Enhancing Text-Only Social Media Interaction with LangChain and Tavily: A Semantic Tagging and AI Reply System

ABSTRACT- This study presents an intelligent system for creating, tagging, and interacting with text-only social media posts using LangChain's large language models (LLMs) and Tavily search integration. The system enables users to compose textual posts that are automatically analyzed to generate relevant context-aware tags through semantic understanding and real-time data. LangChain LLM processes the content to understand user intent and topics, whereas Tavily provides a live external context to refine and recommend accurate hashtags or tags. The system also facilitates comment-and-reply generation using LangChain, allowing automatic initiation or assistance of natural and coherent discussions. Comments and replies maintain contextual continuity, emotional tone, and relevance to the main post. This solution demonstrates how modern language models can enhance user-generated content platforms by supporting richer interactions, discoverability, and content organization, without human moderation. This approach has practical applications in intelligent publishing platforms, content moderation systems, and knowledge sharing communities. The paper is organized into sections presenting the main findings, with references to specific sections indicated by the section numbers. Abbreviations and acronyms were defined at their first occurrence. The online version of the volume will be available on LNCS Online and is accessible to subscribing members. Keywords: LangChain, Tavily Search, LLM, Post-tagging, AI Commenting, AI Reply suggestions