examples/rlm: Add a structured RLM example

[scuffi]

Summary

This adds an end-to-end example of building a recursive language model (RLM) with Cloudflare Computer.

The example keeps long context in a Computer Workspace, uses bounded model calls for semantic interpretation, and uses generated JavaScript for validation and aggregation. This pattern is useful for tasks that need to classify, extract, or compare information across more data than should be placed in one model prompt.

What this adds

The example runs the same official Oolong task through three strategies: direct context, generated JavaScript with Workspace access, and a Structured RLM with Workspace access plus ws:model.

The RLM uses the public WorkerJavaScriptBackend and native executor tool. Generated code receives a read-only Workspace and one trusted model interface:

new WorkerJavaScriptBackend({
  access: "read",
  egress: { mode: "none" },
  trustedModules: {
    "ws:model": createModelCapability(model, hooks),
  },
});

This lets a generated module map model calls across Workspace partitions and reduce their structured results with ordinary JavaScript:

const mapped = await callModel("batch", requests);
const totals = validateAndSum(mapped);
return { answer: largestLabel(totals) };

The UI compares the three approaches side by side and shows the generated module, model workers, token use, duration, and Oolong score. The RLM starts first, then overlaps its map work with both baselines to keep the comparison responsive.

Usage

npm install
npm run build --workspace @cloudflare/computer
npx wrangler login
npm run dev --workspace @cloudflare/example-rlm

Open the Vite URL and run Classify 2,433 records. Workers AI is remote, and a complete comparison can use more than 200,000 model tokens.

The example README covers the architecture, core source files, capability bounds, additional tasks, data provenance, and optional deployment.

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