Boost Smart Home ROI with 10 Plug-and-Play Dify Workflow Examples

Mark Ren
August 15, 2025
3:05 pm
0 comments

Looking for dify workflow examples? Get 10 ready-to-use smart home templates with AI, energy savings, and voice control—optimized for Home Assistant.

In the fast-evolving world of smart home automation, traditional platforms like Home Assistant, Tuya, and HomeKit often fall short with rigid scripting and rule-based flows. That’s where Dify Workflow comes in—empowering integrators and developers with AI-powered automation that thinks like a human. From voice-controlled assistants to real-time energy optimization, this guide features 10 plug-and-play dify workflow examples you can use to build smarter, more efficient homes. Whether you're designing systems for clients or upgrading your setup, these examples integrate seamlessly with Tuya, Home Assistant, and your AI model of choice—making automation truly intelligent.

Developed and tested by ZedIoT’s AIoT engineers, these workflows combine natural language processing, sensor fusion, and no-code logic—designed to boost smart home ROI and accelerate deployment.

10-dify-workflow-examples-smart-home-zediot

Why Traditional Smart Home Automation Falls Short?

Platforms like Home Assistant, Tuya, or Apple HomeKit offer deterministic logic flows, but they often rely on rigid scripting and complex rule chains. This creates friction for developers and integrators who want more dynamic, intelligent automation. Discover how openness on platforms like Xiaomi × Home Assistant is shaping the future.

What Makes Dify Workflows Smarter?

Dify Workflow introduces AI into the automation layer. Instead of hard-coded triggers, it allows:

Natural language-based commands and reasoning
Multi-modal input (vision, voice, sensors)
Real-time decision making with AI models
Seamless integration across APIs, MQTT, and cloud platforms

ZedIoT takes this further by offering prebuilt, battle-tested workflows optimized and smart home automation ideas for its AIoT platform, making the smart home more intelligent and efficient than traditional scenes.

If you’re new to Dify, read our guide on the difference between an agent and a workflow to understand how they complement each other.

10 Dify Workflow Examples for AI Home Automation

Example 1: AI Security Alarm Assistant

What it does

Connects home cameras with AI image recognition to detect intruders who are not household members.
Uses facial recognition and posture analysis to reduce false alarms from pets or delivery staff.

Core Workflow

Subscribe to camera event feeds (MQTT/RTSP AI callback)
Use AI to compare detected faces with a known database
If an unknown face is detected → trigger lights to flash and send audio alerts to smart speakers
Push a snapshot alert to your phone app

Best suited for

Cameras: Hikvision, TP-Link Tapo, Tuya Cameras
AI Models: OpenAI Vision API, YOLOv8, DeepFace

AI Security Alarm Assistant=Cameras + AI image recognition + Alarms

{
  "name": "AI Security Alarm Assistant",
  "version": "1.0",
  "description": "Camera event → Face recognition → Trigger alarm and notification if stranger detected",
  "env": {
    "MQTT_BROKER_URL": "mqtt://broker.local:1883",
    "CAMERA_EVENT_TOPIC": "home/cam/frontdoor/event",
    "KNOWN_FACE_API": "https://your-face-api/identify",
    "ALERT_WEBHOOK": "https://your-app/alert",
    "SPEAKER_TTS_API": "https://your-speaker/tts"
  },
  "triggers": [
    {
      "id": "t1",
      "type": "mqtt",
      "topic": "{{CAMERA_EVENT_TOPIC}}",
      "qos": 1,
      "payload_mapping": "json"
    }
  ],
  "nodes": [
    {
      "id": "n1",
      "type": "condition",
      "name": "Motion Detected?",
      "params": { "expr": "payload.event == 'motion' || payload.event == 'person_detected'" }
    },
    {
      "id": "n2",
      "type": "http",
      "name": "Run Face Recognition",
      "params": {
        "method": "POST",
        "url": "{{KNOWN_FACE_API}}",
        "headers": { "Content-Type": "application/json" },
        "body": { "image_url": "{{payload.snapshot_url}}" }
      }
    },
    {
      "id": "n3",
      "type": "condition",
      "name": "Is Stranger?",
      "params": { "expr": "result.n2.body.is_known == false" }
    },
    {
      "id": "n4",
      "type": "http",
      "name": "Send App Alert",
      "params": {
        "method": "POST",
        "url": "{{ALERT_WEBHOOK}}",
        "body": {
          "title": "Possible Intrusion Detected",
          "message": "A stranger has been detected at your door.",
          "image": "{{payload.snapshot_url}}"
        }
      }
    },
    {
      "id": "n5",
      "type": "http",
      "name": "Broadcast Voice Alert",
      "params": {
        "method": "POST",
        "url": "{{SPEAKER_TTS_API}}",
        "body": { "text": "Warning! A stranger has been detected. Please check your entrance!" }
      }
    }
  ],
  "edges": [
    { "from": "t1", "to": "n1" },
    { "from": "n1", "to": "n2", "condition": "true" },
    { "from": "n2", "to": "n3" },
    { "from": "n3", "to": "n4", "condition": "true" },
    { "from": "n3", "to": "n5", "condition": "true" }
  ]
}

Related reading:[ AI-powered IoT home security ] for real-time protection strategies.

Example 2: Lighting Control - Smart Home Automation for Energy Savings

What it does

Adjusts light brightness and color temperature based on outdoor light levels and indoor activity to save power.

Core Workflow

Use schedules or light sensors to measure illumination
AI calculates the required brightness and color temperature (based on time, weather, and preferences)
Control lights via Tuya API or Zigbee2MQTT
Log energy-saving results

Best suited for

Light sensors: Aqara, Philips Hue, Mi Home sensors
Lighting: Zigbee or Wi-Fi smart bulbs

Smart Energy-Saving Lighting Control=Illuminance + Presence + Brightness/CCT

{
  "name": "Smart Energy‑Saving Lighting Control",
  "version": "1.0",
  "description": "Automatically adjust lighting based on outdoor illuminance and indoor presence",
  "env": {
    "LUX_TOPIC": "home/sensor/lux",
    "PRESENCE_TOPIC": "home/room/living/presence",
    "LIGHT_API": "https://your-iot/light/set",
    "MODEL_API": "https://your-ai/lighting"
  },
  "triggers": [
    { "id": "t1", "type": "mqtt", "topic": "{{LUX_TOPIC}}", "payload_mapping": "json" },
    { "id": "t2", "type": "mqtt", "topic": "{{PRESENCE_TOPIC}}", "payload_mapping": "json" },
    { "id": "t3", "type": "schedule", "cron": "*/10 * * * *" }
  ],
  "nodes": [
    {
      "id": "n1",
      "type": "ai",
      "name": "Compute Optimal Brightness & CCT",
      "params": {
        "model": "gpt-4.1-mini",
        "prompt": "Given outdoor illuminance, current time, presence status, and energy-saving strategy, output recommended brightness (0-100) and correlated color temperature (2700-6500K). Input: {{context}}",
        "inputs": { "context": { "lux": "{{state.lux}}", "presence": "{{state.presence}}", "time": "{{now}}" } }
      }
    },
    {
      "id": "n2",
      "type": "http",
      "name": "Apply Lighting Settings",
      "params": {
        "method": "POST",
        "url": "{{LIGHT_API}}",
        "body": { "brightness": "{{result.n1.output.brightness}}", "ct": "{{result.n1.output.ct}}" }
      }
    }
  ],
  "edges": [
    { "from": "t1", "to": "n1" },
    { "from": "t2", "to": "n1" },
    { "from": "t3", "to": "n1" },
    { "from": "n1", "to": "n2" }
  ],
  "state_reducers": {
    "lux": { "on": "t1", "path": "payload.value" },
    "presence": { "on": "t2", "path": "payload.present" }
  }
}

Example 3: AI-Powered Air Conditioning Control

What it does

Combines weather forecasts, indoor temperature/humidity, and personal comfort preferences for optimal AC settings.

Core Workflow

Gather indoor temperature and humidity data
AI analyzes outdoor weather and historical user preferences
Calculates optimal temperature (e.g., lowers temp when humidity is high)
Adjusts AC mode, temperature, and fan speed

Best suited for

AC control via IR bridge, Home Assistant + Tuya
AI models from OpenAI or Claude

AI Smart AC Temperature Control=Weather + Indoor Conditions + Preferences

{
  "name": "AI Smart AC Temperature Control",
  "version": "1.0",
  "description": "Weather + Indoor Temp & Humidity + Preferences → Optimal AC Settings",
  "env": {
    "INDOOR_THS_TOPIC": "home/sensor/ths/living",
    "WEATHER_API": "https://api.weather.com/v3/...",
    "AC_API": "https://your-iot/ac/set"
  },
  "triggers": [
    { "id": "t1", "type": "mqtt", "topic": "{{INDOOR_THS_TOPIC}}", "payload_mapping": "json" },
    { "id": "t2", "type": "schedule", "cron": "*/5 * * * *" }
  ],
  "nodes": [
    {
      "id": "n1",
      "type": "http",
      "name": "Fetch Outdoor Weather",
      "params": { "method": "GET", "url": "{{WEATHER_API}}?loc=beijing" }
    },
    {
      "id": "n2",
      "type": "ai",
      "name": "Calculate AC Settings",
      "params": {
        "model": "gpt-4.1",
        "prompt": "Based on indoor temperature/humidity and weather data, output mode (cool/heat/auto), temp (°C), and fan speed (low/med/high). Input: Indoor {{payload}}, Weather {{result.n1.body}}"
      }
    },
    {
      "id": "n3",
      "type": "http",
      "name": "Set AC Parameters",
      "params": { "method": "POST", "url": "{{AC_API}}", "body": "{{result.n2.output}}" }
    }
  ],
  "edges": [
    { "from": "t1", "to": "n1" },
    { "from": "t2", "to": "n1" },
    { "from": "n1", "to": "n2" },
    { "from": "n2", "to": "n3" }
  ]
}

Example 4: AI Washing Machine Status Prediction

What it does

Uses power consumption curves and vibration data to predict washing stages and completion time.

Core Workflow

Collect power consumption data in real-time
AI analyzes usage patterns
Identify wash/rinse/spin/complete stages
Push ETA to phone and smart speakers

Best suited for

Energy monitoring: Shelly Plug, Tuya smart plug
Data analysis: Dify with Python execution node

AI Washing Machine Status Prediction=Energy Consumption Curve + Vibration

{
  "name": "AI Washing Machine Status Prediction",
  "version": "1.0",
  "description": "Predict washing stage and completion time based on power consumption curve",
  "env": {
    "POWER_TOPIC": "home/appliance/washer/power",
    "NOTIFY_WEBHOOK": "https://your-app/notify"
  },
  "triggers": [
    { "id": "t1", "type": "mqtt", "topic": "{{POWER_TOPIC}}", "payload_mapping": "json" }
  ],
  "nodes": [
    {
      "id": "n1",
      "type": "ai",
      "name": "Stage Recognition & Time Estimation",
      "params": {
        "model": "gpt-4.1-mini",
        "prompt": "Based on the last 30 minutes of power consumption data, identify the washing stage (wash/rinse/spin/complete) and estimate remaining time (minutes). Input: {{series}}",
        "inputs": { "series": "{{timeseries.last_30m(POWER_TOPIC)}}" }
      }
    },
    {
      "id": "n2",
      "type": "http",
      "name": "Notify User",
      "params": {
        "method": "POST",
        "url": "{{NOTIFY_WEBHOOK}}",
        "body": { "title": "Washing Machine Status Update", "stage": "{{result.n1.output.stage}}", "eta_min": "{{result.n1.output.eta_min}}" }
      }
    }
  ],
  "edges": [
    { "from": "t1", "to": "n1" },
    { "from": "n1", "to": "n2" }
  ]
}

Example 5: Smart Garden Irrigation Assistant

What it does

Waters plants based on soil moisture, weather, and plant type — avoiding unnecessary watering during rain or high humidity.

Core Workflow

Gather soil moisture and weather data
AI decides whether to irrigate
Controls solenoid valves via MQTT/Relay
Logs irrigation data and water savings

Best suited for

Sensors: Tuya soil moisture, Sonoff TH
Devices: 12V solenoid valve + smart relay

Smart Garden Irrigation Assistant=Moisture + Weather + Solenoid Valve

{
  "name": "Smart Garden Irrigation Assistant",
  "version": "1.0",
  "description": "Soil Moisture / Weather / Plant Type → Automatic Irrigation",
  "env": {
    "SOIL_TOPIC": "home/garden/soil",
    "WEATHER_API": "https://api.weather.com/v3/...",
    "VALVE_TOPIC": "home/garden/valve/set"
  },
  "triggers": [
    { "id": "t1", "type": "mqtt", "topic": "{{SOIL_TOPIC}}", "payload_mapping": "json" },
    { "id": "t2", "type": "schedule", "cron": "0 */1 * * *" }
  ],
  "nodes": [
    {
      "id": "n1",
      "type": "http",
      "name": "Fetch Weather Data",
      "params": { "method": "GET", "url": "{{WEATHER_API}}?loc=beijing" }
    },
    {
      "id": "n2",
      "type": "ai",
      "name": "Decide Irrigation Need",
      "params": {
        "model": "gpt-4.1-mini",
        "prompt": "Based on soil moisture {{payload.moisture}} and weather data {{result.n1.body}}, output irrigate (true/false) and duration_sec.",
        "stop": []
      }
    },
    {
      "id": "n3",
      "type": "mqtt_publish",
      "name": "Control Solenoid Valve",
      "params": {
        "broker": "{{MQTT_BROKER_URL}}",
        "topic": "{{VALVE_TOPIC}}",
        "qos": 1,
        "payload": {
          "on": "{{result.n2.output.irrigate}}",
          "duration": "{{result.n2.output.duration_sec}}"
        }
      }
    }
  ],
  "edges": [
    { "from": "t1", "to": "n1" },
    { "from": "t2", "to": "n1" },
    { "from": "n1", "to": "n2" },
    { "from": "n2", "to": "n3", "condition": "result.n2.output.irrigate == true" }
  ]
}

Example 6: AI Voice Household Assistant

What it does

Family members give voice commands like “start the vacuum” via smart speaker or phone. AI recognizes intent and executes tasks.

Core Workflow

Convert speech to text (ASR)
AI identifies intent and extracts parameters
Calls the relevant device API (vacuum, curtains, rice cooker, etc.)
Gives feedback via TTS or app notification

Best suited for

Devices: Xiaomi XiaoAi, Google Nest Audio, Amazon Echo
AI models: OpenAI GPT, Claude, DeepSeek-R1

AI Voice Household Assistant=ASR + Intent Recognition + Appliance Control

{
  "name": "AI Voice Household Assistant",
  "version": "1.0",
  "description": "Voice intent → Control vacuum, curtains, rice cooker, and other appliances",
  "env": {
    "ASR_WEBHOOK": "https://your-asr/callback",
    "DEVICE_API": "https://your-iot/device/command"
  },
  "triggers": [
    { "id": "t1", "type": "webhook", "path": "/voice-intent", "method": "POST" }
  ],
  "nodes": [
    {
      "id": "n1",
      "type": "ai",
      "name": "Intent Recognition & Slot Extraction",
      "params": {
        "model": "gpt-4.1-mini",
        "prompt": "Extract intent (vacuum/curtains/rice cooker/etc.) and slots (room/time/mode) from the user's command. Input: {{payload.text}}"
      }
    },
    {
      "id": "n2",
      "type": "http",
      "name": "Send Device Command",
      "params": {
        "method": "POST",
        "url": "{{DEVICE_API}}",
        "body": {
          "intent": "{{result.n1.output.intent}}",
          "slots": "{{result.n1.output.slots}}"
        }
      }
    }
  ],
  "edges": [
    { "from": "t1", "to": "n1" },
    { "from": "n1", "to": "n2" }
  ]
}

Example 7: Family Arrival Notification & Auto Scene

What it does

Uses GPS and facial recognition to detect when a family member arrives, triggering a personalized “welcome home” scene.

Core Workflow

Confirm identity via phone location or door camera face recognition
AI matches personal scene preferences
Controls lights, AC, music, curtains, etc.
Logs arrival times for household tracking

Best suited for

Location services: Home Assistant Companion, Tuya GeoFence
AI scene matching: Dify + user preference database

Family Arrival Notification & Auto Scene=Location/Face Recognition → Scene Matching

{
  "name": "Family Arrival Notification & Auto Scene",
  "version": "1.0",
  "description": "Location/Face Recognition → Match personalized scene",
  "env": {
    "PRESENCE_WEBHOOK": "https://your-app/presence",
    "SCENE_API": "https://your-iot/scene/run"
  },
  "triggers": [
    { "id": "t1", "type": "webhook", "path": "/presence", "method": "POST" }
  ],
  "nodes": [
    {
      "id": "n1",
      "type": "ai",
      "name": "Match Preferred Scene",
      "params": {
        "model": "gpt-4.1-mini",
        "prompt": "Based on household member {{payload.user}}'s preferences and the current time period, output the list of scenes to trigger."
      }
    },
    {
      "id": "n2",
      "type": "http",
      "name": "Execute Scene",
      "params": {
        "method": "POST",
        "url": "{{SCENE_API}}",
        "body": { "scenes": "{{result.n1.output.scenes}}" }
      }
    }
  ],
  "edges": [
    { "from": "t1", "to": "n1" },
    { "from": "n1", "to": "n2" }
  ]
}

Example 8: AI Kitchen Assistant

What it does

Suggests recipes based on fridge inventory, taste preferences, and weather — then controls kitchen appliances to cook.

Core Workflow

Read fridge inventory from sensors or manual input
AI generates menus based on inventory, preferences, and weather
Controls rice cookers, ovens, etc.
Displays cooking steps on smart displays or apps

Best suited for

Devices: Bosch smart fridge, Mi rice cooker
Recipe generation: Dify with LangChain and knowledge base

AI Kitchen Assistant=Inventory/Preferences/Weather → Recipes + Appliance Control

{
  "name": "AI Kitchen Assistant",
  "version": "1.0",
  "description": "Recommend recipes based on inventory/preferences/weather and control kitchen appliances",
  "env": {
    "INVENTORY_API": "https://your-kitchen/inventory",
    "WEATHER_API": "https://api.weather.com/v3/...",
    "COOKER_API": "https://your-iot/cooker"
  },
  "triggers": [
    { "id": "t1", "type": "webhook", "path": "/menu", "method": "POST" }
  ],
  "nodes": [
    {
      "id": "n1",
      "type": "http",
      "name": "Get Inventory",
      "params": { "method": "GET", "url": "{{INVENTORY_API}}" }
    },
    {
      "id": "n2",
      "type": "http",
      "name": "Get Weather",
      "params": { "method": "GET", "url": "{{WEATHER_API}}?loc=beijing" }
    },
    {
      "id": "n3",
      "type": "ai",
      "name": "Generate Recipe",
      "params": {
        "model": "gpt-4.1",
        "prompt": "Based on inventory {{result.n1.body}}, taste preferences {{payload.preference}}, and weather data {{result.n2.body}}, output a recipe with step-by-step instructions."
      }
    },
    {
      "id": "n4",
      "type": "http",
      "name": "Control Kitchen Appliance",
      "params": {
        "method": "POST",
        "url": "{{COOKER_API}}",
        "body": "{{result.n3.output.program}}"
      }
    }
  ],
  "edges": [
    { "from": "t1", "to": "n1" },
    { "from": "t1", "to": "n2" },
    { "from": "n1", "to": "n3" },
    { "from": "n2", "to": "n3" },
    { "from": "n3", "to": "n4" }
  ]
}

Example 9: AI Pet Care

What it does

Uses cameras, feeders, and environment sensors to remotely monitor pets and send health alerts.

Core Workflow

Camera tracks pet activity and posture
AI decides if feeding or cleaning is needed
Controls feeders and water dispensers
Sends health reports to the owner

Best suited for

Devices: Petcube camera, Tuya feeder
AI: YOLOv8 + action recognition models

AI Pet Care=Camera + Feeder + Health Alerts

{
  "name": "AI Pet Care",
  "version": "1.0",
  "description": "Posture recognition + scheduled feeding + health reports",
  "env": {
    "PET_CAM_TOPIC": "home/cam/pet/event",
    "FEEDER_API": "https://your-iot/feeder",
    "OWNER_NOTIFY": "https://your-app/pet/notify"
  },
  "triggers": [
    { "id": "t1", "type": "mqtt", "topic": "{{PET_CAM_TOPIC}}", "payload_mapping": "json" }
  ],
  "nodes": [
    {
      "id": "n1",
      "type": "ai",
      "name": "Analyze Pet Activity",
      "params": {
        "model": "gpt-4.1-mini",
        "prompt": "Based on detected activity and feeding history, determine if feeding is required or if a cleaning reminder should be sent."
      }
    },
    {
      "id": "n2",
      "type": "http",
      "name": "Control Feeder",
      "params": {
        "method": "POST",
        "url": "{{FEEDER_API}}",
        "body": "{{result.n1.output.feed_cmd}}"
      }
    },
    {
      "id": "n3",
      "type": "http",
      "name": "Send Health Report",
      "params": {
        "method": "POST",
        "url": "{{OWNER_NOTIFY}}",
        "body": "{{result.n1.output.health_report}}"
      }
    }
  ],
  "edges": [
    { "from": "t1", "to": "n1" },
    { "from": "n1", "to": "n2", "condition": "result.n1.output.feed == true" },
    { "from": "n1", "to": "n3" }
  ]
}

Example 10: Whole-Home Energy Optimization Assistant

What it does

Uses electricity prices, consumption curves, and weather predictions to schedule appliances for off-peak hours.

Core Workflow

Combine the electricity price data with real-time consumption
AI predicts the next 24 hours of usage
Schedules appliances (washing machines, water heaters, etc.)
Generates a daily energy-saving report

Best suited for

Energy monitoring: WattPanel, Shelly EM
AI forecasting: Dify + time-series models (Prophet/LSTM)

Whole-Home Energy Optimization Assistant=Electricity Price + Load Forecasting + Appliance Scheduling

{
  "name": "Whole-Home Energy Optimization Assistant",
  "version": "1.0",
  "description": "Electricity price & load forecasting → Appliance scheduling optimization",
  "env": {
    "POWER_STREAM_TOPIC": "home/energy/power",
    "ELECTRICITY_PRICE_API": "https://your-grid/price",
    "SCHEDULER_API": "https://your-iot/scheduler"
  },
  "triggers": [
    { "id": "t1", "type": "schedule", "cron": "0 */1 * * *" }
  ],
  "nodes": [
    {
      "id": "n1",
      "type": "http",
      "name": "Get Electricity Price",
      "params": { "method": "GET", "url": "{{ELECTRICITY_PRICE_API}}" }
    },
    {
      "id": "n2",
      "type": "ai",
      "name": "Forecast 24-Hour Load",
      "params": {
        "model": "gpt-4.1",
        "prompt": "Using the past 24 hours of power consumption data {{timeseries.last_24h(POWER_STREAM_TOPIC)}} and the current electricity price {{result.n1.body}}, output appliance scheduling recommendations to avoid peak hours."
      }
    },
    {
      "id": "n3",
      "type": "http",
      "name": "Send Schedule",
      "params": {
        "method": "POST",
        "url": "{{SCHEDULER_API}}",
        "body": "{{result.n2.output.schedule}}"
      }
    }
  ],
  "edges": [
    { "from": "t1", "to": "n1" },
    { "from": "n1", "to": "n2" },
    { "from": "n2", "to": "n3" }
  ]
}

How to Import and Use These Dify Workflows

Save each JSON as a separate file (wf-security.json, wf-lighting.json, etc.) and import into Dify’s Workflow console.
Replace env variables with your actual settings (MQTT broker, webhooks, device APIs, model names).
Adjust node types and parameters if your Dify version has a different schema naming.
Test in simulation before deployment.

Mermaid diagram: Dify AI Workflow Architecture for Smart Home Automation

flowchart LR

  %% ========= Layers =========
  subgraph Ingest["Ingestion Layer"]
    direction TB
    A["Sensor/Device Inputs"]
  end

  subgraph Orchestration["Orchestration & AI Decisioning"]
    direction TB
    B["Dify Workflow"]
    C["AI Inference"]
  end

  subgraph Execution["Execution & Device Control"]
    direction TB
    D["Issue Control Commands"]
    E["Smart Home Devices"]
  end

  subgraph Feedback["Feedback & Notifications"]
    direction TB
    F["Execution Feedback"]
    G["User App / Smart Speaker"]
  end

  %% ========= Main Path =========
  A -- "MQTT / HTTP Webhook" --> B
  B --> C
  C --> D
  D -- "API / MQTT / Zigbee" --> E
  E --> F
  F -- "Notification" --> G

  %% ========= Styles =========
  classDef ingest fill:#E6F4FF,stroke:#1677FF,color:#0B3D91,stroke-width:1.5px,rounded:10px
  classDef orch   fill:#FFF7E6,stroke:#FAAD14,color:#7C4A03,stroke-width:1.5px,rounded:10px
  classDef exec   fill:#E8FFEE,stroke:#52C41A,color:#124D18,stroke-width:1.5px,rounded:10px
  classDef feed   fill:#F3E5F5,stroke:#8E24AA,color:#4A148C,stroke-width:1.5px,rounded:10px

  class Ingest ingest
  class Orchestration orch
  class Execution exec
  class Feedback feed

Why Choose Dify Workflow for Smart Home Automation?

Traditional platforms like Home Assistant, Tuya Scenes, or Apple HomeKit are great at deterministic rules but lack semantic understanding. Dify Workflow combines rule-based triggers with AI reasoning, offering:

Natural Language Automation – Describe automation in plain language; AI generates the flow.
Multi-Model Integration – Call OpenAI, Claude, DeepSeek, Gemini, etc., in a single flow.
Data Fusion – Merge MQTT, HTTP, and WebSocket data with APIs like weather, electricity, or GPS.
Cross-Platform Control – Integrates with Home Assistant, Tuya, ESPHome, Node-RED, n8n.

To compare automation platforms, see our deep dive on AI-powered workflow tools: n8n vs Dify.

How to Quickly Import These Workflows

Install Dify
- Recommended: one-click deployment via Docker:

docker run -d --name dify \
  -p 3000:3000 \
  -v ./dify-data:/app/data \
  dify/dify:latest

Import Workflow Files
- Log in to the Dify console → go to Workflow Management → click Import JSON File.
- The 10 examples in this article can be imported directly as needed (replace trigger conditions and API keys with your own hardware parameters).
Connect to Your Device Platform
- MQTT devices → configure an MQTT node in Dify (fill in broker address and topic)
- Tuya / Home Assistant → use Webhook or API Key to call device control APIs
- Third-party data sources (weather, electricity prices) → add API call nodes directly to the Workflow
Test & Deploy
- Run tests in the simulator to ensure devices respond correctly
- Once enabled, the Workflow will run continuously in the background, processing triggers in real time

Best Practices for Building AIoT Workflows

Modular Design – Create reusable sub-workflows (e.g., device control module).
AI Validation – Add AI checks before executing to prevent false triggers.
Hybrid Approach – Use traditional automation for fixed rules; Dify for AI-driven scenarios.

Final Thoughts

These 10 dify workflow examples are more than just templates—they’re building blocks for scalable smart home automation. With native support for Tuya, Home Assistant, MQTT, and major AI models, each workflow demonstrates how intelligent automation can simplify control, save energy, and personalize the user experience.

By using these 10 Dify Workflow examples, you can quickly create powerful automations that go beyond basic triggers — making your home smarter and more personalized. As edge AI chips, low-latency models, and local voice recognition become mainstream, AI + Workflow will be the standard in smart homes.

Frequently Asked Questions (FAQ)

What is a Dify workflow example?

A Dify workflow example is a prebuilt automation template that uses AI models to trigger smart home actions based on conditions like camera events, weather, or voice commands. These workflows can integrate with Home Assistant, Tuya, MQTT, and cloud APIs.

Can I use Dify workflows with Home Assistant Automation?

Yes. Dify workflows integrate seamlessly with Home Assistant through API calls, MQTT brokers, or local automation bridges. Many examples in this article are designed specifically for Home Assistant environments.

How do these workflows save energy in smart homes?

Several workflows—like smart lighting and appliance scheduling—use AI to optimize energy usage based on consumption patterns, real-time pricing, and weather forecasts. This makes your smart home automation not just intelligent, but cost-efficient.

Do I need coding skills to use Dify workflows?

No. These workflows are designed to be no-code or low-code. With simple configuration of environment variables and device APIs, integrators can deploy them quickly without deep programming knowledge.

How does ZedIoT support smart home automation with Dify?

ZedIoT provides ready-to-use Dify workflow examples, custom AIoT integration services, and a robust SaaS platform that supports smart home and smart business automation. We help clients reduce development time and boost automation ROI.

Boost Smart Home ROI with 10 Plug-and-Play Dify Workflow Examples

Why Traditional Smart Home Automation Falls Short?

What Makes Dify Workflows Smarter?

10 Dify Workflow Examples for AI Home Automation

Example 1: AI Security Alarm Assistant

Example 2: Lighting Control - Smart Home Automation for Energy Savings

Example 3: AI-Powered Air Conditioning Control

Example 4: AI Washing Machine Status Prediction

Example 5: Smart Garden Irrigation Assistant

Example 6: AI Voice Household Assistant

Example 7: Family Arrival Notification & Auto Scene

Example 8: AI Kitchen Assistant

Example 9: AI Pet Care

Example 10: Whole-Home Energy Optimization Assistant

How to Import and Use These Dify Workflows

Mermaid diagram: Dify AI Workflow Architecture for Smart Home Automation

Why Choose Dify Workflow for Smart Home Automation?

How to Quickly Import These Workflows

Best Practices for Building AIoT Workflows

Final Thoughts

Frequently Asked Questions (FAQ)

What is a Dify workflow example?

Can I use Dify workflows with Home Assistant Automation?

How do these workflows save energy in smart homes?

Do I need coding skills to use Dify workflows?

How does ZedIoT support smart home automation with Dify?

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