LangGraph Made Easy

About this course

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 Messages

State 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, mo

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