Open-source agentic frameworks are gaining traction in AI development by facilitating the creation of dynamic, autonomous systems capable of perceiving, reasoning, and acting independently. The article examines various frameworks, including LangGraph, SmolAgents by HuggingFace, CrewAI, PhiData, and Composio, comparing their architecture, use cases, customization options, and performance. These frameworks are pivotal in enabling multi-agent systems that can break complex tasks into manageable subtasks, fostering collaboration among specialized agents. Open-source solutions are highlighted as key drivers of innovation, allowing developers to customize and extend frameworks while benefiting from community-driven advancements. LangGraph is noted for its graph-based architecture, ideal for structured workflows, while SmolAgents offer lightweight, task-specific solutions. CrewAI excels in collaborative multi-agent systems, PhiData focuses on data-centric applications, and Composio supports complex multi-step operations. Emerging trends such as hierarchical planning and human-in-the-loop systems are shaping the future of agentic frameworks, which are increasingly integrated with technologies like edge computing and retrieval-augmented generation to enhance adaptability and responsiveness.