Authors : Amrutha Sindhu Thota; Singamsetti Syamanth Uma Sai Kiran; Garimella Vasantha Surya Prasad; Paidy Deepak; G Venkata Lakshmi; Mudunuri Sai Surya Narayana Raju; Nuthakki Abhinash

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/2m4rkw6c

Scribd : https://tinyurl.com/2dhprnj6

DOI : https://doi.org/10.38124/ijisrt/25dec1212

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The Social Media Manager Agent is an AI powered system that automates end-to-end social content creation and publishing to enhance the efficiency, consistency, and scalability of digital presence for individuals and businesses. Inspired by data-driven automation in other quality-assessment domains, the agent unifies caption generation, hashtag curation, and image synthesis, followed by seamless publishing to Instagram via the Meta Graph API. The backend, implemented in Flask, orchestrates Perplexity for concise, on-brief captions and platform-aware hashtags, Stability AI’s diffusion models for 1024×1024 creative imagery, and a media pipeline that compresses, persists, and serves assets with stable public URLs for ingestion. The system exposes a simple HTTP interface for a React frontend, providing rapid, non-destructive content generation suitable for iterative creative workflows. The methodology includes prompt engineering for topic-to caption conversion, robust parsing and normalization of model outputs, and standardized media preparation (1080×1080 JPEG) to meet platform constraints. Error-tolerant flows handle variability in LLM responses and external API failures, while publish operations use creation_id–based media containers for reliable posting. Empirical validation across diverse topics demonstrates low-latency generation, consistent adherence to caption length and formatting limits, and dependable publish success when credentials and permissions are correctly configured. The results suggest that AI-driven pipelines can significantly reduce manual effort, improve posting cadence, and elevate content quality through repeatable, scalable automation. This work contributes a practical reference architecture for AI-assisted social media operations, emphasizing unified orchestration of text, image, and publishing services with clear operational safeguards. Future directions include multi-variant generation and ranking for A/B testing, scheduling and analytics feedback loops via platform insights, brandvoice conditioning, content safety filters, and extensions to additional networks such as Facebook and LinkedIn.

Keywords : Social Media Automation, Large Language Models, Diffusion Models, Caption Generation, Hashtag Curation, Instagram Publishing, Flask Backend, React Frontend

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