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ScrapeGraphAI
LLM‑powered web scraping pipelines in just five lines of code
Prompt‑driven Python library that turns websites and documents into structured data using LLMs, with multi‑page, parallel, and low‑code integrations.
Overview
Overview
ScrapeGraphAI lets developers, data scientists, and low‑code users extract structured information from web pages or local documents with a single prompt. By combining large language models with graph‑based logic, it abstracts away HTML parsing and navigation, delivering clean JSON output in minutes.
Capabilities
The library ships with several ready‑made pipelines—SmartScraperGraph for single pages, SearchGraph for top‑N search results, SpeechGraph for audio summaries, and ScriptCreatorGraph for auto‑generated Python scripts. Multi‑graph variants run LLM calls in parallel, boosting throughput. It supports OpenAI, Groq, Azure, Gemini, and local Ollama models, and integrates with LangChain, LlamaIndex, and popular no‑code platforms like Zapier and n8n.
Deployment
Install via pip install scrapegraphai and set up Playwright for rendering. Choose the Python or Node SDK, configure your LLM credentials, and start scraping in five lines of code. Telemetry is optional and can be disabled with an environment variable.
Highlights
Prompt‑driven scraping pipelines requiring minimal code
Multi‑page and parallel graph execution for higher throughput
Supports major LLM providers and local Ollama models
Python and Node SDKs plus low‑code platform integrations
Pros
- Very low entry barrier – start with five lines of code
- Flexible LLM backend, works with cloud APIs or local models
- Rich ecosystem of SDKs and no‑code connectors
- Open‑source and extensible for custom pipelines
Considerations
- Requires LLM API keys or local model setup, adding cost or complexity
- Depends on Playwright for page rendering, adding a runtime dependency
- Telemetry is enabled by default, which may raise privacy concerns
- Overall speed is tied to LLM response latency
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Fit guide
Great for
- Data scientists needing quick structured data from websites
- Developers building AI agents that consume web content
- Low‑code automation platforms seeking web‑scraping capabilities
- Researchers prototyping scraping workflows without deep HTML knowledge
Not ideal when
- High‑frequency large‑scale crawling where LLM costs become prohibitive
- Environments without internet access for external LLM APIs
- Users requiring fine‑grained HTML parsing control
- Projects that must avoid any telemetry collection
How teams use it
Extract company profiles from competitor websites
Structured JSON containing description, founders, and social media links
Generate Python scripts that scrape product listings
Ready‑to‑run script saved to file for repeated execution
Create audio summaries of news articles
MP3 file with spoken summary generated from a single page
Automate multi‑page research across search results
Consolidated dataset compiled from the top N search results
Tech snapshot
Python100%
Makefile1%
Dockerfile1%
Frequently asked questions
Do I need an OpenAI account to use ScrapeGraphAI?
No. The library works with any supported LLM, including OpenAI, Groq, Azure, Gemini, or local Ollama models.
How can I run the library locally without external APIs?
Install Ollama, pull a model (e.g., llama3.2), and configure the `llm` section with the local model name.
What browser engine does ScrapeGraphAI use for rendering?
It relies on Playwright; install it via `playwright install` after adding the package.
Can I disable telemetry collection?
Yes. Set the environment variable `SCRAPEGRAPHAI_TELEMETRY_ENABLED=false` before running the library.
Is there a Node.js version of the SDK?
Yes, a Node SDK (scrapegraph-js) is available for integration in JavaScript projects.
Project at a glance
Active- Stars
- 22,341
- Watchers
- 22,341
- Forks
- 1,942
LicenseMIT
Repo age1 year old
Last commityesterday
Primary languagePython
Last synced 3 hours ago