This workflow follows the Agent → Chat Trigger recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"name": "UPS \u00b7 Gate 5 \u00b7 AI Agent tool path (Track)",
"nodes": [
{
"parameters": {
"options": {}
},
"id": "chat-trigger",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"typeVersion": 1.4,
"position": [
-176,
0
]
},
{
"parameters": {
"promptType": "auto",
"options": {
"systemMessage": "You are a UPS assistant. When the user gives a tracking number or asks about a package, call the UPS tool to look it up, then report the current status and recent scan history in plain language."
}
},
"id": "ai-agent",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 1.7,
"position": [
240,
0
]
},
{
"parameters": {
"model": {
"__rl": true,
"value": "claude-haiku-4-5-20251001",
"mode": "list",
"cachedResultName": "Claude Haiku 4.5"
},
"options": {}
},
"id": "anthropic-chat-model",
"name": "Anthropic Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatAnthropic",
"typeVersion": 1.3,
"position": [
-16,
224
],
"credentials": {
"anthropicApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"sessionIdType": "customKey",
"sessionKey": "={{ $now }}"
},
"id": "simple-memory",
"name": "Simple Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"typeVersion": 1.4,
"position": [
256,
208
]
},
{
"parameters": {
"trackingNumber": "={{ $fromAI('trackingNumber', 'The UPS 1Z tracking number to look up', 'string') }}",
"requestOptions": {}
},
"id": "ups-tool",
"name": "Track a shipment in UPS",
"type": "@nodrel-dev/n8n-nodes-ups.upsTool",
"typeVersion": 1,
"position": [
544,
240
],
"credentials": {
"upsOAuth2Api": {
"name": "<your credential>"
}
}
}
],
"connections": {
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Anthropic Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Simple Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Track a shipment in UPS": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1"
},
"active": false
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
anthropicApiupsOAuth2Api
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
UPS · Gate 5 · AI Agent tool path (Track). Uses chatTrigger, agent, lmChatAnthropic, memoryBufferWindow. Chat trigger; 5 nodes.
Source: https://github.com/nodrel-dev/n8n-ups-node/blob/main/test/workflows/06-agent-track.json — original creator credit. Request a take-down →
Related workflows
Workflows that share integrations, category, or trigger type with this one. All free to copy and import.
This workflow is designed to intelligently route user queries to the most suitable large language model (LLM) based on the type of request received in a chat environment. It uses structured classifica
KI-Agent + Data Table (Praxis-Beispiel). Uses chatTrigger, agent, lmChatAnthropic, memoryBufferWindow. Chat trigger; 12 nodes.
This workflow dynamically chooses between two new powerful Anthropic models — Claude Opus 4 and Claude Sonnet 4 — to handle user queries, based on their complexity and nature, maintaining scalability
KI-Agent Grundlagen (Lern-Workflow). Uses chatTrigger, agent, lmChatAnthropic, memoryBufferWindow. Chat trigger; 11 nodes.
This automation helps marketing and sales teams define their Ideal Customer Profile (ICP) using real LinkedIn profiles of current high-fit customers. By enriching and analyzing profile data, it genera