Jan 16
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In this video, we analyze the architectural differences between the most prominent AI Agent frameworks in 2025. As we move from simple Large Language Models to complex Agentic Systems, choosing the right foundation is critical for production reliability. We discuss the following frameworks and concepts: The Core DNA of Agents: Why 80% of all frameworks consist of the same four pillars: LLM, Tools, Context, and Memory. LangGraph: Exploring "The State Machine" approach using graph theory (Nodes and Edges) for rigid, reliable workflows. CrewAI: Understanding the "Collaborative Crew" metaphor and how role-based delegation works for multi-agent squads. Microsoft AutoGen: A look at the production heavyweight that supports .NET and Python for enterprise-grade multi-agent conversations. OpenAI Agents SDK: A breakdown of "Handoff Logic" and how tool-based conversation passing works natively in the OpenAI ecosystem. The Decision Matrix: We conclude with a comparison matrix to help you choose the right tool based on your specific requirements: Rigid + Complex: LangGraph Rapid Squads: CrewAI Enterprise Infrastructure: AutoGen Native OpenAI: OpenAI SDK Timeline: 0:00 - Introduction to the Agent Landscape 1:05 - The Core DNA: LLM + Tools + Memory 2:15 - LangGraph vs. CrewAI: Graph Theory vs. Role-Based Roles 4:30 - Production Heavyweights: AutoGen & OpenAI SDK 6:10 - Handoff Logic Explained 7:45 - The Decision Matrix: Which framework should you choose? #AIAgents #LangGraph #CrewAI #AutoGen #OpenAI #SoftwareArchitecture #ArtificialIntelligence