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.

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

Platforms

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

Device capture

Settings (vision namespace)

Configured in Settings → Plugins → Plugin configuration → Vision:

Channels

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

• Official DeepSeek Harness (0.1.0-rc.7 lineage), 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