A practical, hands-on guide to building real production AI apps using LangGraph
Build AI agents the way real engineering teams do.
What you’ll learn
- Build agentic workflows in LangGraph, starting from basic LLM calls to complex multi-step graphs with conditional routing and subgraphs..
- Apply TypedDict, Pydantic models, Annotated reducers, and various state-management patterns to design reliable agent state machines..
- Use tools, streaming, message editing, auto-summaries, and memory trimming to build production-ready conversational agents..
- Implement long-term memory using SQLite, evolving memos, structured extraction, and cross-thread memory patterns..
- Design and run stateless and stateful LLM systems using lean I/O, deep-work states, refinements, and state-prompt workflows..
- Integrate external APIs such as weather, web search, and custom tools into LangGraph with live executions and error-safe routing..
- Build advanced agentic patterns including human-in-the-loop planning, guided execution, trust calls, and multi-stage refinement loops..
- Deploy end-to-end LangGraph agents using the Send API with efficient context management and reproducible agent state flows..
Course Content
- Introduction –> 7 lectures • 17min.
- LangGraph Basics –> 3 lectures • 9min.
- Type-Safe Graphs with Pydantic, TypedDict, and Dataclasses –> 3 lectures • 20min.
- State Updates & Reducers in LangGraph –> 2 lectures • 17min.
- Adding Conversation Memory: From One-Shot Prompts to Stateful LLM Turns –> 2 lectures • 14min.
- Teaching Your LLM to Use Tools –> 3 lectures • 28min.
- Managing and Optimizing Chat History in LangGraph –> 3 lectures • 17min.
- Clean I/O & Human-in-the-Loop Refinements –> 4 lectures • 19min.
- Memory, Persistence & Structured Extraction –> 5 lectures • 35min.
- Human in the loop planning & send() Execution –> 2 lectures • 15min.
- Subgraphs, Merged Context, and Live Tool Pipelines –> 3 lectures • 27min.
- Closing –> 1 lecture • 2min.
Requirements
Build AI agents the way real engineering teams do.
This course takes you from zero to building full, production-ready LangGraph applications — the same patterns used in modern AI products like travel assistants, research copilots, conversational agents, and approval-based workflows.
Instead of abstract theory, you learn by building multiple real apps step by step, including a full NYC Travel Concierge with weather, web search, structured itinerary generation, conversation memory, routing, and human-in-the-loop approvals.
You’ll discover how LangGraph uses typed states, nodes, edges, and conditional routing to orchestrate LLMs and tools. You’ll integrate APIs like Tavily Search and OpenWeather, implement tool calls, capture long-term memory, pause the graph for edits, resume execution, and structure outputs using Pydantic. You’ll also learn to design modular subgraphs, inject rolling conversation summaries, and build scalable, debuggable workflows that behave like real AI production systems.
By the end, you’ll know how to:
- Build stateful LangGraph agents using StateGraph, routing, and typed states
- Integrate LLMs with real tools (search, weather, custom utilities)
- Build full multi-step workflows with conditional logic
- Implement MessagesState and MemorySaver for persistent conversation
- Add human-in-the-loop steps using interrupt and resume commands
- Wrap outputs in schemas using Pydantic/BaseModel structured output
- Build modular subgraphs and real-world orchestrations
- Create a full real product: a smart trip-planning agent that summarizes, trims, routes, and generates clean itineraries
Whether you’re a developer building your first agent or a founder prototyping a real AI product, this course is designed to give you the skills and confidence to ship LangGraph-powered applications in the real world.
This is the fastest, cleanest, most practical LangGraph course available — built by a creator who builds alongside you, not above you.