MetaChain：一個完全自動化且無程式碼的LLM智能體框架

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

大型語言模型（LLM）代理展示了在任務自動化和智能決策方面的卓越能力，推動了代理開發框架（如LangChain和AutoGen）的廣泛應用。然而，這些框架主要面向具有豐富技術專業知識的開發人員，這是一個重要限制，考慮到全球僅有0.03％的人口具備必要的編程技能。這種明顯的可及性差距引發了一個基本問題：我們是否可以讓每個人，無論其技術背景如何，僅使用自然語言就能構建自己的LLM代理？為應對這一挑戰，我們介紹了MetaChain-一個完全自動化且高度自我發展的框架，使用戶能夠僅通過自然語言創建和部署LLM代理。作為一個自治代理操作系統運行，MetaChain包括四個關鍵組件：i）代理系統工具，ii）LLM驅動的可操作引擎，iii）自管理文件系統，和iv）自我遊玩代理定制模塊。這個輕量而強大的系統實現了工具、代理和工作流的高效動態創建和修改，無需編碼要求或手動干預。除了無代碼代理開發功能外，MetaChain還作為通用人工智能助手的多功能代理系統。對GAIA基準的全面評估顯示了MetaChain在通用多代理任務中的有效性，超越了現有的最先進方法。此外，MetaChain的檢索增強生成（RAG）相關功能相對於許多其他基於LLM的解決方案表現出一貫優越的性能。

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.