零代码生成大模型智能体，自然语言就能造工具和流程。
AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents
- 用自然语言指令自动生成、管理智能体和工作流，无需编程。
- 在GAIA基准上表现超越现有方法，多智能体任务能力更强。
- 适合非技术人员快速构建自动化助手，降低智能体使用门槛。
大语言模型智能体在任务自动化和智能决策方面表现出色，推动了LangChain和AutoGen等框架的广泛应用。然而，这些框架主要面向具备深厚技术背景的开发者——全球仅有0.03%的人具备编程能力，这一显著的可及性差距引发关键问题：能否让任何人仅通过自然语言就创建自己的大模型智能体？为此，我们提出AutoAgent——一个全自动化、高度自演化、零代码的框架，使用户仅凭自然语言即可创建并部署大模型智能体。作为自主的智能体操作系统，AutoAgent包含四个核心组件：智能体系统工具、基于大模型的动作引擎、自管理文件系统和自博弈定制模块。该轻量但强大的系统可在无需编码或人工干预的情况下，高效动态地创建与修改工具、智能体和工作流。除了零代码开发能力，AutoAgent还可作为通用人工智能助手的多智能体系统。在GAIA基准上的全面评估表明，其在通用多智能体任务中表现优异，超越现有最先进方法。此外，其检索增强生成（RAG）相关能力也持续优于众多基于大模型的解决方案。
原文摘要 · Abstract (English)
Large Language Model (LLM) Agents have demonstrated remarkable capabilities in task automation and intelligent decision-making, driving the widespread adoption of agent development frameworks such as LangChain and AutoGen. However, these frameworks predominantly serve developers with extensive technical expertise - a significant limitation considering that only 0.03 % of the global population possesses the necessary programming skills. This stark accessibility gap raises a fundamental question: Can we enable everyone, regardless of technical background, to build their own LLM agents using natural language alone? To address this challenge, we introduce AutoAgent-a Fully-Automated and highly Self-Developing framework that enables users to create and deploy LLM agents through Natural Language Alone. Operating as an autonomous Agent Operating System, AutoAgent comprises four key components: i) Agentic System Utilities, ii) LLM-powered Actionable Engine, iii) Self-Managing File System, and iv) Self-Play Agent Customization module. This lightweight yet powerful system enables efficient and dynamic creation and modification of tools, agents, and workflows without coding requirements or manual intervention. Beyond its code-free agent development capabilities, AutoAgent also serves as a versatile multi-agent system for General AI Assistants. Comprehensive evaluations on the GAIA benchmark demonstrate AutoAgent's effectiveness in generalist multi-agent tasks, surpassing existing state-of-the-art methods. Furthermore, AutoAgent's Retrieval-Augmented Generation (RAG)-related capabilities have shown consistently superior performance compared to many alternative LLM-based solutions.
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