dsh-plugin-llmtr
用于 DeepSeek Harness LLM 接口的 LLMTR AI 网关提供商：通过一个兼容 OpenAI 的密钥提供 170 多种聊天模型和托管于土耳其的 LLM
插件会安装到这里；不确定时保持 web。
npx -y @deepseek-ai/dsh plugin --profile web add dsh-plugin-llmtr@0.1.0Plugin Llmtr 以 dsh-plugin-llmtr 发布，当前版本为 0.1.0。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。
点击下方 Badge 复制 Markdown，粘贴到 README 即可。
验证 package.json 声明的 GitHub 仓库，即可管理这个公开页面。认领后，Hub 会优先安排当前版本的安全扫描，并在通过后公开展示结果。
认领这个 Plugin →English | Türkçe
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.
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.
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 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.
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.
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 |
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 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.
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.
/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.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.
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.