  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}.
