From Voice to Action: Automating Tasks with Telegram and GPT-4o

We all have that one friend who texts us their to-do list at random hours. Some people keep tasks in their head. Others have seventeen apps that all do slightly different things. The real problem? Task management tools rarely meet us where we already are.

What if you could just message yourself on Telegram and have an AI assistant organize everything into Notion automatically? No app switching. No copy-pasting. Just natural conversation that turns into structured tasks.

This n8n workflow does exactly that. It listens to your Telegram messages, understands what you want through AI, and manages your Notion database accordingly. You can create tasks, update them, or even ask for insights about your workload. Voice messages work too.

Prerequisites: What You Need to Get Started

Before diving into the workflow, you need a few accounts and API credentials set up:

• Telegram Bot: Create a bot through BotFather and get your API token
• Notion Account: Set up a database with properties for title, description, priority, due date, and tags
• OpenAI API Key: For the GPT-4o-mini model that powers intent detection and task processing
• Google Gemini API: For voice message transcription capabilities
• n8n Instance: Either cloud or self-hosted to run the workflow

Key Components in This n8n Workflow

The workflow uses several specialized nodes that work together:

• Telegram Trigger: Listens for incoming messages
• Switch Nodes: Route messages based on type and intent
• Google Gemini Node: Transcribes voice recordings to text
• LangChain LLM Nodes: Power all AI operations with custom prompts
• Notion Nodes: Read and write to your task database
• HTTP Request Node: Handles complex Notion page updates
• Code Nodes: Transform data between workflow steps

How to Build This Workflow: Step by Step

Step 1: Set Up Message Reception and Processing

Start with a Telegram Trigger node that watches for new messages. Connect it to a Switch node that checks whether the incoming message contains text or a voice recording. Voice messages get routed through a Telegram file fetch node, then to Google Gemini for transcription. Text messages skip straight ahead. Both paths merge at a unified text preparation node.

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This design means users can type or talk. The workflow treats both inputs identically from this point forward.

Step 2: Detect What the User Wants

Next comes the intelligence layer. A LangChain node connected to OpenAI analyzes the message and classifies it into one of three intents: create, update, or analyze. The prompt is specific and includes examples like "Create a task to prepare slides by Friday" for create intent, or "Am I too busy tomorrow?" for analyze intent.

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The AI returns a single word. A Code node wraps this with the original text, then a Switch node routes the flow based on which intent was detected. Three branches open up from here.

Step 3: Create Tasks from Natural Language

The create branch uses another LangChain node with a detailed extraction prompt. It pulls out structured fields like title, priority, description, due date, and tags from casual language. If someone says "remind me to buy milk, eggs, and bread tomorrow," it formats the list as bullet points in the description field.

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The extracted JSON flows directly into a Notion node that creates a new database page. Once created, a Telegram node sends back a confirmation message with a link to the new task. Simple, fast, conversational.

Step 4: Update Existing Tasks Intelligently

The update path is more complex. First, an LLM extracts a search string from the user's message. If they say "change the grocery task to high priority," it pulls out "grocery" as the search term.

A Notion node fetches all tasks. A Code node bundles the search string with the task list, then sends both to another LLM. This one finds the best matching task and returns its full JSON. Yet another LLM takes the original user message and the matched task, then generates an updated version with the requested changes applied.

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Finally, an HTTP Request node patches the Notion page with the new data. The user gets a confirmation with the updated task link.

Step 5: Provide Insights on Your Task List

The analyze branch handles questions like "Am I overloaded today?" or "What should I focus on this week?" It fetches all Notion tasks, bundles them with the user's question, and sends everything to an LLM with a thoughtful analysis prompt.

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The AI considers due dates, priorities, and task distribution. It responds in a friendly, conversational tone with actionable suggestions. Maybe you need to reschedule something. Maybe you should add a break. The analysis appears as a regular Telegram message.

Step 6: Understanding What You Get Back

The workflow delivers three types of output depending on what you asked for. When you create a task, you receive a Telegram confirmation message with a direct link to the newly created Notion page. This lets you jump straight to the task if you need to add more details or check that everything was captured correctly.

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For updates, you get a similar confirmation with a link to the modified task. The Notion page itself reflects all your changes, whether that is an updated priority level, a new due date, additional items in the description, or any combination of modifications you requested through your message.

Analysis requests return conversational text responses directly in Telegram. Instead of raw data or charts, you get readable insights that sound like advice from a productivity coach. The AI might tell you that you have seven high-priority tasks due today and suggest which ones could be rescheduled, or it might confirm that your week looks balanced and you are on track with your goals.

Why This Workflow Changes Everything

Traditional task management requires context switching. You stop what you are doing, open an app, fill in forms, click buttons. This workflow removes all of that friction. You message yourself the same way you message friends.

The real power shows up in the details. Voice support means you can capture tasks while driving or cooking. The AI understands natural language, so you do not need to remember specific commands or formats. List formatting happens automatically when you mention multiple items.

The analyze function turns your task database into something smarter. Instead of just storing tasks, the system can reflect back on your workload and help you make better decisions about prioritization and time management.

Real World Use Cases

This workflow adapts to different contexts. Freelancers can capture client requests immediately without breaking focus. Students can manage assignment deadlines through quick voice notes between classes. Teams can use it as a personal inbox that feeds into a shared Notion workspace.

The update capability matters more than it seems at first. Tasks change. Priorities shift. Being able to say "move the report deadline to next Friday" without hunting through your database saves meaningful time over weeks and months.

The insights feature works like having a productivity coach on demand. Ask "what am I forgetting this week?" or "is my schedule realistic?" and get answers based on your actual task data, not generic advice.

Getting Started Today

The workflow connects three tools you might already use. If you have Telegram and Notion, you are halfway there. Add the API keys, import the workflow into n8n, and you have a personal task assistant that lives in your messaging app. Let us customize it specifically for you and your needs to make life easier!