**AutoFlow: Automated Workflow Generation for Large Language Model Agents** **Authors:** Zelong Li **Abstract:** Recent advancements in Large Language Models (LLMs) have significantly improved their ability to understand complex natural language. One key application is LLM-based AI agents, which leverage LLMs and external tools to solve intricate tasks. To ensure these agents follow effective and reliable procedures, manually designed workflows are typically used. However, this process is time-consuming and requires domain knowledge, making large-scale deployment challenging. To address this, AutoFlow is proposed as a framework for automatically generating workflows for agents to solve complex tasks. AutoFlow represents workflows in natural language programs and employs a workflow optimization procedure to iteratively improve workflow quality. The framework offers two methods: fine-tuning-based and in-context-based, suitable for both open-source and closed-source LLMs. Experimental results show that AutoFlow can produce robust and reliable workflows, outperforming manually designed ones in terms of performance and readability. The automatic generation and interpretation of workflows in natural language represent a promising approach for solving complex tasks, especially with the rapid development of LLMs. **Introduction:** The paper introduces AutoFlow, a framework that automatically generates workflows for AI agents to solve complex tasks. It proposes two methods—fine-tuning and in-context learning—to incorporate reinforcement learning in the workflow generation process. The framework uses natural language programs to represent workflows, making them easier to understand and interact with. Experimental results validate the effectiveness of AutoFlow, showing improved performance and reliability compared to manually designed workflows. **Related Work:** The paper reviews related work in LLM agents and workflow design, including reasoning, planning, and coding tasks. It also discusses Automated Machine Learning (AutoML) techniques, which aim to reduce human labor in designing and deploying machine learning techniques. **Preliminary and Background:** The paper introduces the CoRE language, which uses natural language to construct workflows, and explains how LLMs can interpret and execute these workflows. The motivation for AutoFlow is to automate the workflow generation process, reducing human effort and domain expertise. **The AutoFlow Framework:** The framework includes two methods for workflow generation: fine-tuning for open-source LLMs and in-context learning for closed-source LLMs. Both methods use reinforcement learning to optimize the generated workflows based on performance metrics. **Experiments:** Experiments are conducted on both closed-source (GPT-4) and open-source (Mixtral-8x7B) LLMs using the OpenAGI benchmark. Results show that AutoFlow significantly improves performance compared to manually designed workflows, with over 40% improvement in some cases. **Conclusions and Future Work:** The paper concludes by discussing the effectiveness of AutoFlow and suggesting areas for future improvement, such as evaluating different learning methods and exploring alternative learning paradigms.**AutoFlow: Automated Workflow Generation for Large Language Model Agents** **Authors:** Zelong Li **Abstract:** Recent advancements in Large Language Models (LLMs) have significantly improved their ability to understand complex natural language. One key application is LLM-based AI agents, which leverage LLMs and external tools to solve intricate tasks. To ensure these agents follow effective and reliable procedures, manually designed workflows are typically used. However, this process is time-consuming and requires domain knowledge, making large-scale deployment challenging. To address this, AutoFlow is proposed as a framework for automatically generating workflows for agents to solve complex tasks. AutoFlow represents workflows in natural language programs and employs a workflow optimization procedure to iteratively improve workflow quality. The framework offers two methods: fine-tuning-based and in-context-based, suitable for both open-source and closed-source LLMs. Experimental results show that AutoFlow can produce robust and reliable workflows, outperforming manually designed ones in terms of performance and readability. The automatic generation and interpretation of workflows in natural language represent a promising approach for solving complex tasks, especially with the rapid development of LLMs. **Introduction:** The paper introduces AutoFlow, a framework that automatically generates workflows for AI agents to solve complex tasks. It proposes two methods—fine-tuning and in-context learning—to incorporate reinforcement learning in the workflow generation process. The framework uses natural language programs to represent workflows, making them easier to understand and interact with. Experimental results validate the effectiveness of AutoFlow, showing improved performance and reliability compared to manually designed workflows. **Related Work:** The paper reviews related work in LLM agents and workflow design, including reasoning, planning, and coding tasks. It also discusses Automated Machine Learning (AutoML) techniques, which aim to reduce human labor in designing and deploying machine learning techniques. **Preliminary and Background:** The paper introduces the CoRE language, which uses natural language to construct workflows, and explains how LLMs can interpret and execute these workflows. The motivation for AutoFlow is to automate the workflow generation process, reducing human effort and domain expertise. **The AutoFlow Framework:** The framework includes two methods for workflow generation: fine-tuning for open-source LLMs and in-context learning for closed-source LLMs. Both methods use reinforcement learning to optimize the generated workflows based on performance metrics. **Experiments:** Experiments are conducted on both closed-source (GPT-4) and open-source (Mixtral-8x7B) LLMs using the OpenAGI benchmark. Results show that AutoFlow significantly improves performance compared to manually designed workflows, with over 40% improvement in some cases. **Conclusions and Future Work:** The paper concludes by discussing the effectiveness of AutoFlow and suggesting areas for future improvement, such as evaluating different learning methods and exploring alternative learning paradigms.