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dsh-auto-router

Model Inference Updated 2026.08.25

Run the following command in DeepSeek Harness:

dsh plugin install ChaoYuZhang001/dsh-auto-router

Paste the following prompt into your AI chat to install this plugin:

Install this plugin in DeepSeek Harness via the DSH Community Market or by running dsh plugin install ChaoYuZhang001/dsh-auto-router, pulling the package from https://github.com/ChaoYuZhang001/dsh-auto-router into your current Desktop profile; the Auto Router provider will then appear in the model picker.

About this plugin

When multiple model routes are connected in DeepSeek Harness, users often switch between light, heavy, code, and vision models by hand, disrupting flow and risking a wrong pick. dsh-auto-router抽象ifies this decision into a configurable auto-routing layer: the model picker gains an Auto Router provider with six choices, and a single click routes the request to the best-fit model automatically.

By default, the classifier sends images to vision, code snippets to code, long or complex tasks to heavy or medium, and short queries to light. With sticky mode enabled, a session can only upgrade its tier and never oscillate back, keeping multi-turn tasks on a consistent model. Users can also override the tier to force a specific level and skip classification entirely. The package stores, reads, or transmits no API keys; credentials remain in the target route standard DSH settings.

Ideal for developers who already run multiple model routes in DSH and want to cut down manual switching, as well as users who prefer context-aware routing over picking a model every time. It requires only one existing route to work with, with no extra credentials or external services needed.

Use Cases

  • Image analysis requests automatically routed to a vision model
  • Multi-turn coding tasks stay on a code model without oscillation
  • Short queries sent to a light model to reduce latency

Best For

  • DSH users with multiple model routes configured
  • Developers who want to reduce manual model switching
  • Teams that need context-aware automatic model matching