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:
   Building a Smart Telegram Chatbot for Meetup Scheduling Using n8n and AI
   
   Meta Description:
   Learn how to set up an intelligent Telegram bot using n8n, Google Sheets, and OpenRouter AI to help facilitate scheduling and answer questions for meetup groups in Da Nang.
   
   Keywords:
   n8n workflow, Telegram bot, AI chatbot, Google Sheets automation, OpenRouter API, LangChain, meetup assistant, scheduling bot, no-code automation, Telegram AI integration
   
   Third-Party APIs Used:
   
   1. Telegram Bot API – for sending and receiving messages via Telegram.
   2. Google Sheets API – to retrieve scheduling data stored in a shared Google Sheet.
   3. OpenRouter API (for LLM Chat interaction) – to process natural language inputs and generate conversational responses.
   4. LangChain Memory Buffer – to maintain chat context between messages in LLM interactions.
   
   Article:
   
   How to Build an AI-Powered Telegram Bot for Meetup Scheduling with n8n
   
   Managing a meetup community often means juggling constant questions about upcoming events, dates, and times. This is especially true in tech-savvy cities like Da Nang, which hosts growing numbers of digital nomads and tech meetups. Enter the "Telegram-bot AI Da Nang" — an AI-powered chatbot built using n8n, a powerful automation tool that enables no-code workflows. This bot offers effortless access to scheduling details and conversational responses using AI and integrates seamlessly with Telegram and Google Sheets.
   
   In this article, we’ll walk through the mechanics of this workflow, providing insights into how it uses multiple tools to become a responsive, intelligent assistant.
   
   🔁 Workflow Overview
   
   The Telegram-bot AI Da Nang workflow is a sophisticated setup that bridges user queries with real-time data and AI-generated responses. Here’s the high-level process:
   
   - Users send a message to the Telegram bot.
   - The bot retrieves the latest schedule from a Google Sheet.
   - That data is converted into a Markdown table.
   - An AI model processes the user’s question with the schedule as context.
   - The response is delivered back to the user via Telegram.
   
   The entire flow is orchestrated within n8n, ensuring each component interacts fluidly with the others.
   
   🛠 Key Components of the Workflow
   
   1. Telegram Integration
   
   At the top of the workflow sits the Telegram Trigger node. It listens for new messages from users and passes this input through several normalization steps, such as storing the chat ID and message content in a unified structure (handled by custom Set nodes called telegramChatSettings and n8nChatSettings). This standardization allows the workflow to intelligently decide the communication channel (Telegram or native n8n chat) for input/output later in the process.
   
   2. Google Sheets Connection
   
   The heart of the bot’s data lies in a shared Google Spreadsheet. Using the Google Sheets node, the bot fetches direct scheduling data based on a pre-configured URL. Once retrieved, another node—a custom JavaScript function—parses and converts this data into a Markdown-formatted table suitable for language model processing.
   
   This transformation serves as context, telling the AI what dates and events are scheduled before the user's question is interpreted.
   
   3. AI Brains via LangChain & OpenRouter
   
   One of the standout features of this bot is its integration with OpenRouter via the LangChain framework. Using a node called ScheduleBot, the workflow feeds the Markdown version of the schedule to the AI with a predefined system prompt:
   
   “You are a helpful assistant that helps members of a meetup group with scheduling their meetups and answering questions about them..."
   
   This prompt ensures each interaction is focused on helping the user navigate the meetup schedule—no off-topic banter, just clean, relevant responses.
   
   To maintain dialog context across interactions, the workflow uses LangChain’s Memory Buffer with a session ID derived from the Telegram chat itself. This means the AI can "remember" the last few exchanges, making the chatbot conversational and context-aware.
   
   4. Response Generation
   
   Once the large language model generates a reply, the workflow assembles it through the SetResponse node, and a Switch node determines whether to send it through Telegram or n8n’s internal chat interface, based on the mode of interaction.
   
   In the case of Telegram, it simulates typing (SendTyping node) for a human-like delay before responding with the generated text using the Telegram Send Message node.
   
   ✅ Benefits of This Setup
   
   - AI-Powered Conversations: By feeding structured context and a dedicated system prompt to the LLM (large language model), you get accurate, agenda-specific answers.
   - Real-Time Data: The integration with Google Sheets ensures the bot always has the most up-to-date event information.
   - Multi-Platform Support: With separate input handlers for both Telegram and native n8n chat triggers, the solution is flexible and scalable.
   
   🧩 Technologies & Integrations
   
   To achieve this automation magic, the following third-party tools and APIs are used:
   
   - Telegram Bot API – for handling messaging interactions with users.
   - Google Sheets – serving as a dynamic database for meetup schedules.
   - OpenRouter via LangChain – allowing natural language processing through a large language model.
   - LangChain Memory Buffer – preserving user context to create coherent conversations.
   
   🏁 Conclusion
   
   This n8n-powered Telegram chatbot is a prime example of how you can build a smart, responsive, AI-enabled assistant without writing an entire application from scratch. With low-code tools and a bit of creative automation, you can transform static scheduling data into an interactive, conversational assistant that meets your community’s needs—literally and figuratively.
   
   Whether you're managing events, coordinating teams, or just automating support for your Telegram community, this workflow offers a practical template for future-ready bots.
   
   Are you ready to build your own?
   
   🔗 Get started with n8n: https://n8n.io  
   📊 Google Sheets docs: https://developers.google.com/sheets  
   🤖 OpenRouter AI: https://openrouter.ai  
   📘 LangChain framework: https://www.langchain.com

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.