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Description
AegisRunner is an advanced cloud-based platform utilizing AI for autonomous regression testing specifically designed for web applications. By integrating a smart web crawler with AI-driven test generation, it completely removes the need for manual test creation.
The platform operates with a simple input of a URL and autonomously performs several robust functions:
It uses a headless Chromium browser (Playwright) to thoroughly crawl the entire web application, identifying every page, interactive component, form, modal, dropdown, accordion, carousel, and any dynamic states present.
Furthermore, AegisRunner constructs a state graph of the application, representing each unique DOM state as a node and each user interaction—such as clicking, hovering, scrolling, submitting forms, and pagination—as a connecting edge.
Using the crawl data, it employs AI to generate comprehensive Playwright test suites (compatible with OpenRouter, OpenAI, and Anthropic models), eliminating the need for any manual test writing.
After generating the tests, it runs them and provides a detailed report on pass/fail results, including in-depth reports for each test case, accompanied by screenshots and traces.
Remarkably, it boasts a 92.5% pass rate across over 25,000 automatically generated tests, showcasing its effectiveness and reliability in streamlining the testing process for developers and organizations alike.
Description
HyperCrawl is an innovative web crawler tailored specifically for LLM and RAG applications, designed to create efficient retrieval engines. Our primary aim was to enhance the retrieval process by minimizing the time spent crawling various domains. We implemented several advanced techniques to forge a fresh ML-focused approach to web crawling. Rather than loading each webpage sequentially (similar to waiting in line at a grocery store), it simultaneously requests multiple web pages (akin to placing several online orders at once). This strategy effectively eliminates idle waiting time, allowing the crawler to engage in other tasks. By maximizing concurrency, the crawler efficiently manages numerous operations at once, significantly accelerating the retrieval process compared to processing only a limited number of tasks. Additionally, HyperLLM optimizes connection time and resources by reusing established connections, much like opting to use a reusable shopping bag rather than acquiring a new one for every purchase. This innovative approach not only streamlines the crawling process but also enhances overall system performance.
API Access
Has API
API Access
Has API
Screenshots View All
No images available
Integrations
Amazon Web Services (AWS)
Docker
Google Colab
JavaScript
Jupyter Notebook
Python
React
Integrations
Amazon Web Services (AWS)
Docker
Google Colab
JavaScript
Jupyter Notebook
Python
React
Pricing Details
$9
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
AegisRunner
Founded
2025
Country
India
Website
aegisrunner.com
Vendor Details
Company Name
HyperCrawl
Website
hypercrawl.hyperllm.org