The applications of large language models (LLMs)  have expanded well beyond the confines of text processing, signaling a new era where LLMs are envisioned as generalist language agents capable of operating within complex real-world environments.
These environments are often highly expansive, making it impossible for the LLM to process them within its short-term memory.
Motivated by recent research on extending the capabilities of LLMs with tools, this paper investigates the intriguing potential of tools to augment LLMs in handling such complexity.
To this end, we design customized tools to aid in the proactive exploration within these massive environments.
Such tools can serve as amiddlewarelayer shielding the LLM from environmental complexity.
In two representative complex environments—knowledge bases (KBs)  and databases—we demonstrate the significant potential of augmenting language agents with tools in complex environments.
Notably, equipped with these tools, GPT-4 achieves2.8the performance of the best baseline in tasks requiring access to database content and2.2in KB tasks.
Our findings illuminate the path for advancing language agents in complex real-world applications.111Our code and data will be released atOSU_NLP/Fuxi.