This article provides a complete, practical walkthrough of the Limit Webhook Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Build a Conversational AI Voice Assistant with n8n, OpenAI, Google Gemini & ElevenLabs
   
   Meta Description:
   Learn how to build a sophisticated AI voice chatbot using n8n, OpenAI’s speech-to-text, Google Gemini’s large language models, and ElevenLabs’ advanced text-to-speech API. This no-code guide shows how to create context-aware, real-time voice interactions.
   
   Keywords:
   n8n workflow, AI voice assistant, OpenAI speech to text, Google Gemini AI, ElevenLabs API, no-code chatbot, LLM voice bot, conversational AI, text to speech, AI memory, voice chatbot automation
   
   Third-Party APIs Used:
   
   - OpenAI API (Whisper – Speech to Text)
   - Google Gemini API (formerly PaLM via Langchain)
   - ElevenLabs API (Text to Speech)
   
   Article:
   
   Build a Conversational AI Voice Assistant with n8n, OpenAI, Google Gemini & ElevenLabs
   
   Voice-based interactions are becoming integral to how we interface with technology, from virtual assistants to automated customer support. Thanks to the power of no-code platforms like n8n, integrating large language models (LLMs) with speech input and output is now more accessible than ever.
   
   In this article, we'll walk through an n8n workflow that creates a fully functional AI-powered voice chatbot. This system listens to spoken queries via a webhook, transcribes audio with OpenAI Whisper, generates human-like responses using Google Gemini LLM, and replies in natural voice using the ElevenLabs text-to-speech API. Better yet, it maintains context and memory for coherent multi-turn conversations.
   
   Overview of the Workflow
   
   This n8n workflow is aptly named "AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs." It combines several robust technologies into one seamless pipeline. Here’s what it does step-by-step:
   
   1. Voice Input Collection:
   The workflow begins with the Webhook node, which receives a POST request containing a voice recording. This enables integration with frontend applications or voice-enabled devices.
   
   2. Speech to Text:
   The OpenAI - Speech to Text node uses OpenAI’s Whisper model to transcribe the incoming audio. This node processes the binary input (voice file) and converts it into text.
   
   3. Context Retrieval:
   To maintain a natural and coherent conversation, the workflow uses Langchain’s Memory Manager nodes ("Get Chat" and "Window Buffer Memory") to retrieve previous dialogues. These are aggregated using the Aggregate node and fed into the LLM prompt for understanding context.
   
   4. AI Response Generation:
   The Basic LLM Chain uses the Google Gemini Chat Model (models/gemini-1.5-flash) to formulate a response based on both the current user input and past messages. By appending the historical context, it generates smarter and more context-aware answers.
   
   5. Memory Update:
   The Insert Chat node logs the latest question and AI response into memory, enabling true multi-turn conversational flow. This back-and-forth record is stored in a memory buffer such that responses in future sessions account for previous interactions.
   
   6. Natural Voice Output:
   To respond with voice, the AI output is passed to the ElevenLabs - Generate Audio node. ElevenLabs’ advanced neural vocal synthesis API converts this text into speech using a custom-selected voice. The request is handled via an HTTP node due to the lack of native ElevenLabs support in n8n.
   
   7. Response Delivery:
   The final step returns the generated speech (audio binary) directly to the end user using the Respond to Webhook node, completing a full voice-to-voice cycle in real-time.
   
   Why This Workflow Stands Out
   
   Here are a few reasons why this AI Voice Chat workflow is noteworthy:
   
   - Context-Aware Messaging:
   Unlike typical chatbots, this system remembers previous inputs and outputs, allowing for a dynamic and natural dialogue experience over multiple turns.
   
   - LLM Flexibility:
   By integrating Google Gemini through Langchain, the workflow leverages high-performance large language models for intelligent replies.
   
   - Multi-API Integration:
   The combination of OpenAI, Google Gemini, and ElevenLabs ensures real-time processing of audio and generation of lifelike, engaging voice responses.
   
   - No-Code Development:
   With n8n’s visual interface, complex AI logic can be implemented without writing a single line of backend code. Everything from data handling to API interaction is managed through modular nodes.
   
   Getting Started: Prerequisites
   
   To replicate this workflow, you’ll need:
   
   - An n8n instance (self-hosted or cloud)
   - OpenAI API key, with Whisper speech-to-text access
   - Google Gemini API access (formerly known as PaLM; usable via Langchain)
   - ElevenLabs API key and a selected Voice ID from their voice library
   
   The ElevenLabs integration is handled via a standard HTTP node using custom headers. You’ll configure it as follows:
   
   ```json
   {
     "headers": {
       "xi-api-key": "your-elevenlabs-api-key"
     }
   }
   ```
   
   Pro Tip: You can easily swap the ElevenLabs node with OpenAI’s “Generate audio” endpoint if you're more embedded in the OpenAI ecosystem.
   
   Use Cases
   
   This advanced workflow opens the door to numerous real-world applications:
   
   - Smart voice assistants embedded in apps or devices
   - Voice-enabled customer support bots
   - Language learning tools that speak with the user
   - AI storytelling or podcast narration bots
   - Accessible AI interfaces for the visually impaired
   
   Conclusion
   
   The ability to create intelligent, voice-based AI experiences is no longer limited to large tech firms. With tools like n8n and APIs from OpenAI, Google Gemini, and ElevenLabs, developers and enthusiasts alike can build powerful AI voice interfaces in hours, not weeks.
   
   Whether you're crafting an in-house assistant or experimenting with the latest in conversational AI, this n8n workflow provides a modular, extensible, and production-ready foundation.
   
   Now, the only question is: What will your AI assistant sound like?
   
   — END —

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.