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Antifailure
Antifailure builds a disposable copy of your production stack for every pull request. It branches a masked, referentially consistent Postgres from a verified golden, builds your services and then runs them inside a sandbox that cannot reach the internet except where you say it can, drives real workflows with agents that use the accessibility tree the way a person does, and rehearses pending migrations for exclusive locks, table rewrites and query plan regressions before any of it reaches product
Their llms.txt
The beginning of the file, from our validated copy saved 2026-10-27T10:48:19.764Z. Cached copy · Source on antifailure.dev
# Antifailure
> Antifailure builds a disposable copy of your production stack for every pull request. It branches a masked, referentially consistent Postgres from a verified golden, builds your services and then runs them inside a sandbox that cannot reach the internet except where you say it can, drives real workflows with agents that use the accessibility tree the way a person does, and rehearses pending migrations for exclusive locks, table rewrites and query plan regressions before any of it reaches production.
## When to use Antifailure
Use Antifailure when a pull request changes a Postgres-backed application and you need
evidence from a disposable production-shaped environment before merge. It is a fit for
masked database branches, migration lock and rewrite rehearsal, browser workflows,
contained third-party API behavior, and load comparisons against a recorded baseline.
Do not use it as a production database, a general browser automation service, a load
generator against production, or a replacement for unit tests. Start with `af init` in
the repository you want to evaluate; use the control-plane API only when you need shared
organization history or engine ingestion.
Antifailure answers one question: if this pull request merged, what would break? It
answers it by building a disposable copy of the production stack, not by running a
test suite against fixtures.
## The whole thing in four commands, five with a production database
```bash
curl -fsSL https://antifailure.dev/install.sh | sh
af init # reads your repo, writes antifailure.yaml
af golden refresh # only if the manifest names a production database: set
# that variable first, and this makes the masked copy once
af up # database branch from the golden, built services, sealed network
af test # agents run your workflows and return verdicts with evidence
af down # every resource it created, gone
```
On Windows, install from PowerShell with `irm https://antifailure.dev/install.ps1 | iex`;
the commands after it are the same.
af init writes database.source_url_env only when the repository already names its
production variable. With no source, or once a verified golden for the project exists
on the machine, af up is the next command. af start reports every step as observed on
the machine and names the next one.
The installer puts af under ~/.antifailure and puts that on your PATH by appending
one line to the startup file the login shell reads, printing the line and naming
the file. AF_NO_MODIFY_PATH=1 declines it. The terminal that ran the installer
needs the one line it prints, because a running shell cannot see a file written a
second ago. In GitHub Actions it writes GITHUB_PATH instead and touches no
profile, so a later step finds af without a flag.
## What a run produces
- A masked Postgres branch. Masking is compiled to SQL and executed in resumable
chunks, deterministic so the same customer maps to the same
…
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