The landscape of diagramming tools is rapidly evolving, especially with the rise of Large Language Models (LLMs). While developers have long grappled with the classic dilemma of “ease of use” versus “freedom of expression,” a new critical factor has emerged: LLM affinity and token efficiency. This article explores how our approach to diagramming must shift to embrace AI-driven workflows, analyzing popular tools like Mermaid, PlantUML, draw.io, and Excalidraw through the lens of artificial intelligence.
The Persistent Challenge: Balancing Usability and Flexibility
For years, developers have shared common frustrations. Tools like Mermaid.js offer incredible ease of use, seamlessly integrating with platforms like GitHub and and Zenn. However, their automatic layout engines often lead to “line crossing” and limited control over node placement, becoming a source of irritation for those seeking visual perfection.
On the other hand, visual tools such as draw.io (XML) and Excalidraw (JSON) provide unparalleled freedom for creating intricate, aesthetically pleasing diagrams. Yet, this comes at a significant cost: their complex data formats make version control a nightmare (hellish diffs in Git for XML, for example) and can be sluggish to open and process, especially with larger files. The sheer volume of metadata for visual appearance translates into verbose data structures.
LLMs Enter the Fray: The Dawn of Token Efficiency
The advent of powerful LLMs fundamentally alters how we evaluate diagramming tools. Diagrams are no longer solely drawn by humans; they are increasingly generated, edited, and interpreted by AI. In this new paradigm, visual beauty alone is insufficient. The true measure of a tool’s value now includes its “friendliness” to LLMs – in other words, its efficiency.
Why Redundancy is the Enemy: Token Consumption Compared
The verbose nature of formats like JSON and XML, while enabling rich GUI experiences, presents a significant hurdle for LLMs. These formats contain vast amounts of detailed information about element positions, sizes, styles, and metadata, much of which is redundant for an LLM trying to understand the core structure. This “bloat” directly translates to higher token consumption, increasing processing costs and reducing generation speed for AI.
Consider a simple sequence diagram:
| Tool (Data Format) | Estimated Token Count | LLM Affinity |
|---|---|---|
| Mermaid.js (Markdown-like) | 50 | 🥇 Highest |
| PlantUML (Markdown-like) | 80 | 🥈 High |
| Excalidraw (JSON) | 500 | ❌ Low |
| draw.io (XML) | 1200 | ❌ Lowest |
This data clearly illustrates the massive efficiency gap. Markdown-like formats, which focus on describing relationships rather than visual attributes, can be up to 24 times more token-efficient than their XML/JSON counterparts. This efficiency is paramount for reducing AI processing costs and improving performance, echoing research that shows structured formats like Knowledge Graphs can drastically cut token usage in RAG systems.
The Efficiency vs. Expressiveness Trade-off Revisited
Ultimately, choosing a diagramming tool in the LLM era forces a critical decision between “LLM Affinity (Efficiency)” and “Freedom of Expression (Visual Aesthetics).”
- Mermaid/PlantUML (Text-based): Excel in AI generation, token efficiency, and Git integration (clean diffs).
- Excalidraw/draw.io (Structured Data): Offer unparalleled visual control and expressiveness for human-centric design.
Mermaid’s “line crossing problem,” a common developer complaint, is a direct consequence of its automatic layout engine. This seemingly annoying limitation is, in fact, an intentional trade-off. By abstracting layout details, Mermaid prioritizes a clear, structured description of relationships—making it incredibly easy for both humans and LLMs to define the core logic, even if the visual outcome isn’t always perfectly optimized by human standards. It sacrifices some “visual aesthetics” for superior “structural clarity” for AI.
Conclusion: Choosing Your Palette in the AI Era
The “best” diagramming tool remains context-dependent, but the criteria for selection have fundamentally shifted.
| Rank | Tool | Recommended Use | LLM/Git Integration | Compromises |
|---|---|---|---|---|
| Best for AI Workflows | Mermaid.js | Prioritizing AI-driven documentation updates, READMEs, and Wikis. | Excellent (lightweight, Zenn/GitHub native) | Automatic layout, potential for line crossing, limited fine-grained control. |
| Strong for Structured UML | PlantUML | When precise UML diagrams (Class, Component, etc.) are essential. | High (text-based, robust for complex structures) | More complex syntax than Mermaid. |
| For Visual Brainstorming & UI/UX | Excalidraw/draw.io | Ideal for brainstorming, UI mockups, and when visual appeal is paramount. | Lowest (verbose JSON/XML, difficult Git diffs) | Not suitable for AI generation/editing or version control of diagram logic. |
It’s increasingly clear that lightweight, structured “diagram languages” like Mermaid are poised to become the standard for AI-generated and managed code and documentation. While LLMs can already produce reasonable diagrams in these formats, the future promises even greater sophistication. Embracing AI-friendly data formats now is crucial for optimizing workflows, reducing costs, and streamlining documentation processes in the rapidly evolving world of artificial intelligence. Choose your tool wisely, keeping both human understanding and AI efficiency in mind.