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AI Agent Development

LangChain and MCP AI Agent Master Class

The AI Agent Master Class with LangChain and MCP is a practical course that allows you to systematically learn LangChain and MCP (Model Context Protocol) for developing the latest AI services. Going beyond simple theoretical explanations, it encompasses LCEL, memory, RAG, Agent, MCP, and Streamlit projects, structured to enable you to implement a complete chatbot project.

(5.0) 1 reviews

3 learners

Level Basic

Course period 3 months

  • forestsoft
streamlit
streamlit
LangChain
LangChain
openAI API
openAI API
AI Agent
AI Agent
Model Context Protocol
Model Context Protocol
streamlit
streamlit
LangChain
LangChain
openAI API
openAI API
AI Agent
AI Agent
Model Context Protocol
Model Context Protocol

What you will gain after the course

  • Creating Chatbot Services That Can Be Applied in Practice

  • Understanding and Applying LangChain and MCP

Building Practical AI Agents with LangChain x MCP

ChatGPT-initiated LLM-based services have now moved beyond simple question-answering chatbots to movies<her>'s Samantha and Jarvis from <the avengers>, they are evolving into AI agents (artificial intelligence assistants). In this massive wave of change, domestic companies are also actively adopting AI agent technology. Samsung Electronics is developing and applying the AI coding assistant <strong>code.i</strong> to enhance the productivity of in-house developers, while Meritz Fire & Marine Insurance is introducing the Agent-based development platform <strong>WebSquare AI</strong> to significantly reduce development time.</the></her>

As we transition from AI to AI Agents, companies are now facing the challenge of moving beyond the Proof of Concept (PoC) stage of simply being able to build LLM-based services, to implementing services that can create actual business value (PoV). To achieve this, they need to be able to connect existing systems with LLMs in a flexible and stable manner, and in this process, the role of developers who can utilize RAG, VectorDB, Fine-tuning, and LangChain technology stack is becoming increasingly important.

This course guides you through implementing AI agents based on virtual corporate data using LangChain and MCP, adapting to these changes. Starting from the basics of LangChain syntax, you'll learn concepts like RAG, VectorDB, Agent, and MCP, and based on this foundation, you'll be able to create a product recommendation agent service based on actual corporate data.

If you can write basic code in Python and make API requests, you'll be able to follow along without difficulty. Let's start this journey together to seize powerful opportunities in the upcoming AI Agent era.

Features of this course

📌RAG Implementation, MCP Integration, Agent Utilization Rather than abstract concepts, we show you exactly the methods the instructor has personally encountered and organized through hands-on experience.

📌LangChain chain design, memory structure, VectorDB utilization provides insights that can be immediately applied to real projects.

📌 30% Theory · 70% Practice We directly execute all code and verify one by one whether it actually works.

📌Developer-targeted course You don't necessarily need to be a Python expert, but you should have experience with basic API calls or function writing using Python.

📌Complete Project-Based Build a wine recommendation chatbot (Vino Savoir) from start to finish and complete the entire flow of AI Agent construction.

I recommend this for people like this

For those unfamiliar with using Agent & MCP

A practitioner who wants to work with agents but doesn't know how to code them

I'm interested in implementing RAG.
Retrieval-Augmented Generation?
For those curious about how to actually integrate it and create a service

Let's create an AI agent.
I've tried calling the ChatGPT API... but I want to create an AI service connected to company data

After taking the course

  • You can directly create AI agents using LangChain and MCP. Now, instead of simply calling APIs, you can implement smart chatbots and services connected to your company data.

  • You won't be afraid of implementing RAG either. By following these 5 steps one by one - Load → Split → Embed → Store → Retrieval - you can properly master the method of enhancing answers by searching external data.

  • You'll become familiar with agent and MCP integration. From tool selection to execution, you can experience how to connect and extend with actual code.

  • You'll complete a Streamlit-based chatbot project. Rather than simple examples, you'll create practical chatbots like wine recommendation systems, allowing you to build a portfolio with tangible results that you can proudly say "I completed this with my own hands."

  • You'll develop a service-level sense. Rather than just simple CRUD operations, you'll experience data search, external API integration, and AI response optimization, gaining the confidence to apply these skills directly in real-world work.

You'll learn content like this.


Understanding the Overall Structure of LangChain

You can understand the concept of how questions input to LLMs are processed and lead to answers.

AI Chatbot Practice

You can create your own AI Chatbot directly through hands-on projects.

FORESTSOFT, who created this course, is

Pre-enrollment Reference Information

Practice Environment

  • This was written based on Python 3.12 or higher.

  • The hands-on practice will be conducted in both Google Colab and local PC environments. Google Colab allows you to follow along with the practice using a free Google account without any separate subscription.

  • You need to obtain an OpenAI API key for the hands-on practice. The method for obtaining an OpenAI API key will be explained in the lecture.

Learning Materials

  • PDF (E-book) Materials Provided

Prerequisites and Important Notes

  • Someone who knows basic Python syntax

  • It would be good if you have experience with API calls.

  • Lecture materials and code are available for personal learning use by enrolled students only.

Recommended for
these people

Who is this course right for?

  • Someone who wants to create an AI Agent

  • People who want to apply AI Agents to practical work

Need to know before starting?

  • Python Basics

  • Programming Fundamentals

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Learners

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Reviews

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Answers

4.6

Rating

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Courses

We provide education based on the belief that education is the hope for the future.

[Lecture History] Hana Financial Group - DT University: Improving SW Development Productivity with ChatGPT KB Kookmin Bank - Improving Work Productivity Using ChatGPT The Korea Teachers Pension - ChatGPT Utili

[Lecture History]

Hana Financial Group - DT University: Improving SW Development Productivity with ChatGPT

KB Kookmin Bank - Improving Work Productivity Using ChatGPT

Teachers Pension Service - Business Automation using ChatGPT

Seodaemun 50+ Center - ChatGPT and Generative AI Application Guide

Dongguk University - Basic Theories and Trends in ChatGPT and Generative AI

Dankook University High School Affiliated with Software - Dankook SW Universe Forum: Building ChatGPT and Generative AI Capabilities

[Clients]

Multicampus, Hana Financial Group, Samsung Software Academy for Youth (SSAFY), Dongguk University, KB Kookmin Bank, Korea Productivity Center, and many others

Curriculum

All

21 lectures ∙ (8hr 49min)

Published: 
Last updated: 

Reviews

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1 reviews

5.0

1 reviews

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