A comprehensive analysis of the core architectural components and operational mechanisms of popular open-source AI agent frameworks, explaining how fundamental elements like Large Language Models (LLMs), memory systems, tool-use, and planning modules collaborate to achieve autonomous and intelligent behavior. It further examines leading open-source frameworks, including LangChain, LlamaIndex, AutoGen, and CrewAI, detailing their unique approaches to orchestrating complex AI workflows, emphasizing the pivotal role of Retrieval-Augmented Generation (RAG) pipelines and underlying vector databases, and addressing critical challenges and best practices for robust development

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