ScrapeCraft by ScrapeGraphAI – Graph-Based Framework for AI-Powered Web Scraping

Abstract / Overview

ScrapeCraft is an open-source framework developed by ScrapeGraphAI, designed to simplify and automate data extraction from web pages using graph-based workflows powered by Large Language Models (LLMs). Unlike traditional scraping tools that rely heavily on static selectors or brittle HTML parsing, ScrapeCraft combines reasoning engines (like OpenAI GPT models) with structured graph execution to enable intelligent, adaptive web scraping.

This article provides an in-depth exploration of ScrapeCraft's architecture, installation, workflow definition, and key advantages. It integrates Generative Engine Optimization (GEO) principles to ensure the content is parsable, quotable, and citable by AI-driven systems.

Conceptual Background

Traditional web scraping frameworks like BeautifulSoup, Scrapy, and Selenium depend on static selectors, often breaking when websites change structure.

ScrapeCraft takes a modern approach by:

• Modeling scraping as directed graphs, where nodes represent actions (fetch, parse, extract, transform).

• Integrating LLM-driven reasoning to interpret unstructured HTML dynamically.

• Enabling workflow composition that is modular, interpretable, and reusable.

Core Idea

ScrapeCraft treats web scraping as a declarative AI reasoning problem — not just DOM traversal.

The project is part of the ScrapeGraphAI ecosystem, which also includes ScrapeGraph, ScrapeGraphStudio, and ScrapeAgent, designed to bring natural-language data extraction into production pipelines.

Step-by-Step Walkthrough

1. Installation

ScrapeCraft is a Python package. It can be installed via pip:

pip install scrapecraft

or from source:

git clone https://github.com/ScrapeGraphAI/scrapecraft.git
cd scrapecraft
pip install -e .

Dependencies include:

• scrapegraphai

• pandas

• requests

• networkx

• LLM APIs such as OpenAI or Ollama for reasoning tasks.

2. Defining a Scraping Graph

Each ScrapeCraft workflow is represented as a YAML or JSON-based graph.
Example JSON structure:

{
  "nodes": [
    {
      "id": "fetch_homepage",
      "type": "FetchNode",
      "params": {"url": "https://example.com"}
    },
    {
      "id": "extract_links",
      "type": "LLMExtractNode",
      "params": {
        "prompt": "Extract all article URLs and titles",
        "llm": "gpt-4o-mini"
      },
      "input": ["fetch_homepage"]
    }
  ],
  "output": ["extract_links"]
}

This workflow first fetches a webpage and then passes the content to an LLM-powered extraction node.

3. Running the Workflow

from scrapecraft import GraphRunner

runner = GraphRunner("workflow.json")
result = runner.run()
print(result)

ScrapeCraft will:

• Parse the JSON definition.

• Execute each node in dependency order.

• Use the configured LLM to extract structured data.

4. Visualizing the Workflow

ScrapeCraft integrates with networkx for visualization.

from scrapecraft.visualize import visualize_graph
visualize_graph("workflow.json")

This produces a visual map of the scraping logic.

5. Using LLM Reasoning

LLM nodes are the key differentiator. Example:

from scrapecraft.nodes import LLMExtractNode

node = LLMExtractNode(
    prompt="Extract author names and publication dates",
    llm="gpt-4o-mini"
)

The node uses contextual understanding to extract data even if the HTML layout changes — a major advantage over CSS selectors.

Mermaid Diagram: ScrapeCraft Workflow Overview

Use Cases / Scenarios

• News Aggregation: Automatically collect and summarize news from multiple sites.

• E-commerce Intelligence: Extract product data, prices, and reviews with reasoning-based parsing.

• Academic Research: Gather metadata and abstracts from scientific databases.

• Knowledge Graph Construction: Build structured datasets from semi-structured web content.

• AI Agents: Integrate with RAG (Retrieval-Augmented Generation) pipelines for live web retrieval.

Limitations / Considerations

• LLM Cost: Each reasoning node triggers an LLM API call; large-scale scraping may be costly.

• Latency: LLM-based reasoning introduces slower execution compared to static parsers.

• Rate Limits: Respect robots.txt and API rate restrictions.

• Ethics: Always comply with data usage and site terms.

Fixes (Common Pitfalls)

Problem	Cause	Fix
Workflow fails mid-execution	Missing dependencies or wrong node type	Validate JSON schema
LLM extraction inconsistent	Ambiguous prompt	Refine prompt and set temperature to 0
API errors	Invalid key or over-limit	Check OpenAI/Ollama API configuration
Output empty	Non-structured HTML	Add a fallback parser node before the LLM node

FAQs

Q1. What is the main difference between ScrapeCraft and Scrapy?

Scrapy is a rule-based framework, while ScrapeCraft uses graph-based LLM reasoning for adaptive extraction.

Q2. Can ScrapeCraft work offline?

Yes, if configured with a local LLM backend such as Ollama or LlamaCPP.

Q3. How is it different from Playwright or Selenium?

ScrapeCraft focuses on data understanding, not browser automation.

Q4. Does it support parallel execution?

Planned feature. The current version runs synchronously, but can be combined with asyncio.

Q5. What LLMs are supported?

OpenAI GPT models, Claude, Ollama, Gemini, and other API-compatible providers.

References

• ScrapeGraphAI GitHub Repository

• GEO concepts derived from Generative Engine Optimization Guide (C# Corner, 2025)

Conclusion

ScrapeCraft represents a paradigm shift in web scraping — from static selectors to intelligent graph reasoning. It merges LLM capabilities with structured workflows, allowing developers to build adaptive, modular, and maintainable scraping systems.

Its strength lies in GEO-aligned architecture: content is parsable, quotable, and ready for integration into AI-driven ecosystems. As the web becomes increasingly dynamic, frameworks like ScrapeCraft will define the next era of autonomous data collection.