When working with Agentic AI, selecting the right framework is crucial. Each one brings different strengths depending on your project needs — from modular agent designs to large-scale enterprise security. Here's a structured breakdown: ➔ 𝗔𝗗𝗞 (𝗚𝗼𝗼𝗴𝗹𝗲) • Features: Flexible, modular framework for AI agents with Gemini support • Advantages: Rich tool ecosystem, flexible orchestration • Applications: Conversational AI, complex autonomous systems ➔ 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 • Features: Stateful workflows, graph-based execution, human-in-the-loop • Advantages: Dynamic workflows, complex stateful AI, enhanced traceability • Applications: Interactive storytelling, decision-making systems ➔ 𝗖𝗿𝗲𝘄𝗔𝗜 • Features: Role-based agents, dynamic task planning, conflict resolution • Advantages: Scalable teams, collaborative AI, decision optimization • Applications: Project simulations, business strategy, healthcare coordination ➔ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗞𝗲𝗿𝗻𝗲𝗹 • Features: AI SDK integration, security, memory & embeddings • Advantages: Enterprise-grade security, scalable architecture • Applications: Enterprise apps, workflow automation ➔ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗔𝘂𝘁𝗼𝗚𝗲𝗻 • Features: Multi-agent conversations, context management, custom roles • Advantages: Simplifies multi-agent orchestration, robust error handling • Applications: Advanced chatbots, task planning, AI research ➔ 𝗦𝗺𝗼𝗹𝗔𝗴𝗲𝗻𝘁𝘀 • Features: Lightweight, modular multi-agent framework • Advantages: Low-compute overhead, seamless integration • Applications: Research assistants, data analysis, AI workflows ➔ 𝗔𝘂𝘁𝗼𝗚𝗣𝗧 • Features: Goal-oriented task execution, adaptive learning • Advantages: Self-improving, scalable, minimal human intervention • Applications: Content creation, task automation, predictive analysis Choosing the right Agentic AI framework is less about the "most powerful" and more about 𝗺𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸’𝘀 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁'𝘀 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆, 𝘀𝗰𝗮𝗹𝗲, 𝗮𝗻𝗱 𝗴𝗼𝗮𝗹𝘀. → Which one have you used or are excited to try? → Did I miss any emerging frameworks that deserve attention? | 40 comments on LinkedIn
