dsh-plugin-llmtr
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An LLMTR provider route for DeepSeek Harness (dsh).
LLMTR is an OpenAI-compatible AI gateway. One API key reaches 170+ chat models from OpenAI, Anthropic, Google, Qwen, Mistral, xAI and others — plus the models LLMTR runs on its own infrastructure in Türkiye, for deployments that need their prompts to stay in-country.
This plugin registers those models as the llmtr provider route on ctx.llm, so they appear in the harness model picker and can be selected per session, per agent, and per subagent like any other route.
Install
dsh plugin add dsh-plugin-llmtr
The package ships a bundle patch, so plugin add inserts the row for you. Then open Settings → Models, find the LLMTR card, paste a key from llmtr.com, and save. The key is stored in $DSH_HOME/.credentials.yaml; settings retain only its reference.
The model picker fills itself from the gateway's live listing — nothing to type, and models LLMTR adds later appear without an upgrade.
Without the plugin CLI
Add the row to your own cordis.yml or --patch overlay:
- insert:
- id: llm-llmtr
name: dsh-plugin-llmtr
and export the key instead of storing it:
export LLMTR_API_KEY=llmtr-...
Model ids
Model ids are the gateway's own, owner/model, and are sent verbatim:
openai/gpt-5.4 anthropic/claude-... google/gemini-3.7-flash
qwen/qwen3-8-... mistral/... deepseek/deepseek-v4-...
llmtr/muse-glimmer-30b-tr llmtr/trendyol-asure-12b llmtr/gemma-4
Ids starting with llmtr/ are the ones LLMTR hosts in Türkiye; the picker labels them "Hosted in Türkiye on LLMTR infrastructure". Everything else is routed to its upstream provider and processed under that provider's own terms — the plugin does not claim otherwise for them.
Data residency
To offer only the in-country models, narrow the catalog scope:
llm-llmtr:
catalogScope: turkey-hosted
The picker then lists nothing else, so a session cannot be started on a model whose prompts leave the country.
Configuration
Every field is optional and lives in the llm-llmtr section of $DSH_HOME/settings.yaml (or in the plugin row's config). Changes reach the next request without a restart; an in-flight stream keeps the values it started with.
| Field | Default | What it decides |
|---|
apiKeyEnv | LLMTR_API_KEY | Credential reference resolved per request |
baseURL | https://llmtr.com/v1 | Endpoint; $LLMTR_BASE_URL from a trusted launch layer overrides the default |
headers | {} | Extra request headers; authentication and attribution names always win |
catalogScope | chat | chat, turkey-hosted, or all |
catalogTtlMs | 900000 | How long a fetched listing is reused |
models | [] | A pinned list; non-empty replaces the live listing entirely |
modelOverrides | {} | Per-model corrections applied over the live listing |
reasoningEfforts | [] | Effort ids the gateway accepts for your models |
maxTokens | 32768 | Default per-request output cap |
defaultContextWindow | 131072 | Capacity assumed for a model you have not sized |
streamIdleTimeoutMs | 300000 | Idle ceiling while a stream read is outstanding |
retryPolicy | harness default | Provider-owned retry policy |
Context windows
The LLMTR listing publishes ids and operations, not capacities, so every model is sized at defaultContextWindow until you say otherwise. Correct the ones you actually use:
llm-llmtr:
modelOverrides:
openai/gpt-5.4:
contextWindow: 400000
llmtr/muse-glimmer-30b-tr:
contextWindow: 32768
maxTokens: 8192
Reasoning effort
Reasoning output works out of the box: the adapter reads both reasoning_content and reasoning deltas, so a reasoning model's thinking shows up in the transcript with no configuration.
Selecting an effort level is opt-in, because the gateway fans requests out to upstreams that reject reasoning_effort on non-reasoning models. Declare what your models accept and the harness offers exactly those:
llm-llmtr:
reasoningEfforts: [low, medium, high]
With the list empty, no selector is shown and nothing reaches the wire. A request carrying an undeclared effort is refused by name rather than silently dropped.
Pinning a model list
A deployment that must fix exactly which models its users can reach replaces the listing instead of filtering it:
llm-llmtr:
models:
- id: llmtr/muse-glimmer-30b-tr
name: Muse Glimmer 30B
contextWindow: 32768
- id: llmtr/trendyol-asure-12b
With models non-empty the gateway listing is never fetched.
Scope
- Chat completions only. Requests go to
/v1/chat/completions with streaming and usage reporting on. LLMTR's embeddings, image, video, rerank, and realtime models are listed by catalogScope: all but cannot be routed by this adapter, which is why chat is the default.
- Text in, text out. Image content is refused before it is sent, naming the model, rather than being flattened away.
- One attempt per call. Retries belong to the harness retry policy, so a failure is visible once rather than hidden inside the adapter.
Privacy
The plugin sends the harness's standard User-Agent, an x-llmtr-client header naming this package and version, and your API key. No session id, prompt text, file path, or user identifier is added to any header.
Development
npm install
npm test # 80 unit tests, no network and no key required
npm run typecheck
npm run build
tests/mock-server.ts stands in for the gateway, so the suite runs offline.
License
MIT. Portions derive from DeepSeek Harness — see NOTICE.
