Driven by curiosity, humans have continually sought to explore and understand
the world around them, leading to the invention of various tools to satiate
this inquisitiveness. Despite not having the capacity to process and memorize
vast amounts of information in their brains, humans excel in critical thinking,
planning, reflection, and harnessing available tools to interact with and
interpret the world, enabling them to find answers efficiently. The recent
advancements in large language models (LLMs) suggest that machines might also
possess the aforementioned human-like capabilities, allowing them to exhibit
powerful abilities even with a constrained parameter count. In this paper, we
introduce KwaiAgents, a generalized information-seeking agent system based on
LLMs. Within KwaiAgents, we propose an agent system that employs LLMs as its
cognitive core, which is capable of understanding a user's query, behavior
guidelines, and referencing external documents. The agent can also update and
retrieve information from its internal memory, plan and execute actions using a
time-aware search-browse toolkit, and ultimately provide a comprehensive
response. We further investigate the system's performance when powered by LLMs
less advanced than GPT-4, and introduce the Meta-Agent Tuning (MAT) framework,
designed to ensure even an open-sourced 7B or 13B model performs well among
many agent systems. We exploit both benchmark and human evaluations to
systematically validate these capabilities. Extensive experiments show the
superiority of our agent system compared to other autonomous agents and
highlight the enhanced generalized agent-abilities of our fine-tuned LLMs.

我们引入了基于大型语言模型的信息获取代理系统 KwaiAgents，探索了其性能，并介绍了 Meta-Agent Tuning (MAT) 框架以确保在不同代理系统中优化的开源模型的表现。