Agentic AI Frameworks Compared: LangGraph vs. CrewAI vs. AutoGen vs. Semantic Kernel скачать в хорошем качестве

Agentic AI Frameworks Compared: LangGraph vs. CrewAI vs. AutoGen vs. Semantic Kernel

In this deep dive into the 2026 AI landscape, we break down the most powerful Agentic AI Frameworks to help you choose the right stack for your next project. As autonomous agents move from simple chatbots to complex multi-agent "crews," understanding the architectural differences between LangGraph, CrewAI, AutoGen, and Microsoft’s Semantic Kernel is essential for any developer or enterprise leader. We explore how each framework handles state management, planning, and tool integration to turn raw LLMs into goal-driven autonomous systems. We analyze the "Role-Based" philosophy of CrewAI, which excels at collaborative team-based tasks, and compare it to the "Graph-Based" control of LangGraph, which offers unmatched precision for complex, cyclic workflows. Additionally, we look at Microsoft’s AutoGen for its event-driven, distributed multi-agent capabilities and Semantic Kernel as the production-grade middleware for integrating AI into existing C#, Java, and Python enterprise ecosystems. Whether you are building a research agent, a software development crew, or a real-time data analyst, this comparison highlights the trade-offs in scalability, learning curve, and governance. By the end of this video, you will have a clear roadmap for selecting a framework based on your specific needs—ranging from rapid prototyping with no-code tools like Dify to building high-velocity data streams using Apache Kafka and LangChain. We also provide a look into the future of Agentic AI, including the rise of domain-specific frameworks and the shift toward standardized agent communication protocols. Don't forget to check the timestamps below to jump to the specific framework you're most interested in!