AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls
Yu Du Fangyun Wei Hongyang Zhang
We introduce AnyTool, a large language model agent designed to revolutionize
the utilization of a vast array of tools in addressing user queries. We utilize
over 16,000 APIs from Rapid API, operating under the assumption that a subset
of these APIs could potentially resolve the queries. AnyTool primarily
incorporates three elements: an API retriever with a hierarchical structure, a
solver aimed at resolving user queries using a selected set of API candidates,
and a self-reflection mechanism, which re-activates AnyTool if the initial
solution proves impracticable. AnyTool is powered by the function calling
feature of GPT-4, eliminating the need for training external modules. We also
revisit the evaluation protocol introduced by previous works and identify a
limitation in this protocol that leads to an artificially high pass rate. By
revising the evaluation protocol to better reflect practical application
scenarios, we introduce an additional benchmark, termed AnyToolBench.
Experiments across various datasets demonstrate the superiority of our AnyTool
over strong baselines such as ToolLLM and a GPT-4 variant tailored for tool
utilization. For instance, AnyTool outperforms ToolLLM by +35.4% in terms of
average pass rate on ToolBench. Code will be available at
https://github.com/dyabel/AnyTool.