Block 1 - AI Agent
- Type / Role
- @n8n/n8n-nodes-langchain.agent - agent
- Config choices
- Version 1.8
This workflow is provided as-is. Please review and test before using in production.
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...
@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
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Adam Bertram.
Original n8n.io sourceAn 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.
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.
To use this template, you'll need:
<your azure subscription id here> with your actual Azure subscription IDUse 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.
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.
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.
| 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 |
Use the JSON export at /data/workflows/4513/4513.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
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.
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...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
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.