Build Your First Web Scraper in Minutes: Crawly for AI Step-by-Step Tutorial

Web scraping is no longer a luxury for big companies—it’s a vital tool for anyone looking to extract, analyze, and act on web data. In this guide, you’ll learn how to set up and use Crawly for AI, an open-source web scraper, to automate data collection, extract insights, and build powerful workflows.

Step 1: Set Up Your Environment

  1. Install Python
    Make sure you have Python 3.8 or later installed on your system. Download it from python.org.
  2. Clone the Crawly for AI Repository
    Open your terminal and run the following commands: git clone https://github.com/your-repo/crawly-for-ai.git cd crawly-for-ai
  3. Install Dependencies
    Use pip to install the required libraries: pip install -r requirements.txt
  4. Set Up a Virtual Environment (Optional)
    To keep your project isolated: python -m venv env source env/bin/activate # For Linux/Mac env\Scripts\activate # For Windows

Step 2: Create Your First Crawly Script

  1. Start a New Flask Project
    Initialize a basic web application to manage crawling jobs: from flask import Flask app = Flask(__name__) @app.route("/") def home(): return "Crawly for AI is running!" Save this as app.py.
  2. Run the Application
    Launch the app by running: python app.py Open your browser and go to http://127.0.0.1:5000 to verify it’s running.

Step 3: Configure Crawly for Multi-URL Crawling

  1. Choose URLs to Crawl
    Create a urls.txt file with the list of websites you want to scrape. Example: https://example.com https://another-example.com
  2. Modify Crawly Settings
    Edit the script to include multi-URL crawling: from crawly import Crawly def crawl_urls(): crawly = Crawly() with open("urls.txt", "r") as file: urls = file.readlines() results = crawly.crawl(urls) return results

Step 4: Extract and Save Data

  1. Enable CSV Downloads
    Add functionality to save extracted data in CSV format: import csv def save_to_csv(data, filename="output.csv"): with open(filename, mode="w", newline="") as file: writer = csv.writer(file) writer.writerow(["Title", "Link"]) for item in data: writer.writerow([item["title"], item["link"]])
  2. Run the Scraper
    Call the scraping and saving functions: data = crawl_urls() save_to_csv(data)

Step 5: Integrate LLM for Advanced Data Analysis

  1. Install OpenAI or Similar LLM
    Install the OpenAI library: pip install openai
  2. Analyze Extracted Data
    Send the scraped data to an LLM for summarization or keyword extraction: import openai def analyze_with_llm(data): response = openai.Completion.create( engine="text-davinci-003", prompt="Summarize this data: " + str(data), max_tokens=500 ) return response.choices[0].text

Step 6: Test and Troubleshoot

  • Run Your Script
    Execute the final script: python app.py
  • Common Issues
    • If a dependency error occurs, reinstall using: pip install -r requirements.txt --force-reinstall
    • For async-related errors, ensure the package supports asynchronous calls.

Step 7: Scale and Automate

  1. Use Cloud Hosting
    Deploy your scraper to platforms like Heroku, AWS, or Google Cloud for continuous operation.
  2. Integrate with SaaS Tools
    Connect with tools like Stripe for payments or Supabase for database management to create a SaaS product.

Conclusion

With Crawly for AI, you can turn web data into actionable insights effortlessly. Whether you’re a beginner or a pro, this open-source tool provides everything you need to start scraping, analyzing, and automating workflows.

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