Preloop
Runs your GitHub Actions workflow on your own laptop in an isolated, disposable virtual machine that boots in under 200 milliseconds — so you can see if a change actually passes CI in seconds, without pushing a commit and waiting for a runner.
🔗 Visit Preloop
Description
The usual CI feedback loop looks like: commit, push, wait several minutes for a hosted runner to spin up, read the log, repeat. Preloop collapses that loop by running the exact same GitHub Actions workflow locally, each job in its own hardware-isolated microVM that boots in under 200ms — fast enough that testing your uncommitted, in-progress changes feels instant rather than batched.
Because it reads your working tree as-is (including uncommitted and untracked files), a failing step can hold its microVM open with the toolchain, services, and caches exactly as the test left them — you attach a debugger, fix the code, and retry just that step instead of rerunning the whole pipeline from scratch. It's explicitly built around AI coding agents generating code fast enough that manual review can't keep pace: Preloop verifies that generated code actually passes CI before a human even looks at it, using the real GitHub Actions wire protocol rather than a reimplementation.
💬 Our review
The short version: if you or your coding agents are pushing changes faster than hosted CI can turn around, Preloop's under-200ms local microVMs give you a genuine CI pass/fail in the time it takes to save a file — but it's still in beta with pricing undisclosed, so budget it as an early bet rather than a settled cost line.
Compared to `act` — the established open-source tool for running GitHub Actions locally — Preloop's pitch is closer protocol fidelity (reportedly 31/39 compliance scenarios versus act's 29/39) and a debugging workflow built specifically for pausing on failure and reattaching, rather than just re-executing. The tradeoff for that polish is beta-stage maturity: no public pricing, and it's optimized for a specific workflow (AI-agent-generated code verification) that may be more machinery than a small team without heavy agent usage actually needs.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Bêta gratuite, tarification non communiquée
Pros
Démarrage de microVM en moins de 200ms
Compatible workflow GitHub Actions existant, sans réécriture
Débogage live : microVM figée sur échec, retry ciblé sur l'étape
Compatibilité protocole GitHub Actions supérieure à act (31/39 vs 29/39 scénarios)
Cons
Encore en bêta, tarification pas encore publique
Optimisé pour un usage spécifique (vérif de code généré par agents IA), moins pertinent hors de ce cas
Isolation réseau désactivée par défaut par sécurité — peut nécessiter une config manuelle