AI-Powered ServiceNow Chat Triage with GPT-4 — Incident & Request Router

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Built by Tushar Mishra Tushar Mishra
Created on June 05, 2026

Description


Short description

Automatically triage incoming chat messages into Incidents, Service Requests, or Other using an LLM-powered classifier; create Incidents in ServiceNow, submit Service Catalog requests (HTTP), and route everything else to an AI Agent with web search + memory. Includes an optional summarization step for ticket context.

Full description

This n8n template wires a chat trigger to an LLM-based Text Classifier and then routes messages to the appropriate downstream action:

Trigger: When chat message received — incoming messages from your chat channel.
Text Classifier: small LLM prompt/classifier that returns one of three labels: Incident, Request, or Everything Else.
Create Incident (ServiceNow connector): when labeled Incident, the workflow creates a Servicenow Incident record (short fields: short\_description, description, priority, caller).
Submit General Request (HTTP Request): when labeled Request, the workflow calls your Service Catalog API (POST) to place a catalog item / submit a request.
AI Agent: when labeled Everything Else, route to an AI Agent node that:

uses an OpenAI chat model for contextual replies,
can consult SerpAPI (web search) as a tool,
saves relevant context to Simple Memory for future conversations.
Summarization Chain: optional chain to summarize long chat threads into concise ticket descriptions before creating incidents/requests.

This template is ideal for support desks that want automated triage with human-quality context and searchable memory.

Key highlights (what to call out)

Three-way LLM triage**: ensures messages are routed automatically to the correct backend action (Incident vs Service Request vs AI handling).
ServiceNow native connector**: uses the ServiceNow node to create Incidents (safer than raw HTTP for incidents).
Service Catalog via HTTP**: flexible — supports organizations using RESTful catalog endpoints.
Summarization before ticket creation**: produces concise, high-quality short_description and description fields.
AI Agent + Memory + Web Search**: handles non-ticket queries with web-augmented answers and stores context for follow-ups.
Failover & logging**: include a catch node (optional) that logs failures and notifies admins.

Required credentials & inputs (must configure)

ServiceNow**: Instance URL + API user (must have rights to create incidents).
Service Catalog HTTP endpoint**: URL + API key / auth header (for POST).
OpenAI API key** (or other LLM provider): for Text Classifier, Summarization Chain, and AI Agent.
SerpAPI key** (optional): for web search tools inside the AI Agent.
Memory store**: Simple Memory node (or external DB) for conversation history.

Nodes included (quick map)

Trigger: When chat message received
Processor: Text Classifier (OpenAI/LLM)
Branch A: ServiceNow (Create Incident)
Branch B: HTTP Request (Service Catalog POST)
Branch C: AI Agent (OpenAI + SerpAPI + Simple Memory)
Shared: Summarization Chain (used before A or B where enabled)
Optional: Error / Audit logging node, Slack/email notifications

Recommended n8n settings & tips

Use structured outputs** from classifier ({ label: "Incident", confidence: 0.92 }) so you can implement confidence thresholds.
If confidence < 0.7**, route to a human review queue instead of auto-creating a ticket.
Sanitize user PII** before storing in memory or sending to external APIs.
Rate-limit** OpenAI/SerpAPI calls to avoid unexpected bills.
Test the Service Catalog POST body** in Postman first — include sample variables JSON.

Short sample variables JSON (Service Catalog POST)

{
"sysparm_quantity": 1,
"variables": {

"description": "User reports VPN timeout on Windows machine; error code 1234"
}
}

Nodes Used (8)

AI Agent
@n8n/n8n-nodes-langchain.agent
HTTP Request
n8n-nodes-base.httpRequest
OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi
SerpApi (Google Search)
@n8n/n8n-nodes-langchain.toolSerpApi
ServiceNow
n8n-nodes-base.serviceNow
Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow
Summarization Chain
@n8n/n8n-nodes-langchain.chainSummarization
Text Classifier
@n8n/n8n-nodes-langchain.textClassifier