Year: 2025
Type: article
Abstract: This paper provides an in-depth review of the latest AI agent frameworks, focusing on the comparison of their features, architectures, and use cases. We examine well-known frameworks such as LangGraph, CrewAI, OpenAI Swarm, AutoGen, and IBM Watsonx.Ai, highlighting their strengths, weaknesses, and applicability to various domains. Additionally, we categorize the frameworks based on their specific use cases, including general-purpose agents, enterprise solutions, and open-source frameworks. The paper emphasizes the importance of selecting the appropriate framework to build autonomous AI systems and offers insights into future trends and challenges in AI agent development. By analyzing quantitative metrics such as latency, throughput, and scalability, we provide a data-driven evaluation of the frameworks’ performance. Furthermore, we explore the implications of these advancements in real-world applications, including their impact on financial markets, risk management, and enterprise automation. This review serves as a comprehensive guide for developers and researchers seeking to understand the evolving landscape of AI agent frameworks and their potential for future innovation. Since this is very niche and rapidly evolving field with scarcity of journal papers and this work uses white-paper and model documents to organize this literature review for peer reviewed literature creation. Most of the developments discussed in this work is less than 12 months old. This paper provides a comprehensive review of AI agent frameworks by categorizing literature based on publication year, category, and field.
Author: Satyadhar Joshi
Institution: Bank of America
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4407670622