MetaChain：一种用于LLM代理的全自动零代码框架

MetaChain: A Fully-Automated and Zero-Code Framework for LLM Agents

February 9, 2025

作者: Jiabin Tang, Tianyu Fan, Chao Huang

cs.AI

摘要

大型语言模型（LLM）代理展示了在任务自动化和智能决策方面的显著能力，推动了诸如LangChain和AutoGen等代理开发框架的广泛采用。然而，这些框架主要为具有广泛技术专业知识的开发人员提供服务 - 这是一个重要的限制，考虑到全球人口中仅有0.03％拥有必要的编程技能。这种明显的可访问性差距引发了一个基本问题：我们能否让每个人，无论技术背景如何，仅使用自然语言来构建自己的LLM代理？为了解决这一挑战，我们介绍了MetaChain - 一个完全自动化且高度自我发展的框架，使用户能够仅通过自然语言创建和部署LLM代理。作为一个自主代理操作系统，MetaChain包括四个关键组件：i）代理系统实用程序，ii）LLM驱动的可操作引擎，iii）自管理文件系统和iv）自我玩耍代理定制模块。这个轻量而强大的系统使得工具、代理和工作流的高效动态创建和修改成为可能，无需编码要求或手动干预。除了无代码代理开发能力外，MetaChain还作为通用人工智能助手的多功能代理系统。对GAIA基准测试的全面评估显示MetaChain在通用多代理任务中的有效性，超越了现有的最先进方法。此外，MetaChain的检索增强生成（RAG）相关能力相对于许多其他基于LLM的解决方案表现出持续优越的性能。

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 MetaChain-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, MetaChain 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, MetaChain also serves as a versatile multi-agent system for General AI Assistants. Comprehensive evaluations on the GAIA benchmark demonstrate MetaChain's effectiveness in generalist multi-agent tasks, surpassing existing state-of-the-art methods. Furthermore, MetaChain's Retrieval-Augmented Generation (RAG)-related capabilities have shown consistently superior performance compared to many alternative LLM-based solutions.