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What if your AI applications could loop, make conditional decisions, act autonomously, and collaborate with humans—just like real-world systems? In this lesson you’ll learn how to design and build powerful, stateful, production-ready AI workflows using LangGraph. I’m Alireza Chegini, a solution architect working in generative AI. I’ll guide you through designing and building AI agents with LangGraph so you can move beyond single prompts and responses into structured, maintainable, graph-based systems. Modern AI applications require orchestration, memory, decision-making, and control—exactly where LangGraph excels. You’ll learn to build orchestration pipelines that support stateful behavior, cyclical logic, safe termination, and observability so your systems can handle real-world complexity.
You will progress through the following key sections and hands-on labs.
  • Orientation: learning outcomes, prerequisites, and environment setup.
  • Core concepts: what LangGraph is and state graph fundamentals.
  • Orchestration: building your first workflows using nodes, edges, and conditional routing.
  • Stateful agents: memory, persistence, and user-specific state.
  • Robustness: token limits, context optimization, summarization.
  • Human-in-the-loop: approvals, feedback, and real-time controls.
  • Debugging & observability: breakpoints, state editing, and time travel.

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What you’ll build

You’ll start by creating simple nodes and edges, then progress to advanced agents with:
  • Conditional routing and decision nodes.
  • Reducers and cyclical graphs with safe termination.
  • Persistent bookmarks and rehydration of execution state.
  • Observability hooks for logging and tracing.
  • Debugging tools such as breakpoints and state editing.
Next, you’ll construct conversational agents that route conversations, summarize longer contexts to manage token usage, and handle multi-turn workflows with memory.

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Key technical patterns covered

  • Graph-based orchestration patterns (linear, branching, cyclical).
  • State management: session state, user state, and persisted bookmarks.
  • Context optimization: summarization, context windows, and token-budgeting.
  • Safety and termination: guardrails to avoid infinite loops.
  • Human-in-the-loop patterns for approvals and manual corrections.

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Practical labs and debugging

All lessons are hands-on. You’ll implement example agents, add memory, validate inputs, and use LangGraph’s debugging features to inspect and iteratively refine running workflows. Debugging capabilities covered include breakpoints, state editing mid-execution, and time travel to inspect previous states.

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Course format

  • Short conceptual videos to introduce patterns.
  • Guided demos that walk through code and execution traces.
  • Hands-on labs to implement and deploy agents.
  • Example repositories and templates to bootstrap projects.

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Verify installation

Before you begin the labs, confirm the LangGraph package is installed:
Recommended basics: a recent Python version and pip. We’ll cover any additional setup and prerequisites at the start of the lesson so you can follow along without friction.

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Prerequisites & environment checklist

Tip: Store API keys securely (e.g., environment variables or a secrets manager). See the LangGraph docs for provider-specific setup and configuration. Before you begin the labs, we’ll walk through any additional prerequisites and step-by-step environment setup so you can reproduce the demos and exercises confidently.

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