dsh-client-vision

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Give your DeepSeek Harness agent eyes. dsh-client-vision is a screen-capture + external image-recognition plugin for DeepSeek Harness: the agent takes a screenshot (or points at any image), hands it to a vision-capable model through a pluggable channel, and gets back plain text it can actually act on — no multimodal model required.

Compatibility

This revision requires DeepSeek Harness core 0.1.2-alpha.5 or later within the 0.1.x line. It uses the Settings service API introduced in that core release.

Why you want it

• DeepSeek can't see — now it can. The harness model has no image input. This plugin runs the whole "look" outside the model and returns text the agent can reason about, exactly like Codex's semantic vision tool.
• Capture anything, any way. fullscreen / window (with live window enumeration) / region / interactive — grab the browser, a game window, or one corner of the screen.
• Multi-channel by design. Tools are decoupled from recognition backends. The gpt channel ships ready to use; adding Claude, Gemini, or a local model is one analyze() implementation + one registry line — the three tools never change.
• Secret-safe. The API key lives in the harness credentials store (VISION_GPT_API_KEY) — never in settings files, logs, or the conversation transcript.
• Every preset, out of the box. Mounted on the host plane, so code, standard, cordis, minimal — every agent sees the tools. No preset switching.
• Ready to ship. Prebuilt bundles included; three install paths (drop into the monorepo / pnpm publish / tarball).
• Smart payloads. Large captures are auto-downscaled and re-encoded (≤1568px JPEG q80) before they leave the machine.

Capabilities

Tools

Tool	What it does
take_screenshot	Capture the screen: fullscreen (primary display), window (by id from list_windows), region (x, y, width, height), interactive (user selection), android (adb device/emulator), or ios (booted simulator). Returns the PNG path + dimensions.
list_windows	Enumerate on-screen windows (id, app, title) — macOS CGWindowList, Windows Get-Process main handles, Linux X11 (wmctrl/xprop) — pick the browser or game window to capture.
analyze_image	Submit an image (a path, or the most recent screenshot) to the configured vision channel and return a plain-text description.
view_image	One-shot "look at this": capture the screen (or use image_path) and recognize it through the active channel. The screenshot is rendered as an image card in the Web conversation, while the model context receives only the plain-text description — the image bytes never enter the model context.

Platforms

Platform	Capture backend	Window enumeration	Extra requirements
macOS	screencapture (system)	Swift CGWindowList	Screen Recording permission on first use
Windows	PowerShell System.Drawing (system)	Get-Process main window handles	PowerShell System.Drawing
Linux	ImageMagick import	wmctrl + xprop	ImageMagick (convert/identify), wmctrl, x11-utils

mode=interactive (system selection UI) is macOS-only; on Windows and Linux use mode=region with explicit coordinates.

Device capture

Mode	What it captures	Requirements
android	A connected Android device or emulator screen	adb on PATH with a device online (adb devices); works from any host. With several devices online, pass device=<serial>.
ios	The booted iOS simulator	macOS host with Xcode (xcrun simctl)

Settings (vision namespace)

Configured in Settings → Plugins → Plugin configuration → Vision:

Field	Meaning
Endpoint (baseUrl)	Domain + optional path prefix; /chat/completions is appended. e.g. https://api.example.com/v1
Channel	The active recognition backend (currently gpt).
Model	gpt-5.5 / gpt-5.6-sol / gpt-5.6-terra
API key	Stored through the harness credentials service as VISION_GPT_API_KEY; the literal never leaves your machine.

Channels

Channel	Backend	Model	API key
gpt	OpenAI-compatible /chat/completions	gpt-5.5 / gpt-5.6-sol / gpt-5.6-terra	required (e.g. VISION_GPT_API_KEY)
zhipu	Zhipu GLM-4V, OpenAI-compatible /chat/completions	glm-4v-plus / glm-4v-flash	required (e.g. VISION_ZHIPU_API_KEY)
ollama	local Ollama /api/chat (default http://localhost:11434)	llava / llava-llama3 / bakllava / moondream / qwen2-vl / minicpm-v (or any installed vision model)	none

Pick the channel in Settings → Plugins → Vision; the model dropdown follows the channel and the API-key control is hidden for ollama. For ollama the base URL defaults to http://localhost:11434 and the model to llava when left blank.

Multi-channel architecture

model → analyze_image(image, prompt)
          │  reads vision.channel
          ▼
  channels/<id>/analyze()        ← one implementation per backend
          │
  gpt:    POST {baseUrl}/chat/completions   (image_url data URL)
  claude / gemini / local: …    ← add yours here

Adding a channel is deliberately small:

// src/channels/<id>/index.ts
export async function myAnalyze(ctx, call): Promise<string> {
  // call.imageB64, call.mime, call.prompt, call.config, call.signal
  return await fetchYourVisionApi(...)
}

// src/channels/index.ts — one registry line
export const channels = {
  gpt: { label: 'GPT', analyze: gptAnalyze },
  myChannel: { label: 'My Channel', analyze: myAnalyze },
}

The tools (take_screenshot / list_windows / analyze_image) and their schemas never change.

Installation (official — no repo modification)

dsh plugin add installs the packages into your profile; each package declares dsh.bundle, so the rows mount automatically — no patch rows, no repo edits.

Prerequisites

• DeepSeek Harness core 0.1.2-alpha.5 or later within the 0.1.x line, plus dsh and pnpm on PATH.

1. Get the packages (pick one)

a. From this repository (recommended until published to npm):

dsh plugin --profile web add \
  file:/path/to/dsh-client-vision/packages/tool-vision \
  file:/path/to/dsh-client-vision/packages/ui-vision

b. Tarball:

cd packages/tool-vision && npm pack
cd packages/ui-vision   && npm pack
dsh plugin --profile web add file:/path/to/deepseek-ai-dsh-tool-vision-0.1.0-rc.7.tgz \
                            file:/path/to/deepseek-ai-dsh-client-ui-vision-0.1.0-rc.7.tgz

c. npm registry (after publishing):

dsh plugin --profile web add @deepseek-ai/dsh-tool-vision @deepseek-ai/dsh-client-ui-vision

A [WARN] Issues with peer dependencies message is expected and safe to ignore — the peers come from your deployment's own bundles at runtime.

2. Verify

node -e "console.log(JSON.stringify(require(process.env.HOME + '/.dsh/profiles/web/package.json').dsh.profile.bundles))"
# should list dsh-tool-vision and dsh-client-ui-vision

3. Restart + configure

Restart the harness, then Settings → Plugins → Plugin configuration → Vision: set the endpoint, model, and your own API key (VISION_GPT_API_KEY), save.

4. Verify

Ask the agent to "look at the screen" — it should call take_screenshot → analyze_image and describe what it sees.

Uninstall

dsh plugin --profile web remove @deepseek-ai/dsh-tool-vision @deepseek-ai/dsh-client-ui-vision

Alternative: build inside a harness fork

If you run a fork of deepseek-harness (not the official deployment), you can drop the packages into the monorepo instead:

cp -R packages/tool-vision <harness>/packages/vision/tool-vision
cp -R packages/ui-vision   <harness>/packages/client/ui-vision

Then add both to apps/cli/package.json (workspace:^), add ./packages/vision/tool-vision to tsconfig.host.json and ./packages/client/ui-vision to tsconfig.client.json, pnpm install, build (tsdown host + client passes), and restart.

Quick start

1. Restart the harness.
2. The tool catalog now includes take_screenshot / list_windows / analyze_image.
3. Open Settings → Plugins → Plugin configuration → Vision, set the endpoint, model, and your own API key, and save.
4. Ask the agent to "look at the screen" — it will screenshot and describe what it sees.

Development

• This repository is a source distribution: the peer packages (@deepseek-ai/dsh-tools, …) resolve from your deployment. lib/ ships prebuilt, so npm pack works immediately.
• The tsconfig.json files are standalone; the harness monorepo's build pipeline (including the client-bundle tsdown.config.ts) applies in Option A.
• Never commit secrets. The API key stays in each machine's .credentials.yaml.

License

MIT