langchain-scrapegraph 1.3.0b1

Library for extracting structured data from websites using ScrapeGraphAI

🕷️🦜 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.

🔗 ScrapeGraph API & SDKs

If you are looking for a quick solution to integrate ScrapeGraph in your system, check out our powerful API here!

We offer SDKs in both Python and Node.js, making it easy to integrate into your projects. Check them out below:

SDK	Language	GitHub Link
Python SDK	Python	scrapegraph-py
Node.js SDK	Node.js	scrapegraph-js

📦 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)

🌐 SearchscraperTool

Search and extract structured information from the web using natural language prompts.

from langchain_scrapegraph.tools import SearchScraperTool

# Initialize the tool (uses SGAI_API_KEY from environment)
tool = SearchScraperTool()

# Search and extract information using natural language
result = tool.invoke({
    "user_prompt": "What are the key features and pricing of ChatGPT Plus?"
})

print(result)
# {
#     "product": {
#         "name": "ChatGPT Plus",
#         "description": "Premium version of ChatGPT..."
#     },
#     "features": [...],
#     "pricing": {...},
#     "reference_urls": [
#         "https://openai.com/chatgpt",
#         ...
#     ]
# }

🔍 Using Output Schemas with SearchscraperTool

You can define the structure of the output using Pydantic models:

from typing import List, Dict
from pydantic import BaseModel, Field
from langchain_scrapegraph.tools import SearchScraperTool

class ProductInfo(BaseModel):
    name: str = Field(description="Product name")
    features: List[str] = Field(description="List of product features")
    pricing: Dict[str, Any] = Field(description="Pricing information")
    reference_urls: List[str] = Field(description="Source URLs for the information")

# Initialize with schema
tool = SearchScraperTool(llm_output_schema=ProductInfo)

# The output will conform to the ProductInfo schema
result = tool.invoke({
    "user_prompt": "What are the key features and pricing of ChatGPT Plus?"
})

print(result)
# {
#     "name": "ChatGPT Plus",
#     "features": [
#         "GPT-4 access",
#         "Faster response speed",
#         ...
#     ],
#     "pricing": {
#         "amount": 20,
#         "currency": "USD",
#         "period": "monthly"
#     },
#     "reference_urls": [
#         "https://openai.com/chatgpt",
#         ...
#     ]
# }

🌟 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

• API Documentation
• LangChain Documentation
• Examples

💬 Support & Feedback

• 📧 Email: [e-mail]
• 💻 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:

• LangChain
• ScrapeGraph AI

---

Made with ❤️ by ScrapeGraph AI