Automated Telegram Chat Summarization with AI and MongoDB Memory

Overview

This workflow automates the process of summarizing Telegram chat messages using advanced AI models and persistent memory storage. It leverages n8n's Telepilot nodes to monitor chat activity, process messages, and generate concise summaries.

Key Features

• Real-Time Triggers: Initiates on-demand or scheduled via manual and schedule triggers.
• Message Processing: Splits and aggregates incoming Telegram messages for efficient handling.
• AI Summarization: Utilizes LangChain's OpenRouter Chat Model to generate intelligent summaries.
• Contextual Memory: Stores chat context in MongoDB, enabling more accurate and context-aware summaries.
• Time-Based Filtering: Filters messages from the last two hours to ensure relevance.

Benefits

• Saves Time: Automates the tedious task of reading and summarizing chat threads.
• Enhances Productivity: Delivers actionable insights from chat data quickly.
• Improves Collaboration: Keeps teams informed with concise updates.

Use Cases

• Team Communication: Summarize group discussions for daily standups.
• Customer Support: Extract key points from support chats for reporting.
• Project Management: Keep stakeholders updated with chat highlights.

Integrations & Automation

• Telegram: For chat message retrieval.
• LangChain & OpenRouter: For AI-powered summarization.
• MongoDB: For persistent chat memory.

This workflow streamlines chat analysis, providing timely, AI-driven insights for better decision-making.