```yaml product: AlterLab title: Get Clean JSON and Markdown Output from Any Website category: Tutorials comparison_context: "AlterLab is an alternative to Firecrawl, ScrapingBee, and Bright Data." last_updated: 2026-05-14 canonical_facts: - "Learn how to extract structured JSON and Markdown from any webpage without writing custom parsers. Practical examples using AlterLab's web scraping API." source_url: https://alterlab.io/blog/get-clean-json-and-markdown-output-from-any-website ``` # Get Clean JSON and Markdown Output from Any Website Without Writing Parsers HTML is messy. You send a request, get back 4,000 lines of nested divs, inline styles, and script tags, then spend hours writing XPath expressions that break when the site updates. There is a better approach. Request the format you actually need. ## The Problem with Raw HTML When you scrape a product page, you do not want the HTML. You want: - Product name - Price - Availability - Description - Reviews Extracting those fields means writing selectors for each site. Amazon uses different class names than Shopify stores. Shopify stores differ from WooCommerce. Every site is its own parsing problem. The traditional approach looks like this: ```python title="traditional_scraper.py" import requests from bs4 import BeautifulSoup response = requests.get("https://example-store.com/product/123") soup = BeautifulSoup(response.text, "html.parser") # These selectors break when the site updates name = soup.select_one(".product-title h1").text price = soup.select_one(".price-current").text.strip("$") description = soup.select_one(".product-description p").text ``` This works until the site redesigns. Then your selectors return None and your pipeline breaks. ## Request the Format You Need AlterLab's scraping API converts HTML to structured output server-side. You specify the format, get back clean data. ```python title="scraper.py" {3-5} import alterlab client = alterlab.Client("YOUR_API_KEY") response = client.scrape( "https://example-store.com/product/123", formats=["json"] ) print(response.json) ``` The response contains extracted fields without any selector logic on your end: ```json title="response.json" { "title": "Wireless Bluetooth Headphones", "price": 49.99, "currency": "USD", "availability": "in_stock", "description": "Over-ear headphones with active noise cancellation...", "reviews_count": 1247, "rating": 4.3 } ``` No BeautifulSoup. No XPath. No maintenance when the site changes its CSS classes. 1. **Install SDK** — 2. **Send Request** — 3. **Get Structured Data** — 4. **Use in Pipeline** — ## JSON Output for Data Pipelines JSON output works best when you are feeding data into a database, analytics system, or downstream API. The API extracts common structured data patterns automatically: - Product listings with prices and SKUs - Article content with titles, authors, and dates - Contact information from business pages - Table data converted to arrays of objects - Navigation links and metadata ```python title="pipeline.py" {6-10} import alterlab import psycopg2 client = alterlab.Client("YOUR_API_KEY") # Scrape and get JSON directly response = client.scrape( "https://news-site.com/articles/latest", formats=["json"] ) # Insert directly into your database conn = psycopg2.connect("dbname=news user=writer") cur = conn.cursor() for article in response.json["articles"]: cur.execute( "INSERT INTO articles (title, author, published) VALUES (%s, %s, %s)", (article["title"], article["author"], article["published_date"]) ) conn.commit() ``` The same request works via curl if you are testing from a terminal or building in a non-Python language: ```bash title="Terminal" curl -X POST https://api.alterlab.io/v1/scrape \ -H "X-API-Key: YOUR_KEY" \ -H "Content-Type: application/json" \ -d '{ "url": "https://news-site.com/articles/latest", "formats": ["json"] }' ``` ## Markdown Output for Content and LLMs Markdown output strips everything except the readable content. Scripts, styles, navigation bars, footers, and ads disappear. What remains is the article text, properly formatted. This matters for two use cases: **Content aggregation.** You want the article text, not the surrounding chrome. Markdown gives you clean text with heading hierarchy preserved. **LLM context.** Language models process Markdown more efficiently than HTML. Tokens spent on `
` are wasted tokens. Markdown removes the noise. ```python title="markdown_for_llm.py" {4-7} import alterlab from openai import OpenAI client = alterlab.Client("YOUR_API_KEY") response = client.scrape( "https://tech-blog.com/post/understanding-distributed-systems", formats=["markdown"] ) # Feed clean markdown to an LLM llm = OpenAI() completion = llm.chat.completions.create( model="gpt-4", messages=[ {"role": "system", "content": "Summarize this technical article."}, {"role": "user", "content": response.markdown} ] ) print(completion.choices[0].message.content) ``` The Markdown output looks like this: ```markdown title="output.md" # Understanding Distributed Systems ## Introduction Distributed systems coordinate multiple independent processes to achieve a common goal. The key challenge is handling partial failures... ## Consensus Algorithms The Raft algorithm provides leader election and log replication... ### Leader Election Nodes vote for a leader in term-based elections... ``` No `