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dsh-custom-headers

Model Inference Updated 2026.09.15

Run the following command in DeepSeek Harness:

dsh plugin install EPCN-fla/dsh-custom-headers

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

Run dsh plugin install EPCN-fla/dsh-custom-headers in your terminal to install; the source code lives at https://github.com/EPCN-fla/dsh-custom-headers

About this plugin

In real-world deployments, model requests rarely go straight to the provider. They pass through enterprise gateways, custom proxies, or OpenAI-compatible endpoints that demand extra headers—tenant IDs, billing tags, custom auth schemes—often different per model. DSH's provider profile supports a headers field, but it applies to the entire route, making it impossible to target individual models without affecting every one under that route.

dsh-custom-headers fills that gap at model granularity. Define any number of named header profiles in the plugin settings panel, each a set of key-value pairs; then pick one per model from a dropdown in the model directory. On every call the plugin resolves the selected profile into request headers and injects them before the pi-ai adapter dispatches. Change a profile or switch the selection and the next request picks it up immediately—no restart required. All data lives in the DSH user-settings document; deselecting or deleting a profile restores the original state with no residue.

Who benefits: teams routing through enterprise gateways for tenant and cost-center billing; developers hitting non-standard OpenAI-compatible endpoints (self-hosted vLLM, one-api, various relays) that require extra identification headers; and engineers tagging specific model traffic for canary releases or observability. The plugin is MIT-licensed, introduces no external storage, and works exclusively with the pi-ai adapter path.

Use Cases

  • Route requests through enterprise gateways with per-model tenant and billing headers
  • Attach non-standard auth or identification headers for self-hosted vLLM, one-api, and other OpenAI-compatible endpoints
  • Tag specific model traffic to steer it toward canary deployments or backup clusters

Best For

  • Engineering teams that route model traffic through enterprise gateways or custom proxies with per-tenant cost allocation
  • Developers integrating with non-standard OpenAI-compatible endpoints that require extra headers for identification or authentication
  • Platform engineers who use request headers for canary routing, traffic isolation, or targeted log filtering