Automate ITSM ticket classification and resolution using Gemini, Qdrant and ServiceNow

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Important notice

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

1. Workflow Overview

Who’s it for This template is ideal for IT support teams, internal helpdesk automation engineers, and developers building intelligent ticketing systems. It helps streamline ITSM workflows by automa...

Best for

Ticket Management automation workflows
AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatgooglegemini, @n8n/n8n-nodes-langchain.memorybufferwindow, @n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.textclassifier, n8n-nodes-base.servicenow, @n8n/n8n-nodes-langchain.vectorstoreqdrant, n8n-nodes-base.set

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: Automate ITSM ticket classification and resolution using Gemini, Qdrant and ServiceNow
Workflow name: Automate ITSM ticket classification and resolution using Gemini, Qdrant and ServiceNow

Who’s it for

This template is ideal for IT support teams, internal helpdesk automation engineers, and developers building intelligent ticketing systems. It helps streamline ITSM workflows by automatically classifying user queries, retrieving relevant knowledge base entries, and triggering incident creation in ServiceNow.

How it works / What it does

This workflow uses Google Gemini and Qdrant to power an intelligent ITSM assistant. When a user submits a query via chat:

The Text Classifier categorizes the input as an Incident, Request, or Other. Based on the category:

Incidents are automatically logged in ServiceNow. Requests trigger an HTTP call (e.g., for provisioning or access). Other queries are routed to an AI Agent that searches the FAQBase in Qdrant and responds contextually.

The Gemini LLM enriches responses and summaries. The Qdrant Vector Store retrieves semantically similar answers from a pre-embedded FAQ knowledge base. The Summarization Chain condenses incident details for better tracking.

Sticky notes are used throughout the workflow to document each node’s purpose and improve maintainability.

How to set up

Connect your Google Gemini API, Qdrant, and ServiceNow credentials. Populate the FAQBase collection in Qdrant with your ITSM knowledge base. Deploy the webhook to receive chat inputs. Test the flow using the Manual Trigger node. Customize the classifier categories and Gemini prompts as needed.

Requirements

Google Gemini API access Qdrant vector database with embedded FAQ data ServiceNow account with API access n8n instance with LangChain nodes installed

How to customize the workflow

Modify the Text Classifier categories to suit your organization’s ticket types. Add more FAQ entries to Qdrant for broader coverage. Replace the HTTP Request node with integrations relevant to your ITSM tools. Adjust the Gemini prompts to reflect your tone and support style. Extend the workflow with Slack, Teams, or email notifications for ticket updates.

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 2.2

Block 2 - Google Gemini Chat Model

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

Block 3 - Simple Memory

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

Block 4 - When chat message received

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

Block 5 - Text Classifier

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

Block 6 - Google Gemini Chat Model1

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

Block 7 - Create an incident

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

Block 8 - Google Gemini Chat Model2

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

Block 9 - Qdrant Vector Store

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

Block 10 - Edit Fields

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

Block 11 - Qdrant Vector Store1

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

Block 12 - Embeddings Google Gemini

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

Block 13 - When clicking ‘Execute workflow’

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

Block 14 - Embeddings Google Gemini1

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

Block 15 - Default Data Loader

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

Block 16 - Summarization Chain

Type / Role: @n8n/n8n-nodes-langchain.chainSummarization - chainSummarization
Config choices: Version 2.1

Block 17 - HTTP Request

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

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 Note7

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

3. Summary Table

Workflow	Automate ITSM ticket classification and resolution using Gemini, Qdrant and ServiceNow
Complexity	advanced
Nodes	24
Categories	Ticket Management, AI RAG
Author	Nikitha
Published	28 Oct 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/10241/10241.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 Ticket Management workflows AI RAG workflows

Frequently asked questions

What does Automate ITSM ticket classification and resolution using Gemini, Qdrant and ServiceNow 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 Ticket Management, AI RAG use case.

Nikitha

1 workflow

Nodes

@n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.lmchatgooglegemini @n8n/n8n-nodes-langchain.memorybufferwindow @n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.textclassifier n8n-nodes-base.servicenow @n8n/n8n-nodes-langchain.vectorstoreqdrant n8n-nodes-base.set

Complexity

advanced

Published 28 Oct 2025

Likes 0

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Install path: /data/workflows/10241/10241.json

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Automate ITSM ticket classification and resolution using Gemini, Qdrant and ServiceNow

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Who’s it for

How it works / What it does

How to set up

Requirements

How to customize the workflow

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - AI Agent

Block 2 - Google Gemini Chat Model

Block 3 - Simple Memory

Block 4 - When chat message received

Block 5 - Text Classifier

Block 6 - Google Gemini Chat Model1

Block 7 - Create an incident

Block 8 - Google Gemini Chat Model2

Block 9 - Qdrant Vector Store

Block 10 - Edit Fields

Block 11 - Qdrant Vector Store1

Block 12 - Embeddings Google Gemini

Block 13 - When clicking ‘Execute workflow’

Block 14 - Embeddings Google Gemini1

Block 15 - Default Data Loader

Block 16 - Summarization Chain

Block 17 - HTTP Request

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 Note7

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