May 7
Loading video player...
🤖 The complete beginner-to-production guide to building AI Agents with LangGraph — everything in one video. Whether you're just discovering LangGraph or looking to solidify your foundations, this full course walks you through every core concept with real code examples, clear explanations, and a logical progression from simple to complex. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⏱️ CHAPTERS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 00:00 - Introduction 01:04 - Part 1: Building Your First Graph 03:49 - Part 2: State, Reducers & Control Flow 09:54 - Part 3: Persistence, Checkpoints & Memory 14:50 - Part 4: Interrupts & Human-in-the-Loop 18:47 - Part 5: Subgraphs & Multi-Agent Architecture ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ✅ WHAT YOU'LL LEARN ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📌 PART 1 — YOUR FIRST GRAPH → What nodes, edges and state are → How to initialize and compile a StateGraph → How to invoke a graph and read its output 📌 PART 2 — STATE, REDUCERS & CONTROL FLOW → Why state gets overwritten and how reducers fix it → add vs add_messages—when to use each → Passing runtime config with Context → All 4 control flow patterns: • Sequence—linear pipelines • Parallel—concurrent fan-out and fan-in • Conditional Branching—dynamic routing • Control Loops—cycles with safe exit conditions 📌 PART 3—PERSISTENCE, CHECKPOINTS & MEMORY → Giving your agent memory with InMemorySaver → Threads—scoping sessions with thread_id → Checkpoints — snapshots at every execution step → Rewinding to any earlier state with checkpoint_id → Cross-session memory with InMemoryStore → Namespaces, semantic search & production checkpointers 📌 PART 4 — INTERRUPTS & HUMAN-IN-THE-LOOP → Pausing graph execution with interrupt() → The Command object—state updates + routing in one → Building a real email approval workflow → Resuming with Command (resume=True/False) → Real-world use cases for human oversight 📌 PART 5 — SUBGRAPHS & MULTI-AGENT SYSTEMS → Nesting graphs inside graphs → Method 1: Calling a subgraph inside a node → Method 2: Adding a subgraph directly as a node → When to use each approach → How subgraphs power modular multi-agent architectures ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📦 LIBRARIES USED ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • langgraph • langchain-core ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔗 RESOURCES ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • GitHub Code → https://github.com/buildingsaas-lab/agentic-rag-customer-support 🔔 Subscribe and hit the bell so you don't miss the next video! ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🏷️ TAGS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ #langgraph #aiagents #langchain #python #llm #generativeai #machinelearning #humanintheloop #multiagentsystems #fullcourse #artificialintelligence #agenticai #ragarchitecture #claude #aiengineering , #AgenticAI, #AIAgents, #BuildSaaS, #SaaSDevelopment, #SaaSArchitecture, #AISaaS, #ProductionAISystems, #LLMApplications, #AIWorkflows, #AIAutomation, #LangGraph, #MultiAgentSystems, #AIAgentFramework, #AISystemDesign, #RAGArchitecture, #ProductionRAG, #AIInfrastructure, #VectorDatabase, #AIDataPipelines, #FastAPI, #BackendArchitecture, #MicroservicesArchitecture, #APIDevelopment, #CloudArchitecture, #AWSBackend, #SoftwareArchitecture, #DeveloperTutorials, #BuildAIProjects, #AIDeveloper