The booming success of LLMs initiates rapid development in LLM agents. Though the foundation of an LLM agent is the generative model, it is critical to devise the optimal reasoning strategies and agent architectures. Accordingly, LLM agent research advances from the simple chain-of-thought prompting to more complex ReAct and Reflection reasoning strategy; agent architecture also evolves from single agent generation to multi-agent conversation, as well as multi-LLM multi-agent group chat. However, with the existing intricate frameworks and libraries, creating and evaluating new reasoning strategies and agent architectures has become a complex challenge, which hinders research investigation into LLM agents. Thus, we open-source a new AI agent library, AgentLite, which simplifies this process by offering a lightweight, user-friendly platform for innovating LLM agent reasoning, architectures, and applications with ease. AgentLite is a task-oriented framework designed to enhance the ability of agents to break down tasks and facilitate the development of multi-agent systems. Furthermore, we introduce multiple practical applications developed with AgentLite to demonstrate its convenience and flexibility. Get started now at: \url{https://github.com/SalesforceAIResearch/AgentLite}.

翻译：大语言模型（LLM）的蓬勃发展推动了LLM智能体的快速演进。尽管LLM智能体的核心在于生成式模型，但设计最优的推理策略与智能体架构同样至关重要。为此，LLM智能体研究从简单的思维链提示（chain-of-thought prompting）逐步发展为更复杂的ReAct与反思（Reflection）推理策略；智能体架构也从单智能体生成演进至多智能体对话，乃至多LLM多智能体群组聊天。然而，现有复杂框架和库使得新型推理策略与智能体架构的创建与评估成为一项艰巨挑战，这阻碍了LLM智能体的研究探索。为此，我们开源了新一代AI智能体库AgentLite，其通过轻量级、用户友好的平台简化了这一过程，助力用户轻松创新LLM智能体的推理、架构与应用。AgentLite作为面向任务的框架，旨在增强智能体分解任务的能力，并促进多智能体系统的开发。此外，我们还介绍了基于AgentLite开发的多个实用应用，以展示其便捷性与灵活性。立即开始使用：\url{https://github.com/SalesforceAIResearch/AgentLite}