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SocialContentForge: Multi-Agent AI System for Automated Influencer and Brand Content Creation
Subject area: Science,Engineering and Technology · Area of research: Agentic Artificial Intelligence
DOI: https://doi.org/10.64388/IREV9I11-1717762
Abstract
SocialContentForge is a multiagent AI framework that enables influencers and brands to create and publish content more effectively and efficiently across all social media platforms. It solves many of the problems related to creating consistent, high-quality content for multiple channels, while significantly reducing the amount of time it takes to produce that content. SocialContentForge uses sophisticated Generative Artificial In-telligence technologies like Google’s Gemini and a locally run Stable Diffusion V1-5 model for Image Generation in conjunction with OAuth-based connections to popular social media sites such as Instagram, Facebook, LinkedIn, and Twitter/X. Agents within SocialContentForge work together to deliver the content to a given platform and monitor engagement data for that post. It was reported by users that this multi-agent system has reduced the amount of time it takes to generate content by 70-75%. The framework also contains several user assistance features including Global AI chatbot, Customizable brand voice, Platform-specific tone controls, Image Editing Modules and Content moderation which automatically removes content that is potentially inappropriate. Real-time analytics capture engage-ment metrics such as likes, comments, shares, and reach via interactive dashboards to provide users with quick access to their data. The MERN Stack was used to develop SocialContentForge, while images were generated in a hybrid execution model using PyTorch and CUDA, and were integrated through FastAPI and Cloudflare Workers to provide optimal performance, multi-user control, and adherence to ethical content generation practices.
Keywords
Content Generation, Generative AI, Multi-Agent AI Systems, Performance Analytics, Social Media Automation.
References
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How to cite this paper
@article{1717762,
author = {Miral Gopani, Naman Rathod, Rohit Parmar, Sakshi Patel, Prof. Sudhir Dhekane},
title = {SocialContentForge: Multi-Agent AI System for Automated Influencer and Brand Content Creation},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {2804-2817},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717762.pdf},
abstract = {SocialContentForge is a multiagent AI framework that enables influencers and brands to create and publish content more effectively and efficiently across all social media platforms. It solves many of the problems related to creating consistent, high-quality content for multiple channels, while significantly reducing the amount of time it takes to produce that content. SocialContentForge uses sophisticated Generative Artificial In-telligence technologies like Google’s Gemini and a locally run Stable Diffusion V1-5 model for Image Generation in conjunction with OAuth-based connections to popular social media sites such as Instagram, Facebook, LinkedIn, and Twitter/X. Agents within SocialContentForge work together to deliver the content to a given platform and monitor engagement data for that post. It was reported by users that this multi-agent system has reduced the amount of time it takes to generate content by 70-75%. The framework also contains several user assistance features including Global AI chatbot, Customizable brand voice, Platform-specific tone controls, Image Editing Modules and Content moderation which automatically removes content that is potentially inappropriate. Real-time analytics capture engage-ment metrics such as likes, comments, shares, and reach via interactive dashboards to provide users with quick access to their data. The MERN Stack was used to develop SocialContentForge, while images were generated in a hybrid execution model using PyTorch and CUDA, and were integrated through FastAPI and Cloudflare Workers to provide optimal performance, multi-user control, and adherence to ethical content generation practices.},
keywords = {Content Generation, Generative AI, Multi-Agent AI Systems, Performance Analytics, Social Media Automation.},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717762}
}