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A real-time runtime diagnostics suite for ComfyUI, featuring interactive debugging chat, and 50+ fix patterns. Automatically intercepts terminal output from startup, and delivers prioritized fix suggestions with node-level context extraction. Now supports JSON-based pattern management with hot-reload and full i18n support for 9 languages.

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ComfyUI-Doctor

繁體中文 | English | Roadmap & Development Status

A continuous, real-time runtime diagnostics suite for ComfyUI featuring LLM-powered analysis, interactive debugging chat, and 50+ fix patterns. Automatically intercepts all terminal output from startup, captures complete Python tracebacks, and delivers prioritized fix suggestions with node-level context extraction. Now supports JSON-based pattern management with hot-reload and full i18n support for 9 languages (en, zh_TW, zh_CN, ja, de, fr, it, es, ko).


Latest Updates (Jan 2026)

F4: Statistics Dashboard - Click to expand

Track your ComfyUI stability at a glance!

ComfyUI-Doctor now includes a Statistics Dashboard that provides insights into error trends, common issues, and resolution progress.

Features:

  • 📊 Error Trends: Track errors across 24h/7d/30d time ranges
  • 🔥 Top 5 Patterns: See which errors occur most frequently
  • 📈 Category Breakdown: Visualize errors by category (Memory, Workflow, Model Loading, etc.)
  • Resolution Tracking: Monitor resolved vs. unresolved errors
  • 🌍 Full i18n Support: Available in all 9 languages

Statistics Dashboard

How to Use:

  1. Open the Doctor sidebar panel (click the 🏥 icon on the left)
  2. Expand the "📊 Error Statistics" section
  3. View real-time error analytics and trends
  4. Mark errors as resolved/ignored to track your progress

Backend API:

  • GET /doctor/statistics?time_range_days=30 - Fetch statistics
  • POST /doctor/mark_resolved - Update resolution status

Test Coverage: 17/17 backend tests ✅ | 14/18 E2E tests (78% pass rate)

Implementation Details: See .planning/260104-F4_STATISTICS_RECORD.md


T8: Pattern Validation CI - Click to expand

Automated quality checks now protect pattern integrity!

ComfyUI-Doctor now includes continuous integration testing for all error patterns, ensuring zero-defect contributions.

What T8 Validates:

  • JSON Format: All 8 pattern files compile correctly
  • Regex Syntax: All 57 patterns have valid regular expressions
  • i18n Completeness: 100% translation coverage (57 patterns × 9 languages = 513 checks)
  • Schema Compliance: Required fields (id, regex, error_key, priority, category)
  • Metadata Quality: Valid priority ranges (50-95), unique IDs, correct categories

GitHub Actions Integration:

  • Triggers on every push/PR affecting patterns/, i18n.py, or tests
  • Runs in ~3 seconds with $0 cost (GitHub Actions free tier)
  • Blocks merges if validation fails

For Contributors:

# Local validation before commit
python run_pattern_tests.py

# Output:
✅ All 57 patterns have required fields
✅ All 57 regex patterns compile successfully
✅ en: All 57 patterns have translations
✅ zh_TW: All 57 patterns have translations
... (9 languages total)

Test Results: 100% pass rate across all checks

Implementation Details: See .planning/260103-T8_IMPLEMENTATION_RECORD.md


Phase 4B: Pattern System Overhaul (STAGE 1-3 Complete) - Click to expand

ComfyUI-Doctor has undergone a major architecture upgrade with 57+ error patterns and JSON-based pattern management!

STAGE 1: Logger Architecture Fix

  • Implemented SafeStreamWrapper with queue-based background processing
  • Eliminated deadlock risks and race conditions
  • Fixed log interception conflicts with ComfyUI's LogInterceptor

STAGE 2: JSON Pattern Management (F2)

  • New PatternLoader with hot-reload capability (no restart needed!)
  • Patterns now defined in JSON files under patterns/ directory
  • 22 builtin patterns in patterns/builtin/core.json
  • Easy to extend and maintain

STAGE 3: Community Pattern Expansion (F12)

  • 35 new community patterns covering popular extensions:
    • ControlNet (8 patterns): Model loading, preprocessing, image sizing
    • LoRA (6 patterns): Loading errors, compatibility, weight issues
    • VAE (5 patterns): Encoding/decoding failures, precision, tiling
    • AnimateDiff (4 patterns): Model loading, frame count, context length
    • IPAdapter (4 patterns): Model loading, image encoding, compatibility
    • FaceRestore (3 patterns): CodeFormer/GFPGAN models, detection
    • Miscellaneous (5 patterns): Checkpoints, samplers, schedulers, CLIP
  • Full i18n support for English, Traditional Chinese, and Simplified Chinese
  • Total: 57 error patterns (22 builtin + 35 community)

Benefits:

  • ✅ More comprehensive error coverage
  • ✅ Hot-reload patterns without restarting ComfyUI
  • ✅ Community can contribute patterns via JSON files
  • ✅ Cleaner, more maintainable codebase

Previous Updates (Dec 2025) - Click to expand

F9: Multi-language Support Expansion

We've expanded language support from 4 to 9 languages! ComfyUI-Doctor now provides error suggestions in:

  • English (en)
  • 繁體中文 Traditional Chinese (zh_TW)
  • 简体中文 Simplified Chinese (zh_CN)
  • 日本語 Japanese (ja)
  • 🆕 Deutsch German (de)
  • 🆕 Français French (fr)
  • 🆕 Italiano Italian (it)
  • 🆕 Español Spanish (es)
  • 🆕 한국어 Korean (ko)

All 57 error patterns are fully translated across all languages, ensuring consistent diagnostic quality worldwide.

F8: Sidebar Settings Integration

Settings have been streamlined! Configure Doctor directly from the sidebar:

  • Click the ⚙️ icon in the sidebar header to access all settings
  • Language selection (9 languages)
  • AI Provider quick-switch (OpenAI, DeepSeek, Groq, Gemini, Ollama, etc.)
  • Base URL auto-fill when changing providers
  • API Key management (password-protected input)
  • Model name configuration
  • Settings persist across sessions with localStorage
  • Visual feedback on save (✅ Saved! / ❌ Error)

ComfyUI Settings panel now only shows the Enable/Disable toggle - all other settings moved to the sidebar for a cleaner, more integrated experience.


Features

  • Automatic Error Monitoring: Captures all terminal output and detects Python tracebacks in real-time
  • Intelligent Error Analysis: 57+ error patterns (22 builtin + 35 community) with actionable suggestions
  • Node Context Extraction: Identifies which node caused the error (Node ID, Name, Class)
  • System Environment Context: Automatically includes Python version, installed packages (pip list), and OS info in AI analysis
  • Multi-language Support: 9 languages supported (English, 繁體中文, 简体中文, 日本語, Deutsch, Français, Italiano, Español, 한국어)
  • JSON-based Pattern Management: Hot-reload error patterns without restarting ComfyUI
  • Community Pattern Support: Covers ControlNet, LoRA, VAE, AnimateDiff, IPAdapter, FaceRestore, and more
  • Debug Inspector Node: Deep inspection of data flowing through your workflow
  • Error History: Maintains a buffer of recent errors via API
  • RESTful API: Seven endpoints for frontend integration
  • AI-Powered Analysis: One-click LLM error analysis with support for 8+ providers (OpenAI, DeepSeek, Groq, Gemini, Ollama, LMStudio, and more)
  • Interactive Chat Interface: Multi-turn AI debugging assistant integrated into ComfyUI sidebar
  • Interactive Sidebar UI: Visual error panel with node location and instant diagnostics
  • Flexible Configuration: Comprehensive settings panel for behavior customization

🆕 AI Chat Interface

The new interactive chat interface provides a conversational debugging experience directly within ComfyUI's left sidebar. When an error occurs, simply click "Analyze with AI" to start a multi-turn conversation with your preferred LLM.

AI Chat Interface

Key Features:

  • Context-Aware: Automatically includes error details, node information, and workflow context
  • Environment-Aware: Includes Python version, installed packages, and OS info for accurate debugging
  • Streaming Responses: Real-time LLM responses with proper formatting
  • Multi-Turn Conversations: Ask follow-up questions to dig deeper into issues
  • Always Accessible: Input area stays visible at the bottom with sticky positioning
  • Supports 8+ LLM Providers: OpenAI, DeepSeek, Groq, Gemini, Ollama, LMStudio, and more
  • Smart Caching: Package list cached for 24 hours to avoid performance impact

How to Use:

  1. When an error occurs, open the Doctor sidebar (left panel)
  2. Click the "✨ Analyze with AI" button in the error context area
  3. The AI will automatically analyze the error and provide suggestions
  4. Continue the conversation by typing follow-up questions in the input box
  5. Press Enter or click "Send" to submit your message

💡 Free API Tip: Google AI Studio (Gemini) offers a generous free tier with no credit card required. Perfect for getting started with AI-powered debugging without any costs!


Installation

Option 1: Using ComfyUI-Manager (Recommended)

  1. Open ComfyUI and click the Manager button in the menu
  2. Select Install Custom Nodes
  3. Search for ComfyUI-Doctor
  4. Click Install and restart ComfyUI

Option 2: Manual Installation (Git Clone)

  1. Navigate to your ComfyUI custom nodes directory:

    cd ComfyUI/custom_nodes/
  2. Clone this repository:

    git clone https://github.com/rookiestar28/ComfyUI-Doctor.git
  3. Restart ComfyUI

  4. Look for the initialization message in the console:

    [ComfyUI-Doctor] Initializing Smart Debugger...
    [ComfyUI-Doctor] Log file: .../logs/comfyui_debug_2025-12-28.log
    
    ==================== SYSTEM SNAPSHOT ====================
    OS: Windows 11
    Python: 3.12.3
    PyTorch: 2.0.1+cu118
    CUDA Available: True
    ...
    

Usage

Passive Mode (Automatic)

Once installed, ComfyUI-Doctor automatically:

  • Records all console output to logs/ directory
  • Detects errors and provides suggestions
  • Logs system environment information

Example Error Output:

Traceback (most recent call last):
  ...
RuntimeError: CUDA out of memory. Tried to allocate 2.00 GiB

----------------------------------------
ERROR LOCATION: Node ID: #42 | Name: KSampler
SUGGESTION: OOM (Out Of Memory): Your GPU VRAM is full. Try:
   1. Reducing Batch Size
   2. Using '--lowvram' flag
   3. Closing other GPU apps
----------------------------------------

Active Mode (Debug Node)

  1. Right-click on the canvas → Add NodeSmart Debug Node
  2. Connect the node inline with any connection (supports wildcard input *)
  3. Execute your workflow

Example Output:

[DEBUG] Data Inspection:
  Type: Tensor
  Shape: torch.Size([1, 4, 64, 64])
  Dtype: torch.float16
  Device: cuda:0
  Stats (All): Min=-3.2156, Max=4.8912, Mean=0.0023

The node passes data through without affecting workflow execution.


Frontend UI

ComfyUI-Doctor provides an interactive sidebar interface for real-time error monitoring and diagnostics.

Accessing the Doctor Panel

Click the 🏥 Doctor button in the ComfyUI menu (left sidebar) to open the Doctor panel. The panel slides in from the right side of the screen.

Interface Features

Error Report

The Doctor interface consists of two panels:

Left Sidebar Panel (Doctor Sidebar)

Click the 🏥 Doctor icon in ComfyUI's left menu to access:

  • Settings Panel (⚙️ icon): Configure language, AI provider, API keys, and model selection
  • Error Context Card: When an error occurs, displays:
    • 💡 Suggestion: Concise, actionable advice (e.g., "Check input connections and ensure node requirements are met.")
    • Timestamp: When the error occurred
    • Node Context: Node ID and name (if applicable)
    • ✨ Analyze with AI: Launch interactive chat for detailed debugging
  • AI Chat Interface: Multi-turn conversation with your LLM for in-depth error analysis
  • Sticky Input Area: Always accessible at bottom for follow-up questions

Right Error Panel (Latest Diagnosis)

Real-time error notifications in the top-right corner:

Doctor Error Report

  • Status Indicator: Colored dot showing system health
    • 🟢 Green: System running normally, no errors detected
    • 🔴 Red (pulsing): Active error detected
  • Latest Diagnosis Card: Displays the most recent error with:
    • Error Summary: Brief error description (red-themed, collapsible for long errors)
    • 💡 Suggestion: Concise actionable advice (green-themed)
    • Timestamp: When the error occurred
    • Node Context: Node ID, name, and class
    • 🔍 Locate Node on Canvas: Automatically centers and highlights the problematic node

Key Design Principles:

  • Concise Suggestions: Only the actionable advice is shown (e.g., "Check input connections...") instead of verbose error descriptions
  • Visual Separation: Error messages (red) and suggestions (green) are clearly distinguished
  • Smart Truncation: Long errors show first 3 + last 3 lines with collapsible full details
  • Real-time Updates: Both panels automatically update when new errors occur via WebSocket events

AI-Powered Error Analysis

ComfyUI-Doctor integrates with popular LLM services to provide intelligent, context-aware debugging suggestions.

Supported AI Providers

Cloud Services

  • OpenAI (GPT-4, GPT-4o, etc.)
  • DeepSeek (DeepSeek-V2, DeepSeek-Coder)
  • Groq Cloud (Llama 3, Mixtral - ultra-fast LPU inference)
  • Google Gemini (Gemini Pro, Gemini Flash)
  • xAI Grok (Grok-2, Grok-beta)
  • OpenRouter (Access to Claude, GPT-4, and 100+ models)

Local Services (No API Key Required)

  • Ollama (http://127.0.0.1:11434) - Run Llama, Mistral, CodeLlama locally
  • LMStudio (http://localhost:1234/v1) - Local model inference with GUI

💡 Cross-Platform Compatibility: Default URLs can be overridden via environment variables:

  • OLLAMA_BASE_URL - Custom Ollama endpoint (default: http://127.0.0.1:11434)
  • LMSTUDIO_BASE_URL - Custom LMStudio endpoint (default: http://localhost:1234/v1)

This prevents conflicts between Windows and WSL2 Ollama instances, or when running in Docker/custom setups.

Configuration

Settings Panel

Configure AI analysis in the Doctor SidebarSettings panel:

  1. AI Provider: Select from the dropdown menu. The Base URL will auto-fill.
  2. AI Base URL: The API endpoint (auto-populated, but customizable)
  3. AI API Key: Your API key (leave empty for local LLMs like Ollama/LMStudio)
  4. AI Model Name:
    • Select a model from the dropdown list (automatically populated from your provider's API)
    • Click the 🔄 refresh button to reload available models
    • Or check "Enter model name manually" to type a custom model name
  5. Privacy Mode: Select PII sanitization level for cloud AI services (see Privacy Mode (PII Sanitization) section below for details)

Using AI Analysis

  1. Automatically opens the Doctor panel when an error occurs.
  2. Review the built-in suggestions, or click the ✨ Analyze with AI button on the error card.
  3. Wait for the LLM to analyze the error (typically 3-10 seconds).
  4. Review the AI-generated debugging suggestions.

Security Note: Your API key is transmitted securely from frontend to backend for the analysis request only. It is never logged or stored persistently.

Privacy Mode (PII Sanitization)

ComfyUI-Doctor includes automatic PII (Personally Identifiable Information) sanitization to protect your privacy when sending error messages to cloud AI services.

Three Privacy Levels:

Level Description What is Removed Recommended For
None No sanitization Nothing Local LLMs (Ollama, LMStudio)
Basic (Default) Standard protection User paths, API keys, emails, IP addresses Most users with cloud LLMs
Strict Maximum privacy All of Basic + IPv6, SSH fingerprints Enterprise/compliance requirements

What is Sanitized (Basic Level):

  • ✅ Windows user paths: C:\Users\john\file.py<USER_PATH>\file.py
  • ✅ Linux/macOS home: /home/alice/test.py<USER_HOME>/test.py
  • ✅ API keys: sk-abc123...<API_KEY>
  • ✅ Email addresses: user@example.com<EMAIL>
  • ✅ Private IPs: 192.168.1.1<PRIVATE_IP>
  • ✅ URL credentials: https://user:pass@hosthttps://<USER>@host

What is NOT Removed:

  • ❌ Error messages (needed for debugging)
  • ❌ Model names, node names
  • ❌ Workflow structure
  • ❌ Public file paths (/usr/bin/python)

Configure Privacy Mode: Open Doctor Sidebar → Settings → 🔒 Privacy Mode dropdown. Changes apply immediately to all AI analysis requests.

GDPR Compliance: This feature supports GDPR Article 25 (Data Protection by Design) and is recommended for enterprise deployments.

Statistics Dashboard

Statistics Panel

The Statistics Dashboard provides real-time insights into your ComfyUI error patterns and stability trends.

Features:

  • 📊 Error Trends: Total errors and counts for last 24h/7d/30d
  • 🔥 Top Error Patterns: Top 5 most frequent error types with occurrence counts
  • 📈 Category Breakdown: Visual breakdown by error category (Memory, Workflow, Model Loading, Framework, Generic)
  • ✅ Resolution Tracking: Track resolved, unresolved, and ignored errors

How to Use:

  1. Open the Doctor sidebar (click 🏥 icon on left)
  2. Find the 📊 Error Statistics collapsible section
  3. Click to expand and view your error analytics
  4. Mark errors as resolved/ignored directly from error cards to update resolution tracking

Understanding the Data:

  • Total (30d): Cumulative errors in the past 30 days
  • Last 24h: Errors in the last 24 hours (helps identify recent issues)
  • Resolution Rate: Shows progress toward resolving known issues
    • 🟢 Resolved: Issues you've fixed
    • 🟠 Unresolved: Active issues requiring attention
    • Ignored: Non-critical issues you've chosen to ignore
  • Top Patterns: Identifies which error types need priority attention
  • Categories: Helps you understand whether issues are memory-related, workflow problems, model loading failures, etc.

Panel State Persistence: The panel's open/closed state is saved in your browser's localStorage, so your preference persists across sessions.

Example Providers Setup

Provider Base URL Model Example
OpenAI https://api.openai.com/v1 gpt-4o
DeepSeek https://api.deepseek.com/v1 deepseek-chat
Groq https://api.groq.com/openai/v1 llama-3.1-70b-versatile
Gemini https://generativelanguage.googleapis.com/v1beta/openai gemini-1.5-flash
Ollama (Local) http://localhost:11434/v1 llama3.1:8b
LMStudio (Local) http://localhost:1234/v1 Model loaded in LMStudio

Settings

You can customize ComfyUI-Doctor behavior via the ComfyUI Settings panel (Gear icon).

1. Show error notifications

Function: Toggle floating error notification cards (toasts) in the top-right corner. Usage: Disable if you prefer to check errors manually in the sidebar without visual interruptions.

2. Auto-open panel on error

Function: Automatically expands the Doctor sidebar when a new error is detected. Usage: Recommended. Provides immediate access to diagnostic results without manual clicking.

3. Error Check Interval (ms)

Function: Frequency of frontend-backend error checks (in milliseconds). Default: 2000. Usage: Lower values (e.g., 500) give faster feedback but increase load; higher values (e.g., 5000) save resources.

4. Suggestion Language

Function: Language for diagnostic reports and Doctor suggestions. Usage: Currently supports English, Traditional Chinese, Simplified Chinese, and Japanese (more coming soon). Changes apply to new errors.

5. Enable Doctor (requires restart)

Function: Master switch for the log interception system. Usage: Turn off to completely disable Doctor's core functionality (requires ComfyUI restart).

6. AI Provider

Function: Select your preferred LLM service provider from a dropdown menu. Options: OpenAI, DeepSeek, Groq Cloud, Google Gemini, xAI Grok, OpenRouter, Ollama (Local), LMStudio (Local), Custom. Usage: Selecting a provider automatically fills in the appropriate Base URL. For local providers (Ollama/LMStudio), an alert displays available models.

7. AI Base URL

Function: The API endpoint for your LLM service. Usage: Auto-populated when you select a provider, but can be customized for self-hosted or custom endpoints.

8. AI API Key

Function: Your API key for authentication with cloud LLM services. Usage: Required for cloud providers (OpenAI, DeepSeek, etc.). Leave empty for local LLMs (Ollama, LMStudio). Security: The key is only transmitted during analysis requests and is never logged or persisted.

9. AI Model Name

Function: Specify which model to use for error analysis. Usage:

  • Dropdown Mode (default): Select a model from the automatically-populated dropdown list. Click the 🔄 refresh button to reload available models.
  • Manual Input Mode: Check "Enter model name manually" to type a custom model name (e.g., gpt-4o, deepseek-chat, llama3.1:8b).
  • Models are automatically fetched from your selected provider's API when you change providers or click refresh.
  • For local LLMs (Ollama/LMStudio), the dropdown displays all locally available models.

API Endpoints

GET /debugger/last_analysis

Retrieve the most recent error analysis:

curl http://localhost:8188/debugger/last_analysis

Response Example:

{
  "status": "running",
  "log_path": ".../logs/comfyui_debug_2025-12-28.log",
  "language": "zh_TW",
  "supported_languages": ["en", "zh_TW", "zh_CN", "ja"],
  "last_error": "Traceback...",
  "suggestion": "SUGGESTION: ...",
  "timestamp": "2025-12-28T06:49:11",
  "node_context": {
    "node_id": "42",
    "node_name": "KSampler",
    "node_class": "KSamplerNode",
    "custom_node_path": "ComfyUI-Impact-Pack"
  }
}

GET /debugger/history

Retrieve error history (last 20 entries):

curl http://localhost:8188/debugger/history

POST /debugger/set_language

Change the suggestion language (see Language Switching section).

POST /doctor/analyze

Analyze an error using configured LLM service.

Payload:

{
  "error": "Traceback...",
  "node_context": {...},
  "api_key": "your-api-key",
  "base_url": "https://api.openai.com/v1",
  "model": "gpt-4o",
  "language": "en"
}

Response:

{
  "analysis": "AI-generated debugging suggestions..."
}

POST /doctor/verify_key

Verify API key validity by testing connection to the LLM provider.

Payload:

{
  "base_url": "https://api.openai.com/v1",
  "api_key": "your-api-key"
}

Response:

{
  "success": true,
  "message": "API key is valid",
  "is_local": false
}

POST /doctor/list_models

List available models from the configured LLM provider.

Payload:

{
  "base_url": "http://localhost:11434/v1",
  "api_key": ""
}

Response:

{
  "success": true,
  "models": [
    {"id": "llama3.1:8b", "name": "llama3.1:8b"},
    {"id": "mistral:7b", "name": "mistral:7b"}
  ],
  "message": "Found 2 models"
}

Log Files

All logs are stored in:

ComfyUI/custom_nodes/ComfyUI-Doctor/logs/

Filename format: comfyui_debug_YYYY-MM-DD_HH-MM-SS.log

The system automatically retains the 10 most recent log files (configurable via config.json).


Configuration

Create config.json to customize behavior:

{
  "max_log_files": 10,
  "buffer_limit": 100,
  "traceback_timeout_seconds": 5.0,
  "history_size": 20,
  "default_language": "zh_TW",
  "enable_api": true,
  "privacy_mode": "basic"
}

Parameters:

  • max_log_files: Maximum number of log files to retain
  • buffer_limit: Traceback buffer size (line count)
  • traceback_timeout_seconds: Timeout for incomplete tracebacks
  • history_size: Number of errors to keep in history
  • default_language: Default suggestion language
  • enable_api: Enable API endpoints
  • privacy_mode: PII sanitization level - "none", "basic" (default), or "strict" (see Privacy Mode section above)

Supported Error Patterns

ComfyUI-Doctor can detect and provide suggestions for:

  • Type mismatches (e.g., fp16 vs float32)
  • Dimension mismatches
  • CUDA/MPS out of memory
  • Matrix multiplication errors
  • Device/type conflicts
  • Missing Python modules
  • Assertion failures
  • Key/attribute errors
  • Shape mismatches
  • File not found errors
  • SafeTensors loading errors
  • CUDNN execution failures
  • Missing InsightFace library
  • Model/VAE mismatches
  • Invalid prompt JSON

And more...


Tips

  1. Pair with ComfyUI Manager: Install missing custom nodes automatically
  2. Check log files: Full tracebacks are recorded for issue reporting
  3. Use the built-in sidebar: Click the 🏥 Doctor icon in the left menu for real-time diagnostics
  4. Node Debugging: Connect Debug nodes to inspect suspicious data flow

License

MIT License


Contributing

Contributions are welcome! Please feel free to submit a Pull Request.


Support

If you encounter any issues or have suggestions, please open an issue on GitHub.

About

A real-time runtime diagnostics suite for ComfyUI, featuring interactive debugging chat, and 50+ fix patterns. Automatically intercepts terminal output from startup, and delivers prioritized fix suggestions with node-level context extraction. Now supports JSON-based pattern management with hot-reload and full i18n support for 9 languages.

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