AI-powered ticket triage with multi-model classification & knowledge base

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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 This workflow automates enterprise ticket management by combining AI powered classification with knowledge base retrieval. It receives support tickets via webhook, routes them through ...

Best for

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

Tools used

n8n-nodes-base.webhook, n8n-nodes-base.set, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatopenai, @n8n/n8n-nodes-langchain.outputparserstructured, @n8n/n8n-nodes-langchain.mcpclienttool, @n8n/n8n-nodes-langchain.vectorstorepgvector, @n8n/n8n-nodes-langchain.embeddingsopenai

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: AI-powered ticket triage with multi-model classification & knowledge base
Workflow name: AI-powered ticket triage with multi-model classification & knowledge base

How It Works

This workflow automates enterprise ticket management by combining AI-powered classification with knowledge base retrieval. It receives support tickets via webhook, routes them through multiple AI models (OpenAI ChatGPT, NVIDIA's text classification APIs, and embeddings-based search) to determine optimal resolution strategies. The system generates contextual diagnostic logs, formats responses, updates ticket systems, notifies engineers when escalation is needed, and seamlessly integrates with knowledge bases for continuous learning. It solves the critical problem of manual ticket sorting and delayed responses by automating intelligent triage, reducing resolution time, and ensuring consistent quality across support operations. Target audience includes support operations teams, technical support managers, and enterprises managing high-volume ticket queues seeking to improve efficiency and SLA compliance.

Setup Steps

Configure the OpenAI API key in credentials.
Add NVIDIA API credentials for embedding and classification models.
Set up Google Sheets for knowledge base storage and retrieval.
Connect your ticketing system (Jira, Zendesk, or webhook) for incoming tickets.
Link a notification service (Gmail or Slack) for engineer alerts.
Map custom fields to your ticket system schema.

Prerequisites

OpenAI API account with GPT access. NVIDIA API credentials (Embeddings & Classification). Google Sheets for KB management. Ticketing system with webhook capability.

Use Cases

SaaS support teams triaging 100+ daily tickets, reducing manual sorting by 80%. Technical support escalating complex issues intelligently while documenting solutions.

Customization

Swap OpenAI models for Claude or Anthropic APIs. Replace Google Sheets with database systems (PostgreSQL, Airtable).

Benefits

Reduces manual ticket sorting by 70-80%, freeing support staff for complex issues. Decreases average resolution time through intelligent routing.

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 - Incoming Ticket Webhook

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

Block 2 - Workflow Configuration

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

Block 3 - Ticket Classifier Agent

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

Block 4 - OpenAI Chat Model - Classifier

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

Block 5 - Classification Output Parser

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

Block 6 - MCP Server Tools

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

Block 7 - Knowledge Base Search

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

Block 8 - Embeddings OpenAI - Search

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

Block 9 - Check If Solution Found

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

Block 10 - Format Auto-Resolution

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

Block 11 - AI Solution Generator

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

Block 12 - OpenAI Chat Model - Solution

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

Block 13 - Solution Output Parser

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

Block 14 - Create Diagnostic Logs

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

Block 15 - Check If Engineer Needed

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

Block 16 - Notify Engineer

Type / Role: n8n-nodes-base.slack - slack
Config choices: Version 2.4

Block 17 - Update Ticket Status

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

Block 18 - Add to Knowledge Base

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

Block 19 - Embeddings OpenAI - Insert

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

Block 20 - Document Loader

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

Block 21 - Prepare KB Entry

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

Block 22 - MCP Server Tools1

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

Block 23 - Sticky Note

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

Block 24 - Sticky Note1

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

Showing the first 24 of 31 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	AI-powered ticket triage with multi-model classification & knowledge base
Complexity	advanced
Nodes	31
Categories	Ticket Management, AI RAG
Author	Cheng Siong Chin
Published	16 Dec 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/11854/11854.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 AI-powered ticket triage with multi-model classification & knowledge base 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.

Cheng Siong Chin

203 workflows

Nodes

n8n-nodes-base.webhook n8n-nodes-base.set @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.lmchatopenai @n8n/n8n-nodes-langchain.outputparserstructured @n8n/n8n-nodes-langchain.mcpclienttool @n8n/n8n-nodes-langchain.vectorstorepgvector @n8n/n8n-nodes-langchain.embeddingsopenai

Complexity

advanced

Published 16 Dec 2025

Likes 0

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Install path: /data/workflows/11854/11854.json

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AI-powered ticket triage with multi-model classification & knowledge base

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 - Incoming Ticket Webhook

Block 2 - Workflow Configuration

Block 3 - Ticket Classifier Agent

Block 4 - OpenAI Chat Model - Classifier

Block 5 - Classification Output Parser

Block 6 - MCP Server Tools

Block 7 - Knowledge Base Search

Block 8 - Embeddings OpenAI - Search

Block 9 - Check If Solution Found

Block 10 - Format Auto-Resolution

Block 11 - AI Solution Generator

Block 12 - OpenAI Chat Model - Solution

Block 13 - Solution Output Parser

Block 14 - Create Diagnostic Logs

Block 15 - Check If Engineer Needed

Block 16 - Notify Engineer

Block 17 - Update Ticket Status

Block 18 - Add to Knowledge Base

Block 19 - Embeddings OpenAI - Insert

Block 20 - Document Loader

Block 21 - Prepare KB Entry

Block 22 - MCP Server Tools1

Block 23 - Sticky Note

Block 24 - Sticky Note1

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