This article provides a complete, practical walkthrough of the Telegram Stickynote Create Triggered 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: Automating Task Breakdown from Telegram Messages with AI, n8n, and Todoist
   
   Meta Description: Learn how to automate task management from Telegram voice or text messages using n8n, OpenAI, and Todoist. This no-code workflow captures tasks, breaks them into sub-tasks, and schedules them seamlessly.
   
   Keywords: n8n, Telegram automation, OpenAI GPT-4, Whisper API, Todoist integration, task automation, AI task planner, voice to text automation, project management automation, no-code workflow
   
   Third-Party APIs Used:
   
   - Telegram Bot API
   - OpenAI API (GPT-4o and Whisper)
   - Todoist API
   
   Article:
   
   Smart Task Automation via Telegram: How n8n Streamlines Your Projects with AI and Todoist
   
   Task management often begins with a simple idea—jotted down hastily between meetings or spoken aloud during your morning commute. But what if those unstructured thoughts could instantly become well-organized tasks in your project management system? With the combination of n8n, Telegram, OpenAI, and Todoist, this thought-to-task transformation becomes a seamless and automated reality.
   
   This article walks you through a no-code workflow powered by n8n, designed to turn Telegram messages—whether voice or text—into structured, actionable tasks in Todoist using OpenAI’s GPT-4 model.
   
   Overview: What Does This Workflow Do?
   
   This n8n workflow acts as a smart project assistant. It listens to messages from Telegram—either text or voice—transcribes any audio, sends the prompt to an LLM for task decomposition, formats the output for Todoist, and adds each task automatically to your selected project. It even sends you a confirmation message with a link to the created task.
   
   This pipeline is particularly powerful for project planners, busy professionals, and agile teams who need quick conversion of brainstorming ideas into concrete next steps.
   
   Step-by-Step Breakdown of the Workflow
   
   Here’s how the complete automation unfolds behind the scenes:
   
   1. Receiving User Input Through Telegram
   
   The automation begins with the Telegram Trigger node. This listens for incoming messages from your bot—both standard texts and voice messages.
   
   2. Determining Voice or Text Input
   
   A Switch node labeled “Voice or Text?” evaluates the type of message received. If it’s a voice message, it proceeds to retrieve and transcribe it; otherwise, it moves forward using the text message.
   
   3. Handling Voice Messages with OpenAI Whisper
   
   If the input is audio, the Telegram File node fetches the voice file using its file_id. This file is processed through OpenAI Whisper (via the LangChain OpenAI node set to “audio translation”), producing transcribed text suitable for task analysis.
   
   4. Preparing the Text for Task Parsing
   
   The transcribed or original text is standardized by a Set node, making the text ready for processing via a chain of LLM prompts.
   
   5. Breaking Down Projects into Sub-Tasks
   
   The true heart of the automation lies within the “Basic LLM Chain” node, which is powered by GPT-4o-mini. This node uses a carefully crafted system prompt that:
   
   - Understands the context of the user’s project description.
   - Decomposes it into measurable sub-tasks.
   - Formats each task as a JSON object compatible with the Todoist API.
   - Includes only two fields: "content" (task description) and "priority" (1–4 scale).
   
   For example, if your Telegram message says “Project Description: Plan a team offsite,” the LLM responds with multiple subtasks like:
   
   ```json
   [
     {
       "content": "Research potential offsite locations.",
       "priority": 3
     },
     {
       "content": "Determine the budget for the offsite.",
       "priority": 4
     }
   ]
   ```
   
   6. Parsing the Response with Structured Output
   
   The LLM output is passed through an output parser node to extract each structured task element. The schema validation ensures that all data maintains strict adherence to the required Todoist input fields.
   
   7. Creating Tasks in Todoist
   
   Next, each JSON object is sent to Todoist via the "Create Todoist Tasks" node, assigning each task its content and priority. The project ID can be pre-defined or chosen dynamically.
   
   8. Sending a Telegram Confirmation
   
   Once each task is added to Todoist, the user receives a Telegram message confirming task creation and optionally including a link to that task in Todoist.
   
   Key Features and Use Cases
   
   - Voice-Enabled Task Entry: Busy professionals can speak their ideas into Telegram, skipping the keyboard entirely.
   - Natural Language Understanding: Using GPT-4o allows tasks to be inferred from vague phrasing or compound project descriptions.
   - Seamless Project Management: Tasks are delivered directly into Todoist in a structured format.
   - Fully Automated: Once set up, this workflow requires no user interaction beyond the initial message.
   - Cross-Platform Potential: Since n8n is modular, this setup could easily be extended to Slack, email, or Google Assistant.
   
   APIs That Power the Workflow
   
   This automation wouldn’t be possible without the intelligent orchestration of the following tools via API:
   
   1. Telegram Bot API for real-time message intake and communication.
   2. OpenAI’s GPT-4o-mini via LangChain for deep language processing and task decomposition.
   3. OpenAI Whisper for accurate voice-to-text transcription.
   4. Todoist API for creating and managing tasks.
   
   Final Thoughts
   
   This n8n workflow showcases just how powerful no-code automation can be when paired with today’s cutting-edge AI. By turning natural language—spoken or written—into structured tasks automatically added to your project board, you can streamline your productivity and focus on execution.
   
   Whether you're planning a team offsite, launching a new product, or simply trying to keep on top of your to-do list, this workflow transforms simple ideas into actionable outcomes, powered entirely by automation.
   
   Try it out—and never let a fleeting thought slip by again!
   
   — 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.