scrapydvsscrapegraphai
Scrapyd is a service for running Scrapy spiders. It allows you to schedule spiders to run at regular intervals and also allows you to run spiders on remote machines. It is built in Python, and it is meant to be used in a server-client architecture, where the scrapyd server runs on a remote machine, and clients can schedule and control spider runs on the server using an HTTP API. With Scrapyd, you can schedule spider runs on a regular basis, schedule spider runs on demand, and view the status of running spiders.
You can also see the logs of completed spiders, and manage spider settings and
configurations. Scrapyd also provides an API that allows you to schedule spider runs, cancel spider
runs, and view the status of running spiders.
You can install the package via pip by running pip install scrapyd and
then you can run the package by running scrapyd command in your command prompt.
By default, it will start a web server on port 6800, but you can specify a different port using the
`--port`` option.
Scrapyd is a good solution if you need to run Scrapy spiders on a remote machine, or if you need to schedule spider runs on a regular basis. It's also useful if you have multiple spiders, and you need a way to manage and monitor them all in one place.
for more web interface see scrapydweb
ScrapeGraphAI is a Python library that uses large language models (LLMs) to create web scraping pipelines automatically. Instead of writing CSS selectors or XPath expressions, you describe what data you want in natural language and provide a Pydantic schema — the library handles the rest.
Key features include:
- Natural language extraction Describe what you want to extract in plain English (e.g., "Extract all product names and prices") and the LLM figures out how to find and extract the data.
- Pydantic schema output Define the expected output structure using Pydantic models for type-safe, validated extraction results.
- Graph-based pipeline Built on a directed graph architecture where each node performs a specific task (fetching, parsing, extracting, merging). This makes pipelines modular and debuggable.
- Multiple graph types SmartScraperGraph (single page), SearchGraph (search + scrape), SpeechGraph (audio output), and more specialized pipelines.
- Multiple LLM providers Works with OpenAI, Anthropic, Google, Groq, local models via Ollama, and more.
- HTML and JSON support Can extract data from both HTML pages and JSON API responses.
ScrapeGraphAI is particularly useful for rapid prototyping of scrapers and for extracting data from pages with complex or frequently changing layouts where traditional selectors would be brittle.