A more powerful vision toolkit—give text-only models in DeepSeek Harness eyes: image Q&A, long-screenshot OCR, UI restoration, and GUI visual tasks in one toolkit and Skill.
🚀 Paste an image and ask directly | Install with one command | Built-in free vision | Broad use cases
🌐 English | 中文
If you use DeepSeek or another text-only model in DeepSeek Harness (DSH), you may have run into the same problems: the model cannot see a screenshot, generic descriptions miss the point, buttons have no usable coordinates, and a rebuilt page can look “close enough” without a way to measure the remaining difference.
🏆 This project is the first comprehensive vision-tool plugin in the DeepSeek Harness ecosystem: it was initiated before internal beta and built during the beta with reference to agent-vision-toolkit.
Original work: The system and division of responsibilities behind these visual tools, together with the
vision-toolsSkill, were personally created and continuously refined by the author through long-term real-world use and repeated iteration.
- Paste and use it immediately. Paste an image in DSH Web and the text-only route switches to its
(Vision Toolkit)variant without manual path copying or model changes. - A seamless image workflow. Native thumbnails, session history, and workspace paths stay intact; Web can preview artifacts and Headless can continue using the same structured results.
- One command to install. The built-in free Gemini 3.7 Flash vision service is ready after installation, with no API key required.
- Built-in free vision. The shared service works immediately after installation; if shared capacity is temporarily exhausted, it returns a readable
429response with retry guidance. - Vision guided by intent. The agent extracts evidence for the task at hand, such as “Where is the error?” or “Where is the button?”, instead of returning a generic caption.
- A complete screenshot-to-verification loop. Reference images, HTML screenshots, difference regions, and pixel comparison work together for UI restoration.
agent-vision-toolkit gives an agent more than image captions: it can read, locate, crop, trace, rebuild, and verify visual work. DSH Vision Toolkit is its native DeepSeek Harness integration, bringing that workflow into Web and Headless Profiles.
This project has two layers:
- Visual tools and a Skill: the agent learns when to inspect, ground, OCR, crop, trace, or compare pixels.
- Native DSH integration: those capabilities live inside Profiles, sessions, Settings, Artifacts, and the Web UI, with a free Gemini 3.7 Flash vision service ready after installation.
Install and use it immediately. The default setup includes a free Gemini 3.7 Flash vision service and requires no API key. Cropping, pixel diffing, color analysis, foreground extraction, SVG tracing, and HTML screenshots run locally without spending vision API requests.
dsh plugin --profile web add @anionex/dsh-vision-toolkitUpstream toolkit: Anionex/agent-vision-toolkit · Project website: agent-vision.anionex.me
Table of contents
- 2026-08-16 · Windows Python: Added Microsoft Store Python support, fixing first-time isolated-runtime setup failures for affected Windows users.
- 2026-08-17 · Free vision upgrade: Switched the built-in no-key service to Gemini 3.7 Flash and fixed Qwen/Gemini bounding-box coordinate order.
- 2026-08-16 · Better free vision: Switched the built-in no-key service to Groq Qwen3.6, improving image understanding without adding setup steps.
- 2026-08-16 · Image paste: Text-only routes now switch to a
(Vision Toolkit)variant and keep a workspace path, fixing blocked pastes and images that could not be reused later. - 2026-08-16 · More shared capacity: Expanded the free service capacity to reduce peak-time
429responses. - 2026-08-16 · Real model test: Added a full image-request test in Settings, fixing the false confidence caused by a successful
/modelsrequest to a model that still cannot process images.
| The problem | What Vision Toolkit delivers |
|---|---|
| A text-only model cannot see a screenshot | Paste an image in DSH Web; the plugin obtains visual evidence and returns the task-relevant parts to the text model |
| The description is long but misses the point | Ask “Where is the error?” or “What color is the submit button?” and receive an answer focused on that question |
| The model knows an element exists but cannot act on it | Get original-image pixel coordinates and an optional labeled or numbered preview |
| Long-screenshot OCR skips or duplicates lines | Split and audit the image while preserving Markdown, chunks, manifests, and resumable run state |
| UI restoration is judged by feel | Compare the reference and implementation screenshots to get a difference percentage, ranked regions, a heatmap, and JSON |
| Screenshot assets cannot be reused | Produce a crop, transparent PNG, color palette, or editable SVG instead of stopping at prose |
Paste an image into the conversation. A text-only model can switch to its Vision Toolkit variant and inspect the image in the context of the user's question.
Left: the reference screenshot. Right: an editable HTML/CSS result. The result can continue into screenshot rendering and pixel comparison instead of ending as an image description.
Left: a hand-drawn reference. Right: the working interface reconstructed from it.
The repository includes a reproducible UI-restoration example: the agent renders the reference and implementation, then uses difference regions, a heatmap, and a JSON report to guide the next correction.
dsh plugin --profile web add @anionex/dsh-vision-toolkitYou can install it into a Headless Profile too:
dsh plugin --profile headless add @anionex/dsh-vision-toolkitRestart a running Web Profile, then open Settings → Vision Toolkit. The free provider is already configured; run Test vision model to confirm it is reachable.
The first start prepares an isolated runtime, so it needs access to the Python package cache or the network. A normal installation does not require an agent-vision-toolkit source checkout or a local path setting.
Paste a screenshot into the conversation or place an image in the session workspace, then invoke /vision-tools. For example:
Inspect this screenshot. Explain the error and tell me what to fix first.
Find the login button in the top-right corner, return original pixel coordinates, and make a boxed preview.
Crop this icon and convert it to SVG.
Rebuild the page from reference.png. After each pass, render it and run a pixel diff until the major differences are gone.
| Task | Recommended workflow |
|---|---|
| Image Q&A or screenshot debugging | Inspect → answer around the current question → locate details when needed |
| Find a button, icon, or text region | Ground the target → return pixel box → create a labeled preview |
| Extract an icon from a screenshot | Ground → crop → trace to SVG |
| Read a long webpage screenshot | Split → OCR → merge Markdown → audit boundaries |
| Recreate a page or component | Reference → implementation → HTML screenshot → pixel diff → iterate |
| Extract brand visuals | Crop region → analyze dominant colors → extract foreground → export transparent PNG |
The plugin provides 10 tools that can be called independently or composed into a workflow:
| Tool | Best question to ask | Main result |
|---|---|---|
vision_glance |
“What is happening in this image?” | Focused answer, description, OCR, or multi-image comparison |
vision_ground |
“Where is the thing I need?” | Original pixel coordinates and optional boxed preview |
vision_detect |
“Which buttons, icons, or elements are present?” | Numbered element inventory, coordinates, and optional preview |
vision_crop |
“Extract this region as its own image” | PNG or JPEG crop |
vision_trace |
“Turn this shape into an editable vector” | SVG |
vision_pixel_diff |
“Where does the implementation differ from the reference?” | Difference percentage, ranked regions, heatmap, and JSON |
vision_long_screenshot_ocr |
“Read this entire long screenshot” | Markdown, chunks, manifest, and audit output |
vision_extract_foreground |
“Remove the background from this subject” | Transparent PNG |
vision_dominant_colors |
“Which colors dominate this area?” | Palette or ranked candidate colors |
vision_html_screenshot |
“Render this local page at an exact viewport or capture the full page” | PNG and optional CSS pageHeight |
Coordinates always use original-image pixels in x1,y1,x2,y2 form, so grounding output can feed directly into cropping, tracing, or later automation.
For a long HTML document, pass fullPage=true. The requested width and height remain the layout viewport, while the resulting PNG covers the complete document and reports pageHeight in CSS pixels.
The plugin keeps image understanding and deterministic local image processing in one Agent workflow. Expand the flow below for the implementation boundary.
Architecture and image-input behavior
flowchart LR
Image["Screenshot or local HTML"] --> Skill["vision-tools Skill"]
Skill --> Agent["Text agent selects a task"]
Agent --> Vision["Use a vision model when image understanding is needed"]
Agent --> Local["Run crop, SVG, and pixel work locally"]
Vision --> Result["Answer, OCR, coordinates"]
Local --> Artifact["PNG, SVG, heatmap, JSON"]
Result --> Session["Continue reasoning and acting"]
Artifact --> Session
The visual capabilities come from a packaged, pinned agent-vision-toolkit snapshot. The DSH plugin handles installation, session-scoped tool exposure, Credentials, path checks, cancellation, timeouts, result files, and Web presentation. The runtime never fetches upstream main in the background.
The vision-tools Skill is maintained as an explicit adapter of the upstream
SKILL.md plus all five upstream playbooks. Tool names, argument syntax,
Artifact delivery, progressive exposure, and DSH path/lifecycle boundaries are
adapted; the upstream tool-selection rules, coarse-to-fine method, and task SOPs
remain intact. The exact upstream Skill commit, source hashes, adapted hashes,
and reviewable adapter patch are recorded in assets/skill/UPSTREAM.json and
patches/vision-tools-dsh.patch.
For routes that DSH positively identifies as text-only, the plugin registers a sibling <model> (Vision Toolkit) variant. By default, pasting an image in DSH Web switches to that variant and gives the model both a reusable workspace path and a visual description focused on the current task.
The default setup uses:
Base URL: https://vision.anionex.me/v1
Model: gemini-3.7-flash
API Key: https://agent-vision.anionex.me (filled automatically)
Requests that still use the previous qwen/qwen3.6-27b model name remain compatible and are routed to the Qwen backend.
This is a shared zero-configuration entry point, not an unlimited private endpoint. Request safeguards include:
| Limit | Current value |
|---|---|
| Images per request | Up to 5 |
| Image size | 4 MiB per image |
| Decoded pixels | 20,000,000 per image |
| Output | Up to 4,096 tokens per request |
These safeguards prevent unusually large requests from monopolizing memory or request time. When shared capacity is reached, the service returns a readable 429 response with Retry-After instead of collapsing into an unexplained model failure.
Existing clients that still send api_key="free" remain compatible.
For higher quotas, private endpoints, or another model, change the provider in Settings → Vision Toolkit and store the API key as a DSH Credential. Settings stores the Credential reference and never reads the saved secret back into the browser.
Step-by-step Groq tutorial: Get a free Groq API key and use Qwen3.6-27B for image understanding. It includes screenshots for account/API-key setup, the exact Vision Toolkit settings, and working cURL and Python examples.
You can also configure a Profile patch:
- id: vision-toolkit
config:
provider:
baseUrl: https://api.example.com/v1
credential: MY_VISION_KEY
model: your-vision-model
protocol: openaiOpenAI Chat Completions-compatible endpoints and Anthropic Messages are supported. The Web Settings panel exposes the full provider, runtime, timeout, image-limit, and image-input-variant configuration.
- A DeepSeek Harness Web or Headless Profile.
- Node.js
^22.19.0or>=24.0.0. - Python 3.11+; the plugin prepares an isolated environment by default.
- Only
vision_html_screenshotrequires Chrome, Chromium, or Edge. - Inputs must be PNG, JPEG, GIF, or WebP files in the session workspace or an explicitly allowed directory.
Install, upgrade, disable, and uninstall
dsh plugin --profile web update @anionex/dsh-vision-toolkit
dsh plugin --profile web remove @anionex/dsh-vision-toolkitIf you are migrating from the retired @dsh-external/dsh-vision-toolkit, remove the old package first and install @anionex/dsh-vision-toolkit.
To disable the bundle temporarily, set this in the Profile patch:
- id: vision-toolkit
disabled: trueRestart the Web Profile and refresh the page after enabling or upgrading the Web plugin.
In Settings → Vision Toolkit, Check for updates queries the Profile's npm registry. For a direct registry installation, Update and restart installs only the exact version you confirmed, verifies it, and restarts an explicitly opted-in POSIX Web process on a fixed --port. Local/workspace/file/git/URL installs, Windows, dynamic ports, read-only Profiles, and manager-owned processes remain check-only.
The updater revalidates the Profile before mutation, snapshots the original manifest and lockfile, and holds a token-owned cross-process lock. The current Web process exits only after the restart helper confirms that the backup is readable and the lock handoff succeeded. When the Profile was already operational, the replacement must report both the target plugin version and a ready runtime; failed replacements restore the original manifest/lockfile and rebuild dependencies with a frozen lockfile before retrying the previous exact version. If automatic recovery itself fails, the backup and lock are preserved and their paths are written to $DSH_HOME/logs/vision-toolkit-restart.log. Detached restart requires DSH_VISION_TOOLKIT_ALLOW_DETACHED_RESTART=1; unsaved Settings or API-key input blocks installation.
| Problem | What to do |
|---|---|
| Pasting an image still says the model does not support image input | Restart the Web Profile, refresh the page, and confirm the selected route has the (Vision Toolkit) suffix. You can also place the image in the session workspace and invoke /vision-tools |
| The free service returns 429 | Wait for the Retry-After interval, or switch to your own endpoint when you need stable higher volume |
| The image exceeds a size or pixel limit | Crop or resize it first; the error identifies whether bytes or decoded pixels caused the rejection |
| A custom Credential is missing | Enter the API key in Settings → Vision Toolkit and confirm the Credential name matches the provider configuration |
| First-time runtime setup fails | Check Python 3.11+, network or package-cache access, and disk permissions, then retry the model test in Settings |
| Chrome is not found | Install Chrome, Chromium, or Edge. Only HTML screenshot rendering is unavailable; the other tools still work |
| An artifact cannot be previewed | Use Open file or the workspace path in the result. Preview URLs exist only while the Web route is available |
The current release focuses on screenshot understanding, visual grounding, OCR, asset extraction, UI restoration, and pixel-level verification. It is not a video, audio, or camera-input system and does not automatically click GUI controls. Interactive box editing, remote service clusters, model voting, and cross-session visual caches are also outside the current scope.
pnpm install --frozen-lockfile --trust-lockfile
pnpm run verify:portable
pnpm run build
pnpm test
TSX_TSCONFIG_PATH=tsconfig.json pnpm dlx tsx scripts/ui-restoration-example.ts --check- Read CONTRIBUTING.md before contributing.
- Use GitHub Issues for bugs, focused feature requests, and usage questions; see SUPPORT.md for channel guidance.
- Report vulnerabilities privately through SECURITY.md.
- See CHANGELOG.md for releases and FUNDING.md for sponsorship details.
- Visit upstream agent-vision-toolkit for the general toolkit, cross-agent integrations, and visual-task playbooks.
agent-vision-toolkit was created by Anionex. This repository maintains its native DeepSeek Harness integration.
The plugin is available under the MIT License. The packaged upstream snapshot retains its original MIT license in vendor/agent-vision-toolkit/LICENSE.