With the rapid development of large model technology, the application of agent technology in various fields is becoming increasingly widespread, profoundly changing people's work and lifestyles. In complex and dynamic systems, multi-agents achieve complex tasks that are difficult for a single agent to complete through division of labor and collaboration among agents. This paper discusses the integrated application of LangGraph and CrewAI. LangGraph improves the efficiency of information transmission through graph architecture, while CrewAI enhances team collaboration capabilities and system performance through intelligent task allocation and resource management. The main research contents of this paper are: (1) designing the architecture of agents based on LangGraph for precise control; (2) enhancing the capabilities of agents based on CrewAI to complete a variety of tasks. This study aims to delve into the application of LangGraph and CrewAI in multi-agent systems, providing new perspectives for the future development of agent technology, and promoting technological progress and application innovation in the field of large model intelligent agents.

翻译：随着大模型技术的飞速发展，智能体技术在各个领域的应用日益广泛，深刻改变着人们的工作和生活方式。在复杂动态系统中，多智能体通过智能体间的分工协作，实现单个智能体难以完成的复杂任务。本文探讨了LangGraph与CrewAI的集成应用。LangGraph通过图架构提升信息传递效率，而CrewAI则通过智能任务分配与资源管理增强团队协作能力与系统性能。本文主要研究内容包括：（1）基于LangGraph设计智能体架构以实现精准控制；（2）基于CrewAI增强智能体能力以完成多样化任务。本研究旨在深入探索LangGraph与CrewAI在多智能体系统中的应用，为智能体技术的未来发展提供新视角，推动大模型智能体领域的技术进步与应用创新。