Node-RED Make IoT Smart

A powerful AI assistant for Node-RED that makes IoT development smarter and more efficient. Built on LangChain.js framework with modular architecture design, supporting multiple LLM providers, intelligent memory management, and comprehensive tool integration.

Overview

Node-RED Make IoT Smart is an intelligent assistant designed specifically for Node-RED developers. It integrates advanced AI capabilities to help you:

• Learn Node-RED faster with interactive tutorials and smart guidance
• Develop flows more efficiently with AI-powered code generation and optimization
• Solve complex IoT challenges with expert solution recommendations
• Integrate diverse systems seamlessly with intelligent protocol handling
• Manage projects better with automated planning and best practices

Features

AI Assistant

• 🤖 Multi-LLM Support: Compatible with OpenAI, Anthropic, Google, Ollama, and more
• 💬 Interactive Chat Interface: Intuitive sidebar for seamless AI interaction
• 🧠 Intelligent Memory: SQLite-based memory system for context-aware conversations
• 🔧 Tool Integration: Comprehensive MCP (Model Context Protocol) tool support
• 🎯 Scenario-based Assistance: Specialized modes for different development needs

Development Tools

• 📝 Flow Generation: AI-powered Node-RED flow creation and optimization
• 🔍 Code Analysis: Intelligent flow debugging and performance optimization
• 🔗 System Integration: Expert guidance for device and API integration
• 📚 Learning Support: Interactive tutorials and best practice recommendations
• ⚙️ Configuration Management: Automated system setup and optimization

Upcoming Features

• 🌐 Remote Debugging: Advanced debugging capabilities for distributed systems
• 👥 Team Collaboration: Multi-user support with shared workspaces
• 📊 Advanced Analytics: Deep flow analysis and performance insights
• 🚀 Smart Deployment: Intelligent deployment tools and environment management

Installation

Method 1: Install via Node-RED Palette Manager

1. Open Node-RED in your browser
2. Go to Menu → Manage Palette
3. Click the "Install" tab
4. Search for node-red-make-iot-smart
5. Click "Install"

Method 2: Install via npm

npm install node-red-make-iot-smart

Method 3: Install from Source

git clone https://github.com/jimmyfreecoding/node-red-make-iot-smart.git
cd node-red-make-iot-smart
npm install
npm link
cd ~/.node-red
npm link node-red-make-iot-smart

Configuration

Basic Setup

1. Add the Node: Drag the "Make IoT Smart" node from the palette to your flow
2. Configure AI Provider: Double-click the node to open configuration
3. Set API Key: Enter your LLM provider's API key
4. Choose Model: Select the appropriate model for your needs
5. Deploy: Click the Deploy button to activate

AI Provider Configuration

Supported providers and their configuration:

OpenAI

{
  "provider": "openai",
  "apiKey": "your-openai-api-key",
  "model": "gpt-4",
  "baseURL": "https://api.openai.com/v1"
}

Anthropic

{
  "provider": "anthropic",
  "apiKey": "your-anthropic-api-key",
  "model": "claude-3-sonnet-20240229"
}

Google

{
  "provider": "google",
  "apiKey": "your-google-api-key",
  "model": "gemini-pro"
}

Ollama (Local)

{
  "provider": "ollama",
  "baseURL": "http://localhost:11434",
  "model": "llama2"
}

Advanced Configuration

• Memory Settings: Configure conversation history and context retention
• Tool Integration: Enable/disable specific MCP tools
• Scenario Customization: Modify or add custom scenario prompts
• Performance Tuning: Adjust streaming, timeout, and retry settings

Usage

Quick Start

1. Open AI Sidebar: Click the AI assistant icon in the Node-RED sidebar

2. Select Scenario: Choose the appropriate assistance mode:

   • Learning: For Node-RED tutorials and concept explanations
   • Development: For flow creation and code assistance
   • Solution: For IoT architecture and solution design
   • Integration: For system and device integration
   • Configuration: For system setup and optimization
   • Management: For project planning and organization
   • General: For general Node-RED assistance

3. Start Chatting: Type your question or request in the chat interface

4. Apply Suggestions: Use the "Apply" button to implement AI-generated flows

Example Use Cases

Learning Node-RED

User: "I'm new to Node-RED, please explain the basic concepts"
AI: Provides comprehensive explanation of flows, nodes, and wires with examples

Creating Flows

User: "Create a flow that reads temperature sensor data and sends alerts"
AI: Generates complete JSON flow with detailed explanations

System Integration

User: "How do I connect MQTT devices to a REST API?"
AI: Provides integration architecture and implementation steps

Troubleshooting

User: "My flow is running slowly, help me optimize it"
AI: Analyzes performance and suggests optimizations

Supported Scenarios

Learning

Description: Node-RED learning assistant, providing teaching guidance and knowledge answers

Sample Prompts:

• "I'm new to Node-RED, please introduce the basic concepts and core functions of Node-RED"
• "Please explain what Flow, Node and Wire are in Node-RED"
• "How to create my first simple flow in Node-RED? Please provide detailed steps"
• "What are the commonly used core nodes in Node-RED? What are their respective functions?"

Smart Keywords: Automatically triggered when learning-related terms are detected Dynamic Parameters: nodeRedVersion, nodeVersion, currentTime, selectedFlow, selectedNodes, userLevel, mcpTools, lang System Prompt: Professional Node-RED learning assistant that explains complex concepts in simple language, provides practical examples, and adjusts explanation depth based on user skill level.

Solution

Description: IoT solution expert, providing technical solutions and architecture recommendations

Sample Prompts:

• "I need to design a smart home control system, please provide a complete IoT solution architecture"
• "How to use Node-RED to build an Industry 4.0 data collection and monitoring system?"
• "Please design an agricultural IoT solution, including sensor data collection and automation control"
• "I want to establish a smart city environmental monitoring network, what technical solutions are needed?"

Smart Keywords: Automatically triggered when solution design terms are detected Dynamic Parameters: nodeRedVersion, nodeVersion, currentTime, selectedFlow, selectedNodes, projectRequirements, mcpTools, lang System Prompt: Professional IoT solution architect that analyzes business requirements, provides multiple feasible options, compares pros and cons, and recommends optimal technology stacks.

Integration

Description: System integration expert, handling device connections and data integration

Sample Prompts:

• "How to integrate MQTT devices and HTTP APIs in Node-RED? Please provide detailed integration solutions"
• "I need to send sensor data from Modbus devices to cloud databases, how to implement this?"
• "Please help me design a data transformation flow that converts JSON format to XML and sends to third-party systems"
• "How to implement unified data collection and processing for multiple different protocol devices in Node-RED?"

Smart Keywords: Automatically triggered when integration-related terms are detected Dynamic Parameters: nodeRedVersion, nodeVersion, currentTime, selectedFlow, selectedNodes, integrationTargets, mcpTools, lang System Prompt: Professional system integration engineer focusing on IoT device connections, data integration, and API integration with emphasis on stability and security.

Development

Description: Code development assistant, helping to write and optimize Node-RED flows

Sample Prompts:

• "Explanation current flow"
• "Explanation current node"
• "Please help me write a Function node code that implements data filtering and format conversion"
• "How to create a custom node in Node-RED? Please provide complete development steps"
• "My flow is running slowly, please help me analyze performance bottlenecks and provide optimization suggestions"
• "Please design an error handling mechanism to ensure the flow can run stably under abnormal conditions"

Smart Keywords:

• ["create flow", "generate flow", "new flow", "build flow"] → Triggers structured flow creation format

Dynamic Parameters: nodeRedVersion, nodeVersion, currentTime, selectedFlow, selectedNodes, developmentTask, mcpTools, lang System Prompt: Professional Node-RED development engineer that writes high-quality flow logic, provides code review and optimization suggestions, and assists with debugging and custom node creation.

Configuration

Description: System configuration expert, handling Node-RED environment and node configuration

Sample Prompts:

• "Current Node-RED configuration"
• "Current Node-RED diagnostics"
• "How to configure Node-RED security settings, including user authentication and HTTPS?"
• "Please help me optimize Node-RED performance configuration to improve system efficiency"
• "How to install and manage third-party node packages in Node-RED?"
• "I need to configure Node-RED logging and monitoring, how should I set it up?"

Smart Keywords:

• ["current config", "current settings", "system config", "configuration info"] → Triggers get_settings tool
• ["current diagnostics", "system diagnostics", "diagnostic info", "health check"] → Triggers get_diagnostics tool

Dynamic Parameters: nodeRedVersion, nodeVersion, currentTime, selectedFlow, selectedNodes, configurationNeeds, mcpTools, lang System Prompt: Professional Node-RED system administrator that provides configuration recommendations, assists with node management, optimizes performance, and handles security configuration with automatic tool calling for current system information.

Management

Description: Project management assistant, helping with flow organization and project planning

Sample Prompts:

• "Please help me develop an IoT project development plan and milestone arrangement"
• "How to organize and manage large project flow structures in Node-RED?"
• "I need to assess the risks and quality of the current project, please provide analysis suggestions"
• "How to establish Node-RED development standards and best practices for team collaboration?"

Smart Keywords: Automatically triggered when project management terms are detected Dynamic Parameters: nodeRedVersion, nodeVersion, currentTime, selectedFlow, selectedNodes, projectStatus, mcpTools, lang System Prompt: Professional IoT project management expert that analyzes project requirements, provides flow organization recommendations, assists with progress management, and optimizes team collaboration processes.

General

Description: General AI assistant, handling various Node-RED related issues

Sample Prompts:

• "What is Node-RED? What are its main features and application scenarios?"
• "I encountered a Node-RED problem, please help me analyze and solve it"
• "Please recommend some Node-RED learning resources and best practices"
• "How to choose the appropriate Node-RED scenario mode to solve my specific needs?"

Smart Keywords: No specific keywords - serves as fallback for general inquiries Dynamic Parameters: nodeRedVersion, nodeVersion, currentTime, selectedFlow, selectedNodes, lang System Prompt: Professional Node-RED AI assistant that automatically identifies the most suitable processing method, provides accurate solutions, and guides users to specific professional modes when necessary.

Supported AI Providers

API Configuration

Environment Variables

Set environment variables for your preferred AI provider:

# OpenAI
export OPENAI_API_KEY="your-api-key"

# Anthropic
export ANTHROPIC_API_KEY="your-api-key"

# Google
export GOOGLE_API_KEY="your-api-key"

# Azure OpenAI
export AZURE_OPENAI_API_KEY="your-api-key"
export AZURE_OPENAI_ENDPOINT="your-endpoint"

Configuration File

Alternatively, create a configuration file:

{
  "llm": {
    "provider": "openai",
    "apiKey": "your-api-key",
    "model": "gpt-4",
    "temperature": 0.7,
    "maxTokens": 2000
  },
  "memory": {
    "enabled": true,
    "maxHistory": 50,
    "persistPath": "./data/memory.db"
  },
  "tools": {
    "enabled": true,
    "mcpServers": ["filesystem", "web-search"]
  }
}

Advanced Settings

• Streaming: Enable real-time response streaming
• Temperature: Control response creativity (0.0-1.0)
• Max Tokens: Limit response length
• Timeout: Set request timeout duration
• Retry Logic: Configure automatic retry on failures
• Rate Limiting: Manage API usage limits
• Separate Models: Set different models for planning and execution phases

Development

Project Structure

├── ai-sidebar.html          # Main sidebar interface
├── ai-sidebar-config.json   # UI configuration
├── make-iot-smart.html      # Node configuration template
├── make-iot-smart.js        # Backend node implementation
├── lib/
│   ├── langchain-manager.js # LangChain core manager
│   ├── memory-manager.js    # SQLite memory management
│   └── scenario-manager.js  # Scenario-based prompt management
├── config/
│   └── scenarios.json       # Scenario configuration file
├── data/
│   └── memory.db           # SQLite database file
└── package.json            # Package configuration

Technical Architecture

This project is built on the LangChain.js framework with a modular architecture design:

• LangChain Manager: Core AI model management, supporting multiple LLM providers
• Memory Manager: SQLite-based intelligent memory system, supporting short-term and long-term memory
• Scenario Manager: Scenario-based prompt management, supporting JSON configuration and dynamic parameters
• Tool Manager: Unified tool management framework, integrating MCP tools and built-in tools
• API Layer: RESTful API interface, supporting streaming chat and tool execution

Contributing

1. Fork the repository
2. Create a feature branch
3. Make changes and commit
4. Submit a pull request

Roadmap

Phase 1 (Completed)

• ✅ AI assistant integration
• ✅ Multi-provider support
• ✅ Interactive sidebar
• ✅ Configuration management
• ✅ Scenario-based support
• ✅ LangChain.js architecture migration
• ✅ SQLite memory management system
• ✅ MCP tool integration
• ✅ Unified tool management framework

Phase 2 (Coming Soon)

• 🔄 Remote debugging functionality
• 🔄 Team collaboration features
• 🔄 Advanced flow analysis
• 🔄 Smart deployment tools

Phase 3 (Future)

• 📋 Team management system
• 📋 Enterprise features
• 📋 Advanced security options
• 📋 Custom model training

System Requirements

• Node.js >= 18.0.0
• Node-RED >= 2.0.0

License

Licensed under the MIT License. See LICENSE file for details.

Support

AI development is more like art than technology. Mastering LLMs is not a simple task and requires deep understanding of AI models, data, and application scenarios. Each Q&A session may yield different results. Initial versions are often imperfect, but with the improvement of prompt engineering, they will gradually truly meet the daily needs of Node-RED users, whether IT or OT engineers. We welcome more interested people to join the project.

• Issue Reports: GitHub Issues
• Documentation: Wiki
• Discussions: GitHub Discussions

Author

Zheng He

• Email: jhe.zheng@gmail.com
• GitHub: @jimmyfreecoding
• Website: https://www.makeiotsmart.com

Let AI-driven assistance make your IoT development smarter!