The rapid advancement of Large Language Models (LLMs) and autonomous agent architectures has opened a new frontier in AI-driven software engineering. Despite these capabilities, the end-to-end automation of software development remains largely unrealized, owing to the absence of unified, role-structured orchestration frameworks. This survey examines Open AGI, a multi-agent automation framework that organizes specialized LLM-based agents—each representing a distinct engineering role such as Product Manager, Architect, Developer, and Quality Analyst—into a cohesive development pipeline governed by Standard Operating Procedures (SOPs).

Through a systematic review of related frameworks including AutoGPT, LangChain, Microsoft AutoGen, and MetaGPT, this paper identifies critical limitations in existing approaches and articulates the specific contributions of Open AGI in addressing them. Core technical dimensions examined include the role of LLMs as cognitive engines, Retrieval-Augmented Generation (RAG) for dynamic knowledge grounding, multimodal integration for enriched agent perception, and DevOps-compatible deployment capabilities. The paper further delineates open research challenges in persistent memory management, ethical orchestration, inter-agent benchmarking, and scalable multi-agent communication, outlining a structured roadmap for future work in cooperative AI systems for software engineering.

Keywords : Open AGI, Multi-Agent Systems, Artificial Intelligence, Software Automation, Natural Language Programming, Large Language Models (LLMs), SOP-Driven Development.