Skip to content
Stork.AIstork.ai
AI ToolBecomes the API

ScrapeGraphAI Review

ScrapeGraphAI is an LLM-powered web scraping API and suite that extracts structured data from websites using natural language prompts.

shipped Jul 3, 2026codepaid
Domain rating50Monthly visits1.3K/mo
codeimage-generationagents
ScrapeGraphAI — product screenshot

Why it matters

1Offers a free tier with 500 API credits.
2Provides a developer API with comprehensive documentation at https://docs.scrapegraphai.com/introduction.
3Achieved SOC 2 Type 2 Compliance, ensuring data security standards.
4Founded in 2022, with active development including ScrapeGraphAI V2 released around April 2026.

Stork Quadrant

Becomes the API· 25/100

Replaceable as a UI, but kept alive as the API the agents call.

“ScrapeGraphAI is a thin wrapper around LLM capabilities that any builder can replicate in minutes with Claude or GPT-4 directly. The core promise—use an LLM to read a webpage and extract data—is not defensible because the LLM is doing the work, not the tool. Once agents can call browsers natively, this product evaporates.”

— Claude Haiku 4.5, scored 2026-07-08

Defensibility · 0/100

  • Physical-world coupling
  • Regulatory moat
  • Network liquidity
  • Proprietary refreshing data
  • High-trust catastrophic workflows
  • Multi-party coordination
  • Brand / community / taste

An LLM alone could replace

  • Extract structured data from a static or semi-dynamic webpage using natural language instructions
  • Convert unstructured HTML into JSON or CSV format via LLM interpretation
  • Handle selector-free scraping by having an LLM read and parse page content
  • Generate scraping logic on-the-fly without pre-built templates or rules

Agent-Readiness · 55/100

  • Verified MCP
  • Listed on agent surfaces
  • Usage-based pricing— pricing page heuristic match: https://scrapegraphai.com/pricing
  • Headless agent auth— https://docs.scrapegraphai.com/introduction (api-key auth)
  • Public OpenAPI— https://docs.scrapegraphai.com/introduction
  • Active changelog— https://scrapegraphai.com/blog/scrapegraphai-v2 (2026-04-22)
  • llms.txt— https://scrapegraphai.com/llms.txt

How to defend

Pivot to a data moat: build a refreshing index of scraped data (pricing, inventory, job listings, real estate) that updates hourly and becomes the source of truth. Or become the orchestration layer for multi-step scraping workflows where you handle retries, rate limiting, and structured output validation at scale—moving from UI to infrastructure.

  • Ship an MCP server and list it on Stork — biggest single point gain (+25).
  • Get listed in the Anthropic MCP registry, Cursor, or Claude Desktop (+20).
How this score is computed →See the full quadrantHow to defend

About ScrapeGraphAI

Business Model
Subscription SaaS
Free Credits
500 API credits
Founded
2022
Funding
Seed
Platforms
Web, API
Target Audience
Individuals, startups, and teams needing web data extraction services.

Pricing Plans

Free Plan
$0 / monthly
  • • 500 API credits, one-time
  • • 10 requests/min
  • • 1 monitor
  • • 1 concurrent crawl
Starter Plan
$20/mo
  • • 10,000 API credits per month
  • • 100 requests/min
  • • 5 monitors
  • • 3 concurrent crawls
Growth Plan
$100/mo
  • • 100,000 API credits per month
  • • 500 requests/min
  • • 25 monitors
  • • 15 concurrent crawls
Pro Plan
$500/mo
  • • 750,000 API credits per month
  • • 5,000 requests/min
  • • 100 monitors
  • • 50 concurrent crawls
Enterprise
Custom / custom billing
  • • Ad-hoc credits
  • • Custom rate limits
  • • Dedicated support
  • • SLA guarantee

Leadership

Lorenzo PadoanCo-Founder
API DocsGitHubOpen Source

Specs

API Docs

View Documentation →

GitHub

View Repository →

API Available

Yes, public API

Screenshots

overview

What is ScrapeGraphAI?

ScrapeGraphAI is an AI-powered web scraping tool developed by Lorenzo Padoan (Co-Founder) that enables AI developers, data analysts, and businesses to extract structured data from websites and local documents using natural language prompts. It leverages graph-based AI for intelligent, selector-free web scraping tailored to dynamic content extraction.

features

Key Features of ScrapeGraphAI

ScrapeGraphAI integrates several core functionalities to facilitate advanced web data extraction. Its architecture is designed to handle complex web environments and provide reliable, structured output.

  • AI-Powered Extraction: Utilizes Large Language Models (LLMs) for data extraction based on natural language prompts.
  • Zero Maintenance: Automatically adapts to website structural changes, eliminating the need for constant script updates.
  • Automatic Proxy Management: Handles proxy rotation and management to bypass IP blocks and maintain anonymity.
  • JavaScript Rendering: Capable of rendering dynamic web content, essential for modern, interactive websites.
  • Built-in Rate Limiting: Manages request frequency to avoid overwhelming target servers and prevent bans.
  • Cloud-Ready API: Offers a scalable API for integration into various applications and workflows.
  • AI-Agent Ready: Designed to feed structured web data directly to AI agents for enhanced decision-making.
  • Enterprise Support: Provides dedicated support and custom solutions for large-scale deployments.

use cases

Who Should Use ScrapeGraphAI?

ScrapeGraphAI is designed for a diverse range of users requiring automated and intelligent web data extraction, particularly those dealing with dynamic web content or large-scale data collection.

  • AI developers: For building datasets for machine learning and AI training, and integrating live web data into AI agents and RAG pipelines.
  • Data analysts and researchers: For market research, competitive intelligence, news article scraping, and general data analysis.
  • Businesses: For e-commerce data extraction, product price tracking, lead generation, and content aggregation.
  • Developers building scraping-powered platforms: Leveraging the API and SDKs for custom solutions and automating data collection.

how to use

How to Use ScrapeGraphAI

ScrapeGraphAI can be utilized via its open-source Python library or its cloud API, allowing users to define extraction tasks using natural language prompts. The process typically involves specifying a URL and the desired data structure.

  • 1Install the ScrapeGraphAI Python library (e.g., pip install scrapegraphai) or obtain an API key for cloud access.
  • 2Provide a target URL, HTML content, or local document path (XML, JSON, Markdown).
  • 3Define the desired data extraction using natural language prompts, specifying the information to be retrieved.
  • 4Execute the scraping pipeline, which leverages LLMs and graph logic to process the content.
  • 5Receive structured data output, typically in JSON format, ready for further analysis or integration.
  • 6Integrate the extracted data into AI agents, machine learning models, or other applications via SDKs or webhooks.

pricing

ScrapeGraphAI Pricing & Plans

ScrapeGraphAI operates on a freemium model, offering a free tier with limited credits and several paid subscription plans tailored to different usage volumes and feature requirements. All paid plans include access to the API and advanced features.

  • Free Plan: $0 per month, includes 500 API credits.
  • Starter Plan: $20 per month, designed for individual developers and small projects.
  • Growth Plan: $100 per month, suitable for growing teams with increased data needs.
  • Pro Plan: $500 per month, for professional use and higher volume data extraction.
  • Enterprise: Custom pricing, tailored for large organizations requiring specific features, high volume, and dedicated support.

Pros

  • +LLM-powered, selector-free data extraction significantly reduces reliance on traditional CSS selectors or XPath queries.
  • +Automatic adaptation to website structural changes minimizes maintenance efforts and broken scraping scripts.
  • +Open-source Python library and cloud API offer flexible deployment options for various development environments.
  • +Capable of extracting structured data from dynamic content, HTML, XML, JSON, and local documents.
  • +SOC 2 Type 2 Compliant, providing a verified standard for data security and operational integrity.
  • +Active development with significant updates, such as ScrapeGraphAI V2 and new SDKs, enhancing performance and features.

Cons

  • −Reported inconsistencies between the UI Web Playground and API results may necessitate prompt tweaking for desired outcomes.
  • −Some users have noted issues with founder communication and responsiveness to support inquiries.
  • −Requires Python knowledge and understanding of API keys and JSON output, posing a learning curve for purely non-technical users.
  • −The credit system and associated costs can accumulate, particularly with advanced features or 'stealth mode' usage.
  • −Primarily scrapes HTML content around videos and does not process video content itself, such as transcripts or audio.

Policies

Pricing Page

View Pricing→

Similar Tools

ScrapeGraphAI vs Competitors

ScrapeGraphAI competes in the web scraping and data extraction market, differentiating itself through its LLM-powered, graph-based approach to selector-free data extraction. Its primary competitors offer varying strengths in anti-bot measures, platform comprehensiveness, or specific output formats.

1
FirecrawlOn Stork

Firecrawl specializes in converting URLs into clean, LLM-ready Markdown or structured JSON, making it ideal for RAG pipelines and AI agents.

Similar to ScrapeGraphAI, Firecrawl offers LLM-based extraction with natural language prompts for structured data. However, Firecrawl is particularly strong when the primary output needed is Markdown for documentation ingestion or agent context, whereas ScrapeGraphAI emphasizes a broader API for various extraction and monitoring tasks.

2
ApifyOn Stork

Apify provides a comprehensive scraping and automation platform with a marketplace of pre-built 'Actors' for specific scraping tasks, alongside options for custom code and LLM enrichment.

While ScrapeGraphAI focuses on an LLM-powered API for selector-free scraping, Apify offers a broader platform with both no-code pre-built solutions and an SDK for more complex, scalable scraping with LLM integration. Apify is well-suited for known targets with existing actors or when a general scraping platform is desired.

3
ScrapflyOn Stork

Scrapfly excels at bypassing anti-bot protections on challenging websites while providing AI-powered data extraction with natural language prompts.

Scrapfly directly competes with ScrapeGraphAI in LLM-powered data extraction using natural language instructions. Its key advantage over ScrapeGraphAI lies in its robust anti-bot bypass capabilities, ensuring reliable data access from protected sites where other LLM extraction tools might fail.

4
ScrapingBee (AI Web Scraping API)↗

ScrapingBee's AI Web Scraping API allows users to extract structured data using natural language descriptions, automatically adapting to website changes and bypassing anti-bot measures.

Like ScrapeGraphAI, ScrapingBee offers an AI-powered API for structured JSON output from web pages using natural language prompts. It emphasizes ease of use with no coding required for basic extraction and includes built-in capabilities for handling dynamic pages and anti-bot barriers, similar to ScrapeGraphAI's focus on dynamic content.

5
Skrape.ai↗

Skrape.ai uses an LLM to extract data based on a user-defined JSON schema, offering a structured approach to AI-powered scraping.

Skrape.ai is a direct competitor to ScrapeGraphAI, leveraging LLMs for data extraction. While ScrapeGraphAI uses natural language prompts for selector-free extraction, Skrape.ai requires users to define a JSON schema in its playground, which then guides the LLM in scraping the desired data.

More on Stork

Related AI Tools

Other tools in this category, matched by shared tags

HumainumEverMeRoomagineHRmony.aiSutols 3D Ai SlidesEaseGame
Visit ScrapeGraphAI↗
Connect
𝕏
X / Twitterx.com/scrapegraphai
⌘
GitHubgithub.com/ScrapeGraphAI
in
LinkedInwww.linkedin.com/company/101881123/
💬
Discorddiscord.gg/JqvBb7wV8j