Practical overview
When it fits
Use it when a development workflow needs prompt-driven extraction or crawling in Python and can provide an LLM configuration for the pipeline.
Who it is for
- Python developers building AI-assisted web scraping workflows
- Teams collecting structured research data from websites
- Developers preparing web content for Markdown or RAG pipelines
Documented uses
- Extract prompted fields from a webpage
- Crawl or search web content for collection workflows
- Convert webpages into Markdown for downstream processing
- Prepare web-based source material for retrieval-augmented generation
Requirements & limits
The repository is public and lists an MIT license. The documented installation uses pip, with Playwright installation recommended for fetching website content.
- Python is the primary implementation language.
- The documented workflows use LLM configuration and can use different model providers.
- Browser setup is part of the documented website-fetching installation path.
- The project supports website and local-document pipelines, including XML, HTML, JSON, and Markdown.
- The repository documents integrations beyond the core Python library, including SDKs, LLM frameworks, low-code tools, and an MCP server.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install ScrapeGraphAI and prepare browser fetching
Install the Python library and the documented browser dependency needed for fetching website content.
Before you start
- Python environment
- A recommendation in the repository to use a virtual environment
- Access to the Python package installation command
Steps
Install the package with pip install scrapegraphai.
Install the browser dependency with playwright install.
Use a virtual environment to reduce conflicts with other libraries.
Expected outcome
The library and its documented browser-fetching dependency are installed and ready for a Python scraping workflow.
Guide limitations
- This recipe documents setup only; it does not verify a completed scrape.
- The repository does not specify a single LLM provider as mandatory for every workflow.
Configure and run a single-page SmartScraperGraph extraction
Create the documented pipeline for extracting prompted information from one webpage.
Before you start
- An installed ScrapeGraphAI environment
- An LLM configuration
- A source webpage URL
- A Python script using SmartScraperGraph
Steps
Import SmartScraperGraph from scrapegraphai.graphs.
Define an LLM configuration, including the model and relevant settings.
Create SmartScraperGraph with an extraction prompt, source URL, and configuration.
Run the pipeline and inspect the returned dictionary.
Expected outcome
The documented single-page pipeline returns structured extraction results such as descriptions, founder records, and social-media links when those fields are requested.
Guide limitations
- The example requires an LLM configuration; the documented alternatives use different configuration values.
- The example demonstrates one source URL and does not establish results for every website or document type.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 30 September 2026
- Original website title
- ScrapeGraphAI/Scrapegraph-ai · Official homepage title · retained in its source language