🕷️ Web Scraping AI Agent

🎓 FREE Step-by-Step Tutorial

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

AI-powered web scraping using ScrapeGraphAI - extract structured data from websites using natural language prompts. This agent runs locally with the open-source scrapegraphai library.

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📁 What's Inside

Files: ai_scrapper.py, local_ai_scrapper.py

Use the open-source ScrapeGraphAI library that runs on your local machine.

✅ Pros:

• Free to use (no API costs)
• Full control over execution
• Privacy-friendly (all data stays local)

❌ Cons:

• Requires local installation and dependencies
• Limited by your hardware
• Need to manage updates

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🚀 Getting Started

1. Clone the repository

git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd awesome-llm-apps/starter_ai_agents/web_scraping_ai_agent

2. Install dependencies

pip install -r requirements.txt

3. Get your OpenAI API Key

• Sign up for an OpenAI account
• Obtain your API key

4. Run the Streamlit App

streamlit run ai_scrapper.py
# Or for local models:
streamlit run local_ai_scrapper.py

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💡 Use Cases

E-commerce Scraping

# Extract product information
prompt = "Extract product names, prices, and availability"

Content Aggregation

# Convert articles to structured data
prompt = "Extract article title, author, date, and main content"

Competitive Intelligence

# Monitor competitor websites
prompt = "Extract pricing, features, and updates"

Lead Generation

# Extract contact information
prompt = "Find company names, emails, and phone numbers"

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🔧 How It Works

1. You provide your OpenAI API key
2. Select the model (GPT-4o, GPT-5, or local models)
3. Enter the URL and extraction prompt
4. The app uses ScrapeGraphAI to scrape and extract data locally
5. Results are displayed in the app

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📖 Documentation

• ScrapeGraphAI Library: ScrapeGraphAI GitHub

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