  Large Language Models (LLMs) currently struggle with tool invocation and
chaining, as they often hallucinate or miss essential steps in a sequence. We
propose RE-GAINS and EnChAnT, two novel frameworks that empower LLMs to tackle
complex user queries by making API calls to external tools based on tool
descriptions and argument lists. Tools are chained based on the expected
output, without receiving the actual results from each individual call.
EnChAnT, an open-source solution, leverages an LLM format enforcer, OpenChat
3.5 (an LLM), and ToolBench's API Retriever. RE-GAINS utilizes OpenAI models
and embeddings with a specialized prompt based on the $\underline{R}$easoning
vi$\underline{a}$ $\underline{P}$lanning $(RAP)$ framework. Both frameworks are
low cost (0.01\$ per query). Our key contribution is enabling LLMs for tool
invocation and chaining using modifiable, externally described tools.
