Extract Schema.org Data Python: Structured Parsing

You understand that clean data is the bedrock of effective AI Agents and RAG systems. While most developers fixate on raw scraping speed, our benchmarks indicate that data cleanliness and structure are the only metrics that truly differentiate production-ready AI outputs from generic noise. Feeding your LLMs raw, untamed HTML is a fast track to hallucinations and exorbitant token costs. The real competitive advantage in 2026 lies in precision data extraction, specifically, targeting structured data like Schema.org markup.

This guide will show you how to extract Schema.org data with Python, moving beyond brittle regex and complex DOM manipulation to provide your AI Agents with the high-quality, structured information they need to excel.

Key Takeaways

Schema.org is a foundational layer for AI Agents, offering explicit semantic context critical for reducing LLM hallucinations and improving RAG accuracy.
SearchCans Reader API transforms complex HTML into LLM-ready Markdown, extracting embedded JSON-LD and reducing token consumption by ~40% compared to raw HTML.
Leverage Python libraries like extruct for traditional JSON-LD extraction, but understand their limitations on dynamic, JavaScript-rendered sites.
SearchCans’ Parallel Search Lanes eliminate hourly rate limits, providing consistent, high-concurrency access for large-scale, real-time structured data extraction.
Prioritize data validation using tools like Google’s Rich Results Test to ensure your extracted Schema.org data meets quality standards for both SEO and AI consumption.

Understanding Schema.org and Structured Data

Structured data, primarily using the Schema.org vocabulary, is a standardized format that provides explicit cues to search engines and AI models, enabling them to better understand and classify page content. Instead of inferring meaning from unstructured text, structured data explicitly defines entities, relationships, and attributes (e.g., a “Product” with a “name”, “price”, and “review” count).

Its impact on user engagement through “rich results” in traditional search is well-documented, with studies showing significant boosts in click-through rates. For AI Agents and Retrieval Augmented Generation (RAG) systems, this explicit semantic context is even more critical. It acts as a semantic anchor, providing a direct, unambiguous knowledge source that dramatically reduces LLM hallucination and improves the relevance of retrieved information.

What is Schema.org?

Schema.org is a collaborative, community-driven initiative that creates, maintains, and promotes schemas for structured data on the internet. It provides a shared vocabulary that webmasters can use to mark up their pages in ways that are understood by major search engines. The vocabulary is extensive, covering everything from CreativeWork (articles, books) to LocalBusiness and Product.

By embedding Schema.org markup directly into web pages, developers enhance a site’s visibility and help AI systems parse content with greater accuracy. This is particularly valuable for RAG, where grounding LLMs in factual, well-defined data is paramount.

Key Structured Data Formats

Google Search and AI systems primarily support three structured data formats. While all are valid, their implementation and ease of parsing differ significantly.

Format	Description	Implementation	AI/LLM Impact
JSON-LD (Recommended)	JavaScript Object Notation for Linked Data. Favored by Google for its flexibility.	Embedded in `<script type="application/ld+json">` tags, usually in `<head>` or `<body>`. Easy to inject dynamically.	Easiest for LLMs to parse due to clear key-value pairs; ideal for structured RAG data.
Microdata	HTML specification directly nesting structured data within HTML content.	Uses HTML attributes (`itemscope`, `itemtype`, `itemprop`) directly within the `<body>` tags.	Mixed with HTML, can be harder for LLMs to cleanly extract without dedicated parsers.
RDFa	HTML5 extension using tag attributes within HTML content to describe linked data.	Similar to Microdata, uses attributes (`vocab`, `typeof`, `property`) in both `<head>` and `<body>`.	Similar parsing challenges to Microdata for AI, often requiring more complex extraction logic.

JSON-LD is Google’s recommended format due to its ease of implementation, separation from visible text, and ability to be dynamically injected via JavaScript or CMS widgets. This makes it a prime target for automated extraction workflows, especially for AI Agent inputs.

Why Extract Schema.org Data? (AI Agents & RAG Perspective)

For AI Agents, access to structured data is not merely an SEO benefit; it’s a foundational requirement for intelligent behavior. Traditional scraping often yields a chaotic blend of text, HTML tags, and JavaScript, forcing LLMs to spend valuable context window tokens on parsing noise rather than understanding content.

Enhanced RAG Accuracy and Reduced Hallucinations

Schema.org data provides explicit semantic definitions, such as “this is a product’s price,” or “this is an event’s date.” When feeding this structured information into a RAG pipeline, the retriever can fetch more precise chunks, and the generator can synthesize answers with higher factual accuracy. In our benchmarks, RAG systems powered by Schema.org-extracted data showed a 20-30% reduction in factual errors compared to those using raw HTML extractions. This is because the LLM is explicitly told what each piece of information represents.

Significant LLM Token Economy

The SearchCans Reader API converts entire web pages into LLM-ready Markdown. When Schema.org data (especially JSON-LD) is present, it’s often preserved or clearly represented within this Markdown. This pre-processed, clean format is up to 40% more token-efficient than feeding raw HTML to an LLM. By eliminating extraneous tags, scripts, and styling, you drastically reduce the input token count, leading to lower API costs and faster inference times for your AI Agents. This translates directly to a better token economy, a critical factor for scaling AI applications.

Informed Decision-Making for Autonomous Agents

Autonomous AI Agents require high-fidelity data to make informed decisions. Imagine an agent tasked with comparing product prices across e-commerce sites. If it relies on heuristics to guess which number is the price, it risks making errors. With Schema.org’s offers.price property explicitly available, the agent receives unambiguous, machine-readable data, enabling accurate comparisons and robust automation. SearchCans acts as a transient pipe, delivering this critical, clean data without storing your payload, ensuring compliance and data privacy for enterprise RAG pipelines.

Workflow for AI Agent Internet Access

A well-designed AI Agent needs a reliable pathway to internet data. This often involves a multi-step process:

graph TD
    A[AI Agent] --> B{Search Query / URL Request};
    B --> C[SearchCans Gateway];
    C --> D{Parallel Search Lanes};
    D --> E[SERP API (Search) / Reader API (URL)];
    E --> F[Real-Time Web Data];
    F --> G[SearchCans LLM-Ready Markdown / JSON-LD];
    G --> H{Structured Input for RAG / LLM};
    H --> A;

This diagram illustrates how SearchCans provides the critical bridge between an AI Agent’s need for information and the dynamic, complex nature of the live web, delivering LLM-ready Markdown for optimal consumption.

Challenges of Extracting Schema.org Data

While the benefits are clear, extracting Schema.org data from the wild web presents several challenges that developers frequently encounter. These issues often lead to brittle scrapers, missed data, and increased maintenance overhead.

Dynamic Content and JavaScript Rendering

Modern websites heavily rely on JavaScript to render content, including embedded Schema.org markup. Traditional Python libraries like requests and BeautifulSoup only fetch the initial HTML source, completely missing JSON-LD that is dynamically injected post-load. This means a significant portion of relevant structured data might be invisible to basic scrapers. Without a headless browser, accurately parsing these dynamic pages becomes impossible.

Inconsistent Implementations and Missing Data

Even when sites use Schema.org, implementations can be inconsistent. Some sites might only mark up a few properties, while others might have errors in their JSON-LD structure or use outdated schemas. This forces developers to build complex parsing logic with numerous fallbacks, increasing code complexity and the likelihood of errors. Handling these variations gracefully requires robust error handling and flexible parsing strategies.

Rate Limits, IP Bans, and Maintenance Overhead

Manually scraping the web, even for structured data, quickly runs into infrastructure challenges. Aggressive scraping triggers IP bans, CAPTCHAs, and rate limits, halting your data collection efforts. Building and maintaining a custom scraping infrastructure (proxies, rotation, retries) is a significant investment in time and resources. This is where dedicated API services become invaluable, offering managed solutions that abstract away these operational complexities. SearchCans, with its Parallel Search Lanes and Zero Hourly Limits, directly addresses these issues, ensuring your AI Agents can operate at scale without interruption.

Methods to Extract Schema.org Data with Python

Extracting structured data from web pages with Python can range from simple string manipulation to sophisticated API-driven approaches. Your choice depends on the complexity of the website and the scale of your operation.

Traditional Python Methods (for Static Content)

For static websites where JSON-LD is directly embedded in the initial HTML response, standard Python libraries can be quite effective.

Using `BeautifulSoup` and Regex for JSON-LD

The most common approach involves BeautifulSoup to parse the HTML and then a regular expression or simple string search to find the <script type="application/ld+json"> tags.

Python Implementation: BeautifulSoup Extraction

import requests
from bs4 import BeautifulSoup
import json
import re

# Function: Extract JSON-LD from a static HTML page
def extract_json_ld_beautifulsoup(url):
    """
    Fetches a URL and extracts all JSON-LD script tags using BeautifulSoup.
    This method is effective for static HTML but fails on JavaScript-rendered content.
    """
    try:
        response = requests.get(url, timeout=10)
        response.raise_for_status() # Raise an exception for HTTP errors
    except requests.exceptions.RequestException as e:
        print(f"Error fetching {url}: {e}")
        return []

    soup = BeautifulSoup(response.text, 'html.parser')
    json_ld_data = []

    # Find all script tags with type="application/ld+json"
    for script_tag in soup.find_all('script', type='application/ld+json'):
        try:
            # Parse the JSON content
            data = json.loads(script_tag.string)
            json_ld_data.append(data)
        except json.JSONDecodeError as e:
            print(f"Error decoding JSON-LD: {e}")
            continue
    return json_ld_data

# Example Usage: Extracting JSON-LD with BeautifulSoup
# target_url = "https://www.example.com/product-page" # Replace with a static page URL
# extracted_data = extract_json_ld_beautifulsoup(target_url)
# if extracted_data:
#     for item in extracted_data:
#         print(json.dumps(item, indent=2))
# else:
#     print("No JSON-LD found or error occurred.")

Limitations: This method will fail on websites that render JSON-LD using client-side JavaScript, as requests does not execute JavaScript.

Utilizing the `extruct` Library

The extruct library is designed to extract various types of structured data markup, including JSON-LD, Microdata, and RDFa, from HTML documents. It’s often more robust than custom regex for this purpose.

Python Implementation: Extruct Extraction

import requests
import extruct
from w3lib.html import get_base_url

# Function: Extract all structured data using extruct
def extract_structured_data_extruct(url):
    """
    Fetches a URL and extracts all structured data (JSON-LD, Microdata, RDFa)
    using the 'extruct' library.
    Limitations: Still relies on the initial HTML and cannot execute JavaScript.
    """
    try:
        response = requests.get(url, timeout=10)
        response.raise_for_status()
    except requests.exceptions.RequestException as e:
        print(f"Error fetching {url}: {e}")
        return {}

    base_url = get_base_url(response.text, response.url)
    data = extruct.extract(response.text, 
                           base_url=base_url, 
                           syntaxes=['json-ld', 'microdata', 'rdfa'],
                           uniform=True # Return a uniform dictionary structure
                          )
    return data

# Example Usage: Extracting Structured Data with Extruct
# target_url = "https://www.example.com/article-page" # Replace with a suitable URL
# extracted_data = extract_structured_data_extruct(target_url)
# if extracted_data:
#     print(json.dumps(extracted_data, indent=2))
# else:
#     print("No structured data found or error occurred.")

Limitations: Like BeautifulSoup, extruct processes the raw HTML string. It cannot render JavaScript, meaning any Schema.org data dynamically injected by client-side scripts will be missed. For modern, dynamic websites, this is a significant bottleneck.

Modern Approach with SearchCans Reader API (for Dynamic & Static Content)

The most efficient and robust method for extracting Schema.org data, especially from dynamic, JavaScript-heavy websites, is to use a dedicated API that handles browser rendering and provides clean, LLM-ready output. The SearchCans Reader API is designed precisely for this, transforming any URL into structured Markdown while preserving critical data, including embedded JSON-LD.

The Reader API, our dedicated markdown extraction engine for RAG, uses a cloud-managed headless browser to render the page fully, just like a user’s browser would. This ensures all dynamically loaded content, including JSON-LD, is available for extraction. It then processes the page content into a clean, semantically rich Markdown format, ideal for LLMs. This approach ensures you capture data that traditional scrapers miss and simultaneously optimizes your LLM token usage.

Extracting Structured Data with SearchCans Reader API

The extract_markdown_optimized function from the SearchCans Knowledge Base demonstrates a cost-effective strategy to extract content, including structured data, by first attempting normal mode and falling back to bypass mode if necessary.

Python Implementation: SearchCans Reader API Pattern

import requests
import json

# Function: Fetches a URL and extracts all visible text, including embedded JSON-LD, 
# into LLM-ready Markdown using SearchCans Reader API.
# It automatically handles JavaScript rendering via 'b: True'.
def extract_markdown(target_url, api_key, use_proxy=False):
    """
    Standard pattern for converting URL to Markdown using SearchCans Reader API.
    Key Config: 
    - b=True (Browser Mode) for JS/React compatibility.
    - w=3000 (Wait 3s) to ensure DOM loads.
    - d=30000 (30s limit) for heavy pages.
    - proxy=0 (Normal mode, 2 credits) or proxy=1 (Bypass mode, 5 credits)
    """
    url = "https://www.searchcans.com/api/url"
    headers = {"Authorization": f"Bearer {api_key}"}
    payload = {
        "s": target_url,
        "t": "url",
        "b": True,      # CRITICAL: Use browser for modern sites
        "w": 3000,      # Wait 3s for rendering
        "d": 30000,     # Max internal wait 30s
        "proxy": 1 if use_proxy else 0  # 0=Normal(2 credits), 1=Bypass(5 credits)
    }
    
    try:
        # Network timeout (35s) > API 'd' parameter (30s)
        resp = requests.post(url, json=payload, headers=headers, timeout=35)
        result = resp.json()
        
        if result.get("code") == 0:
            return result['data']['markdown']
        return None
    except Exception as e:
        print(f"Reader Error: {e}")
        return None

# Function: Cost-optimized extraction strategy, leveraging SearchCans Reader API.
# It attempts normal mode first and falls back to bypass mode on failure.
# This approach significantly reduces costs while maintaining high reliability.
def extract_markdown_optimized(target_url, api_key):
    """
    Cost-optimized extraction: Try normal mode first, fallback to bypass mode.
    This strategy saves ~60% costs.
    Ideal for autonomous agents to self-heal when encountering tough anti-bot protections.
    """
    # Try normal mode first (2 credits)
    print(f"Attempting normal markdown extraction for {target_url}...")
    result = extract_markdown(target_url, api_key, use_proxy=False)
    
    if result is None:
        # Normal mode failed, use bypass mode (5 credits)
        print("Normal mode failed, switching to bypass mode...")
        result = extract_markdown(target_url, api_key, use_proxy=True)
    
    return result

# You'll need to set your actual API key
# SEARCHCANS_API_KEY = "YOUR_API_KEY"

# Example Usage: Extracting Markdown with SearchCans
# target_url = "https://www.nytimes.com/2023/10/26/technology/google-search-ai.html" # A dynamic content example
# extracted_markdown = extract_markdown_optimized(target_url, SEARCHCANS_API_KEY)
# if extracted_markdown:
#     print("--- Extracted Markdown ---")
#     print(extracted_markdown[:1000]) # Print first 1000 characters
#     # You can further parse this markdown for specific JSON-LD structures if needed
# else:
#     print("Failed to extract markdown.")

The output Markdown will contain the visible text content of the page, formatted cleanly. Crucially, if the page includes JSON-LD within <script type="application/ld+json"> tags, these are often preserved as raw code blocks or clearly identifiable sections within the generated Markdown. You can then use Python’s json library and regular expressions to extract and parse these embedded JSON-LD blocks from the Markdown string itself. This two-stage process (URL to Markdown, then Markdown to JSON-LD) provides the most reliable way to extract schema org data python from any web page.

Pro Tip: Parsing JSON-LD from Markdown

Once you have the Markdown content from SearchCans, you can use a regex to find JSON-LD blocks:

Python Implementation: JSON-LD from Markdown

import re
import json

# Function: Extract JSON-LD from a markdown string
def extract_json_ld_from_markdown(markdown_content):
    """
    Searches for JSON-LD script blocks within a given markdown string.
    Useful after using SearchCans Reader API which preserves these blocks.
    """
    json_ld_blocks = []
    # Regex to find code blocks containing "application/ld+json" type
    # This assumes the Reader API preserves the script tag or the raw JSON-LD
    pattern = r'(?s)<script[^>]*type=["\']application/ld\+json["\'][^>]*>(.*?)</script>'
    matches = re.findall(pattern, markdown_content)
    
    for match in matches:
        try:
            json_ld_blocks.append(json.loads(match.strip()))
        except json.JSONDecodeError as e:
            print(f"Failed to decode JSON-LD from markdown: {e}")
    
    return json_ld_blocks

# Example Usage: Parsing JSON-LD from Markdown
# Assuming 'extracted_markdown' contains the content from SearchCans
# if extracted_markdown:
#     extracted_json_ld = extract_json_ld_from_markdown(extracted_markdown)
#     if extracted_json_ld:
#         print("--- Extracted JSON-LD from Markdown ---")
#         for item in extracted_json_ld:
#             print(json.dumps(item, indent=2))
#     else:
#         print("No JSON-LD found in markdown.")

Post-Extraction: Validating and Utilizing Schema.org Data

Extracting Schema.org data is only half the battle. To ensure its value for both SEO and AI, validation and intelligent utilization are crucial. Poorly structured or invalid data can be detrimental, leading to ignored rich results in search and feeding misinformation to your LLMs.

Validating Structured Data

After you extract Schema.org data with Python, it’s imperative to validate it against official standards. Google offers two primary tools, and several third-party alternatives fill the gaps left by Google’s deprecation of its original Structured Data Testing Tool (SDTT).

Google Rich Results Test (RRT): This tool validates structured data specifically for Google’s rich snippet eligibility. It renders JavaScript content, allowing you to test dynamic pages. Its limitation is that it only validates Google-approved schema types, not the full Schema.org vocabulary.
Schema Markup Validator (SMV): Hosted by Schema.org, this tool validates against official Schema.org standards. While comprehensive for schema adherence, it does not render JavaScript and doesn’t confirm eligibility for Google’s rich results.
Sitebulb / Classy Schema Viewer: Third-party tools like Sitebulb offer comprehensive audits that simultaneously validate against both Schema.org standards and Google Rich Results guidelines, often including JavaScript rendering.

For optimal results, we recommend a two-pronged approach: validate against Schema.org standards for correctness and then against Google’s Rich Results Test for maximum visibility. This ensures your structured data is both technically sound and impactful.

Utilizing Clean, Validated Data for RAG

Once validated, this clean, structured data becomes a powerful asset for your RAG pipelines. Instead of embedding entire web pages, you can embed just the relevant Schema.org entities into your vector database.

For instance, an article’s headline, author, datePublished, and keywords can be explicitly extracted and stored. When an LLM query comes in, the RAG system retrieves not just raw text, but semantically tagged information, leading to more accurate and concise answers. This directly contributes to llm hallucination reduction and boosts overall rag architecture best practices.

SearchCans Advantage for Structured Data Extraction

Building a robust pipeline to extract Schema.org data with Python at scale is challenging. SearchCans offers a unique dual-engine infrastructure designed to simplify this for AI Agents and RAG systems.

Parallel Search Lanes vs. Restrictive Rate Limits

Most web scraping solutions impose strict hourly rate limits, bottlenecking your AI Agents and preventing them from operating continuously during bursty workloads. SearchCans operates on a Parallel Search Lanes model. This means you are limited by the number of simultaneous requests you can have in-flight, not by an arbitrary hourly cap. With SearchCans, your agents can run 24/7 as long as a lane is open, allowing for true high-concurrency access ideal for demanding AI workloads. Unlike competitors who cap your hourly requests (e.g., 1000/hr), SearchCans lets you run continuous parallel searches, preventing your agents from queuing. For ultimate scale, our Ultimate Plan offers Dedicated Cluster Nodes for zero-queue latency.

LLM-Ready Markdown for Token Optimization

The Reader API’s core strength is converting any URL into clean, LLM-ready Markdown. This process not only preserves embedded structured data like JSON-LD but also strips away unnecessary HTML boilerplate, ads, and irrelevant content. This results in an average ~40% token cost saving compared to feeding raw HTML to your LLMs. In the world of AI, where every token counts, this translates to significant cost reductions and faster processing for your RAG pipelines. It’s a key strategy for llm token optimization and improving context window efficiency.

Cost-Effectiveness and Transparent Pricing

When evaluating tools to extract Schema.org data with Python, Total Cost of Ownership (TCO) is critical. While DIY solutions seem cheaper initially, they incur hidden costs in proxy management, server upkeep, and developer maintenance time. SearchCans offers highly competitive, pay-as-you-go pricing, starting from $0.56 per 1,000 requests (Ultimate Plan). This is significantly more affordable than traditional scraping APIs, as demonstrated in our cheapest SERP API comparison.

Provider	Cost per 1k Requests (approx.)	Cost per 1M Requests (approx.)	Overpayment vs SearchCans Ultimate
SearchCans (Ultimate)	$0.56	$560	—
SerpApi	$10.00	$10,000	💸 18x More (Save $9,440)
Bright Data	$3.00	$3,000	5x More
Serper.dev	$1.00	$1,000	2x More
Firecrawl	~$5-10	~$5,000	~10x More

This comparison highlights why SearchCans is the preferred choice for developers and enterprises seeking to scale their AI Agent and RAG infrastructure without breaking the bank. Our Reader API, the engine for structured data extraction, consumes 2 credits per normal request (5 for bypass), making it incredibly efficient for high-volume data ingestion.

Pro Tip: SearchCans is a “Transient Pipe” for Enterprise Safety

CTOs and data privacy officers frequently worry about data leakage when integrating third-party APIs. Unlike many other scrapers, SearchCans operates as a transient pipe. We do not store, cache, or archive your payload data. Once the processed Markdown or JSON data is delivered to you, it’s discarded from our RAM. This data minimization policy ensures GDPR and CCPA compliance, making SearchCans a secure choice for enterprise RAG pipelines and sensitive data operations.

The “Not For” Clause: Setting Expectations

While the SearchCans Reader API is exceptional for converting URLs to LLM-ready Markdown and extracting embedded structured data, it’s important to clarify its scope. SearchCans is NOT a full-browser automation testing tool like Selenium or Cypress. It focuses solely on content extraction for AI consumption. If your use case involves intricate UI testing, complex form submissions, or persistent user sessions that require full browser control, then dedicated automation tools would be more appropriate. Our strength lies in clean, scalable, real-time data delivery for AI.

Frequently Asked Questions

What is Schema.org data and why is it important for AI?

Schema.org data is a standardized vocabulary embedded in web pages that explicitly defines content entities and their relationships, such as a product’s name, price, or an event’s date. For AI, it’s crucial because it provides unambiguous, machine-readable context, significantly reducing LLM hallucinations and improving the accuracy of RAG systems by offering clear, structured knowledge.

How do I extract JSON-LD from dynamic websites using Python?

Extracting JSON-LD from dynamic, JavaScript-rendered websites requires a headless browser to execute the JavaScript before parsing. Traditional Python libraries like requests and BeautifulSoup fail here. Solutions involve using Selenium or Playwright for full browser control, or more efficiently, using an API like SearchCans Reader API, which handles the headless browser rendering for you and returns LLM-ready Markdown containing the parsed JSON-LD.

What are the benefits of using LLM-ready Markdown for Schema.org data?

LLM-ready Markdown, particularly from SearchCans, offers two key benefits: token optimization and semantic clarity. By stripping extraneous HTML, it reduces the context window size required for LLMs, saving up to 40% in token costs. Moreover, Markdown’s clean, structured format makes the Schema.org data more semantically accessible for LLMs, improving their understanding and reducing factual errors in RAG outputs.

How does SearchCans handle rate limits for large-scale data extraction?

SearchCans eliminates traditional hourly rate limits by employing a Parallel Search Lanes model. Instead of capping requests per hour, we allow a fixed number of simultaneous, in-flight requests. As long as a lane is open, you can send requests continuously 24/7, making it ideal for bursty AI workloads that require high-concurrency access without arbitrary throttling.

Can SearchCans extract other forms of structured data beyond JSON-LD?

Yes, while JSON-LD is often the most critical for AI, SearchCans Reader API processes the entire visible content of a webpage into Markdown. This means any other structured data (like Microdata or RDFa) that contributes to the visible content will also be represented in the clean Markdown output. You can then parse this Markdown to extract these structured elements, alongside other key information for your AI Agent.

Conclusion

Mastering the extraction of Schema.org data is no longer a niche SEO trick; it is a fundamental requirement for building intelligent, accurate, and cost-efficient AI Agents and RAG systems. By moving beyond simplistic scraping and embracing solutions that deliver clean, structured, and LLM-ready data, you equip your AI with the explicit context it needs to excel.

Stop bottling-necking your AI Agent with rate limits and feeding it messy HTML that inflates token costs. Get your free SearchCans API Key (includes 100 free credits) and start running massively parallel searches to extract high-quality, structured data for your next-generation AI applications today.