This article provides a complete, practical walkthrough of the Telegram Googlesheets 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:**  
   Build an AI-Powered Telegram Chatbot with Session Memory Using n8n
   
   **Meta Description:**  
   Learn how to create a powerful Telegram chatbot with session management and AI responses using n8n, OpenAI, and Google Sheets. Improve conversations with session control, summaries, and custom queries.
   
   **Keywords:**  
   n8n chatbot, Telegram chatbot, OpenAI GPT-4o, Telegram API, Google Sheets API, chatbot with session memory, AI assistant, chat summarization, n8n workflow example, LangChain, automate Telegram bot
   
   ---
   
   ## Create a Smart Telegram Chatbot with Multiple AI-Powered Sessions — Using n8n
   
   The rise of AI assistants and chatbots has revolutionized how we interact with services. But what if you want your bot to remember different conversations, switch between chats, and even analyze your past messages on demand? If you’re a fan of automation and no-code workflows, this hands-on guide will walk you through setting up exactly that — a multi-session Telegram chatbot powered by OpenAI and n8n.
   
   ### What's Inside the Workflow
   
   This n8n workflow integrates OpenAI’s GPT language model with Telegram’s messaging capabilities, leveraging Google Sheets for session tracking and historical data storage. Users can start a new conversation, resume old ones, retrieve summaries, and ask targeted questions based on chat history — all from within Telegram.
   
   Let’s break down the major components and features of this intelligent chatbot system.
   
   ### Feature Highlights
   
   #### 1. 🤖 Chatbot Conversations with GPT-4o
   The bot uses OpenAI’s GPT-4o mini model via LangChain’s integration to generate responses to user queries. When users send a message, the system retrieves the current session’s context and feeds it to the model, enabling consistent conversations.
   
   #### 2. 🗂 Session Management via Commands
   The chatbot handles sessions dynamically through intuitive commands:
   - `/new` – Starts a new session by registering it in a Google Sheet and marking the old session as “expired”.
   - `/current` – Displays the ID of the currently active session.
   - `/resume SESSION_ID` – Swaps the current session with a previous one.
   - `/summary` – Summarizes the conversation log of the active session using a summarization chain.
   - `/question your question` – Allows the user to ask specific questions based on the conversation's history.
   
   #### 3. 📚 Session Storage with Google Sheets
   Instead of relying on a backend database, all session states — like `"current"`, `"expired"`, and session IDs — are tracked in Google Sheets. The workflow uses Google Sheets for:
   - Logging chatbot prompts and responses with timestamps.
   - Fetching active or historical session data.
   - Managing session states for seamless session switching.
   
   #### 4. 🧠 Context Window and Memory Buffer
   Using LangChain’s `Simple Memory` feature, the chatbot maintains memory for recent exchanges within a session. This ensures multi-turn conversations hold context, which is critical for delivering coherent AI responses.
   
   #### 5. 🧾 Summarization and Semantic Querying
   The bot offers a `/summary` command to deliver concise overviews of chat history. This is achieved via LangChain’s summarization chain powered by OpenAI. Additionally, users can invoke `/question` to ask questions about the session context — ideal for data recall, chat review, or reflection.
   
   ### Workflow Architecture Overview
   
   - **Telegram Trigger**: Listens for incoming messages from users.
   - **Switch Node**: Determines the command type based on user input and routes the workflow accordingly.
   - **Google Sheets Nodes**: Update session info, log interactions, and fetch data based on current or specified sessions.
   - **OpenAI + LangChain Nodes**: Handle AI responses, summarization, and question-answering.
   - **Telegram Nodes**: Send messages back to the user with context-aware AI responses or session updates.
   
   ### User Flow Example
   
   1. A user sends `/new` to begin a new session. The workflow marks previous sessions as expired and assigns a fresh session ID.
   2. Next, the user asks a question like “What is the weather today?” — the message is logged and processed by the GPT-4o model.
   3. After multiple exchanges, the user can send `/summary` to receive a concise recap or `/question What should I do next?` to generate insights from the entire thread.
   4. The user can also `/resume abc123` to switch to a previously active conversation thread.
   
   ---
   
   ### Third-Party APIs and Services Used
   
   1. **Telegram API (Bot API)** – For receiving user commands and sending chat responses.
   2. **OpenAI API (GPT-4o mini)** – For generating AI-powered responses, summaries, and contextual answers.
   3. **Google Sheets API** – For tracking, storing, and managing session IDs, chat logs, and chatbot state configurations.
   4. **LangChain** – For chaining prompts and memory buffers for AI interactions within n8n nodes.
   
   ---
   
   ### Why This Matters
   
   Session control is often overlooked in chatbot design. While most bots operate in a stateless, linear fashion, this solution introduces memory, recall, and context-responsiveness, essential for deeper, more human-like interactions.
   
   Best of all, you can clone the workflow and the session sheet directly to customize it for your business, research, or personal needs.
   
   🔗 Try it out by cloning the template sheet here: [Chatbot with session - Google Sheet](https://docs.google.com/spreadsheets/d/1MCJLAqKP0Y7Qr68ZYoSSBeEVyKI1QgAAZnlEiyqkzXo/edit?usp=sharing)
   
   ---
   
   Through clever use of n8n's no-code interface and the power of GPT, this bot blurs the line between automated responses and intelligent conversation — making it an ideal assistant for modern chat experiences. Whether you're building a customer support agent, journaling bot, or content brainstormer, modularity and memory matter — and with this workflow, you've got it all.

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.