Open_Source_LLM_Agents_Automated_Research_Knowledge_Discovery

This article presents a conceptual review of open-source large language model (LLM) agents for automated research and knowledge discovery. It examines agent architectures, planning, tool use, retrieval-augmented generation, multi-agent collaboration, and automated scientific discovery. It proposes a modular research workflow incorporating scholarly retrieval, computational analysis, evidence synthesis, and human validation. The article also discusses applications, evaluation criteria, reproducibility, citation integrity, security, and responsible deployment. A workflow diagram and comparative tables summarize representative approaches and recommended evaluation dimensions. The manuscript is intended to support further research into transparent, reproducible, and human-supervised research automation.