CITATION.cff

cff-version: 1.2.0
message: If you use this software, please cite it using the following information.
doi:
authors:
  - given-names: Zihao
    family-names: Wu
    orcid: https://orcid.org/0009-0003-2474-2092
    affiliation:
      - Fujian University of Technology
    email: vortez.wohl@gmail.com
    github: https://github.com/vortezwohl
title: Autono
version: 1.0.0
date-released: 2025-04-06
abstract: >
    This paper (project) proposes a highly robust autonomous agent framework based on the ReAct paradigm, designed to solve complex tasks through adaptive decision making and multi-agent collaboration. Unlike traditional frameworks that rely on fixed workflows generated by LLM-based planners, this framework dynamically generates next actions during agent execution based on prior trajectories, thereby enhancing its robustness. To address potential termination issues caused by adaptive execution paths, I propose a timely abandonment strategy incorporating a probabilistic penalty mechanism. For multi-agent collaboration, I introduce a memory transfer mechanism that enables shared and dynamically updated memory among agents. The framework's innovative timely abandonment strategy dynamically adjusts the probability of task abandonment via probabilistic penalties, allowing developers to balance conservative and exploratory tendencies in agent execution strategies by tuning hyperparameters. This significantly improves adaptability and task execution efficiency in complex environments. Additionally, agents can be extended through external tool integration, supported by modular design and MCP protocol compatibility, which enables flexible action space expansion. Through explicit division of labor, the multi-agent collaboration mechanism enables agents to focus on specific task components, thereby significantly improving execution efficiency and quality.
keywords:
  - ReAct
  - Robustness
  - Autonomous Agent
  - Multi-Agent
license: GPL-3.0
url: https://github.com/vortezwohl/Autono