7-Frame trust gap · 2026 edition

The Production Gap · Open-Source AI Tools · 2026

Name: The Production Gap · Open-Source AI Tools · 2026
Creator: Legit.Show
Published: 2026-07-22T12:00:00+00:00

We ran Legit.Show’s 7-Frame production-readiness benchmark across 1616 open-source AI, MCP and developer tools — straight from their repositories. AI coding ships a flawless demo; this is what quietly never makes it to production.

95%

declare no error tracking in their public repo

across 1146 open-source AI & developer tools we measured · according to Legit.Show · 2026

Read from public repositories only — declared dependencies and committed platform config. Integrations enabled in a hosting dashboard, or configured in a private ops repo, are not visible to this method and are not counted either way.

The findings

Wide-open CORS — 100%
Any website can call the API directly.
No error tracking declared — 95%
No error-tracking dependency or platform config declared in the public repo.
No row-level security — 85%
Database tables with no access rules — one user can read another’s data.
No rate limiting declared — 82%
No rate-limiting library or gateway config declared in the public repo — a limit set at a CDN or load balancer is invisible here.
No webhook idempotency — 65%
Webhooks with no dedupe — a duplicate delivery double-charges or double-processes.
Unsanitized AI input — 0%
User input flows straight into the model prompt — it can be hijacked.
Unindexed foreign keys — 0%
Fast with little data, slow or down once it grows.
Committed .env — 0%
Credentials checked into the repo — leaked to anyone who clones it.
Secrets in the browser — 0%
Secret keys shipped to the client — anyone can steal them.

How many controls are missing

Clean — 0 gaps — 30%
1–2 gaps — 64%
3 or more gaps — 6%

By category

Developer Tools — 98% no error tracking
AI & Agents — 90% no error tracking
Other — 96% no error tracking
MCP & Integrations — 96% no error tracking
Infrastructure & DevOps — 84% no error tracking
Productivity — 95% no error tracking

Why these seven

The demo always works — that’s what AI coding is *great* at. The gap is everything a demo never forces you to add: monitoring for when it breaks, limits for when it’s abused, access rules for when there’s more than one user. 486 of 1616 (30%) had none of these gaps. The rest are one incident away from finding out.

The basics most get right

It isn’t carelessness with the obvious stuff: 0% shipped a hard-coded secret key in client code, and only 0% committed a `.env`. The misses are the *invisible* controls a human senior adds by reflex and a model rarely does.

What this is not

A health check, not a verdict. A missing rate limit correlates with “shipped fast, hardened never” — it doesn’t prove the product is bad. Every number is a count over a stated sample, measured from the public repository, fully reproducible. These are floors, not rates. We only see what a project declares on its public surface. Error tracking wired through an environment variable, a rate limit set at a gateway or CDN, an integration enabled in a hosting dashboard — all of it reads as absence here. The real figure is higher than the one we publish, never lower. See [the methodology](https://legit.show/methodology) for the full rule.

Sample composition

Not a random sample — this is what we measured. The mix below is the caveat; judge it for yourself.

Developer Tools: 672
AI & Agents: 341
Other: 251
MCP & Integrations: 90
Infrastructure & DevOps: 73
Productivity: 55
Largest single maker: 1% (jev, 13)

What we measured (1616)

The full list, so anyone can spot-check. Every item links to its public benchmark.

How this was measured →

Listed because the measurement is public and reproducible, not as a verdict on the product.