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Agents and workflows

AI Agents with LangChain and LangGraph

Build Python-based AI agents with LangChain and LangGraph, exploring tools, RAG and agent workflows.

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Course topics

Agents and workflows

  • ReAct agents
  • RAG
  • LangGraph

You inspect the result

Find your fit

Is this a useful starting point?

For software developers familiar with Python, Git, testing and debugging; this is not a beginner course.

Starting point
Intermediate
Course length
19 total hours
Lessons
179 lectures

Course by Eden Marco

Course last updated: 04/2026

Requirements
  • This is not a beginner course. Basic software engineering concepts are needed
  • I assume students will be familiar software engineering subjects such as: git, python, pipenv, environment variables, classes, testing and debugging
  • No Machine Learning experience is needed.
What is included
  • Role Play
  • 19 hours on-demand video
  • 5 articles
  • 1 coding exercise
  • Access on mobile
  • Certificate of completion

DoJo membership: A$250 per month per seat, excluding GST. Includes fortnightly group support and a private member community.

Explore the lessons

Course curriculum.

28 sections • 179 lectures • 19h 8m total length

Introduction5 lectures • 13min
  1. Course Introduction02:43
  2. Course Objectives05:00
  3. Course Structure + How to get the best of Udemy [PLEASE DO NOT SKIP]02:53
  4. Course's Community02:43
  5. Course Resources00:07
The GIST of LangChain- Get started by with your "Hello World" chain9 lectures • 56min
  1. What is LangChain? LangChain Under 6 Minutes06:00
  2. What are we building? LangChain Hello World Chain01:04
  3. Project Setup15:05
  4. LangChain Fundamentals: Prompt Templates, ChatModels, and Chains05:55
  5. Building a LangChain Chain to Summarize Text11:12
  6. Debugging and Tracing Our LangChain Chain01:41
  7. Using Local Open-Weights Models with LangChain and Ollama06:20
  8. Integrating LangSmith for LangChain Application Tracing07:18
  9. LangChain LCEL Fundamentals Quiz 5 questions
  10. LangChain Model Switching: Groq API Integration 1 question
  11. Semantic Versioning in LangChain01:43
THE GIST Of AI Agents9 lectures • 57min
  1. What are AI Agents? A High-Level Overview05:10
  2. What are we building? AI Job Search Agent04:24
  3. The Evolution of LangChain ReAct Agents03:21
  4. Setting Up the Environment for a LangChain Search Agent06:30
  5. Creating Your First LangChain Agent: Tools and LLMs08:03
  6. From Query to Answer: How a LangChain Agent Thinks07:08
  7. Integrating Real-World Search with Tavily and LangChain Tools09:20
  8. Structured Output with LangChain Agents Using Pydantic09:34
  9. [THEORY] Predictable Agent Responses with LangChain Structured Output03:15
Agents Under The Hood (1/4)4 lectures • 18min
  1. Introduction to The Core Architecture of AI Agents: A Deep Dive03:55
  2. What are we building? An E-Commerce Agent01:29
  3. [Theory] The Gist of ReACT07:02
  4. Setup05:45
-------- [Layer 1] The ReAct Loop -------- (2/4)4 lectures • 30min
  1. Writing Tools09:02
  2. Tool Binding and Defensive Prompting05:28
  3. Understanding the ReAct Agent Loop in Langchain11:26
  4. Model Switch03:46
  5. Quiz: AI Agent Loop with LangChain Tool Calling 7 questions
-------- [Layer 2] Raw Function Calling -------- (3/4)3 lectures • 19min
  1. -------- [Layer 2] Manual JSON Schemas vs. LangChain Tool Abstraction --------08:55
  2. Building a ReAct Agent Loop with the Raw Ollama SDK07:41
  3. Recap01:55
  4. Quiz: Raw Function Calling — AI Agent Without LangChain 7 questions
----- [Layer 3] The ReAct Prompt: The Foundation of Function Calling ----- (4/4)5 lectures • 43min
  1. What are we building? Function Calling. Yes we are building Function Calling.05:28
  2. Generating Dynamic Tool Descriptions in Python07:00
  3. Understanding the ReAct Prompt: Building AI Agents Without Function Calling08:05
  4. Implementing Manual Tool Calling for LLMs06:43
  5. Agent Loop With ReAct Prompt15:26
  6. Agents Interview
Function Calling2 lectures • 9min
  1. Intro02:04
  2. [Theory] Understanding Function Calling for LLMs07:04
The GIST of RAG- Embeddings, Vector Databases and, & Retrieval9 lectures • 1hr 45min
  1. Introduction to Retrieval Augmentation Generation (RAG)07:46
  2. Introduction to RAG Implementation13:58
  3. Medium Analyzer- Boilerplate Project Setup12:46
  4. Medium Analyzer- Class Review: TextLoader,TextSplitter,OpenAIEmbeddings,Pinecone09:00
  5. Medium Analyzer- Ingestion Implementation15:01
  6. RECAP01:38
  7. Medium Analyzer- Naive Retrieval Implementation Implementation17:53
  8. Medium Analyzer- 2 Step RAG16:58
  9. LangChain RAG Documentation09:48
  10. RAG Implementation with Vector Stores Quiz 7 questions
Building a documentation assistant (Embeddings, VectorDBs, Retrieval, Memory)16 lectures • 2hr 13min
  1. What are we building? A lightweight Cursor/ Feature Feature (RAG)02:12
  2. Quick Note: Pipenv vs uv00:26
  3. Environment Setup05:33
  4. Ingestion Pipeline Intro02:18
  5. Imports13:08
  6. Tavily Crawling12:27
  7. [Optional] TavilyMap, TavilyExtract for High customizability12:19
  8. [Optional] Crawling Deep Dive16:48
  9. Recap01:06
  10. Chunking (Text Splitting)04:37
  11. Batch Indexing11:28
  12. Retrieval Agent Implementation12:45
  13. Run, Debug, Trace RAG Agent08:21
  14. "Frontend" with Streamlit (UI)19:24
  15. Documentation Helper In Production06:28
  16. RAG Architecture04:04
Prompt Engineering Theory9 lectures • 58min
  1. The GIST of LLMs03:52
  2. What is a Prompt? Composition of a formal prompt02:55
  3. Zero Shot Prompting02:42
  4. Few Shot Prompting08:26
  5. Chain of Thought Prompting08:33
  6. ReAct Prompting07:18
  7. Prompt Engineering Quick Tips08:59
  8. Context Engineering05:20
  9. Context Engineering a System Prompt10:20
Let's Talk About LLM Applications In Production9 lectures • 1hr
  1. LLM Applications in Production08:33
  2. LLM Application Development landscape04:01
  3. LLMs in Production: Privacy & Data Retention08:41
  4. Generative UI/UX featuring CopilotKit04:51
  5. Official LangChain Academy Courses02:22
  6. Open Source LLMs VS Managed LLM Providers (Deepseek)09:05
  7. Confidence in AI Results By Assaf Elovic & Harrison Chase05:00
  8. [NEW] AI FOMO is the New Normal11:09
  9. Finished course? Whats next!06:00
-------------------Introduction To LangGraph -------------------13 lectures • 1hr 23min
  1. What is LangGraph?04:56
  2. Why LangGraph? LangGraph VS LangChain16:42
  3. What are Graphs?03:36
  4. LangGraph & Flow Engineering06:03
  5. LangGraph Core Components05:17
  6. --------- [Hands On] Implementing ReAct AgentExecutor with LangGraph ---------02:20
  7. Quick Note: poetry vs uv00:48
  8. [Hands On] Get Started: Setting Up Your ReAct Agent Project Environment05:09
  9. [Hands On] Coding the Agent's Brain: Implementing the ReAct Runnable08:28
  10. [Hands On] 43. Building Blocks: Defining Your Agent's Nodes in LangGraph05:52
  11. [Hands On] Bringing Your ReAct Agent to Life: Connecting Nodes into a Graph07:21
  12. [Hands On] Running Our LangGraph React Agent with Function Calling07:45
  13. [IMPORTANT] Building Modern LLM Agents: From History to LangGraph v1.008:49
Reflection Agent5 lectures • 33min
  1. What are we building? A Reflection Agent01:26
  2. Project Setup04:44
  3. Creating the Reflector Chain and the Tweet Reviosr Chain04:48
  4. Defining our LangGraph Graph19:17
  5. LangSmith Tracing03:09
Reflexion Agent8 lectures • 58min
  1. What are we building? A Reflexion Agent06:20
  2. Project Setup03:42
  3. Section Resources00:47
  4. Actor Agent V216:40
  5. Revisor Agent04:17
  6. ToolNode - Executing Tools07:10
  7. Building Our LangGraph Graph11:54
  8. Tracing Our Graph07:22
Agentic RAG14 lectures • 1hr 26min
  1. What are Building In this Section- Agentic RAG Architecture02:39
  2. Improving RAG Quality with the Corrective RAG Flow01:24
  3. Boilerplate Setup for an Agentic RAG Agent with LangGraph04:33
  4. Code Structure07:25
  5. LangChain Vector Store Ingestion Pipeline (WebLoader, ChromaDB)06:54
  6. Managing Information Flow in LangGraph: The GraphState02:08
  7. Fetching Context for LLMs: The LangGraph Retrieve Node02:04
  8. Building a Relevance Filter for RAG using LangChain's Structured Output12:53
  9. Implementing a Web Search Node in LangGraph using Tavily API04:53
  10. Creating the LLM Generation Chain and Node for LangGraph05:04
  11. Building and Running the Complete LangGraph Agent07:40
  12. Self RAG- Intro01:30
  13. Self RAG- Implementation15:33
  14. Adaptive RAG11:34
-----------------Introduction to Model Context Protocol (MCP)-------------------5 lectures • 35min
  1. [Theory] Why MCP (Model Context Protocol)04:41
  2. [Theory] How LLMs REALLY Use Tools: Understanding Tool Calling05:06
  3. [Theory] MCP Architecture07:20
  4. [Theory] The GIST of the Protocol with Tool Calling09:00
  5. [Theory] MCP Servers08:44
  6. MCP Quiz 9 questions
Using a Pre-built Server (mcpdoc) with AI Clients (Cursor & Claude)4 lectures • 22min
  1. What are we building? MCP Doc01:11
  2. MCP Inspector02:54
  3. LLM.txt05:33
  4. mcpdoc12:13
Building MCP Servers and Clients with LangChain9 lectures • 50min
  1. Intro00:39
  2. Boilerplate10:01
  3. Servers05:43
  4. What are we MCBuilding?01:10
  5. Simple MCP Server02:42
  6. Bridging the Gap: The LangChain MCP Adapter Explained04:23
  7. Imports08:46
  8. Client08:45
  9. Tracing08:13
Useful tools when developing LLM Applications4 lectures • 19min
  1. [New, Important] Stop Writing Deprecated Code: LangChain's Official MCP Server08:00
  2. LangChain Hub - Downloads prompt from the community04:08
  3. TextSplitting Playground03:52
  4. LangChain VS LlamaIndex02:45
Deep Agents6 lectures • 34min
  1. Introduction to Deep Agents Section01:21
  2. Deep Agents: Taxonomy of Agents: Shallow Agents, Deep Agents, Coding Agents11:11
  3. Deep Agents: How Deep Agents Use Dynamic To-Do Lists to Solve Complex Tasks02:48
  4. Deep Agents: Sub Agents and Hierarchical Delegation06:12
  5. Deep Agents: Subagents context flow06:02
  6. Deep Agents File Systems06:31
Deep Agents Skills5 lectures • 33min
  1. The 3 Layers of AI Agent Skills: From Usage to Source Code01:56
  2. Level 1: Using Agent Skills in the Deep Agents CLI07:57
  3. Layer 2: Tracing AI Agent Skills with LangSmith12:45
  4. RECAP (How LangChain Deep Agents Implement Skill Middleware)01:46
  5. Layer 3: Inside skills.py- The Mechanics of Progressive Disclosure08:13
  6. Agent Skills Quiz 5 questions
LangChain Glossary7 lectures • 39min
  1. ChatModels08:49
  2. Messages04:08
  3. RecursiveCharacterTextSplitter01:55
  4. Document01:23
  5. LangChain Token Limitation Handeling Strategies11:29
  6. LangChain Memory Intro- Co Reference Resolution03:10
  7. LangChain Memory Theory Deepdive (LangGraph)08:31
Industry Insights: Building Production Agents with Assaf Elovic3 lectures • 8min
  1. The Core Architecture of Production-Grade AI03:19
  2. How to Make Users Trust Your AI Agents02:48
  3. Tutorial: Building a Lean AI Feedback Loop01:45
Industry Insights: Building Production Agents with Roy Miara4 lectures • 15min
  1. Intro02:14
  2. AI Agents in Cybersecurity CTF Competitions04:39
  3. Harness Engineering04:41
  4. Managing Variance and Hallucinations in Production Agents03:21
Agent Security1 lecture • 4min
  1. What is LLM App Sec?03:44
The Dark Side of "Vibe Coding": Vulnerabilities in AI-Generated Apps6 lectures • 27min
  1. Introduction04:51
  2. AI Coding Rarely Write SQL Injections or XSS Bugs04:07
  3. AI Agents Struggle with Role-Based Access Control04:20
  4. AI Coding Agents Struggle with Business Logic and SSRF05:51
  5. AI Coding Agents Struggle with Rate Limiting and CSRF04:23
  6. Prompt Engineering Won't Fix Insecure AI Code03:57
Bonus1 lecture • 1min
  1. Bonus00:22

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