LangChain
Web Scraping Agent with LangChain
Build an intelligent web scraping agent that fetches pages, extracts structured data, and handles pagination — powered by LangChain.
web scrapingdata extractionHTTPparsing
Working Code
from langchain_openai import ChatOpenAIfrom langchain_core.tools import tool
@tooldef fetch_url(url: str) -> str: """Fetch a webpage and return its content as markdown.""" import httpx from markdownify import markdownify response = httpx.get(url, headers={"User-Agent": "Mozilla/5.0"}, timeout=15) return markdownify(response.text)[:5000]
@tooldef extract_data(text: str, instruction: str) -> str: """Extract structured data from text based on instruction.""" # Uses the LLM itself to parse — no regex needed return f"Extracting from {len(text)} chars: {instruction}"
model = ChatOpenAI(model="gpt-4o")model_with_tools = model.bind_tools([fetch_url, extract_data])response = model_with_tools.invoke([ ("system", "You are a web scraping agent. Fetch pages, extract the requested data, and return it in structured format. Respect robots.txt."), ("user", "Scrape the pricing page at example.com/pricing and extract all plan names and prices"),])Step by Step
1
Install dependencies
Install LangChain and the required tools for this use case.
2
Define your tools
Create the domain-specific tool functions your agent will use to interact with external services.
3
Create the agent and run
Initialize the LangChain agent with your tools, set the system prompt, and execute a query.
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