anionex/agent-vision-toolkit

If your agent already runs on a text-only model such as DeepSeek but is held back by the lack of multimodality — unable to see images, with every attempt to use an image tool blocked by the system — this repository provides tools, skills, and proxy integrations that let text-only models handle visual tasks on equal or even better footing. The goal is to make the experience of using a text-model agent as seamless as using a multimodal one, and ultimately let a tool-equipped text-model agent outperform a native multimodal agent that does not use this toolkit and its methods.

This repository provides two kinds of components:

1. Vision tool CLIs — multiple CLIs, plus a skill that teaches the agent when to use each one. Any agent that can invoke a shell can use them.
2. Seamless integration (optional upgrade) — a transparent local proxy and single-file native plugins, so images we paste and the agent’s built-in image tools both work seamlessly, with no extra tool installation or additional prompting.

All code has been verified in real Codex + DeepSeek sessions, and the same pipeline has been live-verified end-to-end in Claude Code, Pi, Oh My Pi, and OpenCode.

If this project helps you or gives you some inspiration, feel free to star🌟 & fork.

❤️ Sponsor

Want to sponsor this project? See FUNDING.md or email [email protected].

Latest Update

2026-08-18 — Skill renamed: the included agent skill is now vision-skills (formerly vision-tools), so the name describes the capability rather than the underlying tools.

2026-08-13 — Native DeepSeek Harness support is now available. The new dsh-vision-toolkit linked package brings this toolkit into DSH Web and Headless profiles as a native Profile Bundle. It provides 10 structured visual tools for intent-aware image Q&A, grounding, detection, tracing, cropping, pixel diff, long-screenshot OCR, foreground extraction, dominant-color analysis, and HTML screenshots, while adding DSH Credentials, a managed isolated runtime, previewable Artifacts, Web Settings, and Agent-scoped progressive tool exposure.

The package is tracked here as a Git submodule and maintained independently at Anionex/dsh-vision-toolkit. Clone this repository with --recurse-submodules, or run git submodule update --init --recursive in an existing checkout.

Highlights

• More than image descriptions — it captures what the LLM actually cares about: when viewing an image, it passes along the user’s or model’s latest intent, producing the details needed for the current turn instead of a broad, unfocused description.
• Both pasted images and built-in image tools work: the agent can understand images pasted directly as well as images opened through its built-in tools.
• A battle-tested methodology for visual tasks: the included skill teaches the agent what to inspect, which tool to choose, what sequence to follow, and how to verify the final result.
• One-sentence install: ask your agent to install it — it follows the verified flow end to end, toolkit, skill, and seamless integration included.

Use-case Playbooks

The included vision-skills skill contains complete examples that an agent can follow directly. When to use them, the order in which to call tools, and how to verify the result are all documented in the corresponding skill guides:

Real-world Effects

Infographic restoration: screenshot to HTML in one sentence

Left: the original infographic screenshot. Right: an editable reconstruction built with HTML/CSS. View the HTML source →

UI restoration: sketch to interface in one sentence

Left: the hand-drawn reference. Right: the restored JupyterLab workspace made from it. See the UI restoration playbook for the workflow. Executed in Codex with deepseek-v4-flash.

Fast UI restoration: an approximate first pass

Left: the original page. Right: a fast reconstruction that preserves the main layout, content, and visual hierarchy while allowing approximate colors and library icons. Fast mode targets a first screenshot in about three minutes.

Left: multi-round image Q&A with glance. Right: with ground, DeepSeek V4 locates screen elements to play chess autonomously.

Left: DeepSeek V4 answers a UI style question with similar-style comparisons. Right: DeepSeek V4 debugs a field-name mismatch from a screenshot.

Quick Start

The easiest way to install it is to send this to your agent:

Follow the instructions in https://github.com/Anionex/agent-vision-toolkit to install the vision toolkit and skill locally. If the vision API is not configured, locate the configuration file for the current operating system and guide me through setting VISION_API_KEY, VISION_BASE_URL, and VISION_MODEL.

If you also want the optional seamless integration layer, send this:

Read https://github.com/Anionex/agent-vision-toolkit/blob/main/AGENT_INSTALL.md in full, then install the appropriate vision proxy or native extension/plugin for the agent application we are currently using. If the vision API is not configured, locate the configuration file for the current operating system and guide me through setting VISION_API_KEY, VISION_BASE_URL, and VISION_MODEL.

All you need to prepare is a multimodal API supporting OpenAI Chat Completions, OpenAI Responses, or Anthropic Messages, plus its base URL, API key, and model name. The agent will guide you through writing them to the appropriate configuration file.

After installing the optional integration and restarting the agent, paste an image directly or let the model call its built-in image tool. Pi, Oh My Pi, and OpenCode use single-file native extensions rather than the proxy; see each agent’s documentation.

The Tools

A set of visual tools designed for agents, letting them choose freely based on the situation:

Upgrade: Seamless Integration

This layer makes screenshots pasted into an agent work directly, while also preventing errors when the agent calls its built-in image tools.

All entry points share one configuration. Configure it once and use it everywhere.

How It Works

Descriptions that keep the task in view

Most vision bridges for text-only models simply ask a multimodal model to turn an image into a generic description, then hand that description to the text model and expect it to reconstruct the information it needs. That adds another semantic layer where some information is inevitably lost — the source of the common belief that stitched-together vision solutions must suffer a large performance penalty.

To address this, agent-vision-toolkit tries to recover why the agent wants to look at the image. It extracts the viewing intent from the user message or from the model’s stated reason for calling a built-in image tool, then passes that intent to the vision model as a focus hint. The result is a task-aware description that emphasizes what matters for the current step instead of producing a generic “detailed description” — at lower cost, with higher accuracy and faster responses.

Configuration

Prerequisites

• A coding agent already working with a model, including a text-only model such as DeepSeek V4
• A vision API supporting OpenAI Chat Completions, OpenAI Responses, or Anthropic Messages; select the latter two with VISION_API_PROTOCOL=responses or VISION_API_PROTOCOL=anthropic
• No other configuration is required

FAQ

Limitations

• This is an image-to-text layer; it doesn’t hand vision tokens directly to the text model.
• Overall visual-task quality is determined jointly by the primary LLM and the multimodal LLM.
• The proxy’s cache lives only inside its process and is cleared on restart.

Donation

If this project is valuable to you, you are welcome to buy the developer a coffee ☕️

Community

• Setup and usage help: Support guide and the repository’s issue forms
• Bug reports and feature requests: Issues
• Contributions: Contributing guide
• Security reports: Security policy
• Community standards: Code of Conduct
• User-facing changes: Changelog

About

If agent-vision-toolkit saves you time, you are welcome to star it, share it, contribute, or sponsor the project.

I’m anionex, an AI-native developer who once ranked No. 4 on GitHub’s global developer trending list, with more than 16k stars across my projects. If you would like to follow my future work, follow me on X or GitHub.

Join the Community Group

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