This workflow follows the Chainllm → HTTP Request 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": "TtoDcjgthgA4NTkU",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs",
"tags": [
{
"id": "mqOrNvCDgQLzPA2x",
"name": "Workflows",
"createdAt": "2024-08-07T14:18:53.614Z",
"updatedAt": "2024-08-07T14:18:53.614Z"
}
],
"nodes": [
{
"id": "86cbf150-df4f-42f7-b7b3-e03c32e6f23c",
"name": "Get Chat",
"type": "@n8n/n8n-nodes-langchain.memoryManager",
"position": [
1700,
-400
],
"parameters": {
"options": {}
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "a9153a24-e902-4f29-9b83-447317ce3119",
"name": "Insert Chat",
"type": "@n8n/n8n-nodes-langchain.memoryManager",
"position": [
2540,
-400
],
"parameters": {
"mode": "insert",
"messages": {
"messageValues": [
{
"type": "user",
"message": "={{ $('OpenAI - Speech to Text').item.json[\"text\"] }}"
},
{
"type": "ai",
"message": "={{ $json.text }}"
}
]
}
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "f5c272d4-248b-45a5-87b5-eb659a865d05",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
1664,
-491
],
"parameters": {
"color": 6,
"width": 486.4746124819703,
"height": 238.4911357933579,
"content": "## Get Context"
},
"typeVersion": 1
},
{
"id": "32ad17ca-0045-487d-9387-71c2e73629d4",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
2510,
-489
],
"parameters": {
"color": 6,
"width": 321.2536584847704,
"height": 231.05945912581728,
"content": "## Save Context"
},
"typeVersion": 1
},
{
"id": "17ae4f1a-6192-4c52-8157-3cb47b37e0fb",
"name": "Aggregate",
"type": "n8n-nodes-base.aggregate",
"position": [
2020,
-400
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData",
"destinationFieldName": "context"
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "00b3081e-fbcd-489b-b45a-4e847c346594",
"name": "Window Buffer Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
2080,
-100
],
"parameters": {
"sessionKey": "test-0dacb3b5-4bcd-47dd-8456-dcfd8c258204",
"sessionIdType": "customKey"
},
"typeVersion": 1.2
},
{
"id": "55ca2790-e905-414a-a9f6-7d88a9e5807d",
"name": "Google Gemini Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
2220,
-100
],
"parameters": {
"options": {},
"modelName": "models/gemini-1.5-flash"
},
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "e8b3433f-b205-404c-9f05-504556d6b6dd",
"name": "Respond to Webhook",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
3560,
-400
],
"parameters": {
"options": {},
"respondWith": "binary"
},
"typeVersion": 1.1
},
{
"id": "de296743-5ac7-454b-bf3a-d020cc024511",
"name": "ElevenLabs - Generate Audio",
"type": "n8n-nodes-base.httpRequest",
"position": [
3240,
-400
],
"parameters": {
"url": "=https://api.elevenlabs.io/v1/text-to-speech/{{voice id}}",
"method": "POST",
"options": {},
"sendBody": true,
"sendHeaders": true,
"authentication": "genericCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "text",
"value": "={{ $('Basic LLM Chain').item.json.text }}"
}
]
},
"genericAuthType": "httpCustomAuth",
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"credentials": {
"httpCustomAuth": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "214e15f2-8a16-4598-b4ac-9fc2ec6545e6",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
3040,
-560
],
"parameters": {
"width": 468.73250812192407,
"height": 843.7602354099661,
"content": "* ### For the Text-to-Speech part, we'll use ElevenLabs.io, which is free and offers a variety of voices to choose from. However, you can also use the OpenAI `\"Generate audio\"` node instead.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n* ### Since there is no pre-built node for `\"ElevenLabs\"` in n8n, we'll connect to it through its API using the \"HTTP Request\" node.\n\n## Prerequisites:\n* ### `\"ElevenLabs API Key\"` (you can obtain it from their website).\n* ### `\"Voice ID\"` (you can also get it from ElevenLabs' \"Voice Library\").\n## Setup\n* ### In the URL parameter, replace \"{{voice id}}\" at the end of the URL with the Voice ID you obtained from ElevenLabs.io.\n* ### To set up your API Key, add custom authentication and include the following `JSON` with your acual ElevenLabs API Key:\n```json\n{\n \"headers\": {\n \"xi-api-key\": \"put-your-API-Key-here\"\n }\n}\n```"
},
"typeVersion": 1
},
{
"id": "94ad934c-4a13-47b1-83a5-76fab43b3a47",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1663,
-598
],
"parameters": {
"color": 6,
"width": 487.4293487597613,
"height": 91.01435855269375,
"content": "### The \"Get Chat,\" \"Insert Chat,\" and \"Window Buffer Memory\" nodes will help the LLM model maintain context throughout the conversation."
},
"typeVersion": 1
},
{
"id": "0a96f48d-0d8b-4240-9eab-a681bfd4c8b5",
"name": "Limit",
"type": "n8n-nodes-base.limit",
"position": [
2900,
-400
],
"parameters": {},
"typeVersion": 1
},
{
"id": "9a5d4ddb-6403-4758-858e-9fbe10c421a9",
"name": "Basic LLM Chain",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
2200,
-400
],
"parameters": {
"text": "={{ $('OpenAI - Speech to Text').item.json[\"text\"] }}",
"messages": {
"messageValues": [
{
"type": "AIMessagePromptTemplate",
"message": "=To maintain context and fully understand the user's question, always review the previous conversation between you and him before providing an answer.\nThis is the previous conversation:\n{{ $('Aggregate').item.json[\"context\"].map(m => `\nHuman: ${m.human || 'undefined'}\nAI Assistant: ${m.ai || 'undefined'}\n`).join('') }}"
}
]
},
"promptType": "define"
},
"typeVersion": 1.4
},
{
"id": "f2f99895-9678-41b8-ad28-db40e1e23dc0",
"name": "Webhook",
"type": "n8n-nodes-base.webhook",
"position": [
1320,
-400
],
"parameters": {
"path": "voice_message",
"options": {},
"httpMethod": "POST",
"responseMode": "responseNode"
},
"typeVersion": 2
},
{
"id": "d9a5fb04-4c02-4da4-b690-7b0ecd0ae052",
"name": "OpenAI - Speech to Text",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
1500,
-400
],
"parameters": {
"options": {},
"resource": "audio",
"operation": "transcribe",
"binaryPropertyName": "voice_message"
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.3
}
],
"active": true,
"settings": {
"callerPolicy": "workflowsFromSameOwner",
"executionOrder": "v1",
"saveManualExecutions": true
},
"versionId": "fe5792ca-03d7-4cdd-96db-20f4cd479c7e",
"connections": {
"Limit": {
"main": [
[
{
"node": "ElevenLabs - Generate Audio",
"type": "main",
"index": 0
}
]
]
},
"Webhook": {
"main": [
[
{
"node": "OpenAI - Speech to Text",
"type": "main",
"index": 0
}
]
]
},
"Get Chat": {
"main": [
[
{
"node": "Aggregate",
"type": "main",
"index": 0
}
]
]
},
"Aggregate": {
"main": [
[
{
"node": "Basic LLM Chain",
"type": "main",
"index": 0
}
]
]
},
"Insert Chat": {
"main": [
[
{
"node": "Limit",
"type": "main",
"index": 0
}
]
]
},
"Basic LLM Chain": {
"main": [
[
{
"node": "Insert Chat",
"type": "main",
"index": 0
}
]
]
},
"Window Buffer Memory": {
"ai_memory": [
[
{
"node": "Insert Chat",
"type": "ai_memory",
"index": 0
},
{
"node": "Get Chat",
"type": "ai_memory",
"index": 0
}
]
]
},
"OpenAI - Speech to Text": {
"main": [
[
{
"node": "Get Chat",
"type": "main",
"index": 0
}
]
]
},
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "Basic LLM Chain",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"ElevenLabs - Generate Audio": {
"main": [
[
{
"node": "Respond to Webhook",
"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.
googlePalmApihttpCustomAuthopenAiApi
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
How this works
Enable seamless voice interactions with an AI assistant that remembers past conversations, delivering personalised responses through natural-sounding speech synthesis. This workflow suits developers or teams building chatbots for customer support, virtual assistants, or interactive apps, where maintaining context enhances user experience. It starts with a webhook trigger to receive voice inputs, then leverages memory management to retrieve and update chat history before processing queries via Google Gemini or OpenAI for intelligent replies, culminating in ElevenLabs-generated audio output.
Use this when integrating voice AI into web or mobile apps requiring persistent memory across sessions, such as ongoing customer queries or educational tools. Avoid it for simple text-based chats without voice needs, or if your setup lacks API access to these services, as it demands reliable integrations. Common variations include swapping Gemini for OpenAI exclusively or adding sentiment analysis nodes for more nuanced responses.
About this workflow
AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs. Uses memoryManager, stickyNote, memoryBufferWindow, lmChatGoogleGemini. Webhook trigger; 15 nodes.
Source: https://github.com/Zie619/n8n-workflows — 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.
AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs. Uses memoryManager, stickyNote, memoryBufferWindow, lmChatGoogleGemini. Webhook trigger; 15 nodes.
Intelligent Web Query and Semantic Re-Ranking Flow. Uses stickyNote, dateTime, outputParserAutofixing, outputParserStructured. Webhook trigger; 20 nodes.
Intelligent Web Query and Semantic Re-Ranking Flow. Uses stickyNote, dateTime, outputParserAutofixing, outputParserStructured. Webhook trigger; 20 nodes.
Wait Schedule. Uses googleDrive, lmChatGoogleGemini, outputParserStructured, stickyNote. Scheduled trigger; 34 nodes.
High-Level Service Page SEO Blueprint Report. Uses formTrigger, splitInBatches, httpRequest, lmChatGoogleGemini. Event-driven trigger; 33 nodes.