**agent-vision-toolkit** starts from an argument rather than a model: *an agent's vision capability doesn't have to live in the model — it can live in the harness*. The project has gathered **1,005 stars and 36 forks** in the **17 days** since its **1 August 2026** creation, one of the faster climbs in the agent-tooling space this month. The target user is someone running a coding agent on a strong but **text-only** model — DeepSeek is the running example — who keeps hitting a wall the moment an image enters the conversation. The toolkit ships two layers that can be adopted independently. The first is a set of **vision tool CLIs** plus a `vision-tools` skill that teaches the agent when to reach for each one; any agent that can run a shell command can use them. The second, optional layer is **seamless integration**: a transparent local proxy and single-file native plugins, so that pasted images and the agent's own built-in image tools both start working without extra prompting. The proxy path covers Codex and Claude Code, while **Pi, Oh My Pi and OpenCode use native single-file extensions** instead. The individual tools are narrow by design. **`glance`** answers a question about an image or transcribes its text, and the key claim is that it forwards the model's current intent rather than returning a generic caption — you get the detail the turn actually needs. **`ground`** returns a bounding box in original-image pixel coordinates, with a `--region` flag that re-searches a sub-box and still reports original coordinates, which is the zoom-in path for small UI targets. **`detect`**, **`crop`** and **`trace`** fill out object listing, cropping and vector extraction. Dependencies stay proportional: `glance` needs only **Python 3.11+**, `ground`/`detect`/`crop` add **pillow**, and `trace` adds **numpy** (plus `vtracer` only for its explicit `--outline` fallback). What lifts this above a wrapper collection is the **playbook library** inside the skill. Documented workflows cover long-screenshot and chat-history OCR — finding low-content cut bands, OCRing chunks in order, preserving speakers and timestamps, merging only true overlap and flagging risky boundaries — plus rebuilding a UI from a screenshot, restoring an icon or logo as a transparent PNG or editable SVG, converting a sketch or whiteboard diagram into **Mermaid or Graphviz**, and operating a GUI one verified action at a time. Each playbook specifies tool order and how to verify the result, which is the part most such toolkits leave to the model. Configuration is three environment variables — `VISION_API_KEY`, `VISION_BASE_URL`, `VISION_MODEL` — against any endpoint speaking **OpenAI Chat Completions, OpenAI Responses, or Anthropic Messages**, so OpenRouter, Aliyun DashScope and similar all work. The most recent addition, on **13 August 2026**, is native **DeepSeek Harness** support through the linked `dsh-vision-toolkit` submodule, which exposes **10 structured visual tools** including grounding, tracing, pixel diff, foreground extraction, dominant-color analysis and HTML screenshots. Two caveats are worth stating. The toolkit is a **router, not a vision model** — quality is bounded by whichever multimodal API you point it at, and you pay for those calls. And the **default output language is Chinese**; English descriptions require setting `LANG=en`, which is easy to miss. The project reports end-to-end verification in real Codex + DeepSeek sessions and live runs in Claude Code, Pi, Oh My Pi and OpenCode. Licensed **MIT**.