This article provides a complete, practical walkthrough of the Telegram Googledocs Automation 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:  
   🧠 How to Build a Memory-Powered Telegram AI Assistant Using n8n and DeepSeek AI
   
   Meta Description:  
   Discover how to integrate Telegram with a friendly, memory-enhanced AI assistant using n8n, DeepSeek, and Google Docs. Learn how to enable conversation continuity, personalize chatbot experiences, and store long-term user insights seamlessly.
   
   Keywords:  
   n8n workflow, AI assistant, DeepSeek AI, Telegram bot, chatbot memory, Google Docs API, Telegram Webhook, conversational AI, OpenAI-compatible API, long-term memory chatbot, LangChain, DeepSeek-V3, DeepSeek-Reasnor, webhook automation, smart chatbot, personalized assistant
   
   —
   
   Article:
   
   In an era of AI-powered automation, businesses and developers are seeking smarter, more conversational bots to engage users meaningfully. Enter the ✨ DeepSeek + Telegram AI Agent: a friendly virtual assistant rigged with long-term memory and contextual awareness. Built with the powerful open-source workflow automation tool n8n, this setup is an impressive showcase of how far no-code integrations can go when combined with cutting-edge AI.
   
   In this article, we’ll explore how a Telegram bot can be enhanced with DeepSeek AI for intelligent conversations, and how Google Docs and LangChain integrations enable contextual memory that spans multiple sessions.
   
   🧠 Key Features of the Workflow
   
   Built using n8n, this multi-node automation includes:
   
   1. Telegram Integration with Webhooks
   2. Validation of Specific Bot Users
   3. Voice, Text, and Image Input Handling
   4. DeepSeek AI for Responses (Compatible with OpenAI SDK)
   5. Long-Term Memory Storage via Google Docs
   6. Context-Aware, Personalized Interactions using LangChain
   
   Let’s explore how this workflow operates end-to-end.
   
   🎧 Telegram Interaction & User Validation
   
   The workflow begins with a webhook listening on Telegram for inbound messages using the “Listen for Telegram Events” node. To maintain security and personalization, it includes a validation process where each user’s first name, last name, and Telegram ID are checked against predefined values through a logical condition block.
   
   Messages from unauthorized users receive an error response: “Unable to process your message.”
   
   💬 Message Categorization (Text, Voice, Image)
   
   Once the user is validated, a “Message Router” node determines the message type—be it text, voice, or photo—ensuring only text messages are processed while others are routed to error handlers or could be expanded for future enhancements (e.g., voice-to-text conversion or photo interpretation).
   
   🤖 DeepSeek AI Processing with LangChain
   
   Text messages flow into the heart of the system: an AI Agent node powered by DeepSeek using LangChain integration. DeepSeek has two models available:
   - deepseek-chat (for conversational interactions)
   - deepseek-reasoner (for reasoning and critical thinking)
   
   This assistant is set up with a detailed system prompt outlining behavior rules, personality, and memory management techniques such as:
   - Saving personal facts shared by users
   - Context-aware conversation building
   - Respectful, privacy-preserving responses
   
   This AI not only replies meaningfully but also learns from interactions without making users feel like they’re being "analyzed."
   
   📖 Memory Retrieval & Personalization
   
   Before crafting a response, the AI agent retrieves previously stored long-term user memories from a Google Doc (via the "Retrieve Long Term Memories" node). This memory includes historical preferences, hobbies, or goals mentioned in the chat—providing rich context for the AI’s responses.
   
   A sample memory could be:
   > “User mentioned running a marathon next spring — 2023-11-10”
   
   📝 Saving New Memories
   
   If the AI detects fresh, relevant insights in a message (e.g., a user says “I love Thai food”), it uses LangChain’s memory tools to format and store that information into a predefined Google Docs file. This enables the assistant to ‘remember’ and build upon prior connections in future interactions.
   
   The clever part: The AI never tells the user it saved something—keeping the chat natural and fluid.
   
   📤 Responding to the User
   
   Once a reply is generated, it's piped back to the Telegram chat using the “Telegram Response” node. If the AI fails due to formatting issues or incorrect parameters, a fallback message is sent to keep the user engaged and informed.
   
   🌐 APIs and Services Used
   
   The success of this workflow lies in its seamless integration of multiple third-party services:
   
   1. Telegram Bot API – To receive and respond to messages via webhook
   2. DeepSeek API – Handles AI inference and natural language interaction
   3. Google Docs API – Stores and retrieves long-term user memory data
   4. LangChain AI Framework – Connects the AI agent with tools like Memory and Chat
   5. n8n Workflows – Orchestrates the entire automation seamlessly
   
   🎯 Use Cases
   
   This setup is perfect for:
   - Digital therapy or coaching bots
   - AI journaling assistants
   - Personalized customer support
   - Memory-based trivia or learning companions
   
   Because it uses serverless components and modular steps in n8n, it's both scalable and flexible.
   
   ✅ Final Thoughts
   
   This n8n workflow blends intelligence with emotional intelligence. By adding memory and personalization, it creates a more human, meaningful chatbot experience. Best of all, tools like DeepSeek and n8n make this sophisticated setup accessible even to developers with minimal coding background.
   
   Whether you're an AI enthusiast, automation expert, or digital product creator looking to build smarter interactions—this is how you connect the dots for a truly remarkable AI assistant.
   
   —
   
   Third-Party APIs and Services Used:
   - Telegram Bot API
   - DeepSeek API (deepseek-chat, deepseek-reasoner)
   - Google Docs API
   - LangChain framework (compatible with OpenAI APIs)
   
   Looking to build your own? Start with n8n + Telegram + DeepSeek, and give your bot the memory it needs to care.

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.