* feat: Add reasoning/thinking blocks display in chat interface * feat: add multi-provider options support and replace custom reasoning UI with AI Elements * resolve conflicting reasoning configs and correct provider-specific reasoning parameters * try to solve conflict * fix: simplify reasoning display and remove unnecessary dependencies - Remove Streamdown dependency (~5MB) - reasoning is plain text only - Fix Bedrock providerOptions merging for Claude reasoning configs - Remove unsupported DeepSeek reasoning configuration - Clean up unused environment variables (REASONING_BUDGET_TOKENS, REASONING_EFFORT, DEEPSEEK_REASONING_*) - Remove dead commented code from route.ts Reasoning blocks contain plain thinking text and don't need markdown/diagram/code rendering. * feat: comprehensive reasoning support improvements Major improvements: - Auto-enable reasoning display for all supported models - Fix provider-specific reasoning configurations - Remove unnecessary Streamdown dependency (~5MB) - Clean up debug logging Provider changes: - OpenAI: Auto-enable reasoningSummary for o1/o3/gpt-5 models - Google: Auto-enable includeThoughts for Gemini 2.5/3 models - Bedrock: Restrict reasoningConfig to only Claude/Nova (fixes MiniMax error) - Ollama: Add thinking support for qwen3-like models Other improvements: - Remove ENABLE_REASONING toggle (always enabled) - Fix Bedrock providerOptions merging for Claude - Simplify reasoning component (plain text rendering) - Clean up unused environment variables * fix: critical bugs and documentation gaps in reasoning support Critical fixes: - Fix Bedrock shallow merge bug (deep merge preserves anthropicBeta + reasoningConfig) - Add parseInt validation with parseIntSafe helper (prevents NaN errors) - Validate all numeric env vars with min/max ranges Documentation improvements: - Add BEDROCK_REASONING_BUDGET_TOKENS and BEDROCK_REASONING_EFFORT to env.example - Add OLLAMA_ENABLE_THINKING to env.example - Update JSDoc with accurate env var list and ranges Code cleanup: - Remove debug console.log statements from route.ts - Refactor duplicate providerOptions assignments --------- Co-authored-by: Dayuan Jiang <34411969+DayuanJiang@users.noreply.github.com> Co-authored-by: Dayuan Jiang <jdy.toh@gmail.com>
Next AI Draw.io
A Next.js web application that integrates AI capabilities with draw.io diagrams. Create, modify, and enhance diagrams through natural language commands and AI-assisted visualization.
https://github.com/user-attachments/assets/b2eef5f3-b335-4e71-a755-dc2e80931979
Table of Contents
Examples
Here are some example prompts and their generated diagrams:
Features
- LLM-Powered Diagram Creation: Leverage Large Language Models to create and manipulate draw.io diagrams directly through natural language commands
- Image-Based Diagram Replication: Upload existing diagrams or images and have the AI replicate and enhance them automatically
- Diagram History: Comprehensive version control that tracks all changes, allowing you to view and restore previous versions of your diagrams before the AI editing.
- Interactive Chat Interface: Communicate with AI to refine your diagrams in real-time
- AWS Architecture Diagram Support: Specialized support for generating AWS architecture diagrams
- Animated Connectors: Create dynamic and animated connectors between diagram elements for better visualization
Getting Started
Try it Online
No installation needed! Try the app directly on our demo site:
Note: Due to high traffic, the demo site currently uses minimax-m2. For best results, we recommend self-hosting with Claude Sonnet 4.5 or Claude Opus 4.5.
Bring Your Own API Key: You can use your own API key to bypass usage limits on the demo site. Click the Settings icon in the chat panel to configure your provider and API key. Your key is stored locally in your browser and is never stored on the server.
Run with Docker (Recommended)
If you just want to run it locally, the best way is to use Docker.
First, install Docker if you haven't already: Get Docker
Then run:
docker run -d -p 3000:3000 \
-e AI_PROVIDER=openai \
-e AI_MODEL=gpt-4o \
-e OPENAI_API_KEY=your_api_key \
ghcr.io/dayuanjiang/next-ai-draw-io:latest
Or use an env file:
cp env.example .env
# Edit .env with your configuration
docker run -d -p 3000:3000 --env-file .env ghcr.io/dayuanjiang/next-ai-draw-io:latest
Open http://localhost:3000 in your browser.
Replace the environment variables with your preferred AI provider configuration. See Multi-Provider Support for available options.
Offline Deployment: If
embed.diagrams.netis blocked, see Offline Deployment for configuration options.
Installation
- Clone the repository:
git clone https://github.com/DayuanJiang/next-ai-draw-io
cd next-ai-draw-io
- Install dependencies:
npm install
- Configure your AI provider:
Create a .env.local file in the root directory:
cp env.example .env.local
Edit .env.local and configure your chosen provider:
- Set
AI_PROVIDERto your chosen provider (bedrock, openai, anthropic, google, azure, ollama, openrouter, deepseek, siliconflow) - Set
AI_MODELto the specific model you want to use - Add the required API keys for your provider
TEMPERATURE: Optional temperature setting (e.g.,0for deterministic output). Leave unset for models that don't support it (e.g., reasoning models).ACCESS_CODE_LIST: Optional access password(s), can be comma-separated for multiple passwords.
Warning: If you do not set
ACCESS_CODE_LIST, anyone can access your deployed site directly, which may lead to rapid depletion of your token. It is recommended to set this option.
See the Provider Configuration Guide for detailed setup instructions for each provider.
- Run the development server:
npm run dev
- Open http://localhost:3000 in your browser to see the application.
Deployment
The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.
Check out the Next.js deployment documentation for more details.
Or you can deploy by this button.
Be sure to set the environment variables in the Vercel dashboard as you did in your local .env.local file.
Multi-Provider Support
- AWS Bedrock (default)
- OpenAI
- Anthropic
- Google AI
- Azure OpenAI
- Ollama
- OpenRouter
- DeepSeek
- SiliconFlow
All providers except AWS Bedrock and OpenRouter support custom endpoints.
📖 Detailed Provider Configuration Guide - See setup instructions for each provider.
Model Requirements: This task requires strong model capabilities for generating long-form text with strict formatting constraints (draw.io XML). Recommended models include Claude Sonnet 4.5, GPT-5.1, Gemini 3 Pro, and DeepSeek V3.2/R1.
Note that claude series has trained on draw.io diagrams with cloud architecture logos like AWS, Azue, GCP. So if you want to create cloud architecture diagrams, this is the best choice.
How It Works
The application uses the following technologies:
- Next.js: For the frontend framework and routing
- Vercel AI SDK (
ai+@ai-sdk/*): For streaming AI responses and multi-provider support - react-drawio: For diagram representation and manipulation
Diagrams are represented as XML that can be rendered in draw.io. The AI processes your commands and generates or modifies this XML accordingly.
Project Structure
app/ # Next.js App Router
api/chat/ # Chat API endpoint with AI tools
page.tsx # Main page with DrawIO embed
components/ # React components
chat-panel.tsx # Chat interface with diagram control
chat-input.tsx # User input component with file upload
history-dialog.tsx # Diagram version history viewer
ui/ # UI components (buttons, cards, etc.)
contexts/ # React context providers
diagram-context.tsx # Global diagram state management
lib/ # Utility functions and helpers
ai-providers.ts # Multi-provider AI configuration
utils.ts # XML processing and conversion utilities
public/ # Static assets including example images
Support & Contact
If you find this project useful, please consider sponsoring to help me host the live demo site!
For support or inquiries, please open an issue on the GitHub repository or contact the maintainer at:
- Email: me[at]jiang.jp