This article provides a complete, practical walkthrough of the Telegram Splitout Export 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:
   Powering a Conversational AI with Long-Term Memory: An n8n Workflow for Telegram + Baserow Integration
   
   Meta Description:
   Discover how a no-code n8n workflow integrates Telegram, OpenAI, and Baserow to build an intelligent AI assistant that responds to voice, images, and text while saving notes and long-term memories for personalized, contextual conversations.
   
   Keywords:
   n8n AI assistant, Telegram bot, OpenAI GPT-4o, Baserow memory store, conversational AI, long-term memory AI, no-code automation, AI note-taking, Telegram voice assistant, personal AI workflow
   
   ---
   
   Article:
   
   Building an AI Assistant with Long-Term Memory Using n8n, Telegram, Baserow, and GPT-4o
   
   In the growing landscape of AI, users increasingly expect their digital assistants to not only carry out tasks but to remember context, store important information, and respond intelligently across formats—whether by voice, image, or text. Leveraging no-code automation platform n8n, a new workflow brings this vision to life by integrating OpenAI’s powerful GPT-4o, Telegram, and Baserow to create an all-in-one AI assistant.
   
   This article explores the architecture, tools, and use cases of this intelligent assistant, from capturing memories to analyzing photos—all within a Telegram chat interface.
   
   🧰 Overview of the Workflow
   
   The heart of the assistant is built in n8n, a powerful workflow automation tool. At a high level, the workflow performs the following:
   
   - Receives user input from Telegram (text, voice, or image)
   - Translates inputs into structured contextual data
   - Uses OpenAI’s GPT-4o for intelligent response generation
   - Stores "notes" or "memories" in Baserow (an open-source Airtable alternative)
   - Replies with natural, personalized responses in Telegram
   - Maintains contextual chat memory using PostgreSQL or window memory for more relevant conversations
   
   Let’s break this down by functionality.
   
   🎙️ Multimodal Input via Telegram
   
   The assistant starts with a webhook that listens for messages (text, images, or voice) from Telegram. Messages are smartly routed:
   
   - If it’s audio, it's transcribed into text using OpenAI’s Whisper model.
   - If it’s an image, it is converted, processed into base64, and analyzed using GPT-4o's image capabilities.
   - If it’s plain text, it continues directly to the AI agent module.
   
   This intelligent routing ensures the assistant understands your message regardless of the medium.
   
   🧠 AI Agent with Long-Term Memory
   
   The core intelligence is powered by OpenAI’s GPT-4o via the LangChain integration in n8n. What makes this AI unique is its memory policy:
   
   - “Notes” are individual pieces of important info (e.g., “Remind me to call mom on Friday.”)
   - “Memories” are patterns or preferences derived from longer-term conversations (e.g., “User prefers receiving updates in the morning.”)
   
   Unlike typical chatbots, this assistant evaluates whether a message contains personal or actionable content and autonomously saves it to Baserow without disrupting the user experience.
   
   🗃️ Baserow as a Memory Engine
   
   The memory and note data are stored in Baserow tables titled “Memory Table” and “Notes Table.” They each include fields for the memory or note content and a "Date Added" timestamp. These entries enable persistent storage and retrieval of user context over time.
   
   This design allows the language model to refer back to saved content for more intelligent responses. For instance, if a user asks, “What did I say about my doctor’s appointment?” the AI can check Baserow and answer accurately.
   
   ⚙️ LLM + Memory Integration via LangChain Agent
   
   The workflow uses LangChain’s advanced AI Agent node to combine tools and memory into a single, context-aware agent. It integrates:
   
   - PostgreSQL-based chat memory or memory buffer windows for short-term memory
   - Tool nodes for saving/retrieving from Baserow
   - OpenAI’s GPT-4o as the conversational model
   
   The AI agent receives a carefully constructed system prompt with detailed behavior instructions, including how to use memory, how to structure responses, and how to preserve privacy.
   
   Key behavioral expectations include:
   
   - Save memories or notes proactively, if relevant
   - Avoid revealing internal memory actions
   - Personalize responses using saved user data (like first name)
   - Respect user privacy (no saving passwords or sensitive data)
   
   ✅ Security and Validation
   
   Before processing, incoming Telegram messages are validated via a three-point check: first name, last name, and chat ID. Only approved users—configured in the workflow—are allowed to interact with the assistant. Unauthorized attempts receive a generic error message.
   
   📸 Image Processing with GPT-4o
   
   When a user sends an image with a caption, the assistant extracts the image using Telegram’s file API, converts it to base64, and analyzes it using GPT-4o. The caption is merged with image analysis output and fed into the AI agent for response.
   
   ✨ Use Cases
   
   This workflow unlocks a wide variety of use cases:
   
   - Note-taking over chat (“Note that my passport expires in July.”)
   - Long-term memory tracking (“I usually work out after 6 PM.”)
   - Audio journaling (Send a voice message, get a transcription and a smart reply)
   - Visual Q&A (Send an image for analysis)
   - Personal assistant interactions with context-aware responses
   
   📦 Third-Party APIs Used
   
   1. Telegram API – for handling messaging, voice, and image inputs.
   2. OpenAI API – for text and image processing (GPT-4o), and audio transcription (Whisper).
   3. Baserow API – for storing and retrieving memories and notes in a structured, relational database.
   4. PostgreSQL – for storing chat history in memory mode.
   5. LangChain (via n8n LangChain nodes) – for orchestrating tools, memory, and AI models.
   
   🔧 Extensibility
   
   The underlying structure allows adding more tools like calendar integrations, task managers, or even vector databases like Qdrant to enhance the assistant’s capabilities over time.
   
   —
   
   Conclusion
   
   By combining Telegram, OpenAI, Baserow, and n8n’s advanced automation capabilities, this workflow pioneers a new type of AI assistant—one that listens intelligently across modalities, remembers what matters, and interacts with emotional intelligence. Whether you're building a productivity bot, a journaling companion, or a personal life coach, this framework provides a robust foundation for human-like, memory-augmented AI interactions.
   
   Ready to build yours? Start with this workflow and customize it to reflect your unique vision for assistive intelligence.
   
   —
   
   Want to try this for yourself or collaborate on building advanced automated workflows? Let’s connect!

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.