Library for extracting structured data from websites using ScrapeGraphAI
Project description
🕷️🦜 langchain-scrapegraph
Supercharge your LangChain agents with AI-powered web scraping capabilities. LangChain-ScrapeGraph provides a seamless integration between LangChain and ScrapeGraph AI, enabling your agents to extract structured data from websites using natural language.
📦 Installation
pip install langchain-scrapegraph
🛠️ Available Tools
📝 MarkdownifyTool
Convert any webpage into clean, formatted markdown.
from langchain_scrapegraph.tools import MarkdownifyTool
tool = MarkdownifyTool()
markdown = tool.invoke({"website_url": "https://example.com"})
print(markdown)
🔍 SmartscraperTool
Extract structured data from any webpage using natural language prompts.
from langchain_scrapegraph.tools import SmartscraperTool
# Initialize the tool (uses SGAI_API_KEY from environment)
tool = SmartscraperTool()
# Extract information using natural language
result = tool.invoke({
"website_url": "https://www.example.com",
"user_prompt": "Extract the main heading and first paragraph"
})
print(result)
💻 LocalscraperTool
Extract information from HTML content using AI.
from langchain_scrapegraph.tools import LocalscraperTool
tool = LocalscraperTool()
result = tool.invoke({
"user_prompt": "Extract all contact information",
"website_html": "<html>...</html>"
})
print(result)
🌟 Key Features
- 🐦 LangChain Integration: Seamlessly works with LangChain agents and chains
- 🔍 AI-Powered Extraction: Use natural language to describe what data to extract
- 📊 Structured Output: Get clean, structured data ready for your agents
- 🔄 Flexible Tools: Choose from multiple specialized scraping tools
- ⚡ Async Support: Built-in support for async operations
💡 Use Cases
- 📖 Research Agents: Create agents that gather and analyze web data
- 📊 Data Collection: Automate structured data extraction from websites
- 📝 Content Processing: Convert web content into markdown for further processing
- 🔍 Information Extraction: Extract specific data points using natural language
🤖 Example Agent
from langchain.agents import initialize_agent, AgentType
from langchain_scrapegraph.tools import SmartscraperTool
from langchain_openai import ChatOpenAI
# Initialize tools
tools = [
SmartscraperTool(),
]
# Create an agent
agent = initialize_agent(
tools=tools,
llm=ChatOpenAI(temperature=0),
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
# Use the agent
response = agent.run("""
Visit example.com, make a summary of the content and extract the main heading and first paragraph
""")
⚙️ Configuration
Set your ScrapeGraph API key in your environment:
export SGAI_API_KEY="your-api-key-here"
Or set it programmatically:
import os
os.environ["SGAI_API_KEY"] = "your-api-key-here"
📚 Documentation
💬 Support & Feedback
- 📧 Email: support@scrapegraphai.com
- 💻 GitHub Issues: Create an issue
- 🌟 Feature Requests: Request a feature
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🙏 Acknowledgments
This project is built on top of:
Made with ❤️ by ScrapeGraph AI
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