  Recent advancements in Large Language Models (LLMs) have shown significant
progress in understanding complex natural language. One important application
of LLM is LLM-based AI Agent, which leverages the ability of LLM as well as
external tools for complex-task solving. To make sure LLM Agents follow an
effective and reliable procedure to solve the given task, manually designed
workflows are usually used to guide the working mechanism of agents. However,
manually designing the workflows requires considerable efforts and domain
knowledge, making it difficult to develop and deploy agents on massive scales.
To address these issues, we propose AutoFlow, a framework designed to
automatically generate workflows for agents to solve complex tasks. AutoFlow
takes natural language program as the format of agent workflow and employs a
workflow optimization procedure to iteratively optimize the workflow quality.
Besides, this work offers two workflow generation methods: fine-tuning-based
and in-context-based methods, making the AutoFlow framework applicable to both
open-source and closed-source LLMs. Experimental results show that our
framework can produce robust and reliable agent workflows. We believe that the
automatic generation and interpretation of workflows in natural language
represent a promising paradigm for solving complex tasks, particularly with the
rapid development of LLMs. The source code of this work is available at
https://github.com/agiresearch/AutoFlow.
