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:  
   Building a Telegram Chatbot with DeepSeek AI Agent and Long-Term Memory Using n8n
   
   Meta Description:  
   Learn how to design a smart, context-aware Telegram chatbot powered by DeepSeek AI with long-term memory capabilities using n8n workflows, Google Docs integration, and real-time user validation.
   
   Keywords:  
   n8n workflow, Telegram chatbot, DeepSeek AI, long-term memory, AI assistant, Google Docs API, webhook, chatbot with memory, OpenAI alternative, context-aware chatbot, Telegram bot automation
   
   Third-party APIs Used:
   
   1. Telegram Bot API – For receiving and sending messages via Telegram.
   2. DeepSeek AI API – For AI responses using models deepseek-chat and deepseek-reasoner.
   3. Google Docs API – For managing long-term memory by reading/writing user data.
   
   Article:
   
   How to Build a Context-Aware Telegram Chatbot with DeepSeek AI and Long-Term Memory in n8n
   
   Chatbots are no longer just novelty features—they're evolving into smart digital assistants that remember who you are, what you like, and how they can help you better. In this article, we’ll walk through an advanced n8n workflow that connects Telegram, DeepSeek AI, and Google Docs to create a long-term memory-enabled AI chatbot. This bot can not only respond conversationally but also store and recall meaningful user details across sessions for personalized interactions.
   
   Let’s break it down.
   
   Overview of the Workflow
   
   The core of this intelligent Telegram chatbot is an n8n workflow titled "🐋🤖 DeepSeek AI Agent + Telegram + LONG TERM Memory 🧠." It integrates multiple systems to create a seamless experience where authenticated users can chat with an AI-powered assistant that remembers them over time.
   
   Main Features:
   
   - Validates end-user identity from Telegram messages.
   - Supports multiple message formats (text, voice, image).
   - Communicates with DeepSeek’s conversational and reasoning models to generate replies.
   - Stores user-specific insights into Google Docs for persistent memory.
   - Retrieves stored memory for context-aware conversations.
   - Offers fallback messaging for unrecognized formats.
   
   Let's explore how these components come together.
   
   Step 1: Listening for Telegram Webhook Events
   
   Telegram facilitates real-time interaction using webhooks. A webhook node in n8n (“Listen for Telegram Events”) is configured at a custom endpoint (e.g., /wbot) to capture incoming messages. This ensures immediate message delivery without polling.
   
   Step 2: User Validation
   
   Before processing the message, the workflow runs a validation step to match the sender’s ID, first name, and last name with predefined allowed values. This "Check User & Chat ID" node acts as a security filter.
   
   If the user is invalid, an error message is immediately sent using Telegram’s API.
   
   Step 3: Message Routing
   
   After validation, the incoming message route is evaluated. A switch node ("Message Router") identifies if the message is a voice, text, or image. For this simplified example, only text messages proceed to the reasoning engine.
   
   Text messages land into two main nodes:
   
   - "Retrieve Long Term Memories" from Google Docs
   - "Merge" node to combine current input with memory context
   
   Step 4: AI Agent with DeepSeek Integration
   
   The core component is the "AI Agent" node configured with LangChain’s capabilities and connected to DeepSeek AI. Specifically, it leverages:
   
   - deepseek-chat: For natural, conversational processing of user queries
   - deepseek-reasoner: For more analytical, reasoning-intensive tasks (optionally connected)
   
   The AI agent follows an elaborate system message with clearly defined instructions:
   
   - Strive to capture and store meaningful content from user input (e.g., preferences, goals).
   - Use saved memories to personalize replies.
   - Respond contextually without revealing memory operations to the user.
   - Reject fallback responses like jokes and instead keep the conversation meaningful.
   
   Step 5: Long-Term Memory Management
   
   When personal information is detected in the conversation, the AI agent triggers the "Save Long Term Memories" node using Google Docs Tool. It appends a new memory entry in a shared Google Doc, tagged with the current timestamp.
   
   Each message processed henceforth can optionally pull from this stored information using "Retrieve Long Term Memories," ensuring consistency and personalization.
   
   Step 6: Final Response
   
   The crafted response from the DeepSeek agent—now personalized and contextually aware—is sent back to the user using the "Telegram Response" node.
   
   What Makes This Setup Special?
   
   While many chatbots provide basic Q&A services, this setup takes things a step further by bringing persistent memory into the equation. Here’s what sets it apart:
   
   - Memory Windowing: Uses LangChain-style memory buffers to track ongoing dialogue and enhance session continuity.
   - Long-Term Storage: By leveraging Google Docs, memories can be safely stored, easily edited, and used as persistent context across sessions.
   - DeepSeek API: Unlike popular alternatives like OpenAI, DeepSeek offers a newer, powerful reasoning engine and chat model compatible with OpenAI’s ecosystem.
   - User-Specific Personalization: Only validated users can access the bot functions, adding a layer of security and personality to the agent’s replies.
   
   Integration Summary
   
   Here are the APIs and services involved in this workflow:
   
   1. Telegram Bot API – Initiates conversations, sends/receives messages.
   2. DeepSeek (deepseek-chat & deepseek-reasoner) – Powers the AI assistant with conversation and reasoning capabilities.
   3. Google Docs API – Acts as a memory vault for storing and retrieving user-specific data.
   
   Ideal Use Cases
   
   - Personal AI Assistants on Telegram
   - Habit Trackers with Memory Features
   - Coaches or Therapists Chatbots with Persistent Notes
   - Task Managers with Historical Context
   - Long-Term Customer Support AI
   
   Conclusion
   
   This n8n workflow showcases the power of modular automation combined with modern AI. By integrating real-time chat, intelligent reasoning, and memory persistence, we move one step closer to creating bots that truly know, remember, and serve their users better. Whether you're developing a personal AI assistant or creating a custom VIP chatbot, this architecture is a scalable, privacy-conscious, and powerful starting point.
   
   With just n8n, Telegram, DeepSeek, and Google Docs, you're well on your way to crafting a chatbot with a brain—and a memory.
   
   Want to build it yourself? Fork the workflow, get your API keys ready, and start automating!

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.