@metaharness/router

Route each query to the cheapest model that's good enough. The productized form of the DRACO Phase-2 finding (ruvnet/agent-harness-generator, ADR-040): on cross-domain research, structure/fusion does not beat a strong model on quality — but routing each query to the right, cheapest model is a measured Pareto win. A learned embedding router beat the best fixed model on DRACO, and its accuracy rises monotonically with training data (the learning curve).

Dependency-free, no model files, no network. Bring any embedding model; the router only needs the vectors.

Install

npm install @metaharness/router

Use

import { Router } from '@metaharness/router';

const router = new Router({
  qualityBar: 0.8, // cost-optimal: cheapest candidate predicted to clear 0.8
  candidates: [
    {
      id: 'anthropic/claude-haiku-4.5',
      costPerMTok: 3,
      examples: [ /* { embedding, quality } from your eval logs */ ],
    },
    {
      id: 'anthropic/claude-opus-4',
      costPerMTok: 45,
      examples: [ /* ... */ ],
    },
  ],
});

const pick = router.route(queryEmbedding);
// → { id, predictedQuality, costPerMTok, metBar }

For each candidate the router predicts quality on the new query via k-NN over that candidate's labelled examples (query embedding → quality it achieved), then returns the cheapest candidate predicted to clear qualityBar (or the best-predicted if none clear it / no bar is set). Feed it your own eval logs; the more examples, the closer it gets to the per-query oracle (DRACO's learning curve was still rising at n=19).

Why this and not "a bigger model" / "more structure"

DRACO measured it honestly: structure degrades quality, and a cheap model matches frontier quality at ~10× lower cost. The leverage is choosing the model, not wrapping it. This is that choice as a library.

MIT.

Trained router (kernel ridge regression)

For a learned, regularised router (vs. plain k-NN), train one from the same dataset — no model files, no native deps, pure TS (ADR-043):

import { trainRouter, TrainedRouter } from '@metaharness/router';

const { router, lambda, looQuality } = trainRouter(rows, prices, { qualityBar: 0.8 });
// rows: [{ embedding, scores: { modelId: quality } }]  · λ fit by leave-one-out CV
const pick = router.route(queryEmbedding);

// persist / reload the trained model (portable JSON)
const json = router.toJSON();
const same = TrainedRouter.fromJSON(json);

KRR with a cosine kernel is the regularised generalisation of k-NN; λ (fit by LOO) controls the bias–variance trade-off that hurts k-NN on small data. On the DRACO n=20 dataset it ties k-NN (the data ceiling); it's the router that generalises better as your eval set grows.

Native FastGRNN router (optional, @ruvector/tiny-dancer)

The pure-TS router above needs zero native deps. For a trained native model, the same { embedding, scores } dataset drives a real FastGRNN (Rust/NAPI, gradients + Adam, persisted to .safetensors) — programmatically:

import { trainNativeRouter, NativeRouter, isNativeRouterAvailable } from '@metaharness/router';
// needs `npm i @ruvector/tiny-dancer` (optional peer); falls back to pure-TS otherwise
const res = await trainNativeRouter(rows, prices, { outputPath: './router.safetensors' });
const router = await NativeRouter.load({ modelPath: res.modelPath });

…or with zero code via the published CLI (validated on the DRACO routing dataset — trains at the full embedding dim, e.g. 1536):

npx ruvector tiny-dancer train draco.json --out model.safetensors --prices '{"haiku":1,"opus":15}'
npx ruvector tiny-dancer score model.safetensors --query @query.json   # high → route cheap

Pick the backend automatically with resolveRouterBackend('auto') ('native' when tiny-dancer is installed, else 'js'). See ADR-043 for the measured data ceiling and the train↔route dimension note.