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LangGraph vs Autogen vs CrewAI, which to choose? I've given a breakdown of which is best for you. 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 (𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀) LangGraph is ideal for 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗹𝗲𝘅, 𝗺𝘂𝗹𝘁𝗶-𝘀𝘁𝗲𝗽 𝗔𝗜 𝘄𝗼𝗿𝗸𝗳𝗹𝗼

2 ways AI systems today generate smarter answers. I’ve explained both in simple steps below. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) (𝘴𝘵𝘦𝘱-𝘣𝘺-𝘴𝘵𝘦𝘱) RAG lets AI fetch and use real-time external information to ge

Simple explanation of Traditional RAG vs Agentic RAG vs MCP. 1. 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) • 𝗦𝘁𝗲𝗽 1: 𝗨𝘀𝗲𝗿 𝗮𝘀𝗸𝘀 𝗮 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻. Example: “𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘤𝘢𝘱𝘪�

If you’re building AI agents today, here’s the reality: Calling an LLM isn’t enough anymore. Modern agents need a full system, a framework of interconnected components that help them think, reason, act, adapt, and collaborate autonomously. Here are

3 ways how most AI systems are built. I’ve explained each one step-by-step. 1) 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 (𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽) 1. 𝗦𝗲𝘁 𝘁𝗮𝘀𝗸 – Decide what problem the model should solve. 2. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗱𝗮𝘁𝗮 – Gather lots of example

9 Steps to Build AI Agents from Scratch. I've given a simple step by step explanation. 𝗦𝘁𝗲𝗽 1: 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗠𝗶𝘀𝘀𝗶𝗼𝗻 & 𝗥𝗼𝗹𝗲 • Decide what problem the agent will solve. • Figure out who will use it. • Plan how users will interact

Steps to building Agentic AI systems from scratch. I've given a simple detailed explanation below. 𝗦𝘁𝗲𝗽 1 – 𝗚𝗣𝗨/𝗖𝗣𝗨 𝗣𝗿𝗼𝘃𝗶𝗱𝗲𝗿 (Compute Layer) • This is the engine that powers all AI computations. • You rent computing power to run y

2 main frameworks powering today’s AI workflows. I’ve explained both in simple steps below. 𝗡𝟴𝗡 (𝗟𝗶𝗻𝗲𝗮𝗿 𝗔𝗴𝗲𝗻𝘁 𝗙𝗹𝗼𝘄) (𝘴𝘵𝘦𝘱-𝘣𝘺-𝘴𝘵𝘦𝘱) N8N lets AI follow a 𝘀𝘁𝗿𝗮𝗶𝗴𝗵𝘁, 𝘃𝗶𝘀𝘂𝗮𝗹 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄, moving step-by-step