ScrapeGraph

This notebook provides a quick overview for getting started with ScrapeGraph tools. For detailed documentation of all ScrapeGraph features and configurations head to the API reference.

For more information about ScrapeGraph AI:

Overview

Integration details

Class	Package	Serializable	JS support
SmartScraperTool	langchain-scrapegraph	✅	❌
MarkdownifyTool	langchain-scrapegraph	✅	❌
LocalScraperTool	langchain-scrapegraph	✅	❌
GetCreditsTool	langchain-scrapegraph	✅	❌

Tool features

Tool	Purpose	Input	Output
SmartScraperTool	Extract structured data from websites	URL + prompt	JSON
MarkdownifyTool	Convert webpages to markdown	URL	Markdown text
LocalScraperTool	Extract data from HTML content	HTML + prompt	JSON
GetCreditsTool	Check API credits	None	Credit info

Setup

The integration requires the following packages:

%pip install --quiet -U langchain-scrapegraph

Note: you may need to restart the kernel to use updated packages.

Credentials

You'll need a ScrapeGraph AI API key to use these tools. Get one at scrapegraphai.com.

import getpass
import os

if not os.environ.get("SGAI_API_KEY"):
    os.environ["SGAI_API_KEY"] = getpass.getpass("ScrapeGraph AI API key:\n")

It's also helpful (but not needed) to set up LangSmith for best-in-class observability:

os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()

Instantiation

Here we show how to instantiate instances of the ScrapeGraph tools:

from langchain_scrapegraph.tools import (
    GetCreditsTool,
    LocalScraperTool,
    MarkdownifyTool,
    SmartScraperTool,
)

smartscraper = SmartScraperTool()
markdownify = MarkdownifyTool()
localscraper = LocalScraperTool()
credits = GetCreditsTool()

Invocation

Invoke directly with args

Let's try each tool individually:

# SmartScraper
result = smartscraper.invoke(
    {
        "user_prompt": "Extract the company name and description",
        "website_url": "https://scrapegraphai.com",
    }
)
print("SmartScraper Result:", result)

# Markdownify
markdown = markdownify.invoke({"website_url": "https://scrapegraphai.com"})
print("\nMarkdownify Result (first 200 chars):", markdown[:200])

local_html = """
<html>
    <body>
        <h1>Company Name</h1>
        <p>We are a technology company focused on AI solutions.</p>
        <div class="contact">
            <p>Email: contact@example.com</p>
            <p>Phone: (555) 123-4567</p>
        </div>
    </body>
</html>
"""

# LocalScraper
result_local = localscraper.invoke(
    {
        "user_prompt": "Make a summary of the webpage and extract the email and phone number",
        "website_html": local_html,
    }
)
print("LocalScraper Result:", result_local)

# Check credits
credits_info = credits.invoke({})
print("\nCredits Info:", credits_info)

SmartScraper Result: {'company_name': 'ScrapeGraphAI', 'description': "ScrapeGraphAI is a powerful AI web scraping tool that turns entire websites into clean, structured data through a simple API. It's designed to help developers and AI companies extract valuable data from websites efficiently and transform it into formats that are ready for use in LLM applications and data analysis."}

Markdownify Result (first 200 chars): [![ScrapeGraphAI Logo](https://scrapegraphai.com/images/scrapegraphai_logo.svg)ScrapeGraphAI](https://scrapegraphai.com/)

PartnersPricingFAQ[Blog](https://scrapegraphai.com/blog)DocsLog inSign up

Op
LocalScraper Result: {'company_name': 'Company Name', 'description': 'We are a technology company focused on AI solutions.', 'contact': {'email': 'contact@example.com', 'phone': '(555) 123-4567'}}

Credits Info: {'remaining_credits': 49679, 'total_credits_used': 914}

Invoke with ToolCall

We can also invoke the tool with a model-generated ToolCall:

model_generated_tool_call = {
    "args": {
        "user_prompt": "Extract the main heading and description",
        "website_url": "https://scrapegraphai.com",
    },
    "id": "1",
    "name": smartscraper.name,
    "type": "tool_call",
}
smartscraper.invoke(model_generated_tool_call)

ToolMessage(content='{"main_heading": "Get the data you need from any website", "description": "Easily extract and gather information with just a few lines of code with a simple api. Turn websites into clean and usable structured data."}', name='SmartScraper', tool_call_id='1')

Chaining

Let's use our tools with an LLM to analyze a website:

Select chat model:

pip install -qU langchain-openai

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableConfig, chain

prompt = ChatPromptTemplate(
    [
        (
            "system",
            "You are a helpful assistant that can use tools to extract structured information from websites.",
        ),
        ("human", "{user_input}"),
        ("placeholder", "{messages}"),
    ]
)

llm_with_tools = llm.bind_tools([smartscraper], tool_choice=smartscraper.name)
llm_chain = prompt | llm_with_tools


@chain
def tool_chain(user_input: str, config: RunnableConfig):
    input_ = {"user_input": user_input}
    ai_msg = llm_chain.invoke(input_, config=config)
    tool_msgs = smartscraper.batch(ai_msg.tool_calls, config=config)
    return llm_chain.invoke({**input_, "messages": [ai_msg, *tool_msgs]}, config=config)


tool_chain.invoke(
    "What does ScrapeGraph AI do? Extract this information from their website https://scrapegraphai.com"
)

API Reference:ChatPromptTemplate | RunnableConfig | chain

AIMessage(content='ScrapeGraph AI is an AI-powered web scraping tool that efficiently extracts and converts website data into structured formats via a simple API. It caters to developers, data scientists, and AI researchers, offering features like easy integration, support for dynamic content, and scalability for large projects. It supports various website types, including business, e-commerce, and educational sites. Contact: contact@scrapegraphai.com.', additional_kwargs={'tool_calls': [{'id': 'call_shkRPyjyAtfjH9ffG5rSy9xj', 'function': {'arguments': '{"user_prompt":"Extract details about the products, services, and key features offered by ScrapeGraph AI, as well as any unique selling points or innovations mentioned on the website.","website_url":"https://scrapegraphai.com"}', 'name': 'SmartScraper'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 47, 'prompt_tokens': 480, 'total_tokens': 527, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_c7ca0ebaca', 'finish_reason': 'stop', 'logprobs': None}, id='run-45a12c86-d499-4273-8c59-0db926799bc7-0', tool_calls=[{'name': 'SmartScraper', 'args': {'user_prompt': 'Extract details about the products, services, and key features offered by ScrapeGraph AI, as well as any unique selling points or innovations mentioned on the website.', 'website_url': 'https://scrapegraphai.com'}, 'id': 'call_shkRPyjyAtfjH9ffG5rSy9xj', 'type': 'tool_call'}], usage_metadata={'input_tokens': 480, 'output_tokens': 47, 'total_tokens': 527, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})

API reference

For detailed documentation of all ScrapeGraph features and configurations head to the Langchain API reference: https://python.langchain.com/docs/integrations/tools/scrapegraph

Or to the official SDK repo: https://github.com/ScrapeGraphAI/langchain-scrapegraph

Tool conceptual guide
Tool how-to guides

Overview​

Integration details​

Tool features​

Setup​

Credentials​

Instantiation​

Invocation​

Invoke directly with args​

Invoke with ToolCall​

Chaining​

API reference​

Related​

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