Middleware for LLMs: Tools Are Instrumental for Language Agents in
  Complex Environments
Yu Gu Yiheng Shu Hao Yu Xiao Liu Yuxiao Dong Jie Tang Jayanth Srinivasa Hugo Latapie Yu Su
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 a
middleware layer 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
achieves 2.8X the performance of the best baseline in tasks requiring access to
database content and 2.2X in KB tasks. Our findings illuminate the path for
advancing language agents in complex real-world applications.