AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs
15 nodes · memoryManager, aggregate, memoryBufferWindow, lmChatGoogleGemini, respondToWebhook, httpRequest
- Engine
- n8n
- Nodes
- 15
- Trigger
- scheduled/triggered
- Source file
- 1289_Limit_Webhook_Automation_Webhook.json
Workflow flow
Window Buffer Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow
Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini
Webhook
Webhook
OpenAI - Speech to Text
@n8n/n8n-nodes-langchain.openAi
Get Chat
@n8n/n8n-nodes-langchain.memoryManager
Aggregate
Aggregate
Basic LLM Chain
@n8n/n8n-nodes-langchain.chainLlm
Insert Chat
@n8n/n8n-nodes-langchain.memoryManager
Limit
Limit
ElevenLabs - Generate Audio
HTTP
Respond to Webhook
RespondToWebhook
11 steps · flows top to bottom, branches left to right
Browse by integration
Import into n8n
- Download the n8n JSON file (full or sanitized).
- In n8n: Workflows → Import from file.
- Open each node and reconnect credentials to your accounts.
- Test with manual execution before enabling triggers.
Sanitized JSON removes credential IDs — safer for sharing. Flow graph JSON is OpenRuna's lightweight format for diagrams and other tools, not n8n import.
Overview
AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs is a free n8n workflow on OpenRuna — 15 nodes connecting memoryManager, aggregate, memoryBufferWindow, lmChatGoogleGemini, respondToWebhook, httpRequest, limit, chainLlm. Download the JSON, view the flow diagram, and import it into your n8n instance.
What this workflow does
"AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs" automates a memoryManager-to-aggregate flow in n8n across 15 nodes. 15 nodes · memoryManager, aggregate, memoryBufferWindow, lmChatGoogleGemini, respondToWebhook, httpRequest Execution begins at the scheduled/triggered trigger and flows through the 15 configured nodes. OpenRuna renders the node graph and offers the JSON export so you can import and adapt it in minutes. Use it to learn integration patterns, fork it for your own use case, or deploy it quickly after reconnecting credentials.
Use cases
- Share it with your team as a documented reference for scheduled/triggered automations in the memoryManager stack.
- Study how data moves from memoryManager to aggregate before building your own variant.
- Use it as a starting template when wiring memoryManager, aggregate, memoryBufferWindow, lmChatGoogleGemini, respondToWebhook, httpRequest, limit, chainLlm — adjust node parameters to your accounts and record IDs.
- Import "AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs" into n8n, reconnect credentials for memoryManager, aggregate, memoryBufferWindow, lmChatGoogleGemini, respondToWebhook, httpRequest, limit, chainLlm, and run the same automation in your own workspace.
Example output
Imported into n8n, "AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs" exposes 15 configured nodes. Trigger type: scheduled/triggered. Integrations: memoryManager, aggregate, memoryBufferWindow, lmChatGoogleGemini, respondToWebhook, httpRequest, limit, chainLlm. Reconnect the memoryManager credential first, then walk the remaining nodes top to bottom. Run it once in test mode to verify any branch logic (Switch/IF nodes) before enabling production triggers.
Tips by platform
Claude
Paste the exported JSON for "AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs" into Claude and ask it to explain each of the 15 nodes. Claude is good at suggesting credential mappings for memoryManager, aggregate, memoryBufferWindow, lmChatGoogleGemini, respondToWebhook, httpRequest, limit, chainLlm, plus error-handling and idempotency improvements before you run it in production.
ChatGPT
Have ChatGPT generate a checklist for adapting "AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs" to your accounts — which memoryManager, aggregate, memoryBufferWindow, lmChatGoogleGemini, respondToWebhook, httpRequest, limit, chainLlm credentials to create and which node parameters to change first.
Cursor
Open the downloaded JSON in Cursor and use Agent mode to refactor hard-coded URLs into env vars, or to generate tests for any Code nodes. Keep the OpenRuna flow diagram open so you do not break connection order across the 15 nodes.
Frequently asked questions
- How do I import "AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs" into n8n?
- Click Download n8n JSON on this page, then in n8n choose Workflows → Import from file and select the downloaded file. Reconnect credentials on each node — the export carries credential IDs from the original author's instance, not working secrets.
- Which integrations does "AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs" use?
- It uses: memoryManager, aggregate, memoryBufferWindow, lmChatGoogleGemini, respondToWebhook, httpRequest, limit, chainLlm. The flow diagram above shows how each service connects across the 15 nodes. Filter OpenRuna by integration to find related workflows and tools.
- Can I import a sanitized export without credentials?
- Yes. Use "Download sanitized JSON" to strip credential references before sharing internally. You will still add your own memoryManager and aggregate credentials in n8n after import.
- What trigger type does "AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs" use?
- Trigger type: scheduled/triggered. After import, confirm the trigger node (webhook, schedule, or app event) matches your environment, then activate the workflow when you are ready to run it.
- Is "AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs" free to use?
- OpenRuna catalogs community n8n workflows under open licenses. Check the source corpus link for the original license; commercial use may require verifying the third-party API terms for memoryManager, aggregate, memoryBufferWindow, lmChatGoogleGemini, respondToWebhook, httpRequest, limit, chainLlm.
Related resources
- Agent
PraisonAI
24/7 AI employee team for automating complex challenges. Low-code multi-agent framework with handoffs, guardrails, memory, RAG, and 100+ LLM providers
- Agent
Prompt Optimizer
AI prompt optimization tool with multi-round iterative improvements, dual-mode optimization for system and user prompts, and multi-model support. Available as web app, desktop app, Chrome extension, and Docker deployment. AGPL-3.0 licensed
- Agent
Archon
Workflow engine for deterministic AI coding agents. Define development processes as YAML workflows (planning → implementation → validation → review → PR) with isolated Git worktrees for parallel execution. MIT licensed
- Agent
A2A Protocol
Agent2Agent (A2A) open protocol enabling communication and interoperability between opaque agentic applications. Donated to Linux Foundation by Google with 50+ technology partners. Apache 2.0 licensed
- Agent
CAMEL
First and best multi-agent framework for building scalable agent systems. Apache 2.0 licensed with extensive tooling for agent communication and task automation
- Agent
Neuron AI
PHP Agentic Framework for building production-ready AI driven applications. Connect components (LLMs, vector DBs, memory) to agents that can interact with your data. MIT licensed
- Agent
smolagents
Lightweight agent framework centered on tool use and code-executing workflows
- Agent
Supermemory
Memory API and app for AI agents that provides fast, scalable, context-aware storage and retrieval across projects. MIT licensed
