Real-time IoT anomaly detection with MQTT, GPT-4o-mini AI, and multi-channel alerts

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

How It Works MQTT ingests real time sensor data from connected devices. The workflow normalizes the values and trains or retrains machine learning models on a defined schedule. An AI agent detects ...

Best for

Engineering automation workflows
AI Summarization automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.mqtttrigger, n8n-nodes-base.set, n8n-nodes-base.code, n8n-nodes-base.postgres, n8n-nodes-base.aggregate, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.if

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Cheng Siong Chin.

Original n8n.io source

1.1 Workflow description

Title: Real-time IoT anomaly detection with MQTT, GPT-4o-mini AI, and multi-channel alerts
Workflow name: Real-time IoT anomaly detection with MQTT, GPT-4o-mini AI, and multi-channel alerts

How It Works

MQTT ingests real-time sensor data from connected devices. The workflow normalizes the values and trains or retrains machine learning models on a defined schedule. An AI agent detects anomalies, validates the results for accuracy, and ensures reliable alerts. Detected issues are then routed to dashboards for visualization and sent via email notifications to relevant stakeholders, enabling timely monitoring and response.

Setup Steps

MQTT: Configure broker connection, set topic subscriptions, and verify data flow.
ML Model: Define retraining schedule and specify historical data sources for model updates.
AI Agent: Connect Claude or OpenAI APIs and configure anomaly validation prompts.
Alerts: Set dashboard URL and email recipients to receive real-time notifications.

Prerequisites

MQTT broker credentials; historical training data; OpenAI/Claude API key; dashboard access; email service

Use Cases

IoT sensor monitoring; server performance tracking; network traffic anomalies; application log analysis; predictive maintenance alerts

Customization

Adjust sensitivity thresholds; swap ML models; modify notification channels; add Slack/Teams integration; customize validation rules

Benefits

Reduces detection latency 95%; eliminates manual monitoring; prevents false alerts; enables rapid incident response; improves system reliability

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - MQTT Sensor Data Ingestion

Type / Role: n8n-nodes-base.mqttTrigger - mqttTrigger
Config choices: Version 1

Block 2 - Workflow Configuration

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 3 - Edge Preprocessing & Validation

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 4 - Normalize Sensor Data

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 5 - Store Raw Data in Time-Series DB

Type / Role: n8n-nodes-base.postgres - postgres
Config choices: Version 2.6

Block 6 - Aggregate Sensor Readings

Type / Role: n8n-nodes-base.aggregate - aggregate
Config choices: Version 1

Block 7 - AI Anomaly Detection Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3

Block 8 - OpenAI Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.2

Block 9 - Check for Anomalies

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2.2

Block 10 - Store Anomaly Records

Type / Role: n8n-nodes-base.postgres - postgres
Config choices: Version 2.6

Block 11 - Send Alert to Dashboard API

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.3

Block 12 - Send Slack Alert

Type / Role: n8n-nodes-base.slack - slack
Config choices: Version 2.3

Block 13 - Send Email Alert

Type / Role: n8n-nodes-base.emailSend - emailSend
Config choices: Version 2.1

Block 14 - Model Retraining Schedule

Type / Role: n8n-nodes-base.scheduleTrigger - scheduleTrigger
Config choices: Version 1.2

Block 15 - Fetch Historical Data for Retraining

Type / Role: n8n-nodes-base.postgres - postgres
Config choices: Version 2.6

Block 16 - Retrain ML Model via API

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.3

Block 17 - Statistical Anomaly Detection

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 18 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 19 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 20 - Sticky Note2

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 21 - Sticky Note3

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 22 - Sticky Note4

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 23 - Sticky Note5

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 24 - Sticky Note6

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Showing the first 24 of 28 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Real-time IoT anomaly detection with MQTT, GPT-4o-mini AI, and multi-channel alerts
Complexity	advanced
Nodes	28
Categories	Engineering, AI Summarization
Author	Cheng Siong Chin
Published	12 Nov 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/10759/10759.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Engineering workflows AI Summarization workflows

Frequently asked questions

What does Real-time IoT anomaly detection with MQTT, GPT-4o-mini AI, and multi-channel alerts do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Engineering, AI Summarization use case.

Cheng Siong Chin

203 workflows

Nodes

n8n-nodes-base.mqtttrigger n8n-nodes-base.set n8n-nodes-base.code n8n-nodes-base.postgres n8n-nodes-base.aggregate @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.lmchatopenai n8n-nodes-base.if

Complexity

advanced

Published 12 Nov 2025

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/10759/10759.json

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Real-time IoT anomaly detection with MQTT, GPT-4o-mini AI, and multi-channel alerts

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

How It Works

Setup Steps

Prerequisites

Use Cases

Customization

Benefits

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - MQTT Sensor Data Ingestion

Block 2 - Workflow Configuration

Block 3 - Edge Preprocessing & Validation

Block 4 - Normalize Sensor Data

Block 5 - Store Raw Data in Time-Series DB

Block 6 - Aggregate Sensor Readings

Block 7 - AI Anomaly Detection Agent

Block 8 - OpenAI Chat Model

Block 9 - Check for Anomalies

Block 10 - Store Anomaly Records

Block 11 - Send Alert to Dashboard API

Block 12 - Send Slack Alert

Block 13 - Send Email Alert

Block 14 - Model Retraining Schedule

Block 15 - Fetch Historical Data for Retraining

Block 16 - Retrain ML Model via API

Block 17 - Statistical Anomaly Detection

Block 18 - Sticky Note

Block 19 - Sticky Note1

Block 20 - Sticky Note2

Block 21 - Sticky Note3

Block 22 - Sticky Note4

Block 23 - Sticky Note5

Block 24 - Sticky Note6

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions