This workflow follows the Agent → OpenAI Chat 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": "q2MJWAqpKF2BCJkq",
"name": "LangChain - Example - Code Node Example",
"tags": [
{
"id": "snf16n0p2UrGP838",
"name": "LangChain - Example",
"createdAt": "2023-09-25T16:21:55.962Z",
"updatedAt": "2023-09-25T16:21:55.962Z"
}
],
"nodes": [
{
"id": "ad1a920e-1048-4b58-9c4a-a0469a1f189d",
"name": "OpenAI",
"type": "@n8n/n8n-nodes-langchain.lmOpenAi",
"position": [
900,
628
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "7dd04ecd-f169-455c-9c90-140140e37542",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
800,
340
],
"parameters": {
"width": 432,
"height": 237,
"content": "## Self-coded LLM Chain Node"
},
"typeVersion": 1
},
{
"id": "05ad7d68-5dc8-42f2-8274-fcb5bdeb68cb",
"name": "When clicking \"Execute Workflow\"",
"type": "n8n-nodes-base.manualTrigger",
"position": [
280,
428
],
"parameters": {},
"typeVersion": 1
},
{
"id": "39e2fd34-3261-44a1-aa55-96f169d55aad",
"name": "Set",
"type": "n8n-nodes-base.set",
"position": [
620,
428
],
"parameters": {
"values": {
"string": [
{
"name": "input",
"value": "Tell me a joke"
}
]
},
"options": {}
},
"typeVersion": 2
},
{
"id": "42a3184c-0c62-4e79-9220-7a93e313317e",
"name": "Set1",
"type": "n8n-nodes-base.set",
"position": [
620,
820
],
"parameters": {
"values": {
"string": [
{
"name": "input",
"value": "What year was Einstein born?"
}
]
},
"options": {}
},
"typeVersion": 2
},
{
"id": "4e2af29d-7fc4-484b-8028-1b9a84d60172",
"name": "Chat OpenAI",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
731,
1108
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "334e9176-3a18-4838-84cb-70e8154f1a30",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
880,
1028
],
"parameters": {
"width": 320.2172923777021,
"height": 231,
"content": "## Self-coded Tool Node"
},
"typeVersion": 1
},
{
"id": "05e0d5c6-df18-42ba-99b6-a2b65633a14d",
"name": "Custom - Wikipedia",
"type": "@n8n/n8n-nodes-langchain.code",
"position": [
971,
1108
],
"parameters": {
"code": {
"supplyData": {
"code": "console.log('Custom Wikipedia Node runs');\nconst { WikipediaQueryRun } = require('langchain/tools');\nreturn new WikipediaQueryRun();"
}
},
"outputs": {
"output": [
{
"type": "ai_tool"
}
]
}
},
"typeVersion": 1
},
{
"id": "9c729e9a-f173-430c-8bcd-74101b614891",
"name": "Custom - LLM Chain Node",
"type": "@n8n/n8n-nodes-langchain.code",
"position": [
880,
428
],
"parameters": {
"code": {
"execute": {
"code": "const { PromptTemplate } = require('langchain/prompts');\n\nconst query = $input.item.json.input;\nconst prompt = PromptTemplate.fromTemplate(query);\nconst llm = await this.getInputConnectionData('ai_languageModel', 0);\nlet chain = prompt.pipe(llm);\nconst output = await chain.invoke();\nreturn [ {json: { output } } ];"
}
},
"inputs": {
"input": [
{
"type": "main"
},
{
"type": "ai_languageModel",
"required": true,
"maxConnections": 1
}
]
},
"outputs": {
"output": [
{
"type": "main"
}
]
}
},
"typeVersion": 1
},
{
"id": "6427bbf0-49a6-4810-9744-87d88151e914",
"name": "Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
880,
820
],
"parameters": {
"options": {}
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "e14a709d-08fe-4ed7-903a-fb2bae80b28a",
"connections": {
"Set": {
"main": [
[
{
"node": "Custom - LLM Chain Node",
"type": "main",
"index": 0
}
]
]
},
"Set1": {
"main": [
[
{
"node": "Agent",
"type": "main",
"index": 0
}
]
]
},
"OpenAI": {
"ai_languageModel": [
[
{
"node": "Custom - LLM Chain Node",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Chat OpenAI": {
"ai_languageModel": [
[
{
"node": "Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Custom - Wikipedia": {
"ai_tool": [
[
{
"node": "Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"When clicking \"Execute Workflow\"": {
"main": [
[
{
"node": "Set",
"type": "main",
"index": 0
},
{
"node": "Set1",
"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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How this works
This workflow harnesses LangChain's capabilities to process queries through AI-driven code execution, delivering precise and customised responses by integrating OpenAI models with a flexible code node. It suits developers and AI enthusiasts seeking to automate complex tasks like querying Wikipedia data or running bespoke scripts without building everything from scratch. The key step involves the custom code node, which takes inputs from Chat OpenAI and OpenAI nodes to execute tailored logic, streamlining experimentation with event-triggered AI chains.
Use this workflow for prototyping AI agents that require code-level customisation, such as analysing external data sources in real-time, especially when triggered by specific events. Avoid it for simple data transfers or non-AI automations, where basic nodes suffice without LangChain overhead. Common variations include swapping the Wikipedia code for database queries or API calls to adapt to different analytical needs.
About this workflow
LangChain - Example - Code Node Example. Uses lmOpenAi, stickyNote, manualTrigger, lmChatOpenAi. Event-driven trigger; 10 nodes.
Source: https://github.com/Zie619/n8n-workflows — original creator credit. Request a take-down →
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