This article provides a complete, practical walkthrough of the Telegram Wait Automation 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:
   How to Build a Buffered Telegram Chatbot Using n8n, Supabase, and OpenAI
   
   Meta Description:
   Learn how to create an intelligent Telegram chatbot workflow with message buffering using n8n, Supabase, and OpenAI. Improve conversation quality by consolidating rapid messages into a single AI-generated response.
   
   Keywords:
   Telegram chatbot, n8n workflow, Supabase chatbot, OpenAI GPT integration, chatbot message buffering, AI assistant on Telegram, LangChain AI chatbot, Telegram bot with memory, Supabase queue, gpt-4o-mini, AI workflow automation
   
   Third-Party APIs Used:
   
   - Telegram API (via Telegram node and Telegram Trigger node)
   - Supabase (PostgreSQL storage and API integration)
   - OpenAI API (via LangChain GPT-4o-mini model)
   - PostgreSQL (used via Supabase connection for chat memory in LangChain)
   
   Article:
   
   Buffered AI-Powered Conversations in Telegram Using n8n, Supabase, and OpenAI
   
   Chatbots have become a staple in communication platforms like Telegram, WhatsApp, and Messenger. However, traditional bots often falter when users send multiple short, rapid-fire messages that are contextually part of a single thought. Responding to each message individually creates a clunky and awkward experience.
   
   To tackle this challenge, we’ve built a low-code, scalable AI-assisted workflow in n8n that buffers incoming Telegram messages, aggregates them into a coherent conversation, and replies with a unified response using OpenAI’s GPT model.
   
   In this article, we’ll walk through this Telegram chatbot architecture that leverages Supabase for message queuing and LangChain for AI-powered, memory-supported conversations.
   
   🧠 Why Message Buffering Matters
   
   When users interact with AI via messaging platforms, conversations are rarely typed out in a single message. People send multiple lines — often mid-thought — expecting the bot to respond once they’ve finished "speaking."
   
   This can derail linear AI comprehension and lead to disjointed results. Our solution introduces a 10-second buffering period to detect and group messages before generating a reply.
   
   🚀 How the Workflow Works
   
   1. Telegram Trigger: Receiving the Message
   
   The journey begins with the Telegram Trigger node in n8n. It listens for new messages and captures essential metadata, such as user ID and message content.
   
   2. Supabase: Buffering Messages
   
   Upon receiving a message, the bot saves the data into a Supabase PostgreSQL table named message_queue. This storage table includes:
   
   - user_id — the sender's Telegram user ID
   - message — the message text itself
   - message_id — the Telegram-assigned message identifier
   
   This enables tracking and sequencing of messages belonging to the same user session.
   
   3. Wait Node: Delaying the Response
   
   Once a message is stored, the workflow pauses for 10 seconds using the Wait node. This short delay allows time for additional messages to arrive and be stored in the queue.
   
   4. Fetch and Sort Messages
   
   The workflow then retrieves all messages for that particular user from the Supabase queue using the Get Queued Messages node. These are sorted in chronological order by message_id to preserve natural conversational flow.
   
   5. Check if the Current Message is the Last
   
   A Check Most Recent Message IF node determines whether the most recent message received is the last in the queue. If it isn’t, the system does nothing, waiting for additional input. If it is the most recent, the process continues.
   
   6. Aggregate Messages
   
   If it’s the last message, the messages are aggregated into a single text entity — effectively treating multiple messages as a paragraph of text from the user.
   
   7. AI Processing via LangChain & OpenAI
   
   The aggregated message is passed to LangChain’s AI Agent node, powered by OpenAI’s GPT-4o-mini model. Previously collected chat history is retrieved from a PostgreSQL-backed LangChain-compatible chat memory using the Postgres Chat Memory node. This keeps sessions consistent and personalized.
   
   8. Cleanup and Response
   
   After generating an intelligent response, the following steps are executed:
   
   - Delete Queued Messages: All stored messages for the user are removed from Supabase.
   - Reply: The AI-generated text is sent back to the user via Telegram.
   
   ⚙️ Setup Instructions
   
   To replicate this setup, follow these steps:
   
   1. Supabase Table Creation:
   
   Create a table named message_queue with columns:
   
   - user_id (uint8)
   - message (text)
   - message_id (uint8)
   
   2. Integration Credentials:
   
   Add your credentials in n8n for:
   
   - Telegram API
   - Supabase API
   - OpenAI API (for GPT-4o-mini via LangChain)
   - PostgreSQL (for chat memory)
   
   3. Workflow Activation:
   
   Activate the n8n workflow and start sending multiple messages to your Telegram bot to test.
   
   4. Wait & Respond:
   
   The bot will collect your messages, wait 10 seconds, then respond intelligently to the entire message batch.
   
   🛠 Modifications You Can Make
   
   - Adjust the Wait amount to increase or decrease the buffering time.
   - Modify the AI model by replacing GPT-4o-mini with another supported OpenAI or third-party alternative.
   - Add a custom system instruction or persona in LangChain to tailor responses to a specific use case.
   
   💡 Use Cases
   
   This Telegram workflow is ideal for:
   
   - Customer support bots
   - Personal AI assistants
   - Multi-turn conversation bots
   - Language learning applications
   - Task bots that require context from previous interactions
   
   🔚 Conclusion
   
   Using this powerful n8n setup, developers and no-code builders alike can create conversationally fluent AI Telegram bots. By leveraging Supabase for buffering and LangChain + OpenAI for intelligence, this workflow transforms scattered messages into unified, context-aware responses.
   
   It’s a step closer to human-like conversation — delivered in real time through Telegram.
   
   Ready to build a smarter chatbot? Try cloning this workflow inside your n8n instance and start asking your bot questions… like a human would.

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.