title: MetaChain: A Fully-Automated and Zero-Code Framework for LLM Agents
summary: 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.
paper_authors._id: 67aaecec114e64d6e15e7f41
paper_authors.name: Jiabin Tang
paper_authors._id: 67aaecec114e64d6e15e7f42
paper_authors.name: Tianyu Fan
paper_authors._id: 67aaecec114e64d6e15e7f43
paper_authors.name: Chao Huang
paper_title: MetaChain: A Fully-Automated and Zero-Code Framework for LLM Agents
paper_summary: 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.
paper_ai_summary: MetaChain, a fully-automated natural language-based framework, enables non-technical users to create and deploy LLM agents efficiently, demonstrating superior performance on multi-agent tasks and retrieval-augmented generation.
paper_ai_keywords: LLM Agents task automation intelligent decision-making LangChain AutoGen agent development frameworks Natural Language Alone Agentic System Utilities LLM-powered Actionable Engine Self-Managing File System Self-Play Agent Customization module Multi-Agent System General AI Assistants GAIA benchmark Retrieval-Augmented Generation (RAG)