After months of development, polishing, and community feedback, the library is leaving beta and entering a production/stable development status.

We are happy to announce Crawlee for Python v1.0.

From now on, Crawlee for Python will strictly follow semantic versioning. You can now rely on it as a stable foundation for your crawling and scraping projects, knowing that breaking changes will only occur in major releases.

What's new in Crawlee for Python v1​

• New storage client system
• Adaptive Playwright crawler
• Impit HTTP client
• Sitemap request loader
• Robots exclusion standard
• Fingerprinting
• Open telemetry

Key steps we'll cover:

1. Project setup
2. Analyzing YouTube and determining a scraping strategy
3. Configuring YouTube
4. Extracting YouTube data
5. Enhancing the scraper capabilities
6. Creating a YouTube Actor on the Apify platform
7. Deploying to Apify

Which hashtags are trending now? What is an influencer's engagement rate? What topics are important for a content creator? You can find answers to these and many other questions by analyzing TikTok data. However, for analysis, you need to extract the data in a convenient format. In this blog, we'll explore how to scrape TikTok using Crawlee for Python.

Key steps we'll cover:

1. Project setup
2. Analyzing TikTok and determining a scraping strategy
3. Configuring Crawlee
4. Extracting TikTok data
5. Creating TikTok Actor on the Apify platform
6. Deploying to Apify

Key steps we will cover:

1. Project setup
2. Development of the Bluesky crawler in Python
3. Create Apify Actor for Bluesky crawler
4. Conclusion and repository access

A key capability is its standby mode, which runs the Actor as a persistent API server. This removes the usual start-up times - a common pain point in many systems - and lets users make direct API calls to interact with the system immediately.

This blog explains how SuperScraper works, highlights its implementation details, and provides code snippets to demonstrate its core functionality.

Crunchbase tracks details that matter: locations, business focus, founders, and investment histories. Manual extraction from such a large dataset isn't practical -automation is essential for transforming this information into an analyzable format.

By the end of this blog, we'll explore three different ways to extract data from Crunchbase using Crawlee for Python. We'll fully implement two of them and discuss the specifics and challenges of the third. This will help us better understand how important it is to properly choose the right data source.

Key steps we'll cover:

1. Project setup
2. Choosing the data source
3. Implementing sitemap-based crawler
4. Analysis of search-based approach and its limitations
5. Implementing the official API crawler
6. Conclusion and repository access

What data will we extract from Google Maps?​

We’ll collect information about hotels in a specific city. You can also customize your search to meet your requirements. For example, you might search for "hotels near me", "5-star hotels in Bombay", or other similar queries.

We’ll extract important data, including the hotel name, rating, review count, price, a link to the hotel page on Google Maps, and all available amenities. Here’s an example of what the extracted data will look like:

{
    "name": "Vividus Hotels, Bangalore",
    "rating": "4.3",
    "reviews": "633",
    "price": "₹3,667",
    "amenities": [
        "Pool available",
        "Free breakfast available",
        "Free Wi-Fi available",
        "Free parking available"
    ],
    "link": "https://www.google.com/maps/place/Vividus+Hotels+,+Bangalore/..."
}