Mario This workflow corresponds to n8n.io template #3820 — we link there as the canonical source.
This workflow follows the Chainllm → 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 →
{
"id": "dQC8kExvbCrovWf0",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Dynamically switch between LLMs Template",
"tags": [],
"nodes": [
{
"id": "962c4b29-c244-4d68-93e1-cacd41b436fc",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
220,
80
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "6fc4f336-09e3-4e79-94e9-e5eff04e4089",
"name": "Switch Model",
"type": "@n8n/n8n-nodes-langchain.code",
"position": [
540,
320
],
"parameters": {
"code": {
"supplyData": {
"code": "let llms = await this.getInputConnectionData('ai_languageModel', 0);\nllms.reverse(); // reverse array, so the order matches the UI elements\n\nconst llm_index = $input.item.json.llm_index;\nif (!Number.isInteger(llm_index)) {\n console.log(\"'llm_index' is udefined or not a valid integer\");\n throw new Error(\"'llm_index' is udefined or not a valid integer\");\n}\n\nif(typeof llms[llm_index] === 'undefined') {\n console.log(`No LLM found with index ${llm_index}`);\n throw new Error(`No LLM found with index ${llm_index}`);\n}\n\nreturn llms[llm_index];"
}
},
"inputs": {
"input": [
{
"type": "ai_languageModel",
"required": true
}
]
},
"outputs": {
"output": [
{
"type": "ai_languageModel"
}
]
}
},
"typeVersion": 1
},
{
"id": "68511483-355b-45c1-915f-e7517c42b809",
"name": "Set LLM index",
"type": "n8n-nodes-base.set",
"position": [
440,
80
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "24b4d30e-484a-4cc1-a691-0653ed764296",
"name": "llm_index",
"type": "number",
"value": "={{ $json.llm_index || 0 }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "adc2f24c-0ad6-4057-bb3b-b46563c72ee8",
"name": "Increase LLM index",
"type": "n8n-nodes-base.set",
"position": [
1420,
-200
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "24b4d30e-484a-4cc1-a691-0653ed764296",
"name": "llm_index",
"type": "number",
"value": "={{ $('Set LLM index').item.json.llm_index + 1 }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "eace2dd7-9550-47ba-a4c3-4f065f80757b",
"name": "No Operation, do nothing",
"type": "n8n-nodes-base.noOp",
"position": [
1640,
540
],
"parameters": {},
"typeVersion": 1
},
{
"id": "c1735d1c-5dc4-4bd5-9dde-3bb04b8811c3",
"name": "Check for expected error",
"type": "n8n-nodes-base.if",
"position": [
1040,
160
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "3253e1f2-172e-4af4-a492-3b9c6e9e4797",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.error }}",
"rightValue": "Error in sub-node Switch Model"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "4a259078-aa74-4725-9e91-d2775bbd577f",
"name": "Loop finished without results",
"type": "n8n-nodes-base.set",
"position": [
1260,
60
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "b352627d-d692-47f8-8f8c-885b68073843",
"name": "output",
"type": "string",
"value": "The loop finished without a satisfying result"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "3b527ed3-a700-403d-8e3c-d0d55a83c9ea",
"name": "Unexpected error",
"type": "n8n-nodes-base.set",
"position": [
1260,
260
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "b352627d-d692-47f8-8f8c-885b68073843",
"name": "output",
"type": "string",
"value": "An unexpected error happened"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "2a48a244-25ab-4330-9e89-3f8a52b7fd0a",
"name": "Return result",
"type": "n8n-nodes-base.set",
"position": [
1420,
-460
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "b352627d-d692-47f8-8f8c-885b68073843",
"name": "output",
"type": "string",
"value": "={{ $json.text || $json.output }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "79da2795-800a-423d-ad5b-ec3b0498a5e6",
"name": "OpenAI 4o-mini",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
460,
580
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "c5884632-4f21-4e1e-a86d-77e3b18119b9",
"name": "OpenAI 4o",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
640,
580
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o",
"cachedResultName": "gpt-4o"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "0693ac6a-fd1e-4a1f-b7be-bd4a1021b6c1",
"name": "OpenAI o1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
820,
580
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "o1",
"cachedResultName": "o1"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "f9fa467a-804d-4abf-84e3-06a88f9142b4",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1100,
-100
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "7c6bf364-1844-484f-8a1c-1ff87286c686",
"name": "Validate response",
"type": "@n8n/n8n-nodes-langchain.sentimentAnalysis",
"position": [
1040,
-300
],
"parameters": {
"options": {
"categories": "pass, fail",
"systemPromptTemplate": "You are a highly intelligent and accurate sentiment analyzer. Analyze the sentiment of the provided text. Categorize it into one of the following: {categories}. Use the provided formatting instructions. Only output the JSON.\n\n> Evaluate the following customer support response. Give a short JSON answer with a field \u201cquality\u201d: \u201cpass\u201d or \u201cfail\u201d. Only return \u201cpass\u201d if the response:\n\n1. Acknowledges both the broken keyboard and the late delivery \n2. Uses a polite and empathetic tone \n3. Offers a clear resolution or next step (like refund, replacement, or contact support)"
},
"inputText": "={{ $json.text }}"
},
"typeVersion": 1
},
{
"id": "a7be0179-e246-4f75-8863-d03eefe9d8ac",
"name": "Generate response",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"onError": "continueErrorOutput",
"position": [
660,
80
],
"parameters": {
"text": "={{ $('When chat message received').item.json.chatInput }}",
"messages": {
"messageValues": [
{
"message": "=You\u2019re an AI assistant replying to a customer who is upset about a faulty product and late delivery. The customer uses sarcasm and is vague. Write a short, polite response, offering help."
}
]
},
"promptType": "define"
},
"retryOnFail": false,
"typeVersion": 1.6
},
{
"id": "273f4025-2aeb-4a67-859a-690a3a086f82",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
380,
-160
],
"parameters": {
"width": 480,
"height": 140,
"content": "### Customer complaint - example\n\nI really *love* waiting two weeks just to get a keyboard that doesn\u2019t even work. Great job. Any chance I could actually use the thing I paid for sometime this month?"
},
"typeVersion": 1
},
{
"id": "a7806fab-fdc2-4feb-be53-fcea81ede105",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
380,
0
],
"parameters": {
"color": 7,
"width": 220,
"height": 240,
"content": "Defines the LLM node by index which should be used."
},
"typeVersion": 1
},
{
"id": "0117d8d8-672e-458a-a9dd-30b50e05f343",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
480,
240
],
"parameters": {
"color": 7,
"width": 380,
"height": 200,
"content": "Dynamically connects the LLM by the index provided in the previous node."
},
"typeVersion": 1
},
{
"id": "66066bad-4fd3-4e68-88bb-0b95fd9a6e49",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
980,
60
],
"parameters": {
"color": 7,
"width": 220,
"height": 260,
"content": "Check if LangChain Code Node ran into error. _Currently only supports error output from main Node_"
},
"typeVersion": 1
},
{
"id": "b9101226-0035-4de3-8720-f783d13e0cca",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
600,
0
],
"parameters": {
"color": 7,
"width": 380,
"height": 240,
"content": "Generates a polite answer based on the customers complaint."
},
"typeVersion": 1
},
{
"id": "ee7d70ee-2eb7-494f-ad74-2cb6108ba0ed",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
980,
-360
],
"parameters": {
"color": 7,
"width": 380,
"height": 220,
"content": "Analyses the generated answer by certain criteria"
},
"typeVersion": 1
},
{
"id": "03bde6f5-27b1-4568-96fb-5ece77d7b2e5",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
1360,
-280
],
"parameters": {
"color": 7,
"width": 220,
"height": 240,
"content": "Increases the index to choose the next available LLM on the next run"
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "52381ffc-bdf4-4243-bc35-462dedb929bd",
"connections": {
"OpenAI 4o": {
"ai_languageModel": [
[
{
"node": "Switch Model",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"OpenAI o1": {
"ai_languageModel": [
[
{
"node": "Switch Model",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Switch Model": {
"ai_outputParser": [
[]
],
"ai_languageModel": [
[
{
"node": "Generate response",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Set LLM index": {
"main": [
[
{
"node": "Generate response",
"type": "main",
"index": 0
}
]
]
},
"OpenAI 4o-mini": {
"ai_languageModel": [
[
{
"node": "Switch Model",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Generate response": {
"main": [
[
{
"node": "Validate response",
"type": "main",
"index": 0
}
],
[
{
"node": "Check for expected error",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "Validate response",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Validate response": {
"main": [
[
{
"node": "Return result",
"type": "main",
"index": 0
}
],
[
{
"node": "Increase LLM index",
"type": "main",
"index": 0
}
]
]
},
"Increase LLM index": {
"main": [
[
{
"node": "No Operation, do nothing",
"type": "main",
"index": 0
}
]
]
},
"Check for expected error": {
"main": [
[
{
"node": "Loop finished without results",
"type": "main",
"index": 0
}
],
[
{
"node": "Unexpected error",
"type": "main",
"index": 0
}
]
]
},
"No Operation, do nothing": {
"main": [
[
{
"node": "Set LLM index",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Set LLM index",
"type": "main",
"index": 0
}
]
]
}
}
}
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.
openAiApi
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About this workflow
This example workflow demonstrates a way to connect multiple LLMs to a single AI Agent/LangChain Node and programmatically use one – or in this case loop through them.
Source: https://n8n.io/workflows/3820/ — original creator credit. Request a take-down →
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