Reading
Introducing a visual reasoning engine designed specifically for AI agents, supporting screenshot analysis, UI review, image comparison, and architecture diagram interpretation, with multi-mode visual analysis capabilities for both local and cloud environments.
Section 01
Introducing a visual reasoning engine designed specifically for AI agents, supporting screenshot analysis, UI review, image comparison, and architecture diagram interpretation, with multi-mode visual analysis capabilities for both local and cloud environments.
Section 02
Section 03
mq-image-analyze is a visual reasoning engine, not a traditional image generation tool. Its core mission is to convert screenshots, charts, UI interface states, and various visual content into structured data for secure use by AI agents (such as mq-agent) and MCP (Model Context Protocol) workflows.
In the current AI ecosystem, text processing capabilities are quite mature, but visual understanding remains a weak link. mq-image-analyze is designed to fill this gap; it acts as the "eyes" of AI agents, enabling machines to truly "understand" image content.
The project's core philosophy can be summarized as: Vision → Reasoning → Experience. This three-layer architecture emphasizes that generation is optional and secondary; the real value lies in understanding and analysis.
Section 04
The Vision Layer is responsible for extracting basic information from images, including:
This layer mainly relies on computer vision technologies, such as YOLOv8 for object detection, OpenCV for image processing, and PIL for basic image operations.
Section 05
The Reasoning Layer performs higher-level semantic understanding based on the basic information extracted by the Vision Layer:
This layer combines traditional computer vision technologies with modern multimodal large language models (such as BakLLaVA, Llama 3.2 Vision, GPT-4.1, etc.)
Section 06
The Experience Layer is oriented towards end-users and developers, providing a friendly interactive interface:
Section 07
mq-image-analyze provides three different visual analysis modes to adapt to different usage scenarios and performance requirements:
Section 08
By default, it uses BakLLaVA via Ollama, suitable for:
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