Policies

A policy receives the normalized request and the candidates that survived capability filtering. It returns a model ID and may choose a reasoning effort.

A local policy

import type { RoutingPolicy } from "@mupt-ai/dari-router";

const policy: RoutingPolicy = ({ request, candidates }) => {
  const wantsTools = request.tools.length > 0;
  const preferred = wantsTools ? "anthropic/claude-sonnet-4-6" : "openai/gpt-5.4-mini";
  const selected = candidates.find((candidate) => candidate.id === preferred)
    ?? candidates[0]!;

  return {
    model: selected.id,
    reasoningEffort: selected.defaultReasoningEffort,
    reason: wantsTools ? "This candidate supports the requested tools." : "Use the fast candidate.",
  };
};

Policies may be synchronous or asynchronous. Router Core validates the returned model and reasoning effort. Omit reasoningEffort to use the candidate’s default. The TypeScript field is reasoningEffort; wire JSON uses reasoning_effort.

Inspect or serve a selection

router.evaluatePolicy(request) is stateless: it runs the policy without reading or writing leases and without firing hooks. Use it for previews, logs, or tests. router.select(request) is the authoritative serving primitive. It reads an existing lease, applies a new lease returned by the policy, and fires onSelection. router.fetch() uses the same semantics.

import { openAIChatRequest } from "@mupt-ai/dari-router/protocols";

const request = openAIChatRequest({
  model: "my-router",
  messages: [{ role: "user", content: "Explain cache locality." }],
});

const preview = await router.evaluatePolicy(request);
const servingSelection = await router.select(request);
console.log(preview.decision, servingSelection.decision);

Self-hosted selector policy

The advanced createDariRoutingPolicy uses a selector model (an LLM that answers routing questions) and the deterministic policy engine to assemble evidence, ask the selector, and validate its answer. It is public from /policy-engine because it is more opinionated than the core policy interface.

import { createPiRuntime, createRouter } from "@mupt-ai/dari-router";
import { createDariRoutingPolicy } from "@mupt-ai/dari-router/policy-engine";

const pi = await createPiRuntime({ apiKey: process.env.OPENAI_API_KEY! });
const policy = createDariRoutingPolicy({
  runtime: pi,
  selectorModel: "openai/gpt-5.4-mini",
  selectorContextWindowChars: 400_000,
  pricing: (model) => myPricing[model] ?? null,
  averageOutputTokensByModel: myAverageOutputTokensByModel,
});

const router = createRouter({
  models: [pi.model("openai/gpt-5.4-mini"), pi.model("openai/gpt-5.4")],
  policy,
  executor: pi,
});

runtime: pi is valid here: it configures the selector used by createDariRoutingPolicy. It is not a createRouter option. For self-hosted createDariRoutingPolicy, provide your own pricing and observed output-token averages. Router Core does not ship or infer them. Cache state and model metadata remain optional; unknown cache state means the engine makes no cache-savings claim.

Custom rules

const policy = createDariRoutingPolicy({
  runtime: pi,
  selectorModel: "openai/gpt-5.4-mini",
  pricing: (model) => myPricing[model] ?? null,
  averageOutputTokensByModel: myAverageOutputTokensByModel,
  strategy: "custom",
  customConfig: {
    rules: [
      { when: "architecture or difficult debugging", use: "openai/gpt-5.4", thinking_level: "high" },
      { when: "simple factual questions", use: "openai/gpt-5.4-mini" },
    ],
    default: "openai/gpt-5.4-mini",
  },
});

The selector must return an eligible model and effort. For a fully local deterministic policy, write a RoutingPolicy directly; for full preparation/finalization control, use the advanced policy engine.

Get Started

Build A Router

Advanced

A local policy

Inspect or serve a selection

Self-hosted selector policy

Custom rules

​A local policy

​Inspect or serve a selection

​Self-hosted selector policy

​Custom rules

A local policy

Inspect or serve a selection

Self-hosted selector policy

Custom rules