AI voice chat using webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs
Ready for 3.0What this workflow does
This AI workflow has 11 nodes and runs when an HTTP POST request hits its webhook URL. Along the way it connects Google Gemini Chat Model and OpenAI.
How it runs
Trigger: When an HTTP POST request hits its webhook URL.
- OpenAI: transcribe (audio)
- Get Chat (Chat Memory Manager): uses Chat Memory Manager, with memory: Window Buffer Memory
- Aggregate: combines items into one
- Basic LLM Chain: sends a prompt to an LLM, with model: Google Gemini Chat Model
- Insert Chat (Chat Memory Manager): uses Chat Memory Manager, with memory: Window Buffer Memory
- Limit: keeps the first few items
- ElevenLabs - Generate Audio (HTTP Request): calls an HTTP API (POST)
- Respond to Webhook: sends the response back to the webhook caller
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (10)
- Chat Memory Manager
@n8n/n8n-nodes-langchain.memoryManager - Aggregate
n8n-nodes-base.aggregate - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - Respond to Webhook
n8n-nodes-base.respondToWebhook - HTTP Request
n8n-nodes-base.httpRequest - Limit
n8n-nodes-base.limit - Basic LLM Chain
@n8n/n8n-nodes-langchain.chainLlm - Webhook
n8n-nodes-base.webhook - OpenAI
@n8n/n8n-nodes-langchain.openAi
From the author
Who is this for? This workflow is designed for businesses or developers looking to integrate voice-based chat applications with dynamic responses and conversational memory. What problem does this solve? It automates AI-powered voice conversations, maintaining…