web-scraper

Agentic Web Scraper Expert (2026 Edition)

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English

Orchestration & Integration

Connects and orchestrates with relevant domain skills like browser-automation-expert, ai-llm-integration-expert, brainstorming, and zero-to-prod-orchestrator to ensure cohesive agentic execution.

Description

Advanced Agentic Web Scraping utilizing modern multi-strategy data extraction. Leverages Crawl4AI v4 and Firecrawl to convert raw DOMs into LLM-friendly Markdown. Implements Agentic Extraction loops where the LLM guides the scraper dynamically based on page state. Incorporates strategies for bypassing anti-bot measures (Cloudflare Turnstile, Datadome) and navigating dynamic Shadow DOMs.

Trigger Conditions

• Extracting structured data from websites for analysis, training data, or content pipelines.
• Scraping dynamic JavaScript-rendered pages and complex SPAs.
• Converting web pages to clean Markdown for LLM context or RAG pipelines.
• Dealing with anti-bot protections or complex Shadow DOM architectures during scraping.
• Implementing an automated agentic data extraction loop.

Extracting DOM into LLM-Friendly Markdown

Use Crawl4AI v4 for high-performance async extraction and Firecrawl for seamless LLM-ready conversion.

Crawl4AI v4 (Async Python):

import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode

async def extract_markdown(url: str):
    config = BrowserConfig(headless=True, bypass_csp=True)
    run_config = CrawlerRunConfig(
        cache_mode=CacheMode.ENABLED,
        remove_overlay_elements=True,
        word_count_threshold=50
    )
    
    async with AsyncWebCrawler(config=config) as crawler:
        result = await crawler.arun(url=url, config=run_config)
        # Returns clean, AI-optimized markdown ready for LLM consumption
        return result.markdown.fit_markdown

Firecrawl (Managed API):

from firecrawl import FirecrawlApp
from pydantic import BaseModel

app = FirecrawlApp(api_key="fc-xxxx")

class ExtractionSchema(BaseModel):
    title: str
    content: str
    key_metrics: list[str]

# Single API call to extract structured data based on JSON schema
result = app.scrape_url(
    "https://example.com/data",
    formats=["extract", "markdown"],
    extract={"schema": ExtractionSchema.model_json_schema()}
)
print(result.markdown) # Clean markdown
print(result.extract)  # Structured JSON