Telegram to Todoist Task Automation with OpenAI & n8n

1. Opening Problem Statement

Meet Sarah, a project manager juggling multiple tasks and teams daily. On the move, she often sends voice messages or texts via Telegram to herself or her task bot, detailing what needs to be done next for ongoing projects. However, managing these tasks manually in Todoist has been time-consuming and error-prone. She spends close to an hour every day just transcribing notes and breaking down her project plans into actionable tasks manually. This inefficiency leads to missed deadlines and frequent confusion within her team.

To solve this, Sarah needs a seamless way to convert her conversational task inputs from Telegram — whether voice recordings or text messages — directly into prioritized Todoist tasks without lifting more than a finger.

2. What This Automation Does ⚙️

This n8n workflow takes the hassle out of task management by automating the conversion of Telegram messages into structured Todoist tasks. When you run this workflow, it:

• Listens for incoming voice or text messages in Telegram via a webhook trigger.
• If a voice message is received, it automatically fetches and transcribes it into text using OpenAI’s Whisper API.
• Processes the text input with the OpenAI Chat Model (GPT-4o-mini) to intelligently break down the task or project description into clear, actionable subtasks.
• Formats each subtask in the JSON structure required by Todoist, including specific priority levels.
• Creates tasks in a specified Todoist project, making management and tracking straightforward.
• Sends a confirmation message back to the Telegram chat with a link to the created task for quick reference.

Overall, this workflow can save Sarah over an hour daily, eliminating manual transcription and task breakdown errors.

3. Prerequisites ⚙️

• n8n automation platform account (cloud or self-hosted) 🔌
• Telegram Bot API account with created bot and credentials 📱
• OpenAI API account with access to GPT-4o-mini and Whisper for audio transcription 🔑
• Todoist account with API access and project ID where tasks will be created 📋
• Basic knowledge of n8n workflow setup and credentials linking

4. Step-by-Step Guide to Build This Workflow

Step 1: Set up Telegram Trigger to Receive Messages

In n8n, click + New Node, select Telegram Trigger. Configure it to listen for message updates. Connect your Telegram Bot API credentials. This node will listen to incoming messages from your chosen Telegram bot.

Expected outcome: The node captures voice or text messages sent to the bot.

Common mistake: Forgetting to select the correct update type. Ensure “message” is checked.

Step 2: Add a Switch Node to Determine Message Type

Add a Switch node named Voice or Text? to evaluate if the incoming Telegram message contains a voice note or text. Use the condition to check if {{$json["message"].voice.file_id}} exists for voice or {{$json.message.text}} for text.

Expected outcome: The workflow routes the message appropriately based on type.

Common mistake: Condition logic errors can cause misrouting; verify expressions carefully.

Step 3: Fetch the Voice Message File (If Voice)

For voice messages, add the Telegram node configured with “Get File” operation and file ID sourced from the voice message data. This will retrieve the audio file URL for transcription.

Expected outcome: Audio file is fetched and passed downstream.

Common mistake: Incorrect mapping of file ID leading to failed fetch; ensure JSON path is exact.

Step 4: Transcribe Voice to Text Using OpenAI Whisper

Add the OpenAI node with audio translate operation. Configure it to send the fetched audio file URL for transcription. Use your OpenAI credentials here.

Expected outcome: Voice message is converted into text for further processing.

Common mistake: Audio format issues or API key problems stopping transcription.

Step 5: Prepare Text Messages for Language Model Input

Use a Set node called Prepare for LLM to assign the text (from either direct text messages or transcribed voice) to the field text for the next node.

Expected outcome: Clean text data ready for AI analysis.

Common mistake: Forgetting to direct text from both the voice and text routes properly.

Step 6: Use the OpenAI Chat Model to Extract Sub-Tasks

Add the OpenAI Chat Model node configured with the GPT-4o-mini model. This node receives the project/task description text and breaks it down into subtasks with priorities using a detailed system prompt inside a chained LLM node.

Key prompt detail: It returns subtasks in JSON format containing “content” and “priority” only, formatted specifically for Todoist.

Expected outcome: AI-generated list of actionable subtasks ready for task creation.

Common mistake: Using the wrong prompt or model version causing output format errors.

Step 7: Parse and Transform AI Output into Structured Tasks

Add the Output Parser Structured node to validate and extract the expected JSON objects (with content and priority fields) from the AI response for further use.

Expected outcome: Valid JSON array of subtasks, matching Todoist API requirements.

Common mistake: Schema mismatch leading to parsing errors; ensure example JSON is accurate.

Step 8: Create Tasks in Todoist

Use the Todoist node to create tasks in the specified Todoist project. Map task content and priority from the previous node outputs. Ensure your Todoist API credentials and Project ID are correct.

Expected outcome: Tasks appear in your Todoist project immediately.

Common mistake: Wrong project ID or invalid credentials causing API failure.

Step 9: Send Confirmation Back to Telegram

Add a Telegram node configured to send a message. The message confirms task creation with dynamic placeholders for task content and Todoist URL/link. Use the message chat ID from the original trigger for the reply.

Expected outcome: User receives quick feedback on successful task creation.

Common mistake: Incorrect chat ID mapping results in message failure.

5. Customizations ✏️

• Change Todoist Project: In the Create Todoist Tasks node, update the project parameter to target different Todoist projects for categorizing tasks.
• Adjust AI Model & Prompt: Modify the system prompt in the Basic LLM Chain node to tune task decomposition behavior or switch to a different OpenAI model for cost/performance trade-offs.
• Handle Additional Telegram Message Types: Extend the Voice or Text? switch node to detect images or documents, adding processing nodes accordingly.
• Task Priority Logic: Customize the Basic LLM Chain node’s prompt for setting priority rules based on task urgency or deadlines.

6. Troubleshooting 🔧

Problem: “No tasks created in Todoist after sending message.”
Cause: Incorrect Todoist project ID or API credentials.
Solution: Check and update Todoist node with correct project ID and verify API key permissions.

Problem: “Voice message not transcribed.”
Cause: OpenAI Whisper API issues or invalid audio fetch.
Solution: Confirm audio file URL is fetched correctly, and OpenAI credentials are valid.

Problem: “AI response parsing errors.”
Cause: Mismatch in expected JSON format in Output Parser node.
Solution: Adjust JSON schema example and confirm AI output follows the expected structure.

7. Pre-Production Checklist ✅

• Verify Telegram bot is receiving all message types as expected (voice & text).
• Run tests sending both voice and text messages, confirming transcription and subtasks creation.
• Double-check API credentials for Telegram, OpenAI, and Todoist in n8n settings.
• Ensure Output Parser structured JSON schema matches AI-generated output format.

8. Deployment Guide

After all nodes are configured and tested individually, activate the workflow in n8n. Make sure the Telegram webhook is set up correctly to point to your n8n instance. Monitor regular logs in n8n to catch any errors or performance bottlenecks. You can also enable retry policies on nodes like Todoist or OpenAI for resilience.

9. FAQs

Q: Can I use other AI models instead of GPT-4o-mini?
A: Yes, you can swap the model in the OpenAI Chat Model node but ensure the prompt and JSON output format remain consistent.

Q: Does this automation consume OpenAI credits?
A: Yes, transcription and chat completions use OpenAI API credits depending on usage volume.

Q: Is my data secure?
A: All messages and tasks are processed in your n8n instance under your API credentials. Use secure hosting for safety.

10. Conclusion

By building this Telegram to Todoist workflow with OpenAI and n8n, you’ve automated a tedious manual task chain. You now save hours daily, reduce errors, and scale your task management with minimal effort.

Next, consider automating task reminders via Telegram, integrating calendar events, or expanding to multitool project workflows for even greater productivity.

Automation is empowering—you’ve taken a big step towards smarter work with this powerful, tailored workflow. Keep exploring and refining!