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

• URL: http://arxiv.org/abs/2502.05957v2
• Date: Tue, 18 Feb 2025 06:23:25 GMT
• Title: AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents
• Authors: Jiabin Tang, Tianyu Fan, Chao Huang,
• Abstract summary: Large Language Model (LLM) Agents have demonstrated remarkable capabilities in task automation and intelligent decision-making.<n>These frameworks predominantly serve developers with extensive technical expertise.<n>Only 0.03 % of the global population possesses the necessary programming skills.
• Score: 4.57755315319748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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