Browser-use + LightRAG Agent That Can Scrape 99% websites with LLM

Introduction

In this tutorial, we will explore how to create a powerful chatbot using Browser-use and LightRAG with a local large language model (LLM). This AI agent can scrape data from almost any website and respond to inquiries about that data. By the end of this guide, you’ll have a solid understanding of the tools involved and how to implement them effectively.

Step 1: Understand LightRAG

• LightRAG is a versatile framework for developing AI agents.
• It provides advantages over other similar tools, such as Graphrag, by being more efficient in scraping data.
• Familiarize yourself with its capabilities and how it operates to best utilize it in your project.

Step 2: Set Up Browser-use

• Browser-use is a tool that allows for effective web scraping.
• Access the Browser-use GitHub repository here.
• Follow the installation instructions provided in the repository to set it up on your local machine.

Step 3: Install LightRAG

• Obtain LightRAG from its GitHub repository here.
• Follow the installation steps, ensuring that all dependencies are correctly installed.
• Make sure to check for compatibility with your local LLM setup.

Step 4: Code Your Chatbot

• Begin coding your chatbot by integrating Browser-use and LightRAG.
• Use the following code snippet as a starting point for your chatbot’s functionality:

# Example code to initialize LightRAG with Browser-use
from lightrag import LightRAG
from browser_use import Browser

# Initialize Browser
browser = Browser()

# Initialize LightRAG
agent = LightRAG(browser)

# Define your scraping logic here
def scrape_website(url)
    data = browser.scrape(url)
    return data

# Define how the chatbot will respond
def chatbot_response(question)
    return agent.answer(question)

• Customize the scraping logic and the response mechanism to suit your specific needs.

Step 5: Test Your Chatbot

• After coding, run your chatbot to ensure it correctly scrapes data and responds to questions.
• Test with various websites to evaluate its scraping capabilities and response accuracy.
• Adjust your scraping logic based on the results to improve performance.

Step 6: Optimize and Expand

• Once the basic functionality is working, consider optimizing the chatbot's performance.
• Implement features such as error handling, data caching, and user prompt customization.
• Expand your chatbot’s capabilities by integrating more complex scraping techniques or additional AI features.

Conclusion

In this tutorial, we covered the essential steps to create a chatbot using Browser-use and LightRAG. You learned how to set up both tools, write the initial code, and test your chatbot's functionality. As you continue to refine your chatbot, explore additional features and optimizations to enhance its capabilities. Happy coding!