This article provides a complete, practical walkthrough of the Telegram Stickynote Automate 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:  
   AI-Powered Speech Coaching with Telegram and n8n: A Smart Multimodal Workflow
   
   Meta Description:  
   Discover how an advanced n8n workflow integrates Telegram, OpenAI, and Google Gemini to create an AI speechwriting assistant. From feedback analysis to full speech synthesis, this intelligent automation streamlines your preparation process—via voice or text.
   
   Keywords:  
   Speechwriting, n8n workflow, AI coach, Telegram bot, Google Gemini, OpenAI Whisper, LangChain, multimodal AI, speech feedback, AI agent, conversational AI, voice-to-text, chatbot automation
   
   Third-Party APIs Used:
   
   1. Telegram Bot API  
   2. OpenAI API (for transcription and LLM processing)  
   3. Google Gemini API (formerly PaLM, via LangChain's ChatModel)  
   4. LangChain (used through n8n integrations)
   
   —
   
   Article:  
   
   AI-Powered Speech Coaching with Telegram and n8n: A Smart Multimodal Workflow
   
   In a world where effective communication is key, preparing a compelling speech often involves more than just writing a script—it’s about structure, delivery, feedback, and improvement. Now, imagine you could automate all of that with the help of AI, right from your favorite messaging app. That’s precisely what a powerful n8n workflow achieves by turning Telegram into a full-featured AI speech coach.
   
   This no-code workflow blends natural language processing, audio transcription, chatbot responsiveness, and cloud-based AI models to help anyone prepare, analyze, and improve speeches on demand. Let’s break down how this automation works—and how it brings together cutting-edge tools like OpenAI, Google Gemini, LangChain, and Telegram.
   
   From Raw Voice to Actionable Feedback
   
   The workflow begins by listening to incoming Telegram messages. Whether users send a typed message or a voice recording, it intelligently determines the message type and processes it accordingly. If it’s a voice message, it uses the Telegram Bot API to retrieve the audio, then passes it to OpenAI's Whisper model for fast and accurate transcription. This transcribed text is treated the same way as a typed message thereafter.
   
   Dynamic Routing Based on User Intent
   
   The core of the workflow is a logic-driven switch node that analyzes the content of the incoming message and routes it to different AI operations:
   
   - If the user sends “generate speech,” the system prompts the AI to synthesize a new speech based on previously gathered user feedback.
   - If the message includes “new speech,” it initiates a new coaching session, acting as an intelligent assistant that guides the user through brainstorming and structuring a fresh presentation.
   - For any other messages, it assumes the user is submitting a speech draft and responds with detailed feedback.
   
   Dynamic System Prompting
   
   At this stage, one of three system prompts is dynamically assigned to the AI model based on the detected user intent. Each prompt is crafted to guide the AI agent accordingly—whether it’s providing feedback, drafting a new speech, or leading a preparation conversation. These prompts are injected into the AI Agent node that leverages LangChain to facilitate human-like interactions.
   
   Powerful Language Understanding with Google Gemini
   
   Behind the scenes, the workflow uses Google’s Gemini AI (formerly known as PaLM) via LangChain's ChatModel integration to drive all conversational intelligence. This foundation ensures high-quality, context-sensitive responses that evolve as the user shares more information or makes adjustments.
   
   Memory Management: Contextual, Not Confusing
   
   To prevent conversational “hallucinations” and make sure that responses remain relevant, the workflow includes a memory wipe step before starting fresh interactions. At the same time, it uses a memory buffer to maintain short-term history when helpful, especially during iterative speech refinement. This ensures that the AI remains smart but not misleading.
   
   Making Messages Telegram-Ready
   
   After the AI responds, the text goes through a sanitization phase. Since Telegram interprets characters like asterisks, underscores, and brackets as formatting instructions, special code is used to strip these characters. Next, another Python code node chunks the cleaned response into pieces of 4,000 characters or fewer—the maximum allowed by Telegram—to ensure smooth delivery.
   
   Seamless User Feedback Loop via Telegram
   
   Finally, the prepared, split-up, and sanitized response is sent back to the user in Telegram. Since the whole workflow is triggered by the Telegram bot API, users interact with the system naturally as if they were chatting with a human coach—only much faster and more tireless.
   
   Multimodal Intelligence at Your Fingertips
   
   What sets this solution apart is its ability to handle multimodal input (voice or text), contextual prompting, memory-sensitive interaction, and output management within a single workflow. The result is a powerful, user-friendly digital assistant that helps users ideate, practice, and perfect their speeches—no technical skills required.
   
   In essence, this AI-enhanced n8n workflow acts as your always-available speech assistant. Whether you're rehearsing a keynote, preparing a pitch, or delivering a toast, you'll have AI-powered guidance tailored to your unique voice and intent—all accessible from your Telegram chat window.
   
   —
   
   Conclusion
   
   With this n8n Telegram-integrated workflow, speechwriting is no longer a solitary task or a source of anxiety. It becomes an interactive, insightful, and even conversational experience. By fusing automation with state-of-the-art AI, users gain not just assistance—but confidence.
   
   Whether you're a public speaker, student, or business leader, the future of AI coaching is already here—and it fits in your pocket.

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.