LLM-Agent-UMF：基於LLM的代理統一建模框架，用於實現多主動/被動核心代理的無縫集成。

LLM-Agent-UMF: LLM-based Agent Unified Modeling Framework for Seamless Integration of Multi Active/Passive Core-Agents

September 17, 2024

作者: Amine B. Hassouna, Hana Chaari, Ines Belhaj

cs.AI

摘要

基於LLM的代理人中整合工具克服了獨立LLM和傳統代理人有限能力的困難。然而，這些技術的結合以及在幾項最新工作中提出的增強方案，都採用了非統一的軟件架構，導致缺乏模塊化。事實上，它們主要專注於功能，忽略了代理人內部組件界限的定義。這導致研究人員之間術語和架構上的不明確性，我們通過提出一個統一框架來解決這些問題，從功能和軟件架構的角度為基於LLM的代理人的開發建立清晰基礎。 我們的框架，LLM-Agent-UMF（基於LLM的代理人統一建模框架），清楚區分了代理人的不同組件，將LLM和工具與一個新引入的元素核心代理人區分開來，核心代理人起著代理人的中央協調者的作用，包括五個模塊：規劃、記憶、檔案、行動和安全，後者在以往的工作中經常被忽略。核心代理人的內部結構差異導致我們將它們分類為被動和主動類型。基於此，我們提出了不同的多核心代理人架構，結合了各種個別代理人的獨特特徵。 為了評估目的，我們將此框架應用於一組最新代理人，從而展示其與它們的功能的一致性，並澄清被忽視的架構方面。此外，我們通過將不同的代理人集成到混合主動/被動核心代理人系統中，徹底評估了我們提出的四種架構。這種分析為潛在改進提供了清晰見解，並突出了結合特定代理人所涉及的挑戰。

English

The integration of tools in LLM-based agents overcame the difficulties of standalone LLMs and traditional agents' limited capabilities. However, the conjunction of these technologies and the proposed enhancements in several state-of-the-art works followed a non-unified software architecture resulting in a lack of modularity. Indeed, they focused mainly on functionalities and overlooked the definition of the component's boundaries within the agent. This caused terminological and architectural ambiguities between researchers which we addressed in this paper by proposing a unified framework that establishes a clear foundation for LLM-based agents' development from both functional and software architectural perspectives. Our framework, LLM-Agent-UMF (LLM-based Agent Unified Modeling Framework), clearly distinguishes between the different components of an agent, setting LLMs, and tools apart from a newly introduced element: the core-agent, playing the role of the central coordinator of the agent which comprises five modules: planning, memory, profile, action, and security, the latter often neglected in previous works. Differences in the internal structure of core-agents led us to classify them into a taxonomy of passive and active types. Based on this, we proposed different multi-core agent architectures combining unique characteristics of various individual agents. For evaluation purposes, we applied this framework to a selection of state-of-the-art agents, thereby demonstrating its alignment with their functionalities and clarifying the overlooked architectural aspects. Moreover, we thoroughly assessed four of our proposed architectures by integrating distinctive agents into hybrid active/passive core-agents' systems. This analysis provided clear insights into potential improvements and highlighted the challenges involved in the combination of specific agents.