The text discusses using large language models (LLMs) in applications by leveraging multi-agent frameworks, which simulate a team of agents that collaborate to accomplish tasks. These frameworks can be used when the LLM's behavior is not sufficient on its own and additional tools need to be integrated into the application. The key benefits of multi-agent frameworks include increased flexibility, better handling of complex tasks, and improved collaboration among agents. However, each framework has its pros and cons, such as complexity, customizability, and dependency on specific technologies. The text highlights four popular multi-agent frameworks: AutoGen, MetaGPT, CrewAI, and LangGraph, each with its strengths and weaknesses, and emphasizes the importance of evaluating these frameworks to ensure proper use in production environments.