This article provides a complete, practical walkthrough of the Http Telegram Automate Webhook 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 a Memory-Aware AI Telegram Bot with n8n, OpenAI & Supabase
   
   **Meta Description**:  
   Learn how to create a smart, context-aware Telegram chatbot using n8n, OpenAI Assistants, and Supabase. Store user sessions, maintain conversation threads, and deliver personalized AI-powered replies in real time.
   
   **Keywords**:  
   AI Telegram bot, n8n workflow, Supabase memory, OpenAI assistant, OpenAI GPT-4o, Telegram automation, low-code bot, Telegram bot with memory, context-aware chatbot, Supabase n8n integration
   
   ---
   
   # Build a Smart Telegram Bot with AI Memory Using n8n, OpenAI, and Supabase
   
   In the fast-evolving landscape of AI chatbots, context awareness and memory are becoming essential for providing a more natural and intelligent user experience. Most bots today are reactive—only responding to a message in isolation with no regard for past interactions. That changes with this powerful low-code n8n workflow that integrates Telegram, OpenAI's Assistants v2, and Supabase to create a smart, memory-enabled Telegram chatbot.
   
   Created by Mark Shcherbakov from the 5minAI community, this setup allows your bot to remember users, create unique OpenAI thread sessions, and deliver uniquely contextual responses based on previous conversations.
   
   Let’s break down how it works!
   
   ---
   
   ## Core Features of the Workflow
   
   - 💬 Telegram Bot: Receives and sends messages to users via a bot created using [Botfather](https://t.me/botfather).
   - 🗂 Supabase Database: Stores `telegram_id` and `openai_thread_id` to associate each user with a persistent OpenAI thread, enabling conversation memory.
   - 🧠 OpenAI Assistant: Interacts with GPT-based assistants created via [OpenAI Assistants v2](https://platform.openai.com/assistants) for natural, contextual dialog.
   - 🔄 Continuous Messaging Flow: Handles message threads and responses seamlessly via HTTP requests to OpenAI and Telegram APIs.
   
   ---
   
   ## How the Workflow Operates (Overview)
   
   1. **Trigger via Telegram Bot**  
      A user starts a chat with the bot. The `Get New Message` node listens for incoming messages using Telegram’s webhook infrastructure.
   
   2. **Check for Existing User**  
      The bot queries Supabase to check if the user already exists in the `telegram_users` table using their `telegram_id`.
   
   3. **Handle New vs Existing Users**
      - If the user is new, the workflow:
        - Creates a new conversation `thread_id` via OpenAI API.
        - Stores `telegram_id` and `thread_id` in Supabase.
      - If user exists, the saved thread ID is retrieved.
   
   4. **Send User Message to OpenAI Assistant**  
      User’s message is sent to their corresponding OpenAI thread using the `OPENAI - Send message` node.
   
   5. **Run Assistant**  
      Executes the assistant’s logic (defined during setup in OpenAI GUI) to generate a reply.
   
   6. **Fetch Assistant Response**  
      The assistant’s latest reply is fetched from the thread.
   
   7. **Send Response via Telegram**  
      The final assistant message is sent back to the user in chat.
   
   ---
   
   ## Why Memory Matters (and How Supabase Enables It)
   
   Unlike stateless bots that treat every message as a fresh query, this workflow leverages Supabase to store user metadata. Specifically, each user's `telegram_id` is tied to a unique `openai_thread_id`. This lets OpenAI track ongoing conversations as distinct threads per user—giving the assistant the ability to generate more relevant and personalized responses. This opens the door for use cases like:
   
   - Customer support with conversation history
   - Tutoring bots that track learning progress
   - Virtual assistants for scheduling and reminders
   - Habit tracking and journaling tools
   
   ---
   
   ## How to Set It Up 🚀
   
   Follow these streamlined steps to replicate the workflow:
   
   1. ✅ **Create a Telegram Bot:**
      - Open BotFather on Telegram.
      - Use `/newbot`, give it a name and username.
      - Copy the bot token.
   
   2. 🛠️ **Set Up Supabase:**
      - Create a project on [Supabase](https://supabase.com).
      - Use this SQL snippet to define your table:
   
      ```sql
      create table
        public.telegram_users (
          id uuid not null default gen_random_uuid (),
          date_created timestamp with time zone not null default (now() at time zone 'utc'::text),
          telegram_id bigint null,
          openai_thread_id text null,
          constraint telegram_users_pkey primary key (id)
        );
      ```
   
   3. 🧠 **Create an OpenAI Assistant:**
      - Go to [OpenAI platform](https://platform.openai.com/assistants), create a new Assistant.
      - Choose a model (e.g., GPT-4o or GPT-3.5), set the behavior or personality of the bot.
      - Copy the generated Assistant ID.
   
   4. ⚙️ **Configure Your n8n Workflow:**
      - Set up credentials for Telegram, Supabase, and OpenAI.
      - Make sure each HTTP node (API call) is authorized with the correct credentials.
      - Add your OpenAI Assistant ID in the `OPENAI - Run assistant` node.
   
   5. 📹 **Need Visual Help?**  
      Watch the 5-minute setup [YouTube tutorial](https://www.youtube.com/watch?v=kS41gut8l0g).
   
   ---
   
   ## Third-party APIs Used
   
   This workflow smartly integrates the following external APIs:
   
   | API         | Purpose                                          |
   |-------------|--------------------------------------------------|
   | Telegram Bot API | To send/receive messages via the chat interface |
   | OpenAI API (Assistants v2) | To create threads and interact with custom AI models |
   | Supabase (Database) | To persist user sessions and maintain thread associations |
   
   ---
   
   ## Final Output: What the User Experiences
   
   When a user sends a message like “Hello, how are you?” the AI bot responds with:
   
   > “Hello! I'm just a program, but I'm here and ready to help you. How can I assist you today?”
   
   Behind the scenes, the bot has just initiated—or continued—a personalized thread with OpenAI’s assistant using the user's Telegram ID, enabling an experience that feels continuous and intelligent.
   
   ---
   
   ## Conclusion: Smarter Bots Start With Memory
   
   What elevates this bot from the rest is its use of session memory. With the support of Supabase, every user has a persistent conversational thread. Combine that with OpenAI’s powerful assistant framework and n8n’s visual, low-code approach, and you can build enterprise-grade bots without writing a single line of code.
   
   🧩 Clone it, tweak it, integrate it—and watch your AI assistant evolve into a true conversation partner.
   
   —
   
   ✨ Created by [Mark Shcherbakov](https://www.linkedin.com/in/marklowcoding/) from the [5minAI Community](https://www.skool.com/5minai-2861)  
   🛠️ Template Powered by n8n + OpenAI + Supabase  
   📸 Visual tutorial: https://www.youtube.com/watch?v=kS41gut8l0g

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.