriate to reduce API calls

🔧 Troubleshooting

Common Issues

"Invalid API key" errors

  • Verify your .env file is in the correct location
  • Check that API keys are valid and have sufficient credits
  • Ensure no extra spaces or quotes in the .env file

Connection timeout errors

  • Check your internet connection
  • Some websites may block automated requests
  • Try with a different website to test functionality

Python import errors

uv add --upgrade mcp langchain-mcp-adapters langgraph langchain-openai python-dotenv

🤝 Contributing

Contributions are welcome! Here's how:

  1. Fork the repository
  2. Create a feature branch
git switch -c feature/amazing-feature
  1. Commit and push your changes
git commit -m "Add amazing feature"
git push origin feature/amazing-feature
  1. Open a pull request

📝 License

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

Made with ❤️ by Smaranjit Ghose

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