This article provides a complete, practical walkthrough of the Telegram Code 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:
   Automating Meetup Scheduling with AI: A Deep Dive into the “Telegram-bot AI Da Nang” n8n Workflow
   
   Meta Description:
   Discover how the “Telegram-bot AI Da Nang” n8n workflow automates meetup scheduling using AI, Telegram, and Google Sheets. Explore how it enhances user interaction and answers scheduling queries in real time.
   
   Keywords:
   n8n workflow, Telegram bot AI, Google Sheets n8n integration, AI chatbot scheduling, LangChain memory, OpenRouter, GPT chatbot, automation for meetups, Telegram AI automation, LangChain n8n
   
   Article:
   
   Automating Meetup Scheduling with AI: A Deep Dive into the “Telegram-bot AI Da Nang” n8n Workflow
   
   Managing event schedules and helping users find timely answers to meetup-related questions can be quite the overhead, especially for community managers juggling multiple platforms. Fortunately, automation tools like n8n make it possible to build smart, interactive assistant bots using intuitive, no-code or low-code workflows. One project that leverages this power beautifully is the active n8n workflow titled “Telegram-bot AI Da Nang.”
   
   This intelligent bot engages with users on Telegram or within n8n’s own chat interface, responds to scheduling queries, accesses live data from Google Sheets, and generates conversational replies using large language models (LLMs). Let’s walk through how this powerful, AI-enhanced bot works and the technologies it integrates.
   
   The High-Level Overview
   
   At its core, the Telegram-bot AI Da Nang workflow achieves four key functionalities:
   
   - Accepts chat input from Telegram and n8n’s native chat interface
   - Normalizes user input and prepares it for processing
   - Fetches the latest schedule data from a shared Google Sheet
   - Contextualizes that data for an AI language model to generate helpful, natural-language responses
   - Delivers the response back to Telegram or n8n, depending on the originating chat platform
   
   By orchestrating third-party APIs such as Google Sheets, Telegram Bot API, and LLMs via OpenRouter, this workflow becomes a virtual event manager.
   
   Step 1: Input Triggers and Normalization
   
   The workflow starts with two primary triggers:
   
   - telegramInput: Listens for new messages on Telegram via the Telegram Trigger node.
   - n8nInput: Enables testing or internal use via the n8n LangChain ChatTrigger node.
   
   Each input is routed separately through distinct paths (`telegramChatSettings` and `n8nChatSettings` respectively), but both converge into a common `Settings` node. Here, critical variables like inputMessage, chatId, and communication mode (Telegram or n8n) are standardized — essential for downstream components to work seamlessly regardless of the source.
   
   Step 2: Retrieving and Structuring Meetup Data
   
   One key insight of this bot is its use of a live Google Sheet as the source of truth for the meetup schedule. This is abstracted through three connected nodes:
   
   - Settings → Injects the `scheduleURL` which links to the Google Sheet
   - Schedule → Uses the Google Sheets API to fetch real-time data from a tab called "Schedule" based on that URL
   - ScheduleToMarkdown → Runs custom JavaScript code that formats the tabular data into a Markdown table for easier parsing and display
   
   This Markdown conversion is vital because it turns raw, cell-based data into a structured string the AI language model can understand and reference.
   
   Step 3: AI-Powered Conversation Handling
   
   With both the user’s intent (inputMessage) and the latest schedule embedded in Markdown form, the bot leverages the power of LangChain’s scheduling agent, “ScheduleBot.” This agent is configured with:
   
   - Memory: Powered by LangChain Memory Buffer Window and indexed by chat ID, meaning it can remember previous conversations in ongoing sessions.
   - Language Model: All prompts are routed through OpenRouter, enabling versatile LLM use (like GPT variants).
   
   The bot uses a descriptive system message that sets context:
   
   "You are a helpful assistant that helps members of a meetup group with scheduling their meetups and answering questions about them. The current version of the schedule in tabular format is the following:"
   
   This primes the model to answer appropriately using accurate event data.
   
   Step 4: Response Transmission
   
   Once the bot generates a natural-language answer, it’s assembled in `SetResponse` and passed through a `Switch` node to determine the right response channel — Telegram (`telegramResponse`) or n8n (`n8nResponse`). This keeps responses targeted and avoids misrouting.
   
   For Telegram specifically, users even see a "typing…" indicator (`SendTyping`) that creates a sense of a real-time, human-like interaction.
   
   Enhanced Usability and Debugging
   
   Additional sticky notes and design annotations in the workflow make it transparent and easy to debug. With clearly labeled sticky notes ("Normalize Input", "AI Processing", "Retrieve Data"), designers understand what each stage does, speeding up maintenance and updates.
   
   Third-Party Integrations Used
   
   This workflow integrates the following third-party APIs and services:
   
   1. Telegram Bot API
       - Used for triggering messages and sending chat responses
   2. Google Sheets API (via OAuth2)
       - To fetch the latest schedule dynamically from a Google Spreadsheet
   3. OpenRouter (LLM API)
       - An interface to advanced language models like GPT for dynamic, AI-generated responses
   4. LangChain
       - Memory and agent modules that handle question processing, memory, and prompt management
   
   Final Thoughts
   
   The Telegram-bot AI Da Nang is a shining example of how low-code platforms like n8n can be combined with AI and cloud tools to build practical, productivity-enhancing solutions. Whether managing local meetups or any recurring event, such an AI bot can save time, boost engagement, and drive operational excellence.
   
   It exemplifies not only technical acumen but also a smart understanding of user experience and system interoperability.
   
   If you’re exploring the world of automation, chatbots, or AI-assisted tools for community management, this workflow is an excellent template to learn from or build upon.
   
   —
   
   Get started with n8n and bring your bots to life — intelligently, contextually, and beautifully automated.

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.