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Скачать или смотреть LangGraph Advanced – Build Multi Agent AI Systems with Supervisor Architecture

  • GrabDuck!
  • 2025-08-19
  • 2171
LangGraph Advanced – Build Multi Agent AI Systems with Supervisor Architecture
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Описание к видео LangGraph Advanced – Build Multi Agent AI Systems with Supervisor Architecture

In this video, we dive into LangGraph Advanced – Build Multi-Agent AI Systems with Supervisor Architecture, showing how to split one overcomplicated agent into multiple smaller AI agents coordinated by a supervisor. You’ll see how a research agent and a trading agent can work together under supervision to handle a real-world request like “invest $1,000 in AI,” with clear responsibilities and cleaner flows.

This lesson is part of the Building AI Agents with LangGraph: A Beginner’s Guide | Agentic AI Course and explores key ideas like prebuilt agents, tools, ReAct architecture, supervisor orchestration, and autonomous AI agents. Whether you’re just learning how to create, build, or make AI agents—or want a deeper langgraph tutorial covering langchain agents and generic agent architecture—this step-by-step walkthrough makes it simple and practical.

I get it—tech content takes time to research, code, and polish. If my videos have helped you save time, avoid headaches, or just made debugging less painful, a coffee would be highly appreciated! https://buymeacoffee.com/grabduck

00:00 – Intro & lesson goals
01:12 – From single “fat” agent to complexity problems
06:42 – Why we need Supervisor architecture
07:57 – Splitting into Research and Trading agents
09:16 – Building and testing the Supervisor

🌟 Related Courses & Tutorials

• LangGraph Introduction Series – Learn how to build AI agents with LangGraph in this step‑by‑step course. From basic graphs to memory, human‑in‑the‑loop (HITL), streaming, FastAPI, and React integration — this playlist covers everything you need to create autonomous AI agents and real‑world applications.

   • LangGraph Intro  

• LangSmith Introduction Series – Master AI app monitoring and debugging with LangSmith. This tutorial series covers tracing, run types, and step‑by‑step methods for understanding, testing, and improving LLM and LangChain applications. Perfect for developers learning how to monitor AI apps and build more reliable AI agents.

   • LangSmith Intro  

💡 Explore More:
Check out the GitHub repo with all the code we used in this session: https://github.com/esurovtsev/langgra...

#aidevelopment #aiintegration #aiprogramming

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Hi, I’m Evgeny, a freelance software engineer passionate about solving complex problems with technologies like Java, Kotlin, Spring Boot, TypeScript, Python, and Node.js. On this channel, you’ll find content centered around AI, search technologies, and security, with in-depth coding sessions and theoretical discussions to enhance your knowledge. Join me as we explore the latest innovations in backend development and beyond.

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