【評判】LangChain & LangGraph AI Engineer: Agents, Agentic RAG & MCP

Build AI agents with the latest LangChain and LangGraph, from your first tool-calling agent to multi-step agentic systems
Build an agent loop from scratch to understand exactly how AI agents work under the hood
Build RAG and Agentic RAG systems with embeddings and vector databases (Pinecone, FAISS, Chroma)
Design Reflection, Reflexion and multi-step agents as LangGraph graphs with nodes, edges and state
Create your own MCP server and client with LangChain and connect them to Cursor and Claude
Build a Deep Agent that plans and uses Skills and middleware to handle complex tasks
Explain the AI engineering fundamentals behind agents: tokens, context windows, embeddings and function calling
Debug agents by tracing every LLM and tool call in LangSmith
Apply the technical forward deployed engineer (FDE) toolkit to ship agents into real customer environments

This course contains the use of artificial intelligence :)

2026- COURSE WAS RE-RECORDED and supports- LangChain Version 1.2+ Jev

**Ideal students are software developers / data scientists / AI/ML Engineers**

Welcome to the AI Engineering with LangChain and LangGraph course.

In this course you will learn how to design and build AI agents and agentic AI systems using LangChain and LangGraph, the most powerful frameworks for developing modern LLM applications.

Agentic AI Engineering focuses on building AI systems that can reason, plan, use tools, and autonomously complete tasks. With LangChain and LangGraph, you will build AI agents, RAG systems, and advanced LLM applications and understand the challenges of deploying such to production.

What is LangChain?
LangChain is an open-source development framework designed to simplify creating applications powered by large language models (LLMs).

Using LangChain, LangGraph, MCP, and modern LLM frameworks, you will build production-ready AI agents, multi-agent systems, and advanced RAG applications.

Please note that this is not a course for beginners. This course assumes that you have a background in software engineering and are proficient in Python. I will be using Pycharm IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts .

You will build real-world Agentic AI systems using LangChain and LangGraph:

• Search Agent

• Documentation Helper – A chatbot over Python package docs (and any data you choose), using advanced retrieval and RAG.

• Prompt Engineering Theory

• Context Engineering Theory

• Introduction to LangGraph

• Model Context Protocol (MCP)

• Deep Agents

Agentic AI Engineering Topics Covered:

Agentic AI Fundamentals

• AI Agents

• Agentic AI architectures

• Multi-agent systems

• AI engineering principles

LLM and Prompt Engineering

• Prompt Engineering

• Few-Shot Prompting

• Chain of Thought

• ReAct prompting

• Context Engineering

Agent Frameworks

• LangChain

• LangGraph

• Model Context Protocol (MCP)

• Skills

• Tool Calling

AI Agent Infrastructure

• Vector databases (Pinecone, FAISS, Chroma)

• Retrieval Augmented Generation (RAG)

• Memory systems

• LangSmith tracing

System One Decision Models (Jev by TypeSafe AI)

• What is Jev?

• Noul, Score, Choice

• Running Locally Decision Models

Throughout the course, you will work on hands-on exercises and real-world projects to reinforce your understanding of the concepts and techniques covered. By the end of the course, you will be proficient in using LangChain to create powerful, efficient, and versatile LLM applications for a wide array of usages.

Why This Course?

• Up-to-date: Covers LangChain V.1+ and the latest LangGraph ecosystem.

• Practical: Real projects, real APIs, real-world skills.

• Career-boosting: Stay ahead in the LLM and GenAI job market.

• Step-by-step guidance: Clear, concise, no wasted time.

• Flexible: Use any Python IDE (Pycharm shown, but not required).

Targeting a Forward Deployed Engineer (FDE) role? This course covers the technical FDE toolkit that AI labs and enterprise AI teams hire for:

• RAG and Agentic RAG over customer data → RAG Foundations, Documentation Assistant, Agentic RAG

• Tool calling, structured outputs and agent workflows → Search Agent, ReAct from scratch, LangGraph

• Integrating agents with customer systems → MCP servers & clients

• Production, observability and security → AI Applications in Production, LangSmith tracing, Agent Security

• Agent harness engineering → Deep Agents, Skills, Middleware & Harness Engineering

This course is ideal for developers who want to learn Agentic AI Engineering, AI agents with Python, and LLM application development.

You will learn how to design agent architectures, implement tool-using agents, and build scalable agentic AI systems using LangChain and LangGraph.

DISCLAIMERS

1. Please note that this is not a course for beginners. This course assumes that you have a background in software engineering and are proficient in Python.
   I will be using Pycharm IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts.