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AI Agent Development

AI Agent Development Using LangGraph (feat. MCP)

LangGraph, packed with a major corporation's AI Agent lead's know-how. We deliver knowledge gained from real-world challenges.

(4.9) 124 reviews

1,604 learners

  • jasonkang
ai활용
에이전트
prompt engineering
LLM
AI Agent
LangGraph
Model Context Protocol

Reviews from Early Learners

What you will gain after the course

  • LLM Agent

  • LLM

  • Prompt Engineering

  • Retrieval Augmented Generation(RAG)

  • AI Agent

LLM Agent Development!
Easier and More Powerful with LangGraph



LLM agents play a key role in understanding user requirements, automating complex tasks, and solving problems. However, the process of designing and implementing agents is structurally complex and involves many repetitive tasks, making it challenging. LangGraph simplifies this process, helping you efficiently develop powerful LLM agents.


Theory is compact
Debugging and optimization are like real combat

Only the essentials from extensive official documentation!

The official LangGraph documentation is extensive, but the necessary information is limited. I've prepared a curriculum focused on key concepts directly selected based on the experience of working engineers.

Exactly as used in the field!

We show you the prompt writing and debugging process exactly as it is, without editing. Through the course, you can experience how actual engineers solve errors and optimize prompts.

I recommend this for people like this

Developers with LangChain experience

If you've experienced the limitations of LangChain, this course can give you wings for agent development

Developers Curious About LLM Agents

A working professional explains the Agentic AI mentioned by NVIDIA's Jensen Huang at CES 2025

Tech entrepreneurs and startup teams

If you want to develop AI-based products or services, you can learn the latest agent development technologies.

After taking the course

  • Understanding the Differences Between LangGraph and LangChain: By grasping the structural differences and usage methods of these two frameworks, you can select the most suitable tool for your project.

  • Agent Design and Implementation: You can design various agents such as Retrieval agents, Self-RAG, Corrective RAG, and automate workflows.

  • Complex Workflow Configuration: You can design workflows that efficiently handle complex tasks by utilizing Multi-Agent systems and RouteLLM.

  • Tool Utilization Skills: You can leverage various tools within LangGraph to extend the agent's functionality and enhance problem-solving capabilities.


You'll learn this kind of content

1⃣ Prompt Engineering Strategies

Even when performing the same function, you need to write prompts differently depending on the model you use. Learn how to efficiently write prompts suitable for different situations using LangGraph's PromptTemplate and
ChatPromptTemplate.

2⃣ LLM Agent Optimization Tips

Instead of using expensive premium models like gpt-4o, it's more efficient to break tasks into smaller units and repeatedly utilize lightweight models like gpt-4o-mini. Learn how to optimize cost and performance by dividing prompts into smaller units.

3⃣ Everything About Using LangGraph Tools

You'll learn how to use LangChain's basic tools, as well as how to extend functionality by developing custom tools that agents can directly utilize as needed. Additionally, you can implement more reliable agents by designing human-in-the-loop systems where humans intervene in the workflow.

The person who created this lecture

  • (Former) GS Group GenAI Platform Development and Operations

  • (Former) Series C Medical AI Startup Tech Lead

  • (Former) Hanghae Plus AI Course Coach

GS Group Hackathon coaching and the know-how gained from developing/operating various real-world projects.

Do you have any questions?

Q. What are the differences between LangChain and LangGraph?

LangChain primarily connects tasks in a chain format, while LangGraph uses a graph structure to compose more complex workflows. LangGraph supports various agent tasks through flexible node connections.

Q. I'm new to LangChain - is it okay to take this course?

If you have experience using Python, there shouldn't be any problem taking the course, but if you're not familiar with LangChain syntax, it might be difficult to understand.

If you're new to LangChain, I recommend the instructor's beginner course

Q. What should I do if there are parts I don't understand while taking the course?

If you have any questions while taking the course, please feel free to post them as Inflearn questions anytime! I'll respond as quickly as possible,
and if necessary, I'll update the course through additional filming.

Pre-enrollment Reference Information

Practice Environment

  • Operating System and Version (OS): MacOS

    • You can follow the course regardless of your operating system, whether it's Windows, Linux, etc., as long as you have an environment that can run Python.

  • Tools Used:

    • All live coding is conducted in a Notebook environment.

    • There's no particular editor I recommend, but in the lectures I use Cursor

Learning Materials

  • The source code for the Notebooks used in the lectures is provided as a GitHub Repository

    • The lecture videos include supplementary explanations through "comments" and "Markdown" that are not present in the videos.

  • We provide Notion pages for theoretical explanations

Prerequisites and Important Notes

  • Essential Knowledge: Python

  • Selected Knowledge: LangChain

    • This course is an intermediate-level course targeting those who have experience with LangChain.

Recommended for
these people

Who is this course right for?

  • Developer interested in LLM

  • Developer deploying/operating LLM Applications

  • Developer who wants to advance LLM Applications

Need to know before starting?

  • Python

Hello
This is

14,501

Learners

949

Reviews

446

Answers

4.9

Rating

9

Courses

Curriculum

All

29 lectures ∙ (6hr 20min)

Course Materials:

Published: 
Last updated: 

Reviews

All

124 reviews

4.9

124 reviews

  • johnsonmoshy69777님의 프로필 이미지
    johnsonmoshy69777

    Reviews 1

    Average Rating 5.0

    5

    100% enrolled

    실습 위주의 훌륭한 강의입니다! LangGraph, MCP, RAG 같은 복잡한 내용을 쉽게 설명해 주셔서 이해하기 쉬웠고, 실무에 바로 적용할 수 있었습니다. 강사님의 설명이 정말 명확하고 유익했어요. 추천드립니다!

    • jasonkang
      Instructor

      친절한 말씀 정말 감사합니다! 강의가 도움이 되었다니 기쁘네요. 저는 직장에서 다른 엔지니어들에게 LangGraph를 사용하고 실제 프로젝트에서 AI 에이전트를 구축하는 방법을 가르치고 있는데, 그런 실무 경험이 자연스럽게 이 강의에도 반영된 것 같습니다. 그런 실무 배경이 개념을 더 접근하기 쉽고 적용 가능하게 만드는 데 도움이 되었다니 정말 좋네요.

  • qkenr1321559님의 프로필 이미지
    qkenr1321559

    Reviews 6

    Average Rating 5.0

    Edited

    5

    100% enrolled

    I received so much help from the Langchain basics to the RAG lectures, so I also took the LangGraph course. They explain the content that can be used directly in the field with high quality and in a very detailed manner. You mentioned in the lecture that you have plans to publish a book, and I would appreciate it if you could mention it in a community or something when it is published. I am interested in purchasing it.

    • jasonkang
      Instructor

      Wow, that's a really touching review! 🥹🙏 I prepared my lecture so that it can be applied directly to practical work, so I feel very rewarded to receive such good feedback. Thank you so much for being with me from LangChain to LangGraph! I'm also working hard to prepare for the publication of the book, and I'll be sure to let you know in the community! The fact that you are interested in this gives me great strength. I will continue to repay you with helpful lectures and content. Thank you again for leaving such a sincere review!

  • wjswnsdud3541933님의 프로필 이미지
    wjswnsdud3541933

    Reviews 2

    Average Rating 5.0

    5

    32% enrolled

    • bjorkii6169님의 프로필 이미지
      bjorkii6169

      Reviews 4

      Average Rating 5.0

      5

      32% enrolled

      • anne5041님의 프로필 이미지
        anne5041

        Reviews 1

        Average Rating 5.0

        5

        32% enrolled

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