Policies

After Router Core filters candidates by capability, your policy picks one.

Write A Policy

A policy receives the normalized request, eligible candidates, and an abort signal:

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

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

  return {
    model: selected.id,
    reasoningEffort: selected.defaultReasoningEffort,
    reason: request.tools.length > 0
      ? "Use the preferred model for tool calls."
      : "Use the smaller model for plain text.",
  };
};

Policies can be sync or async. They can use a heuristic, benchmark data, a learned model, a remote service, or another LLM. Router Core validates the result against eligible candidates. reasoningEffort is optional — omitting it applies the candidate’s default. The field is reasoningEffort in TypeScript, reasoning_effort in JSON, and thinking_level in Dari custom rules.

Inspect Without Executing

Call router.select() to see a decision without making a provider call:

import { openAIChatRequest } from "@dari/router-core";

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

anthropicRequest(payload) provides the equivalent normalization for Anthropic Messages.

Dari’s Selector Policy

createDariRoutingPolicy() uses a separate LLM call (the selector) to choose the route. Its answer is validated before execution.

import { createDariRoutingPolicy, createPiRuntime, createRouter } from "@dari/router-core";

const pi = await createPiRuntime({ apiKey: process.env.OPENAI_API_KEY! });
const policy = createDariRoutingPolicy({
  runtime: pi,
  selectorModel: "openai/gpt-5.4-mini",
  selectorContextWindowChars: 400_000,
});

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

selectorModel doesn’t need to be a routed candidate. selectorContextWindowChars is a character budget for the selector prompt, not a token count. Optional callbacks add evidence to the choice:

pricing — model and cache rates
evals — benchmark scorecards
state — observed provider-cache prefixes
metadata — richer model capabilities

None are required to run the policy.

Custom Rules Strategy

Use strategy: "custom" to give the selector natural-language rules:

const policy = createDariRoutingPolicy({
  runtime: pi,
  selectorModel: "openai/gpt-5.4-mini",
  selectorContextWindowChars: 400_000,
  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 judges each rule’s when text against the request. Its answer must name an eligible pair or Router Core returns a policy error.

Get Started

Build A Router

Advanced

Write A Policy

Inspect Without Executing

Dari’s Selector Policy

Custom Rules Strategy

​Write A Policy

​Inspect Without Executing

​Dari’s Selector Policy

​Custom Rules Strategy

Write A Policy

Inspect Without Executing

Dari’s Selector Policy

Custom Rules Strategy