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Tech riderRev. 4 Oct 2026

Frontend Regression Validator

Ch 097.2of 26 Visual Regression Testing Software
  1. 1Runs onAPI, Linux, Mac, self-hosted, Web
  2. 2CostsFree plan
  3. 3Visual diff modesai-assisted
  4. 4CI/CD integrationsYes
4 lines stated Written from the maker's own pages: github.com
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The Frontend Regression Validator homepage

Overview

Frontend Regression Validator is ranked #9 of 26 in visual regression testing software on Specifiction. 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

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