ScrapeGraphAI
ScrapeGraphAI provides AI-powered web data infrastructure that turns websites into clean, structured data using natural-language prompts. Its open-source library and managed cloud API are designed for AI agents, data analytics, research, and automation workflows.
At a Glance
- Developers and AI-agent builders
- Startups and bootstrapped companies
- Data scientists and analytics teams
- Enterprise data and research teams
- +2 more
AI Tools by ScrapeGraphAI
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ScrapeGraphAI
Python LLM Web Scraping Library
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Latest News
ScrapeGraphAI announces SOC 2 Type 2 compliance, an enterprise security milestone.
ScrapeGraphAI announces a partnership with Orthogonal, an API marketplace for autonomous agents.
ScrapeGraphAI publishes its MCP Server integration for Claude, Cursor, and other MCP clients.
ScrapeGraphAI announces the Hivemind/Activeloop agent-skills collaboration.
Products & Services
MIT-licensed, self-hosted Python library using LLMs and graph logic to build scraping pipelines for websites and local XML, HTML, JSON, and Markdown documents.
Hosted cloud API with scrape, extract, search, crawl, monitor, events, and history capabilities, managed rendering, proxy/anti-bot support, and pay-per-credit billing.
Scrape converts URLs to Markdown, HTML, screenshots, links, images, summaries, or branding analysis; Extract returns structured JSON from natural-language prompts and optional schemas.
Search combines web search with extraction; Crawl explores multi-page sites; Monitor performs recurring checks and change detection with webhook notifications.
Market Position
ScrapeGraphAI is positioned as an LLM-native alternative to selector-based scraping and general scraping infrastructure: users describe the desired data in natural language and can receive schema-validated JSON, while the managed service adds rendering, proxy rotation, crawling, monitoring, SDKs, MCP, and agent integrations. The broader AI web-scraping comparison market includes Firecrawl, Apify, Browse.AI, Browserless, Hyperbrowser, Octoparse, and Bright Data.
Leadership
Founders
Marco Perini
Founder and technical lead; identified in the project README via the Perini LinkedIn profile (linkedin.com/in/perinim/).
Marco Vinciguerra
Founder and software engineer; his LinkedIn profile describes him as Founder of ScrapeGraphAI, and the project README lists him as Founder & Software Engineer.
Lorenzo Padoan
Co-founder and CEO/product engineer; his LinkedIn profile identifies him as Co-founder - CEO of ScrapeGraphAI and previously a Research Engineer at Motion Analytica.
Executive Team
Lorenzo Padoan
Co-founder and CEO
Previously a Research Engineer at Motion Analytica; ScrapeGraphAI LinkedIn identifies him as Co-founder - CEO.
Marco Vinciguerra
Founder and Software Engineer
Founder of ScrapeGraphAI and software engineer; his LinkedIn profile says he studied at Università degli Studi di Bergamo.
Founding Story
ScrapeGraphAI began as an open-source Python project in 2024 to simplify web and document extraction by combining large-language models with direct graph logic, replacing brittle, selector-heavy scraping pipelines with natural-language-driven workflows. It subsequently expanded into a managed cloud API for production use.
Business Model
Revenue Model
The managed cloud API uses subscription plans that include monthly API credits, plus one-time credit top-ups; usage is billed in credits by endpoint and output format. The open-source library is MIT licensed and self-hosted, while the managed API is paid.
Pricing Tiers
500 one-time API credits, 10 requests/minute, 1 monitor, and 1 concurrent crawl.
10,000 API credits/month, 100 requests/minute, 5 monitors, and 3 concurrent crawls.
100,000 API credits/month, 500 requests/minute, 25 monitors, 15 concurrent crawls, and basic proxy rotation.
750,000 API credits/month, 5,000 requests/minute, 100 monitors, 50 concurrent crawls, advanced proxy rotation, and priority support.
Ad-hoc credits, custom rate limits, dedicated support, and SLA guarantee.
1,000 credits for $5, 10,000 for $40, or 50,000 for $150; packs do not expire.
Target Markets
- Developers and AI-agent builders
- Startups and bootstrapped companies
- Data scientists and analytics teams
- Enterprise data and research teams
- Sales and marketing teams
- Businesses in e-commerce, market research, real estate, and automation
- AI-agent web research and live web access
- Lead enrichment and lead generation
- Product and price monitoring
- Market research and competitor intelligence
- Product/catalog and review extraction
- LLM training datasets and structured-output research
- Waveful, which used ScrapeGraphAI to identify high-reach creators across its 4M+ user base
- Vibiz, which reported reaching $750K ARR in six months using ScrapeGraphAI for lead discovery, brand knowledge, and fresh web data
- Hundreds of startups, according to ScrapeGraphAI's startup program page