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Generate Azure VM timeline reports with Google Gemini AI chat assistant

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Generate Azure VM timeline reports with Google Gemini AI chat assistant preview
Open on n8n.io

Important notice

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

1. Workflow Overview

An AI powered chat assistant that analyzes Azure virtual machine activity and generates detailed timeline reports showing VM state changes, performance metrics, and operational events over time. Ho...

Best for

  • DevOps automation workflows
  • AI Chatbot automation workflows
  • intermediate n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.agent, n8n-nodes-base.set, @n8n/n8n-nodes-langchain.toolhttprequest, @n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.lmchatgooglegemini, @n8n/n8n-nodes-langchain.memorybufferwindow, @n8n/n8n-nodes-langchain.toolcode

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Adam Bertram.

Original n8n.io source

1.1 Workflow description

Title
Generate Azure VM timeline reports with Google Gemini AI chat assistant
Workflow name
Generate Azure VM timeline reports with Google Gemini AI chat assistant

An AI-powered chat assistant that analyzes Azure virtual machine activity and generates detailed timeline reports showing VM state changes, performance metrics, and operational events over time.

How It Works

The workflow starts with a chat trigger that accepts user queries about Azure VM analysis. A Google Gemini AI agent processes these requests and uses six specialized tools to gather comprehensive VM data from Azure APIs. The agent queries resource groups, retrieves VM configurations and instance views, pulls performance metrics (CPU, network, disk I/O), and collects activity log events. It then analyzes this data to create timeline reports showing what happened to VMs during specified periods, defaulting to the last 90 days unless the user specifies otherwise.

Prerequisites

To use this template, you'll need:

  • n8n instance (cloud or self-hosted)
  • Azure subscription with virtual machines
  • Microsoft Azure Monitor OAuth2 API credentials
  • Google Gemini API credentials
  • Proper Azure permissions to read VM data and activity logs

Setup Instructions

  1. Import the template into n8n.
  2. Configure credentials:
    • Add Microsoft Azure Monitor OAuth2 API credentials with read permissions for VMs and activity logs
    • Add Google Gemini API credentials
  3. Update workflow parameters:
    • Open the "Set Common Variables" node
    • Replace <your azure subscription id here> with your actual Azure subscription ID
  4. Configure triggers:
    • The chat trigger will automatically generate a webhook URL for receiving chat messages
    • No additional trigger configuration needed
  5. Test the setup to ensure it works.

Security Considerations

Use minimum required Azure permissions (Reader role on subscription or resource groups). Store API credentials securely in n8n credential store. The Azure Monitor API has rate limits, so avoid excessive concurrent requests. Chat sessions use session-based memory that persists during conversations but doesn't retain data between separate chat sessions.

Extending the Template

You can add more Azure monitoring tools like disk metrics, network security group logs, or Application Insights data. The AI agent can be enhanced with additional tools for Azure cost analysis, security recommendations, or automated remediation actions. You could also integrate with alerting systems or export reports to external storage or reporting platforms.

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 - AI Agent

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 1.8

Block 2 - Set Common Variables

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

Block 3 - Get VM Performance Stats

Type / Role
@n8n/n8n-nodes-langchain.toolHttpRequest - toolHttpRequest
Config choices
Version 1.1

Block 4 - When chat message received

Type / Role
@n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices
Version 1.1

Block 5 - Google Gemini Chat Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices
Version 1

Block 6 - Simple Memory

Type / Role
@n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices
Version 1.3

Block 7 - Get Azure Resource Groups

Type / Role
@n8n/n8n-nodes-langchain.toolHttpRequest - toolHttpRequest
Config choices
Version 1.1

Block 8 - Get VM Information

Type / Role
@n8n/n8n-nodes-langchain.toolHttpRequest - toolHttpRequest
Config choices
Version 1.1

Block 9 - Get VM Events

Type / Role
@n8n/n8n-nodes-langchain.toolHttpRequest - toolHttpRequest
Config choices
Version 1.1

Block 10 - Get Current Date

Type / Role
@n8n/n8n-nodes-langchain.toolCode - toolCode
Config choices
Version 1.1

Block 11 - Get VM Instance View

Type / Role
@n8n/n8n-nodes-langchain.toolHttpRequest - toolHttpRequest
Config choices
Version 1.1

3. Summary Table

Workflow Generate Azure VM timeline reports with Google Gemini AI chat assistant
Complexity intermediate
Nodes 11
Categories DevOps, AI Chatbot
Author Adam Bertram
Published 30 May 2025

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/4513/4513.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

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

  4. 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. 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.

Frequently asked questions

What does Generate Azure VM timeline reports with Google Gemini AI chat assistant do?

An AI powered chat assistant that analyzes Azure virtual machine activity and generates detailed timeline reports showing VM state changes, performance metrics, and operational events over time. Ho...

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 DevOps, AI Chatbot use case.