Algorithm
Overview
Latest algorithm tutorials mirror the fragment catalog under config/algorithm/.
Algorithms only matter after a decision matches and exposes multiple candidate models in modelRefs. The router then uses decision.algorithm to choose or coordinate those candidates.
Key Advantages
- Separates route eligibility from model selection policy.
- Lets one decision keep several candidate models without inlining ranking logic.
- Supports both one-model ranking and multi-model orchestration.
- Mirrors the repo fragment tree exactly: one tutorial page per algorithm under
config/algorithm/selection/andconfig/algorithm/looper/.
What Problem Does It Solve?
Once a route matches, the router still needs a principled way to choose among candidate models. Without an algorithm layer, teams either hard-code one winner or duplicate ranking logic across routes.
Algorithms solve that by making the post-match selection policy explicit and reusable.
When to Use
Use algorithm/ when:
modelRefscontains more than one candidate- route policy depends on latency, feedback, semantic fit, or online exploration
- one decision should orchestrate several models instead of choosing exactly one
- you want model choice to evolve without changing the decision rule itself
Configuration
In canonical v0.3 YAML, algorithms live inside each matched decision:
routing:
decisions:
- name: computer-science-reasoning
rules:
operator: AND
conditions:
- type: domain
name: "computer science"
modelRefs:
- model: qwen2.5:7b
- model: qwen3:14b
algorithm:
type: router_dc
router_dc:
temperature: 0.07
The repo now keeps one tutorial page per algorithm.