AI coding agents still write your SDK's old API — so I built a type-checker to measure it

Here's a bug I kept hitting. I'd ask an AI assistant to write some code against a library — Prisma, the Vercel AI SDK, Zod — and the code would look completely right. Clean, idiomatic, exactly the shape I expected. Then I'd run it, and it wouldn't compile.

The reason was always the same: the library had shipped a new major version, and the model was writing the previous major's API from memory. parameters instead of inputSchema. required_error instead of error. A new PrismaClient({ datasources }) call that no longer exists. Small things — but enough to break the build on the first try.

Models are frozen at their training cutoff. Libraries are not. So there's a gap between "the API the model reaches for" and "the API you actually have installed" — and that gap is widest right after a library ships a breaking change.

I wanted to know: how big is that gap, exactly — and can I measure it objectively? So I built SDKProof.

The method: let the compiler be the judge

The trick to measuring this without hand-waving is to not use an LLM to grade an LLM. Instead:

1. 
   Generate. Have a model solve 10–15 realistic tasks for a given SDK ("define a tool that returns the weather", "make an optional field with a custom error message").


2. 
   Type-check. Drop each solution into a fixture that has the real, current package installed, and run tsc --noEmit.


3. 
   Score. Pass = compiles clean. Fail = the compiler's own diagnostics, classified into failure patterns.

No LLM judge. No "looks plausible." The installed package's type definitions are the ground truth, and tsc is the referee. A pass means the code would actually build against the version you have.

One design detail that matters: the prompts name the functions but never the option names. I ask for "a tool with a description and an input schema," not "use inputSchema." That way I'm measuring what the model naturally reaches for — not whether it can echo back a name I already handed it.

What I found

Running claude-opus-4-8 across three SDKs:

Here's a representative miss. Ask for a tool definition with the AI SDK and you tend to get this:

import { tool } from 'ai';

import { z } from 'zod';



const weatherTool = tool({

  description: 'Get the weather for a city',

  parameters: z.object({ city: z.string() }), // ✗ this was the v4 API

  execute: async ({ city }) => getWeather(city),

});

Looks right. But against ai v7, tsc says:

error TS2353: Object literal may only specify known properties,

and 'parameters' does not exist in type 'Tool<...>'.

Because the option was renamed to inputSchema:

const weatherTool = tool({

  description: 'Get the weather for a city',

  inputSchema: z.object({ city: z.string() }), // ✓ v5+

  execute: async ({ city }) => getWeather(city),

});

Everything else about the code is fine. It's one renamed key — and it's exactly the kind of thing that slips past a quick read but stops the build cold.

The pattern: readiness tracks the drift window

The interesting part isn't any single score — it's why they differ.

Notice the newest breaking change scores worst. The Vercel AI SDK and Zod shipped their renames in 2025; by now the model has largely absorbed them and mostly gets them right (90/100). Prisma 7 is more recent, and the model hasn't caught up (80/100) — it still writes setup calls that were removed.

That's the whole thesis: a model's "readiness" for an SDK tracks how recently that SDK changed. Which means this isn't a one-time audit. The gap:

• 
  re-opens every time a library ships a new major, and


• 
  shifts every time a model is retrained.

So it's something to monitor, not measure once. A library that scores 95 today can drop to 70 the week it ships v-next — then climb back as the next model generation learns it.

Honest limitations

I'd rather you trust the number than oversell it:

• 
  Type-check only. It measures whether the code uses the real, current API surface — not whether it runs correctly. Compiling clean ≠ bug-free.


• 
  One model, for now. Comparing across models over time is the plan; a single model is a starting point, not the whole story.


• 
  A snapshot. Every score is tied to a specific package version and model version. Both move.

None of that breaks the signal — it just scopes it. "Does the model reach for API surface that still exists?" is a real, useful question, and the compiler answers it without opinion.

Why it matters

• 
  If you use AI to write code: be most skeptical right after a library ships a major. That's exactly when "looks right" and "compiles" diverge. Pin your versions and read the diff.


• 
  If you maintain an SDK: the day you ship a breaking major, AI-assisted users hit broken code on their first try — until the models catch up. That's a support-and-perception cost that's currently invisible. SDKProof makes it visible.

It's open source — add your own SDK

Adding an SDK is about an afternoon: install the package, add a tsconfig, write a tasks file, register it. The harness handles generation, type-checking, and scoring.

• 
  Board + full breakdowns: sdkproof.dev
  


• 
  Code: github.com/Kalpitrathore/sdkproof
  

I'm looking for the next SDKs to score. What library have you watched an AI assistant get wrong since its last major? Tell me and I'll run it.