Skip to content

requestiumvsskyvern

BSD-3-Clause 8 4 1,837
79.9 thousand (month) Dec 28 2012 0.5.1(2026-01-19 16:59:35 ago)
21,046 17 148 AGPL-3.0
Feb 01 2024 250.9 thousand (month) 1.0.29(2026-04-02 14:42:44 ago)

Requestium is a Python library that merges the power of Requests, Selenium, and Parsel into a single integrated tool for automatizing web actions.

The library was created for writing web automation scripts that are written using mostly Requests but that are able to seamlessly switch to Selenium for the JavaScript heavy parts of the website, while maintaining the session.

Requestium adds independent improvements to both Requests and Selenium, and every new feature is lazily evaluated, so its useful even if writing scripts that use only Requests or Selenium.

Skyvern is an AI-powered browser automation tool that uses large language models (LLMs) and computer vision to interact with websites. Instead of relying on DOM selectors, Skyvern takes screenshots of web pages and uses visual understanding to identify and interact with elements, making it highly resilient to website changes.

Key features include:

  • Vision-based interaction Uses screenshots and computer vision (multimodal LLMs) to understand page layout and identify interactive elements visually, rather than through DOM inspection alone.
  • No selectors needed Describe tasks in natural language and Skyvern figures out what to click, type, and navigate without CSS selectors or XPath.
  • Complex workflow automation Can handle multi-step workflows like form filling, navigation through menus, file uploads, and multi-page processes.
  • Self-correcting When actions fail, Skyvern can analyze the resulting page state and adjust its approach, recovering from errors autonomously.
  • API-first design Provides a REST API for triggering and managing automation tasks programmatically.
  • Open source with cloud option Core engine is open source and can be self-hosted. Also available as a managed cloud service.

Skyvern is particularly effective for automating tasks on websites with complex or dynamic UIs where traditional selector-based automation breaks frequently. It achieved 85.85% accuracy on the WebVoyager benchmark.

Highlights


ai-powerednatural-languageanti-detect

Example Use


```python from requestium import Session, Keys session = Session(webdriver_path='./chromedriver', browser='chrome-headless', default_timeout=15) # then session object can be used like requests and parsel: title = session.get('http://samplesite.com').xpath('//title/text()').extract_first(default='Default Title') # other advance functions like POST requests and proxy settings are also available: s.post('http://www.samplesite.com/sample', data={'field1': 'data1'}) s.proxies.update({'http': 'http://10.11.4.254:3128', 'https': 'https://10.11.4.252:3128'}) # session can also be used like selenium as it exposes all selenium functions. # like typing keys: s.driver.find_element_by_xpath("//input[@class='user_name']").send_keys('James Bond', Keys.ENTER) ```
```python import requests # Skyvern runs as a service - interact via REST API SKYVERN_API = "http://localhost:8000/api/v1" # Create a task with natural language instructions task = requests.post( f"{SKYVERN_API}/tasks", json={ "url": "https://example.com/contact", "navigation_goal": "Fill out the contact form with test data and submit it", "data_extraction_goal": "Extract the confirmation message after submission", "navigation_payload": { "name": "John Doe", "email": "john@example.com", "message": "Hello, this is a test message", }, }, ).json() task_id = task["task_id"] # Check task status result = requests.get(f"{SKYVERN_API}/tasks/{task_id}").json() print(result["status"]) # "completed" print(result["extracted_information"]) # confirmation message ```

Alternatives / Similar


Was this page helpful?