Scrapegraph-ai
ScrapeGraphAI is a Python library that uses large language models (LLMs) to automate web scraping and data extraction from websites and documents. Users define what data they want via natural language prompts, and the library handles the extraction logic without writing traditional scraping code.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | ScrapeGraphAI/Scrapegraph-ai |
| Owner | ScrapeGraphAI |
| Primary language | Python |
| License | MIT — OSI-approved |
| Stars | 28.2k |
| Forks | 2.7k |
| Open issues | 1 |
| Latest release | v2.1.4 (2026-06-23) |
| Last updated | 2026-06-25 |
| Source | https://github.com/ScrapeGraphAI/Scrapegraph-ai |
What Scrapegraph-ai is
Built on Python with LLM-driven graph-based pipelines, ScrapeGraphAI orchestrates web scraping tasks by delegating extraction logic to configurable LLMs (OpenAI, Groq, Gemini, Ollama, etc.) and uses Playwright for browser automation. It supports multiple pipeline types (SmartScraperGraph, SearchGraph, SmartScraperMultiGraph, etc.) for single-page and multi-page extraction workflows.
Get the Scrapegraph-ai source
Clone the repository and explore it locally.
git clone https://github.com/ScrapeGraphAI/Scrapegraph-ai.gitcd Scrapegraph-ai# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires Playwright installation and browser setup (`playwright install`). Plan for browser driver maintenance and potential OS-level dependencies.
- LLM API costs accumulate per scraping run. Estimate token usage and select cost-effective models (e.g., `gpt-4o-mini`) or local Ollama instances for high-volume workloads.
- Extraction quality depends on prompt clarity and LLM capability. Iterative prompt engineering and output validation are necessary.
- Multi-page workflows use parallel LLM calls; monitor API rate limits and costs. Implement retry logic and error handling for failed requests.
- Extracted data may contain inconsistencies or hallucinations. Always validate outputs against source data, especially in production pipelines.
When to avoid it — and what to weigh
- Need guaranteed extraction accuracy or legal compliance — LLM-driven extraction can hallucinate or misinterpret data. For regulated industries (finance, healthcare, legal), validate all outputs and do not rely on this as a sole source of truth without human review.
- Scraping high-volume, low-latency requirements — LLM inference adds latency. If you need sub-second extraction of thousands of pages, traditional parsers or specialized crawlers are more suitable.
- Extensive anti-bot or JavaScript rendering complexity — While Playwright is included, the library does not offer the anti-detection or stealth features of managed crawling services. Heavy JS-rendered sites may require additional configuration.
- Zero API dependencies or offline-only requirements — Most practical configurations require external LLM APIs (OpenAI, Groq, Gemini). Local Ollama is an option, but self-hosting Ollama introduces infrastructure overhead.
License & commercial use
Licensed under MIT (MIT License), a permissive open-source license allowing commercial use, modification, and distribution with minimal restrictions. No patent clauses.
MIT license permits commercial use without restrictions. However, ensure compliance with terms of service of external LLM providers (OpenAI, Groq, etc.) and websites being scraped. Web scraping legality varies by jurisdiction and target site; obtain necessary permissions and review ToS. The library itself poses no commercial licensing barrier, but operational and legal responsibility rests with the user.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
Self-hosted deployment requires standard Python application security practices: isolate API keys in environment variables, validate LLM inputs to prevent prompt injection attacks, and sanitize user-supplied prompts. Browser automation (Playwright) runs in user-controlled environment; monitor for malicious websites during testing. External LLM API calls transmit website content and prompts to third-party services; review data privacy with your organization. No explicit security audit or vulnerability disclosure program mentioned; monitor GitHub for CVEs. Local Ollama deployments do not transmit data externally but require dedicated resources and model validation.
Alternatives to consider
Firecrawl
Managed API-first alternative with built-in anti-bot handling, headless browser rendering, and structured data extraction. Suitable if you prefer not to manage LLM integration or Playwright dependencies. Typically commercial (SaaS).
Beautiful Soup + Selenium / Scrapy
Traditional Python scraping stack offering more control over parsing logic and explicit error handling. Lower latency and no LLM costs, but requires manual XPath/CSS selector writing and more boilerplate code. Best for deterministic, stable website structures.
Anthropic Claude via direct API + custom parsing
Lightweight alternative: send raw HTML to Claude and parse structured responses without graph-based orchestration. Simpler for one-off extractions but lacks multi-page coordination, parallel execution, and tool ecosystem of ScrapeGraphAI.
Build on Scrapegraph-ai with DEV.co software developers
Start with the quick install guide, explore example prompts, and validate extraction quality on your target websites. For production use, plan LLM costs and implement robust validation workflows.
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Scrapegraph-ai FAQ
Do I need to provide my own LLM API key?
Is this library production-ready?
What are the main differences between the open-source library and the managed API?
How much does it cost to use this?
Custom software development services
Adopting Scrapegraph-ai is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate ai frameworks software in production.
Ready to build intelligent scraping pipelines?
Start with the quick install guide, explore example prompts, and validate extraction quality on your target websites. For production use, plan LLM costs and implement robust validation workflows.