# Web Scraping Automation: How to Run Scrapers on a Schedule

![Web Scraping Automation: How to Run Scrapers on a Schedule image](https://www.firecrawl.dev/images/blog/scheduling-scrapers-images/automated-web-scraping-free-2025.jpg)

## Introduction

Web scraping is an essential skill for programmers in this data-driven world. Whether you’re tracking prices, monitoring competitors, or gathering research data, automated web scraping can save you countless hours of manual work. In this comprehensive guide, you’ll learn how to schedule and automate your Python web scrapers using completely free tools and services.

### Why automate your web scraper?

Manual or one-off web scraping can be time-consuming and error-prone. You need to repeatedly run scripts, update data frequently, and sometimes work during off-hours just to gather information.

Scheduling your web scrapers automates this entire process. It collects data at optimal times without manual intervention, ensures consistency, and frees up your valuable time for actual data analysis rather than repetitive data gathering.

### What tools can you use to automate web scrapers in Python?

If you are building scrapers in Python, you have many completely free options to schedule them.

In terms of local scraping, Python’s `schedule` library is very intuitive and fastest to set up. There is also the built-in `asyncio` for concurrent scraping. There are also system-level automation tools like cron jobs for macOS/Linux and the Task Scheduler for Windows.

There are also many cloud-based solutions like GitHub Actions (completely free), PythonAnywhere (free tier), Google Cloud Functions (free tier), and Heroku (free tier with limitations).

In this guide, we will start with basic local scheduling and progress to using GitHub Actions, all while following best practices and ethical scraping guidelines.

Let’s begin by setting up your development environment and writing your first scheduled web scraper.

### Common challenges in automating web scrapers

Scheduling web scrapers is an easy process, as you will discover through this tutorial. The real challenge lies in ensuring that the scrapers don’t break the day after they are put on schedule. Common issues include websites changing their HTML structure, implementing new anti-bot measures, or modifying their robots.txt policies. Additionally, network issues, rate limiting, and IP blocking can cause scheduled scrapers to fail.

For these reasons, it is almost impossible to build long-lasting scrapers written in Python frameworks. But the web scraping landscape is changing as more AI-based tools are emerging, like [Firecrawl](https://www.firecrawl.dev/).

Firecrawl provides an AI-powered web scraping API that can identify and extract data from HTML elements based on semantic descriptions in Python classes. While traditional scrapers rely on specific HTML selectors that can break when websites change, Firecrawl’s AI approach helps maintain scraper reliability over time.

For demonstration purposes, we’ll implement examples using Firecrawl, though the scheduling techniques covered in this tutorial can be applied to any Python web scraper built with common libraries like BeautifulSoup, Scrapy, Selenium, or `lxml`. If you want to follow along with a scraper of your own, make sure to have it in a script and ready to go.

## Prerequisites

This article assumes that you are already comfortable with web scraping and its related concepts like HTML structure, CSS selectors, HTTP requests, and handling rate limits. If you need a refresher on web scraping basics, check out [this introductory guide to web scraping with Python](https://realpython.com/python-web-scraping-practical-introduction/).

Otherwise, let’s jump in by setting up the tools we will use for the tutorial.

### Environment setup

We will mainly use Firecrawl in this article, so, please make sure that you sign up at [firecrawl.dev](https://www.firecrawl.dev/), choose the free plan and get an API token.

Then, create a new working directory on your machine to follow along in this tutorial:

```bash
mkdir learn-scheduling
cd learn-scheduling

```

It is always a best practice to create a new virtual environment for projects. In this tutorial, we will use Python’s `virtualenv`:

```bash
python -m venv venv
source venv/bin/activate  # For Unix/macOS
venv\Scripts\activate     # For Windows

```

Now, let’s install the libraries we will use:

```bash
pip install requests beautifulsoup4 firecrawl-py python-dotenv

```

We will touch on what each library does as we use them.

### Firecrawl API key setup

Since we will push our code to a GitHub repository later, you will need to save your Firecrawl API key securely by using a `.env` file:

```bash
touch .env
echo "FIRECRAWL_API_KEY='your-key-here'" >> .env

```

Also, create a `.gitignore` file and add the `.env` to it so that it isn’t pushed to GitHub:

```bash
touch .gitignore
echo ".env" >> .gitignore

```

## Writing a Basic Web Scraper

In this tutorial, we will build a scraper for [the Hacker News homepage](https://news.ycombinator.com/) that extracts post title, URL, author, rank, number of upvotes and date.

![Hacker News Homepage showing posts to be scraped using automated scheduling with Firecrawl](https://www.firecrawl.dev/images/blog/scheduling-scrapers-images/hacker_news_homepage.png)

Like we mentioned, we will build the scraper in Firecrawl but I have also prepared an [identical scraper written in BeautifulSoup](https://github.com/firecrawl/firecrawl/tree/main/examples/blog-articles/scheduling_scrapers) if you want a more traditional approach.

In your working directory, create a new `firecrawl_scraper.py` script and import the following packages:

```python