Frontend Regression Validator

No phone app

6.3No. 15 of 23
in Visual Regression Testing Software

Recognised40% of the score22
Phone app26% of the score0
Documented20% of the score83
Free plan14% of the score100

Free plan: Yes
Runs on: api, Linux, Mac, self-hosted, Web

Summary

Frontend Regression Validator is ranked #15 of 23 in visual regression testing software on Samsung Mobile US Press. It runs on API, Linux, macOS, Self-hosted, Web. There is a free plan.

Frontend Regression Validator plans and pricing

All plans

Open-source Free Apache-2.0 licensed software · run locally or with Docker · no paid plans stated github.com · 4 Oct 2026

Compared on visual regression testing software

Visual diff modes: ai-assistedgithub.com
CI/CD integrations: Yesgithub.com

Facts

Purpose: FRED is an open-source visual regression tool for automatically comparing baseline and updated website instances.github.com · 4 Oct 2026
Checks: It compares console and network logs, screenshots, and optionally screenshots using machine-learning analysis.github.com · 4 Oct 2026
Visual AI: Its image-segmentation analysis identifies high-level text and image structures to reduce false positives from dynamic content.github.com · 4 Oct 2026
Scalability: FRED has an internal queue and can process websites in parallel depending on available RAM, CPUs, or GPUs.github.com · 4 Oct 2026
Interfaces: Users interact with FRED through a web UI or API.github.com · 4 Oct 2026
Workflow: A comparison starts with two URLs, which FRED crawls to find pages to render and compare.github.com · 4 Oct 2026
Results: Results are saved locally and include divergence scores and links to raw and analysis images.github.com · 4 Oct 2026
Deployment: The repository documents running FRED as a Docker container or as a local process.github.com · 4 Oct 2026
Resource needs: The README recommends at least 8 GB of Docker memory and preferably 16 GB, especially when using machine learning.github.com · 4 Oct 2026
Runtime: The documented rule of thumb for a machine-learning-enabled crawl is one minute or less per page, while crawl time varies with site complexity.github.com · 4 Oct 2026
Model training: Version 2.x does not include code to train or retrain the machine-learning model; the repository points users to the v1 folder for that code.github.com · 4 Oct 2026
License: The repository includes an Apache License 2.0, which grants no-charge, royalty-free rights subject to its terms.github.com · 4 Oct 2026
Integrations: The README describes API calls and Docker deployment but does not name third-party integrations.github.com · 4 Oct 2026