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dsh-reasoning-settings

已验证 · 实测可装 JuneLearn

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JavaScript语言
2026-08-16最近推送
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功能简介

让 DeepSeek Harness 的第三方 API 支持低、中、高等推理强度,并可为每次子 Agent 调用选择模型|Add Low, Medium, High, and other reasoning levels to third-party APIs, with model selection for each subagent call

✅
我们的评价
可用 — 实测通过,早期项目

让 DeepSeek Harness 的第三方 API 支持低、中、高等推理强度,并可为每次子 Agent 调用选择模型|Add Low, Medium, High, and other reasoning levels to third-party APIs, with model selection for each subagent call 实测能干净安装、正常启动。早期项目,但功能可用。

「已验证」表示我们的自动化 CI 在干净 profile 里实际执行了 dsh plugin add 并启动成功——仅此而已。功能描述与版本兼容性均为作者声明。这不是安全审计,也不代表对第三方代码的背书。

README

DSH Reasoning Settings

A DeepSeek Harness Web settings plugin that adds an independent reasoning effort (thinking intensity) page for custom llm-pi-ai providers. The official UI only exposes effort selection for first-party DeepSeek models; this plugin makes it work for third-party / relay APIs too.

锟斤拷锟斤拷说锟斤拷

Third-party service recommendation (referral link)

If you are looking for an OpenAI-compatible API relay, you can take a look at WPIronman API. This is my referral link, and I may receive a referral benefit if you register through it; any promotion or benefit is subject to the provider's current terms. This plugin is independent of that service and does not require any particular relay. Evaluate pricing, reliability, and privacy terms before choosing a provider.

New users can redeem the gift quota with code 99F509ABC6C38F77.

Features

  • Auto-reads the custom providers and models you already added on the official Models page.
  • Declares a per-model effort level from off / minimal / low / medium / high / xhigh / max.
  • Customizes the actual wire value sent to the API for each level.
  • Sets a provider-wide default effort, restricted to the levels supported by every model in that provider.
  • Configures the reasoning wire format for openai-completions providers.
  • Writes back to llm-pi-ai.providers.* through the official settings.mutate API 锟斤拷 API keys are never touched.
  • Fixes in-process subagents inheriting the Agent's create-time official model instead of the parent's live third-party provider/model selection.
  • Makes subagents inherit an explicit parent reasoning effort, or fall back to the target provider's configured default effort.
  • Resolves model-only child overrides such as grok-4.5 against user-configured third-party providers instead of an unrelated official provider.
  • Extends the ordinary subagent and subagent_fork tools with per-call provider, model, and reasoning_effort fields, without replacing their official lifecycle, background, or result behavior.

Live demo

Reasoning effort settings page

Model and reasoning effort picker

Installation

Prerequisites

  • Install Node.js. DSH currently supports Node.js 22.19.x or version 24 and newer; Node.js 24 LTS is recommended. Node.js includes npm and npx.
  • Install Git so the plugin can be fetched from its GitHub repository.
  • Install pnpm. Both methods require pnpm because dsh plugin invokes it in the profile directory to install or remove plugins.
  • Your network must reach registry.npmjs.org and github.com. Configure a working network proxy if npm or GitHub is unavailable or unstable in your region.
  • Method 1 does not require a DeepSeek Harness source checkout. Method 2 additionally requires that checkout.

Check the basic environment first:

node --version
npx --version
git --version
corepack enable
pnpm --version

If corepack enable fails with a permission error, run it once from an Administrator PowerShell. Alternatively, use another option from the pnpm installation guide.

If downloads remain on a spinner or fail with ECONNRESET, ETIMEDOUT, or a GitHub connection error, set a proxy for the current PowerShell window. Port 7890 is only an example; replace it with your proxy's actual port:

$proxy = "http://127.0.0.1:7890"
$env:HTTP_PROXY = $proxy
$env:HTTPS_PROXY = $proxy
$env:npm_config_proxy = $proxy
$env:npm_config_https_proxy = $proxy

These variables only affect the current PowerShell window and disappear when it is closed.

Method 1: npx (recommended for regular users)

This method does not require a DeepSeek Harness source checkout or a global dsh installation, but Git and pnpm must already be available. On its first run, npx downloads @deepseek-ai/dsh and its dependencies, which can take several minutes:

npx --yes -p @deepseek-ai/dsh dsh plugin --profile web add github:JuneLearn/dsh-reasoning-settings

Start Web through the same package runner after installation:

npx --yes -p @deepseek-ai/dsh dsh web

Method 2: pnpm with the Harness source tree (recommended for developers)

Use this method if you already cloned deepseek-harness and want to run its source directly. First confirm pnpm is available:

pnpm --version

Enter the DeepSeek Harness source root. Install its dependencies once, then install the plugin:

cd D:\deepseek-harness
pnpm install
pnpm dsh plugin --profile web add github:JuneLearn/dsh-reasoning-settings

Start Web from that source directory afterward:

cd D:\deepseek-harness
pnpm dsh web

The package's dsh.bundle declaration adds the plugin to the Web profile automatically. Neither method requires editing cordis.patch.yml. Web listens on http://127.0.0.1:3080 by default; it uses another port only when the default is occupied or you explicitly select one.

Upgrade

Run the corresponding install command again to upgrade. No uninstall or profile-patch maintenance is required.

npx method:

npx --yes -p @deepseek-ai/dsh dsh plugin --profile web add github:JuneLearn/dsh-reasoning-settings

pnpm source method:

cd D:\deepseek-harness
pnpm dsh plugin --profile web add github:JuneLearn/dsh-reasoning-settings

Uninstall

npx method:

npx --yes -p @deepseek-ai/dsh dsh plugin --profile web remove dsh-reasoning-settings

pnpm source method:

cd D:\deepseek-harness
pnpm dsh plugin --profile web remove dsh-reasoning-settings

DSH removes both the dependency and its bundle layer. Restart dsh web; the Reasoning effort settings page is removed.

Usage

  1. Add your custom provider and models on the official Models page first.
  2. Open Settings > Reasoning effort.
  3. Pick the supported levels for each model and set the provider default effort.
  4. Click Save under that provider.
  5. Start a new session and choose the model and reasoning effort in the model picker.

The plugin only declares which effort levels Harness may select and send. Whether the relay API actually honors a value is up to the relay: if a request comes back with HTTP 400, uncheck the levels that model does not support or adjust their wire values.

Subagent routing and reasoning

The server entry fixes model inheritance for Harness 0.1.0-rc.5 in-process subagent, subagent_fork, and Workflow children:

  1. An explicit child provider + model route remains authoritative when it differs from the parent's create-time route.
  2. A model-only override is resolved against user-configured third-party providers; duplicate model ids prefer the parent's current provider.
  3. With no child override, the child inherits the parent's live provider/model instead of its create-time official default.
  4. An explicit child effort remains authoritative; otherwise the parent's effort is inherited only for the same target route.
  5. With no explicit effort, the plugin leaves it absent so llm-pi-ai applies the target provider's reasoning default.

The plugin also augments the existing model-facing subagent and subagent_fork tools for each live Agent. Their original arguments remain available, with three optional fields added:

  • provider: exact configured Provider id;
  • model: exact model id owned by that Provider;
  • reasoning_effort: optional off / minimal / low / medium / high / xhigh / max level.

provider and model must be supplied together. Omitting both preserves normal inheritance. Omitting reasoning_effort after selecting a different route uses that Provider/model's configured default. The tool descriptions enumerate exact configured pairs and instruct the parent model to delegate instead of answering directly when a user requests another Provider/model.

Do not use shorthand such as 锟斤拷the 5.6 model锟斤拷 when multiple exact ids exist. For example, if a Provider contains both gpt-5.6-sol and gpt-5.6-terra, ask for one exact pair:

Use subagent with provider=wpironman-gpt, model=gpt-5.6-terra,
reasoning_effort=max to write a 100-character random essay.

If the requested pair equals the current route, the child still uses that same route, so the result will naturally look like the parent model. The structured tool-call arguments and the child's durable request/header are the reliable verification points.

Per-call targeting affects local in-process children only. Codex, Claude Code, and ACP subagents run in separate processes and retain their own model configuration. Workflow retains its own structured provider/model phase fields and still benefits from the inheritance correction when no phase target is supplied.

All switches default to true and may be changed in the plugin mount:

- insert:
    - id: ui-settings-reasoning
      name: dsh-reasoning-settings
      config:
        subagentRouting: true
        inheritRoute: true
        resolveModelOnly: true
        inheritReasoning: true

Set subagentRouting: false to disable the entire server-side correction.

Development

pnpm test   # or: node tests/plugin.test.mjs

Compatibility

Built against the public dual-end plugin, settings-slot, settings.mutate, agent-scoped tool shadowing, Agent lifecycle, and agent/request waterfall interfaces of DeepSeek Harness 0.1.0-rc.5. Harness is still in Developer Preview; if the plugin stops loading or subagent routing changes after an upgrade, check those interfaces and the subagent session metadata.

License

MIT

安装

🧩 让 Agent 自动装(推荐)

装一次目录插件,之后本站所有插件都能让 DeepSeek Harness 自动找、自动装:

dsh plugin add dshbase-catalog

然后对 agent 说「帮我装 dsh-reasoning-settings」,它会在目录里找到并自动安装。文档:dshbase-catalog · 已验证场景包。

该插件是 GitHub 源码(未发 npm)——直接从仓库装:

Web profile:

dsh plugin --profile web add github:JuneLearn/dsh-reasoning-settings

Headless(CLI)profile:

dsh plugin --profile headless add github:JuneLearn/dsh-reasoning-settings

实测报告

验证通过:从 GitHub 源码完成 L1 安装 + L2 加载 + L3 运行(dsh 0.1.0-rc.6)。

使用场景

把一个新模型、provider 或路由策略接入循环,让 dsh 能为任务选对脑子。

适合谁

同时用多个模型或 provider、想让成本/质量/延迟自动平衡的人。

二次开发建议

provider 适配器和路由启发式是缝——加后端、调回退链,或加按任务的模型选择。

安全:尚未扫描——我们的每日静态扫描将很快覆盖它。

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