What You Will Learn

• Integrate voice capabilities into n8n workflows
• Build file-based (asynchronous) audio exchanges via Telegram
• Create real-time conversational agents with ElevenLabs and n8n webhooks
• Use Perplexity/OpenRouter for live research and summarisation
• Securely manage API keys and deploy workflows to production

Study Guide

Introduction: Transforming AI Agents into Voice Assistants in Minutes

Imagine a world where your AI agents don’t just respond to typed prompts, but listen, speak, and engage in natural conversation,seamlessly, in real-time, or on-demand. That world isn’t science fiction: it’s accessible, practical, and surprisingly easy to build with the right tools. This course is your step-by-step guide to turning an ordinary AI agent into a powerful voice assistant using n8n and ElevenLabs, with real-world messaging integration (like Telegram) and dynamic, research-driven intelligence (via OpenRouter and Perplexity).

This isn’t just about connecting APIs; it’s about unlocking a new dimension of AI interaction. By the end of this course, you’ll know,from first principles to advanced workflow design,how to:

• Integrate voice capabilities into n8n workflows
• Leverage ElevenLabs for both one-way and real-time voice interactions
• Configure AI agents that process, reason, and respond with personality
• Automate complex, modular workflows and securely manage them
• Deliver human-grade voice assistants fast, without the friction

Whether you’re automating business processes, building customer-facing bots, or exploring the future of voice-enabled interfaces, you’ll leave this course armed with the skills, strategies, and best practices to bring your AI voice assistant to life,quickly, securely, and smartly.

Understanding the Core Concepts and Tools

Before you can build, you need to understand the building blocks. Let’s break down each core component and why it matters in your voice assistant journey.

n8n: The Automation Brain
n8n (pronounced “n-eight-n”) is your open-source command center for workflow automation. Think of it as the connective tissue between your apps, APIs, and services. You design workflows visually, linking triggers and actions as “nodes.”

• Example 1: Automate sales lead follow-up by triggering a workflow when a new email arrives, extracting the contact info, and sending it to a CRM.
• Example 2: Connect a web form submission to a Telegram alert, instantly notifying your team of new sign-ups.

Why it matters for voice assistants: n8n is where your AI, messaging platforms, and voice tools come together, orchestrating the entire conversation from input to output.

ElevenLabs: The Voice Engine
ElevenLabs is the specialist in AI voice technology, offering:

• Speech-to-Text (STT): Converting spoken audio to written text for AI processing.
• Text-to-Speech (TTS): Turning AI-generated text responses into natural-sounding voice audio.
• Conversational AI Agents: Enabling real-time, interactive, back-and-forth voice conversations, including tool-calling capabilities.

• Example 1: Use ElevenLabs to transcribe voice memos from customers, then send the text to your support team or AI agent.
• Example 2: Generate dynamic, lifelike spoken responses for an AI assistant that answers user questions in your app.

Why it matters for voice assistants: ElevenLabs is the bridge between human speech and digital intelligence, letting your AI agent listen and speak like a real person.

Telegram: The User Interface
Telegram is a secure, popular messaging app. In these workflows, it serves as the user-facing channel for sending and receiving voice messages.

• Example 1: A user sends a voice message to your Telegram bot, triggering an n8n workflow.
• Example 2: Your AI agent sends an audio reply back to the user’s chat, completing the conversation loop.

Why it matters for voice assistants: It’s where your users interact directly with your voice-enabled AI, making the experience tangible and personal.

AI Agents in n8n: The Intelligence Layer
n8n’s AI agent nodes connect to Large Language Models (LLMs) via services like OpenRouter or Perplexity. They process, understand, and generate responses to user inputs.

• Example 1: Take a transcribed user question and generate a witty, helpful answer.
• Example 2: Summarize a long research result into a concise, three-sentence response.

Why it matters for voice assistants: This is where the “smarts” live,reasoning, context, personality, and more.

Webhooks: The Real-Time Data Conduit
A webhook is an HTTP endpoint that receives real-time data from external services. In these workflows, webhooks link ElevenLabs agents and n8n workflows.

• Example 1: ElevenLabs agent sends a “searchQuery” to n8n via a webhook for live web research.
• Example 2: n8n responds to ElevenLabs with a summarised answer to play back to the user in real time.

Why it matters for voice assistants: Webhooks make it possible for different platforms to “talk” to each other instantly, enabling dynamic, tool-augmented conversations.

API Keys: The Gatekeepers
API keys are secret tokens that authenticate and authorize your n8n workflows to connect with third-party services like ElevenLabs, OpenRouter, or Perplexity.

• Example 1: Your n8n workflow uses your ElevenLabs API key to convert text into speech.
• Example 2: You use a Perplexity API key to perform web research directly from n8n.

Why it matters for voice assistants: Without API keys, your workflows can’t securely access the services that make real-time voice and intelligence possible.

Two Core Methods for Integrating Voice: File-Based vs. Real-Time Conversations

The heart of this course is understanding the two main ways you can add voice to your AI workflows. Each has distinct strengths, limitations, and ideal use cases.

Method 1: File-Based Audio Exchange (Asynchronous Voice Interaction)

This approach is about simplicity and reliability. Here, a user sends an audio file (like a voice message) to your Telegram bot. The workflow processes it, and returns an audio file with the AI’s response. It’s a single exchange, not a flowing conversation.

Step-by-Step Workflow:

1. Telegram Trigger Node: This node “wakes up” your workflow when a new message (like a voice memo) is sent in Telegram.
   
   • Example 1: A customer says, “What’s the weather like today?” in a Telegram voice message.
   • Example 2: An employee sends a quick verbal update for the daily standup.
2. Telegram Get File Node: The trigger only provides metadata (like the file’s ID), not the audio itself. The Get File node downloads the actual voice file for processing.
   
   • Example 1: The workflow fetches the .ogg audio file from Telegram’s servers.
   • Example 2: Downloads a user’s question as binary data for transcription.
3. ElevenLabs Transcribe Audio/Video Node: Converts the downloaded audio into text. You need an ElevenLabs API key with access to transcription.
   
   • Example 1: “What’s the weather like today?” is transcribed from the user’s speech to written text.
   • Example 2: A complex technical question is accurately transcribed for the AI to answer.
4. AI Agent (n8n): Processes the transcribed text. The agent uses a system prompt to define its role and personality, and is connected to a chat model (such as OpenRouter). The “user message” is set to the transcribed text.
   
   • Example 1: System prompt: “You’re a helpful assistant who is extremely funny.” The AI responds with a humorous weather update.
   • Example 2: System prompt: “You’re an expert project manager.” The AI provides actionable standup feedback.
5. ElevenLabs Convert Text to Speech Node: Takes the AI’s text response and generates an audio file in a chosen voice.
   
   • Example 1: The AI’s weather report is spoken in a cheerful, realistic voice.
   • Example 2: The project manager’s summary is rendered in a professional, confident tone.
6. Telegram Send Audio File Node: Sends the new audio file back to the original Telegram chat using the chat ID.
   
   • Example 1: The user receives a spoken answer to their question in Telegram.
   • Example 2: The team hears their project update, read aloud by the AI assistant.
7. Workflow Activation: Set the n8n workflow to “active” to ensure it runs automatically for every new message.

Key Details and Best Practices:

• Link the “user message” of the AI agent node directly to the output of the transcription node (not to the default chat trigger).
• Choose the ElevenLabs voice that matches your assistant’s persona,experiment with multiple voices for different use cases.
• Activate the workflow for 24/7 operation (don’t leave it in test mode).
• All API keys (Telegram, ElevenLabs, OpenRouter) must be securely stored in n8n’s credentials manager.

Limitations and Use Cases:

• Best for scenarios where a rapid, one-off answer is needed,like Q&A, simple support, or voice-annotated tasks.
• Not suitable for dynamic, flowing conversations or follow-up questions. Each exchange is independent.

Method 2: Real-Time Conversational Voice Agent (Continuous Dialogue)

This approach enables a natural, ongoing conversation between user and AI agent. ElevenLabs’ Conversational AI agent feature, combined with n8n’s tool-calling integration, lets your AI both converse and perform actions (like live web search) on demand.

Step-by-Step Agent and Workflow Setup:

1. Create an Agent in ElevenLabs: Start with a blank agent in the Conversational AI section.
   
   • Example 1: Set up an agent named “ResearchBot” to answer general knowledge queries.
   • Example 2: Create “ConciergeAI” for hotel guests to ask about amenities, local events, or directions.
2. Configure Language and Voice: Choose the default language and voice for the agent.
   
   • Example 1: English with a friendly, upbeat male voice.
   • Example 2: Spanish with a warm, inviting female voice.
3. Initial Greeting (Optional): Set the agent’s first message (e.g., “How can I help you today?”).
4. Add a Custom Tool (Webhook): This is the game-changer. You add a tool to the agent,a webhook that points to your n8n workflow.
   
   • Tool Name & Description: “NIDN”, “Call this tool to search the web.”
   • Method: POST (for sending data to n8n).
   • n8n Webhook URL: Use the webhook URL from your n8n workflow (test or production, see “Testing and Production” below).
   • Response Timeout: Set high enough for complex operations (like web research and summarization).
   • Body Parameter: Define what the agent will send (usually “searchQuery” or similar, mapping directly to the user’s spoken request).
5. System Prompt Engineering: Use the “describe with AI” function or manually craft the agent’s system prompt. This is where you define:
   • The agent’s role/personality (“You are a knowledgeable, concise assistant.”)
   • The rules of engagement (“If you need to perform a web search, use the NIDN tool.”)
   • The process for extracting the search query from user speech (“When the user asks a factual question, trigger the NIDN tool with their query.”)
   • Example 1: “You are a friendly travel assistant. For travel-related questions, use the NIDN tool to search the web and provide the latest information.”
   • Example 2: “You are a medical reference bot. If a user asks for symptoms or treatments, use the NIDN tool to check for recent updates.”

n8n Workflow for the Agent’s Tool Call:

1. Webhook Trigger Node: Set up as a POST endpoint, waiting to receive data (like searchQuery) from ElevenLabs.
   
   • Example 1: Receives “What are the latest COVID travel guidelines for Germany?”
   • Example 2: Receives “Who won the Champions League last year?”
2. Perplexity Message Model Node: Uses the searchQuery to perform live web research through Perplexity. Requires a Perplexity API key.
   
   • Example 1: Sends the query to Perplexity, which returns several paragraphs of up-to-date information.
   • Example 2: Researches the latest football scores and details.
3. AI Agent (n8n) for Summarization: Instead of duplicating full reasoning (to avoid “double processing”), this agent simply summarizes the potentially lengthy output from Perplexity.
   
   • System Prompt: “You are an expert research agent. Create a concise summary of the provided content. Limit your answer to three sentences.”
   • Input: The full research output from Perplexity.
   • Output: A clear, brief, user-ready answer.
   • Example 1: Summarizes German travel guidelines into three actionable points.
   • Example 2: Condenses a sports match report into a quick update.
4. Respond to Webhook Node: Sends this summary back to the ElevenLabs agent, which delivers it to the user in natural speech.
   
   • Example 1: The user hears a spoken, concise summary of travel guidelines.
   • Example 2: The agent reports the match results, spoken in a lively tone.

Testing and Going Live: Critical Details

• During development, use the “test” webhook URL generated by n8n (contains /test). This is temporary and only works while the workflow is running in test mode.
• After testing, switch to the “production” URL for your ElevenLabs agent’s webhook tool. Remove /test from the URL,this ensures your agent works continuously, not just during manual tests.
• Activate the workflow in n8n to handle real users and real data.
• Protect your API keys and webhook URLs,never expose them in public channels or unsecured environments.

Agent Autonomy and User Experience:

• The ElevenLabs agent can independently decide when the conversation is complete, ending the call when the user’s intent is fulfilled.
• For best results, ensure your agent’s system prompt clearly defines when to end a conversation, how to handle clarifications, and how to escalate if needed (e.g., “If you’re unsure, ask the user to repeat or clarify.”).

Limitations and Use Cases:

• Best for hands-free, interactive support,customer service, digital concierges, or AI-powered phone agents.
• Enables follow-up questions, context retention, and dynamic tool-calling (like web search or database lookup).
• Requires careful prompt engineering to ensure smooth, helpful conversations.

Workflow Automation Principles: Making It All Work Together

Automation isn’t just about plugging in APIs. It’s about designing robust, modular, and secure workflows that deliver real value. Let’s dig into key principles and best practices for building your voice assistant workflows.

Trigger Nodes: The Starting Point
Every workflow begins with a trigger,an event that tells n8n to start processing.

• Example 1: Telegram Trigger for an incoming voice message.
• Example 2: Webhook Trigger for an ElevenLabs tool call from a conversational agent.

Best Practice: Always design your trigger to capture just enough context to route the request correctly,track user IDs, timestamps, or message types for more advanced flows.

Sequential Processing: The Assembly Line
Nodes are executed in order. Each output becomes the input for the next node, like steps on an assembly line.

• Example 1: Download audio → Transcribe to text → AI processing → Convert to speech → Send audio reply.
• Example 2: Receive search query → Research via Perplexity → Summarize → Send back to agent.

Best Practice: Keep your workflows modular,break complex processes into smaller, reusable segments.

Data Flow: Information as a Living Stream
Data moves from node to node, often transformed at each step. Using n8n’s schema interface, you can drag and drop specific fields to map outputs to inputs.

• Example 1: Drag the “transcribed text” field from the ElevenLabs transcription node into the “user message” field of the AI agent node.
• Example 2: Pass the “chat ID” from the Telegram trigger through to the Send Audio File node.

Best Practice: Always verify the data types match (e.g., don’t pass binary audio into a text field). Use logging or debug nodes to inspect data as it flows.

API Keys and Credentials: Security First
Every integration with a third-party service (ElevenLabs, OpenRouter, Perplexity, Telegram) requires an API key.

• Example 1: Store your ElevenLabs API key in n8n’s credentials manager and reference it in every relevant node.
• Example 2: Rotate your API keys regularly and audit their usage for unusual activity.

Best Practice: Never hard-code API keys directly in node fields or expose them in logs or public repositories.

AI Agent Configuration and Prompt Engineering

To get the most out of your AI agents,whether in n8n or ElevenLabs,you need to master prompt engineering. This is where you define the agent’s behavior, personality, and how it interacts with users and external tools.

System Prompting: Personality and Rules of Engagement
The system prompt is the “prime directive” for your AI agent.

• Example 1: “You are a helpful assistant who is extremely funny. Always answer with a joke or pun.”
• Example 2: “You are an expert medical researcher. Always provide concise, evidence-based answers.”

Tips:

• Be explicit about goals (“summarize in three sentences” or “ask clarifying questions if unsure”).
• Define the boundaries (“never provide legal advice” or “always cite a source if possible”).
• Tailor the prompt to your use case,customer support, research, entertainment, etc.

User Message Input: Connecting to Transcribed Speech
In n8n, don’t use the default chat trigger as the AI’s input. Instead, set the “user message” to the output of the ElevenLabs transcription node.

• Example 1: Drag the “text” output from the transcription node into the AI agent’s user message field.
• Example 2: For multi-turn conversations, pass conversation history as context for more natural dialogue.

Best Practice: Always check the mapping,if the wrong data is used, your agent may respond out of context or with irrelevant information.

Tool Calling: Extending the Agent’s Reach
In ElevenLabs, you can define custom tools (like a webhook to n8n) that the agent can use to perform specific tasks,web search, database lookup, or triggering business processes.

• Example 1: “Call this tool to search the web.” The agent sends a query to n8n, which uses Perplexity to research the answer.
• Example 2: “Call this tool to create a new support ticket.” The agent triggers an n8n workflow to log an issue in your help desk system.

Tips:

• Describe the tool’s purpose clearly in the system prompt (“only use this tool for external research”).
• Define what data (parameters) the agent should send (“searchQuery” as the user’s question).
• Ensure the agent knows when not to use the tool (e.g., for simple, factual responses it can answer itself).

Production vs. Test Workflows: Moving from Lab to Live

Testing is essential,but so is the transition to production. Here’s what you need to know to ensure your workflows run reliably and securely for real users.

Test Webhook URLs: n8n provides temporary webhook URLs (with /test in the path) for testing your workflows. These are only active while you’re running the workflow in test mode in the editor.

• Example 1: During initial setup, point your ElevenLabs agent’s tool to the test webhook URL to debug the integration.
• Example 2: Use test mode to simulate different user queries and check the workflow’s responses.

Production Webhook URLs: When you activate your n8n workflow, you must switch the ElevenLabs tool’s webhook URL to the production version (no /test in the path). This ensures the agent can reach your workflow at all times.

• Example 1: After testing, update the webhook URL in ElevenLabs and verify the workflow operates for live users.
• Example 2: Document the production URL and restrict access to trusted systems or users.

Security Considerations:

• Keep webhook URLs private,exposing them can allow unauthorized access to your workflows.
• Use authentication mechanisms on your webhooks where possible (e.g., secret tokens in the header).
• Regularly rotate API keys and audit access logs.

Deep Dive: Full Data Flow Examples

Let’s walk through two complete, practical examples,one for each core integration method,to solidify your understanding.

Example 1: File-Based Audio Exchange (One-Way Interaction)

• User sends a voice message (“What’s the capital of France?”) to your Telegram bot.
• Telegram Trigger node wakes the workflow, capturing metadata and file ID.
• Telegram Get File node downloads the actual audio file.
• ElevenLabs Transcribe Audio/Video node converts the speech to text (“What’s the capital of France?”).
• AI Agent (n8n) receives the transcribed text, with system prompt: “You are a funny assistant.” The agent replies, “The capital of France is Paris,oui oui!”
• ElevenLabs Convert Text to Speech node generates an audio file in a lighthearted French accent.
• Telegram Send Audio File node sends the response back to the user.

Example 2: Real-Time Conversational Voice Agent (Tool-Calling Integration)

• User speaks to ElevenLabs agent: “Can you find the latest news about electric cars?”
• Agent’s system prompt instructs: “For web searches, use the NIDN tool.”
• Agent extracts the query and calls the n8n webhook tool, sending { searchQuery: “latest news about electric cars” }.
• n8n Webhook Trigger node receives the request.
• Perplexity node performs live web research, returns several paragraphs about recent electric car announcements.
• AI Agent (n8n) summarizes: “The latest news includes several new electric vehicle launches and advances in battery technology.”
• Respond to Webhook node sends the summary back to ElevenLabs agent.
• Agent speaks the concise summary to the user, and ends the session when appropriate.

Optimization Strategies and Best Practices

To build robust, efficient, and user-friendly AI voice assistants, keep these guiding principles in mind:

• Modular Design: Break workflows into small, focused steps. This makes debugging, scaling, and reusing components much easier.
  Example: Separate your transcription, AI reasoning, and speech generation into distinct nodes.
• API Key Management: Store keys securely, rotate them regularly, and restrict their permissions to only what’s necessary.
  Example: Use n8n’s built-in credentials manager for all API keys.
• Prompt Engineering: The quality and clarity of your system prompts directly influence your agents’ effectiveness. Experiment, iterate, and test with real user queries.
  Example: If your agent gives verbose answers, refine the prompt to “Provide concise, direct answers in two sentences or less.”
• Workflow Activation: Always set your workflows to “active” for continuous operation. Inactive workflows won’t respond to live events.
  Example: After testing, double-check the workflow status in n8n before sharing with users.
• Iterative Testing: Don’t wait until the end,test each part of your workflow as you build. This helps catch errors early.
  Example: Test the transcription node independently before connecting it to the AI agent.
• Optimizing Responses: Summarize long AI outputs (especially from research tools) so users get quick, actionable information.
  Example: Use a summarization agent to condense Perplexity results before sending them to the voice agent.
• Avoiding Double Processing: Understand where logic and reasoning happen. Let the ElevenLabs agent manage conversational flow/tool-calling, and use n8n for tasks like summarization or external action.
  Example: Don’t send the same user message through two separate AI reasoning agents unless necessary.

Transitioning from Test to Production: What to Watch For

One of the most common mistakes is forgetting to update webhook URLs or activate workflows before going live. Here’s your checklist for a smooth launch:

• Switch all webhook URLs in ElevenLabs from the test to the production version.
• Ensure all workflows in n8n are set to “active.”
• Lock down access to production API keys and webhooks.
• Set up monitoring,use n8n’s logging features to track errors or failed messages.
• Communicate with users about expected capabilities and limitations.

Security Fundamentals: Protecting Your Data and Workflows

Voice assistants process sensitive data,user messages, audio files, even potential personal information. Protect your system and your users by:

• Keeping all API keys secret. Never share them in public documentation or code.
• Restricting webhook URLs to trusted sources if possible (e.g., IP whitelisting, secret tokens).
• Monitoring for abuse,unexpected spikes in traffic may indicate a compromised workflow.
• Regularly updating all integrations and dependencies for security patches.

Common Pitfalls and How to Avoid Them

• Forgetting to Map the Correct Input: Always link the AI agent’s “user message” to the transcribed text, not the default trigger.
• Leaving Workflows Inactive: If your workflow isn’t “active,” it won’t respond to live events,always double-check status before going live.
• Exposing API Keys or Webhook URLs: Treat all credentials as sensitive. Use n8n’s credential store and restrict webhook access.
• Overloading Agents with Duplicate Logic: Don’t let both ElevenLabs and n8n handle full conversational reasoning,decide where each piece of logic belongs.
• Neglecting to Summarize Long Outputs: Users want concise answers,use a summarization node for lengthy research results.

Resources for Further Learning and Workflow Sharing

If you want more workflow templates, advanced tutorials, or a community to troubleshoot with, look for:

• Free community forums and workflow download libraries (such as those offered by Complete AI Training).
• Paid communities and in-depth courses (e.g., “Agent Zero” and “10 Hours to 10 Seconds”) for advanced strategies and support.

Conclusion: Bringing It All Together

Let’s recap what you’ve learned:

• You can turn any AI agent into a voice assistant,file-based or real-time,in minutes, not months, using the right combination of n8n, ElevenLabs, and messaging tools like Telegram.
• Mastering both the file-based (asynchronous) and conversational (real-time, tool-calling) integration methods gives you flexibility to match any use case.
• Every great voice assistant workflow is rooted in solid automation principles,modular design, secure credential management, and clear data mapping.
• Prompt engineering is the art and science that makes your agents effective, friendly, and on-brand.
• Testing, activating, and securing your workflows is critical for real-world reliability and user trust.

By applying these skills, you’re not just automating routine tasks,you’re building the next generation of AI-powered, human-centric voice experiences. The tools are in your hands. The only limit is your imagination.

Frequently Asked Questions

This FAQ section is crafted to answer the most common,and uncommon,questions about turning your AI agent into a voice assistant using n8n and ElevenLabs. Whether you’re just starting out or looking to refine advanced workflows, these answers cover technical integration, practical use cases, troubleshooting, and best practices. The goal is to give you direct, actionable information that makes building and deploying AI-powered voice assistants approachable and effective for business professionals.

What are the two primary methods for integrating voice into n8n workflows or agents?

The two primary methods for integrating voice into n8n workflows or agents are:

• Converting Text to Speech and sending an audio file: This involves using a service like ElevenLabs to transform text generated by an AI agent into an audio file, which can then be sent to the user. This method is suitable for scenarios where a direct, real-time conversation isn't strictly necessary, but audio output is desired. For example, a user sends a voice message, it's transcribed, an AI processes it and responds in text, and then that text is converted to an audio file and sent back.
• Real-time conversational voice agents: This method leverages a dedicated voice agent service, such as ElevenLabs' conversational AI feature, to facilitate a continuous, real-time voice conversation. This approach allows for dynamic back-and-forth interactions where the AI agent can listen, process, respond, and even initiate tool calls based on the conversation flow.

How can a voice file received via Telegram be processed and responded to with an AI agent?

To process a voice file received via Telegram and respond with an AI agent, the following steps are typically involved:

1. Telegram Trigger: An n8n workflow starts with a Telegram node configured as a "message received" trigger. When a voice message is sent to the Telegram channel, this node captures the voice file, identified by its audio/ogg type.
2. Download File: A subsequent Telegram node is used to download the received voice file. The file ID from the initial trigger output is used to specify which file to download.
3. Transcribe Audio: The downloaded voice file needs to be converted into text for the AI agent to understand. This is done using a service like ElevenLabs (or OpenAI's transcribe recording node). An ElevenLabs "transcribe audio or video" node takes the binary voice file and converts it into a text string.
4. AI Agent Processing: The transcribed text is then fed into an n8n AI agent node. The user message for the AI agent is set to the output of the transcription node. A system prompt can be added to define the AI's persona or purpose (e.g., "helpful assistant who is extremely funny"). A chat model (like OpenRouter) is connected to the agent to enable it to generate a response.
5. Convert Text to Speech: The AI agent's text response is then converted back into speech using an ElevenLabs "convert text to speech" node. This node requires the text to be converted and a chosen voice (either from a list or by a specific voice ID).
6. Send Audio File via Telegram: Finally, another Telegram node is used to send the generated audio file back to the user. This node is configured to "send an audio file," using the chat ID from the initial Telegram trigger and the binary audio data from the ElevenLabs text-to-speech conversion.

This entire sequence creates an audio-in, audio-out interaction, though not in real-time conversation.

What is the role of ElevenLabs and n8n in creating these voice assistants?

ElevenLabs and n8n play distinct yet complementary roles in creating these voice assistants:

• ElevenLabs: Primarily functions as the voice processing engine. It provides core functionalities such as:
  • Text-to-Speech (TTS): Converting written text into natural-sounding spoken audio.
  • Speech-to-Text (STT) / Transcription: Transcribing spoken audio into written text.
  • Conversational AI Agents: Offering a platform to build and manage real-time voice agents, handling the conversational flow, voice input/output, and tool calling based on predefined instructions.
  • Voice Management: Providing a library of diverse voices and the ability to preview and select them for use.
• n8n: Serves as the workflow automation and integration platform. It acts as the orchestrator, connecting different services and automating the data flow. Its key roles include:
  • Triggering Workflows: Initiating processes based on external events (e.g., a message received in Telegram, a webhook call).
  • Data Handling: Downloading, managing, and passing various data types (like binary audio files, text strings) between different nodes.
  • API Integration: Connecting to and utilising APIs of services like ElevenLabs, OpenAI, OpenRouter, and Perplexity for their specific functionalities.
  • AI Agent Orchestration: Hosting and configuring AI agents that process text, generate responses, and make decisions based on defined prompts and connected chat models.
  • Tool Calling: Enabling AI agents to interact with external tools (like web search via Perplexity) to gather information or perform actions.
  • Response Management: Sending processed data or generated files back to the user through various channels.

In essence, ElevenLabs handles the "voice" aspect, while n8n manages the "logic" and "connectivity" of the entire system.

Explain the process of setting up a real-time conversational voice agent using ElevenLabs and n8n.

Setting up a real-time conversational voice agent involves a more intricate interaction between ElevenLabs and n8n, focusing on continuous dialogue and tool integration:

1. ElevenLabs Agent Creation:
   • Create a new conversational AI agent in ElevenLabs and configure basic settings like language and the first message.
   • Define "tool calling" capabilities, allowing the agent to trigger external actions.
2. n8n Webhook Setup:
   • Add a "Webhook" node in n8n, configured for "POST" requests, which ElevenLabs will call.
   • Copy the webhook URL and set a high response timeout for complex operations.
3. ElevenLabs Tool Definition:
   • Add a custom tool in ElevenLabs that uses the n8n webhook URL and specifies the data to send (e.g., searchQuery).
4. System Prompting:
   • Define the agent’s personality and tool usage instructions using the "Describe with AI" function or a custom system prompt.
5. n8n Research and Summarisation:
   • When the agent calls the tool, n8n receives the query, passes it to a research tool (like Perplexity), and then summarises the response using a dedicated AI agent node.
6. n8n Webhook Response:
   • The summarised response is sent back to ElevenLabs via the "Respond to Webhook" node.
7. Testing and Activation:
   • Test the agent and update webhook URLs for production.

This setup allows the ElevenLabs agent to manage the real-time voice conversation, trigger n8n for research and summarisation, and deliver concise, spoken answers to the user.

What are the common challenges or considerations when building these voice-enabled workflows?

Several challenges and considerations arise when building voice-enabled workflows:

• Transcription Accuracy: Quality speech-to-text is essential. Background noise, accents, or unclear speech can cause errors in transcription.
• API Key Management and Security: API keys give access to paid services and user data. Store them securely and restrict access to your workflows.
• Workflow Latency: Delays in processing can make real-time conversations feel sluggish. Each step,downloading, transcribing, AI processing, and text-to-speech,should be optimised for speed.
• Prompt Engineering: Crafting effective system prompts for AI agents is a challenge. The prompt defines the agent’s persona, purpose, and tool usage. Iterative testing is often necessary.
• Tool Calling Logic: Agents need to know when to call a tool and what to send. This requires precise instructions in system prompts and tool descriptions.
• Response Conciseness: Summaries are critical, especially after research steps, to avoid overwhelming the user with information.
• Error Handling: Plan for API timeouts, transcription failures, and unexpected input to avoid workflow breakdowns.
• Cost Management: Monitor usage, as each transcription, research, and text-to-speech call may incur fees.
• Testing vs. Production: Switching webhook URLs and configurations is required for live deployment. Forgetting this can result in the workflow not functioning in production.

How does the integration ensure that the AI agent's response is delivered back to the user as an audio file?

The integration ensures the AI agent's response is delivered back to the user as an audio file through a two-step process:

1. Text-to-Speech Conversion (ElevenLabs):
   • The AI agent’s text response is passed to an ElevenLabs "convert text to speech" node, which outputs an audio file.
2. Sending Audio File via Telegram (n8n):
   • The audio file (as binary data) is routed to a Telegram node configured to "send an audio file" using the chat ID from the original trigger, ensuring the response reaches the correct user.

This closes the loop from user voice input to AI-generated, spoken output.

What is the benefit of using an additional AI agent in n8n for summarisation, even when a conversational agent is already present in ElevenLabs?

Using an additional AI agent in n8n for summarisation brings optimisation and specialisation:

• Avoids Double Processing: The ElevenLabs agent manages conversation and tool calling, while the n8n agent focuses solely on summarising research results. This reduces redundant reasoning and speeds up responses.
• Specialised Summaries: The n8n agent can be prompted specifically for concise, digestible summaries (e.g., "three sentences"), leading to more useful responses for users.
• Workflow Modularity: Keeping summarisation in n8n allows for easier updates, testing, and data management.
• Efficiency: The summarisation step can be optimised for speed and clarity, improving overall user experience.

How does Complete AI Training support individuals looking to integrate AI into their work?

Complete AI Training supports individuals by providing comprehensive learning resources and community engagement:

• Tailored Training Programs: Programs are designed for over 220 professions, focusing on practical, job-relevant AI integration.
• Diverse Learning Materials: Includes video courses, custom GPTs, (audio)books, an AI tools database, and prompt courses.
• Community Support: Offers both free and paid communities for sharing knowledge, downloadable workflows, and peer support.
• Structured Classroom Courses: Deep-dive courses on AI automation principles and building time-saving automations.

This approach equips professionals with the knowledge and resources to make AI part of their daily workflow.

What is n8n and why is it used in voice assistant workflows?

n8n is an open-source workflow automation tool that lets you visually connect and automate processes across apps and services. It acts as the central hub for routing data, triggering actions, and integrating APIs. In the context of voice assistants, n8n connects messaging apps (like Telegram), AI agents, transcription services, and text-to-speech engines, enabling seamless, automated interactions. For example, it can receive a voice message, pass it to a transcription service, process the resulting text with an AI agent, and deliver the final spoken response,without manual intervention.

What is ElevenLabs and what role does it play in the workflow?

ElevenLabs is a platform providing advanced AI speech technology, including text-to-speech, speech-to-text (transcription), and real-time conversational agents. In these workflows, it handles the conversion between spoken and written language. For instance, it transcribes incoming audio to text for AI processing and generates high-quality, natural-sounding speech from AI responses. Its voice library allows you to choose voices that fit your assistant's personality or brand.

Why is Telegram used as the interface for sending and receiving voice messages?

Telegram is a popular messaging app with strong support for bots and automation. It provides reliable APIs for sending/receiving voice messages and integrates smoothly with n8n. This makes it easy to trigger workflows when users send voice messages and to deliver audio responses back to them. Telegram is also cross-platform, ensuring accessibility for users on different devices.

What is a webhook and why is it important in these workflows?

A webhook is an HTTP endpoint that listens for real-time data from external services. It enables event-driven communication between systems. In these workflows, webhooks allow ElevenLabs agents to trigger n8n workflows (for tasks like web research) and receive responses. Webhooks also let you connect n8n with almost any service that supports outbound HTTP calls, making your automation flexible and powerful.

Why do I need API keys, and how should I manage them?

API keys are unique codes that authenticate and authorise requests to external services like ElevenLabs, OpenRouter, and Perplexity. They grant access to paid features and user data. You should store them securely (using n8n’s credential storage or environment variables), avoid sharing them publicly, and regularly review permissions. Mismanaging API keys can result in service abuse or unexpected costs.

What is the function of the ElevenLabs "Transcribe Audio/Video" node in n8n?

The "Transcribe Audio/Video" node converts audio files (like Telegram voice messages) into text. This is essential for enabling AI agents to process spoken language as text. Once transcribed, the text can be analysed, summarised, or responded to by an AI agent before being converted back to speech for the user.