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ease
features
design
support

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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

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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

Alternatives

Alternatives