Comparative Analysis of Open-Source Frameworks for Agentic AI Systems: Capabilities, Design Philosophies, and Development Experiences
Jianzheng Shi, David Kuo Chuen Lee, Weibiao Xu, Yue Wang · World Scientific Annual Review of Fintech · 2024
This study presents a comprehensive comparative analysis of eight prominent open-source frameworks designed to facilitate the development of agentic AI systems: LangGraph, Agno, SmolAgents, Mastra, PydanticAI, Atomic Agents, CrewAI, and AutoGen. As the domain of agentic AI evolves at a remarkable pace, developers face increasing challenges in selecting frameworks that best align with their project requirements. Through a detailed examination of architectural designs, abstraction strategies, multi-agent capabilities, and developer experiences, this study elucidates the distinct design philosophies that underpin these frameworks. A fundamental tension emerges between systems that prioritize agent autonomy and those emphasizing structured engineering control, each carrying profound implications for system reliability, debugging complexity, and development efficiency. The insights provided herein contribute to a deeper understanding of the current landscape of agentic AI development tools, offering practical guidance for framework selection based on project demands and developer expertise.