Next-AI-Draw-IO: AI Diagram Generator Hits #2 on GitHub

Next-ai-draw-io, a Next.js app that generates diagrams from natural language commands, hit #2 on GitHub Trending today with 635 stars in 24 hours. The open-source tool integrates AI models (OpenAI, Anthropic, Bedrock, and six others) with draw.io’s XML format, letting developers describe diagrams conversationally—”Create a microservices architecture with API gateway and database”—and get editable diagrams in seconds. Unlike AI image generators that produce static pictures, this manipulates actual draw.io XML, meaning output is fully editable in the familiar draw.io editor.

Developers spend hours manually creating architecture diagrams, flowcharts, and ERDs. Next-ai-draw-io automates the tedious scaffolding while keeping output editable for refinement. The timing matters: advanced AI models can now generate valid draw.io XML, something impossible with earlier models that produced malformed structures or nonsensical layouts.

From Natural Language to Editable Diagrams: How It Works

The tool converts conversational prompts into properly formatted draw.io XML using the Vercel AI SDK. Type “Create an AWS architecture with CloudFront, S3, and Lambda,” and the AI generates mxGraphModel XML—draw.io’s format since 2005—with correct shapes, positioning, connectors, and AWS-specific icons. The technical challenge is significant: draw.io XML requires precise structure with mxCell elements, spatial coordinates, styling attributes, and connector relationships.

The application supports nine AI providers: AWS Bedrock, OpenAI, Anthropic, Google AI, Azure OpenAI, Ollama, OpenRouter, DeepSeek, and SiliconFlow. However, model quality matters critically. Documentation explicitly recommends Claude Sonnet 4.5, GPT-5.1, or Gemini 3 Pro—weaker models produce malformed XML or illogical layouts. The demo site degrades to minimax-m2 under load, resulting in poor output. For real evaluation, deploy your own instance with advanced models.

The tech stack is modern: Next.js 16.x, React 19.x, and react-drawio for XML rendering. Output is standard draw.io XML, fully compatible with draw.io desktop or web for manual refinement after AI scaffolding.

More Than Text-to-Diagram: Key Features

Beyond basic natural language creation, next-ai-draw-io delivers several production-ready features. Image replication stands out: upload whiteboard photos and AI recreates them as editable XML. This solves the “digitize architecture meeting sketches” problem that plagues development teams. Take a photo of that hastily drawn system diagram on the conference room whiteboard, upload it, and get a clean, editable diagram.

Version control tracks changes like Git with undo and revert capabilities. Experiment freely without losing work. Cloud architecture specialization offers native AWS, GCP, and Azure icon sets—not generic boxes—with Claude models excelling at cloud diagrams according to documentation. Multi-provider support means you’re not locked to one AI vendor, and local Ollama support enables fully offline usage (though quality depends on your local model).

Recent improvements include prompt caching to reduce API costs, mobile-optimized layouts, and Langfuse integration for LLM observability. The reasoning display feature shows AI thinking processes for compatible models, helping you understand how the AI interprets your requests.

Related: CopilotKit v1.50: Build AI Copilots in React Fast

When to Use Next-AI-Draw-IO (and When Not To)

This AI diagram generator fits between code-as-diagram tools (Mermaid, PlantUML) and visual SaaS tools (Lucidchart). Use it for rapid prototyping and complex visual diagrams where natural language beats syntax. Skip it for simple flowcharts—Mermaid is faster and integrates directly with GitHub. Skip it for pixel-perfect designs—manual draw.io gives full control. Skip it when reproducibility is critical—AI output varies.

Compared to Mermaid, next-ai-draw-io trades GitHub integration and code syntax for natural language and complex visuals. Mermaid wins for simple flowcharts and sequence diagrams embedded in documentation. Next-ai-draw-io wins for intricate architecture diagrams where describing relationships conversationally is faster than learning diagram syntax.

Compared to PlantUML, the trade-off is learning curve versus consistency. PlantUML requires syntax mastery but produces reproducible results. Next-ai-draw-io is conversational and faster to start, but AI introduces variance—generate the same prompt twice and get slightly different layouts.

Compared to Lucidchart, it’s open-source self-hosting versus professional SaaS collaboration. Lucidchart offers enterprise features and team workflows. Next-ai-draw-io offers freedom from subscriptions and vendor lock-in, though you pay for AI API calls instead.

The honest assessment: next-ai-draw-io is excellent for prototyping, not final polished diagrams. Use it to scaffold architecture quickly, then refine manually in draw.io. Don’t expect perfect output—advanced models (Claude Sonnet 4.5, GPT-5.1) produce good results, but you’ll still tweak positioning and styling.

Deploy and Start Creating in Minutes

Four deployment options exist: try the online demo at next-ai-drawio.jiang.jp with no setup, run Docker containerized deployment, clone the GitHub repository for local development (npm install, configure provider), or use Vercel’s one-click deploy. Configuration requires three environment variables: AI_PROVIDER, AI_MODEL, and the relevant API key.

Here’s minimal configuration:

AI_PROVIDER=anthropic
AI_MODEL=claude-sonnet-4-5-20251022
ANTHROPIC_API_KEY=your_api_key_here
ACCESS_CODE_LIST=my_secure_password
TEMPERATURE=0.3

Critical security warning from documentation: set ACCESS_CODE_LIST for public deployments to prevent token depletion. Without password protection, someone could drain your API credits by hammering your endpoint. This isn’t theoretical—it happens.

Iterative refinement works best. Start broad: “Create a microservices architecture.” Then refine: “Add authentication service.” Then: “Connect services via message queue.” Then: “Use AWS icons.” This approach beats one massive prompt.

Use Cases and Real-World Limitations

Best use cases include rapid prototyping in design meetings—describe architecture verbally and get instant visuals instead of spending 20 minutes manually placing boxes. Converting text documentation to diagrams streamlines workflow when writing technical specs. Whiteboard digitization transforms meeting photos into editable diagrams. Cloud architecture planning leverages native AWS, GCP, and Azure icons for infrastructure design.

Limitations are real. AI struggles with diagrams exceeding 50 elements—break complex systems into smaller diagrams. Generating quality output requires expensive models (Claude Sonnet 4.5, GPT-5.1), and costs accumulate with heavy iterative refinement. Each conversation turn consumes API tokens. Prompt caching helps but doesn’t eliminate costs. AI output varies—generate the same prompt twice and get different layouts. Most diagrams need manual refinement in draw.io for polished results.

The cost reality: advanced models produce great results but at a price. Budget accordingly for production use, or stick with Mermaid for cost-sensitive projects. The demo site’s degradation to weaker models under load proves this point—quality costs money.

Key Takeaways

• Next-ai-draw-io generates editable draw.io XML from natural language, not static images—output integrates with existing draw.io workflows.
• Best for rapid prototyping and complex architecture diagrams where natural language beats syntax. Not a replacement for Mermaid (simple flowcharts) or manual draw.io (pixel-perfect control).
• Requires advanced AI models (Claude Sonnet 4.5, GPT-5.1) for quality results. Weaker models produce malformed XML or poor layouts. Budget for API costs.
• Image replication, version control, and cloud architecture specialization make this production-ready, not just a demo tool.
• Open-source and self-hostable (Apache 2.0 license). Deploy your own instance to avoid demo site degradation. Set ACCESS_CODE_LIST to prevent token depletion.

For comparison with other diagramming approaches, review this diagram-as-code tools comparison.