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Agents and workflows

AI Agents with LangChain and LangGraph: Deployment

Build AI agent projects in Python with LangChain and LangGraph, covering RAG, multi-agent systems and deployment.

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Course topics

Agents and workflows

  • RAG and memory
  • Multi-agent systems
  • Deployment

You inspect the result

Find your fit

Is this a useful starting point?

For intermediate Python developers comfortable with command-line tools; an OpenAI API key is listed.

Starting point
Intermediate
Course length
17 total hours
Lessons
155 lectures

Course by Paulo Dichone | Software Engineer, AWS Cloud Practitioner & Instructor

Course last updated: 05/2026

Requirements
  • Python intermediate level — comfortable with functions, classes, decorators, and type hints
  • Basic command line familiarity — creating directories, running scripts, installing packages
  • A code editor such as VS Code (free) and an OpenAI API key (costs $2–5 for the entire course)
  • No prior LangChain or LangGraph experience needed — Section 1 covers all foundations from scratch
What is included
  • 17 hours on-demand video
  • 4 articles
  • 10 downloadable resources
  • 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.

11 sections • 155 lectures • 17h 6m total length

Introduction3 lectures • 7min
  1. Introduction, Prerequisites and What You'll Build02:03
  2. Why LangChain and LangGraph - Rapid Growth01:55
  3. Course Structure02:40
Download Course Source Code1 lecture • 1min
  1. Download Source Code00:06
LangChain Foundations - A Deep Dive27 lectures • 2hr 18min
  1. The LangChain Ecosystem - Overview07:53
  2. Setup Development Environment01:31
  3. API Setup and Verification (OpenAI and Anthropic)07:03
  4. The V 1.0 Architecture (LangChain)02:15
  5. LangChain Core Concepts - LCEL and Runnable Chains - Hands-on06:23
  6. LCEL - Batch Execution Demo03:36
  7. Demo Stream Realtime Output with LCEL02:34
  8. Demo Schema Inspection03:29
  9. Your Turn - Build Your First Chain with LangChain01:15
  10. Solution - Your First Chain01:56
  11. Quick Check in00:55
  12. LCEL - The Pipe Operator Overview00:55
  13. New Model Initiation and Available Models in 202604:33
  14. Why Output Parsers in LangChain00:50
  15. Model Configuration and Cost Optimization Strategies06:30
  16. Working with LLMs in LangChain - Multi Providers Configuration12:53
  17. Your Turn - Exercise - Create a Multi-model Setup01:07
  18. Solution02:01
  19. Prompt Templates and Messages - Deep Dive07:19
  20. Hands-on: Prompt Messages10:41
  21. Hands-on: Prompt Templates Code Run Through and Testing06:15
  22. Hands-on: Why Output Parsers and Structured Outputs06:55
  23. Hands-on: Output Parsers and Structured Outputs09:15
  24. Running the Final Output Parser and Structured Output Script - Demo04:23
  25. Smart Q&A Bot Architecture - Overview01:12
  26. Project 1 - Smart Q&A Bot - Overview01:13
  27. Project 1 - Final Touches23:10
  28. LangChain Foundations - Section 2 Knowledge Check 4 questions
Chain Patterns5 lectures • 34min
  1. Chains, RAG and Memory & Chain Patterns - Overview06:48
  2. Hands-on: Basic and Parallel Chains07:57
  3. Hands-on: Demo Passthrough Runnable07:27
  4. Hands-on: Chain Branching04:57
  5. Hands-on: Debugging06:45
Document Loading, Chunking & Embeddings - Loaders, Splitters, Vector Stores27 lectures • 2hr 4min
  1. LangChain Document Loaders - Overview06:14
  2. Hands-on: TextLoaders03:43
  3. Hands-on: WebLoader02:52
  4. Hands-on: Lazy Loader02:15
  5. Hands-on: Document Structure03:34
  6. Hands-on: Loading PDF Files02:54
  7. Why Chunking Matters and Text Splitting Deep Dive08:19
  8. Hands-on: Text Splitter - RecursiveCharacterTextSplitter05:21
  9. Hands-on: Chunk Comparison03:24
  10. Hands-on: Overlap Importance In Code05:22
  11. Hands-on: Markdown Header Splitter02:57
  12. Hands-on: Code Splitter03:32
  13. PDF Document Splitting03:41
  14. RAG Overview10:12
  15. Vector and Embeddings Full Overview06:29
  16. Hands-on: OpenAI Embedding04:41
  17. Free Embedding Models02:17
  18. Hands-on: Embeddings Deep Dive - Basics - Similarity Search06:37
  19. Hands-on: Embedding Caching04:06
  20. Vector Stores: Architecture and Production Options - Overview05:45
  21. Hands-on: Setting Up Chroma and Running Chroma Basics06:17
  22. Important: Python Version for ChromaDB (Use 3.11 or 3.12)00:56
  23. Hands-on: Similarity Search with Scores03:39
  24. Hands-on: Metadata Filtering02:58
  25. Hands-on: Chroma DB Persistence06:13
  26. Hands-on: Vector Store as a Retriever for Chains06:31
  27. Exercise and Solution: Vector Stores03:38
RAG and Memory - A Comprehensive Dive25 lectures • 2hr 43min
  1. RAG Architecture and Best Practices - Overview06:37
  2. Hands-on: Basic RAG Pipeline08:39
  3. Hands-on: RAG with Resources03:12
  4. Hands-on: RAG with Fallback02:11
  5. Hands-on: RAG with Structured Outputs05:51
  6. Hands-on: RAG Pipeline Exercise03:31
  7. From Basic RAG to Advanced RAG - Overview09:23
  8. Hands-on: Advanced RAG - Multi-Query Retriever06:02
  9. Hands-on: Advanced RAG - Contextual Compression08:13
  10. Hands-on: Advanced RAG - Hybrid Search07:13
  11. Hands-on: Advanced RAG - Parent Document Retriever04:28
  12. Hands-on: Advanced RAG - Combining Multi-Query and Compression Strategies05:07
  13. Memory in LangChain - Full Overview07:25
  14. Hands-on: Conversation Memory - Basics05:45
  15. Hands-on: Multiple Sessions Memory03:30
  16. Hands-on: Message Trimming04:28
  17. Hands-on: Windowed Memory07:38
  18. Hands-on: Summary Memory10:40
  19. Exercise and Solution: Persistent Memory08:57
  20. Project: AI Research Assistant - Overview03:27
  21. Project: AI Research Assistant - Indexing Documents (Part 1)07:56
  22. Project: AI Research Assistant - LLM Prompt and Output Parser (Part 2)09:22
  23. Project: AI Research Assistant - Adding Memory (Part 3)08:42
  24. Project: AI Research Assistant - Multi-Query Implementation (Part 4)07:38
  25. Project: AI Research Assistant - Structured Output - Final Part06:43
  26. Chains, RAG and Memory - Section 3 Knowledge Check 4 questions
LangGraph - A Full Deep Dive19 lectures • 2hr 31min
  1. LangGraph and Its Pillars - Overview10:43
  2. Hands-on: Create Your First Simple Graph08:50
  3. Hands-on: Understanding Reducers and Accumulating State08:39
  4. Hands-on: Message State - The Chat Pattern05:13
  5. Hands-on: Multi-Node Pipelines - Chaining LLM Calls15:17
  6. Exercise: Build Your First Node04:17
  7. Hands-on: Full LangGraph Step-by-Step Workflow05:43
  8. Edges and Conditional Edges Deep Dive04:24
  9. Hands-on: Basic Routing with Literal Routing Types07:33
  10. Hands-on: Conditional Looping04:20
  11. Hands-on: Multipath Routing05:36
  12. Hands-on: Cycles and Loops - Self-Correcting Code Writer08:54
  13. Hands-on: Iterative Research Agent with Loops and Cycles06:23
  14. Human in the Loop - Overview05:39
  15. Hands-on: Human Input - Interrupt for Approval12:09
  16. Full Human in the Loop Workflow10:44
  17. Hands-on: Checkpointing Deep Dive16:05
  18. Checkpoint Internals Deep Dive07:08
  19. LangGraph Summary and Key Takeaways03:32
  20. LangGraph - Section 4 Knowledge Check 5 questions
Multi-Agent Systems with LangGraph and LangChain17 lectures • 2hr 37min
  1. Introduction to Multi-Agents and the ReAct Pattern07:02
  2. Hands-on: Tool Calling Agent12:51
  3. Custom Tool with Error Handling06:15
  4. Hands-on: The Supervisor Agent16:48
  5. Hands-on: Agent Handoffs in LangGraph15:17
  6. Hands-on: Map-Reduce Strategy10:57
  7. How Agents Communicate: Reducers Deep Dive11:55
  8. Hands-on: Message Passing Pattern04:38
  9. Hands-on: Shared Field State05:36
  10. Hands-on: The Blackboard Iterative Refinement10:31
  11. Hierarchical Architecture Deep Dive04:15
  12. Hands-on: Single Department in Isolation04:21
  13. Hands-on: Hierarchical Routing and Full Tracing12:58
  14. Section Project: Multi-Agent Research System Overview05:44
  15. Hands-on: State Schema and Send API Overview12:08
  16. Final Project: Research System from the Ground Up14:06
  17. Multi-Agent Systems: Section Summary01:09
  18. Multi-Agent Systems - Section 5 Knowledge Check 5 questions
Production Deployment - Deploying AI Agents29 lectures • 4hr 9min
  1. Introduction to Observability and Benefits Deep Dive08:48
  2. Setting Up LangSmith and Testing Observability Traces07:56
  3. Production Test Commands File - Get This First00:05
  4. PII, Security Categories, and Prompt Injection Attacks05:20
  5. Hands-on: Defense in Depth - Input Sanitization and PII Detection09:19
  6. Hands-on: The LLM Guard - The Smart Bouncer06:03
  7. Hands-on: Output Validator and Summary10:16
  8. Production Considerations00:43
  9. LLM Testing and Unit Tests08:23
  10. Hands-on: Integration Testing04:00
  11. Hands-on: LLM-as-Judge Evaluation04:12
  12. Hands-on: Regression Testing04:26
  13. Hands-on: LangSmith Datasets - Production Approach09:44
  14. Hands-on: LLM Testing Pyramid01:47
  15. Understanding Error Handling and the Retry Decorator07:46
  16. Hands-on: Circuit Breaker06:48
  17. Hands-on: Fallback Chain04:51
  18. Hands-on: Graph-Based Retry06:28
  19. Hands-on: Cost Reduction - Model Router07:58
  20. Hands-on: Semantic Caching05:30
  21. Hands-on: Token Budgeting07:16
  22. Pillars of Production Visibility - Observability and Monitoring11:34
  23. Project: Production-Ready API - Project Scaffold and the Big Picture18:13
  24. Project: Security Layer - First Line of Defense12:06
  25. Project: Production Cache and Monitoring - Save Money, See Everything08:36
  26. Project: The LangGraph Agent - Brain with Safety Net09:41
  27. Project: FastAPI - Wiring Everything Together22:46
  28. Project: Production LangGraph API - Final Build and Dockerization15:37
  29. Deployment to Render - Your API Goes Live22:46
  30. Production Deployment - Section 6 Knowledge Check 5 questions
Wrap up1 lecture • 4min
  1. Wrap up and Next Steps04:17
Bonus1 lecture • 1min
  1. Bonus00:08

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