Build DeepSeek AI Agent with Telegram & Long-Term Memory in n8n

Opening Problem Statement

Meet Sarah, a content creator who manages multiple Telegram groups and wants a smart assistant that remembers past conversations and personal preferences to provide personalized responses. Without an integrated memory system, Sarah struggles with repetitive queries, inconsistent interactions, and spends hours manually reviewing chat histories. This leads to lost engagement opportunities and an inefficient management process.

Sarah’s main pain points include: spending over 4 hours daily manually referencing previous chats, missing context for follow-up questions, and the inability to deliver personalized user experiences on Telegram. A tailored AI chatbot with long-term memory integration could transform her workflow.

What This Automation Does

This n8n workflow leverages the DeepSeek AI agent integrated with Telegram and Google Docs to create a sophisticated chatbot capable of long-term memory retention. Here’s what happens when the workflow runs:

• Receives and validates Telegram messages via webhook ensuring interaction is with authorized users only.
• Routes different message types (text, audio, images) appropriately for processing.
• Uses DeepSeek AI “deepseek-chat” model to generate context-aware, personalized responses on Telegram.
• Retrieves past conversation memories stored in Google Docs to keep responses relevant and consistent over time.
• Saves new noteworthy user information back into Google Docs as long-term memory to enhance future interactions.
• Handles errors gracefully by sending friendly messages when processing fails.

This workflow saves Sarah several hours daily by eliminating manual memory checks and boosts engagement through personalized AI responses.

Prerequisites ⚙️

• n8n account with webhook support for receiving Telegram messages.
• Telegram bot with a valid API token configured via the Telegram node in n8n. 📧
• Google Docs account connected to n8n for storing and retrieving long-term chat memories. 📁
• DeepSeek API access via OpenAI-compatible credentials for AI processing. 🔐
• Basic knowledge of webhook setup on Telegram (bot webhook URL pointing to n8n webhook).

Step-by-Step Guide

1. Set Up Telegram Bot Webhook Listener

In n8n, start by adding a Webhook node named Listen for Telegram Events. Set the HTTP Method to POST and define the path as wbot. This URL will be the endpoint for Telegram’s bot webhook.

Follow Telegram’s official method to set your webhook URL via:

https://api.telegram.org/bot{your_bot_token}/setWebhook?url=https://your-n8n-instance.com/wbot

You should see confirmation from Telegram indicating the webhook is set successfully.

Common mistake: Forgetting to use HTTPS for the URL or misconfiguring the path will cause webhook failures.

2. Validate User and Chat Identity

Use a Set node named Validation to define properties representing the authorized user’s first name, last name, and Telegram ID exactly as in your Telegram records.

Configure an If node Check User & Chat ID with conditions comparing incoming message sender data to the validation data. This ensures the workflow processes messages only from recognized users.

If the user is unauthorized, the workflow sends a polite error message using the Telegram node configured as Error message.

Expected result: Only approved users interact with the AI agent; unwanted messages get a polite decline.

3. Route Messages by Type

Use a Switch node called Message Router to separate incoming Telegram messages based on content type such as text, voice (audio), or images.

The router has outputs for each type—customize processing downstream accordingly. Messages not matching these types flow into an error message node.

Common mistake here is not handling the ‘extra’ output properly, which results in lost or unhandled messages.

4. Retrieve Historical Long-Term Memories

Integrate the Google Docs node Retrieve Long Term Memories to pull the content of a designated Google Doc where user memories are stored. This document acts as the AI’s persistent memory store.

The retrieved content is merged with the current message data using a Merge node for combined AI context.

Make sure your Google Docs node has correct OAuth2 credentials.

5. Process User Text via DeepSeek AI Agent

The core AI node is the AI Agent node configured with the “deepseek-chat” model from DeepSeek.

It receives the current Telegram text message and the long-term memories merged into its input context, then produces personalized, context-aware replies.

The prompt includes system instructions emphasizing memory management, personalized responses, and respectful privacy.

Common mistake: missing to map the merged memory content into the AI prompt’s context can degrade response quality.

6. Save New Memories to Google Docs

Newly extracted key information from conversations is saved back into the long-term memory document using the Google Docs Tool node named Save Long Term Memories.

Text is appended with a timestamp to maintain memory chronology.

7. Send AI-Generated Response Back to Telegram

Use a Telegram node Telegram Response to send the AI’s reply back to the user’s chat. Set the ‘chatId’ parameter dynamically from the incoming message, and use HTML parse mode for formatting.

Expected outcome: Users receive coherent, personalized, and contextually relevant answers.

8. Handle Errors Gracefully

If processing fails at any point, the workflow uses a Telegram node Error message to notify users politely without exposing technical details.

Customizations ✏️

• Add More Authorized Users: In the Validation node, append or duplicate the user credentials checks and adjust the Check User & Chat ID node conditions to handle multiple users.
• Extend Message Types: Enhance the Message Router node by adding cases for video or documents to process other Telegram content types.
• Change AI Model: Switch between DeepSeek’s deepseek-chat and deepseek-reasoner models by redirecting the AI Agent input to the respective node in the workflow to try different AI capabilities.
• Use Other Storage: Modify the Google Docs nodes to integrate with Google Sheets or an SQL database to store memories in alternative formats.

Troubleshooting 🔧

• Problem: “Webhook not receiving Telegram updates”
  Cause: Incorrect webhook URL or Telegram bot not set up properly.
  Solution: Re-check the webhook URL in Telegram BotFather, ensure HTTPS and correct path usage, and verify n8n webhook node is active.
• Problem: “AI Agent returns generic or irrelevant answers”
  Cause: Missing or incorrect long-term memories input.
  Solution: Verify Google Docs retrieval node pulls correct content and that merged context is correctly passed to the AI Agent node.
• Problem: “Unauthorized users getting in or no response sent”
  Cause: Validation conditions not matching exact user info.
  Solution: Update Validation node with accurate user data from Telegram and test with known user accounts.

Pre-Production Checklist ✅

• Verify Telegram Bot token and Google Docs OAuth2 credentials are set correctly in n8n.
• Test webhook endpoint with Telegram’s setWebhook method and confirm receipt of updates.
• Check the Validation and Check User & Chat ID nodes to ensure only authorized users are processed.
• Run tests sending text messages and verify AI generates relevant, memory-enhanced responses.
• Confirm Google Docs nodes successfully read and write to the designated memory document.

Deployment Guide

Activate the workflow in n8n by toggling from inactive to active in the workflow editor.

Ensure the webhook endpoint remained stable and publicly accessible for Telegram to push updates.

Monitor execution logs in n8n to troubleshoot any delivery or AI response issues.

Consider setting up alerts on failures or retries if running at scale.

FAQs

• Can I use another AI model instead of DeepSeek?
  Yes, if the model supports OpenAI-compatible API requests, you can configure it in the AI Agent node.
• Does this workflow consume a lot of API credits?
  Usage depends on message volume; batching memories and limiting token counts per request help control costs.
• Is user data stored securely?
  Yes, data is stored securely within your Google Docs account and transmissions use encrypted HTTPS channels.
• Can this handle many concurrent Telegram users?
  The current setup is designed for a limited user base, but can be scaled with additional validation and memory management strategies.

Conclusion

In this post, you’ve built a powerful AI assistant using DeepSeek, Telegram, and Google Docs long-term memory with n8n automation. This system transforms user interactions by remembering past conversations and personalizing replies, saving countless hours of manual follow-up for Sarah or any user deploying it.

With this foundation, you can explore next steps like adding multi-user support, expanding accepted message types, or integrating more advanced AI reasoning models.

Get ready to delight your Telegram community with a smart, context-aware AI that learns continuously!