> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Introduction > Course introducing LangGraph for building stateful, graph-based AI workflows and agents with orchestration, memory, debugging, human-in-the-loop, and hands-on labs. 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. The image is a screenshot of a website offering a course called "LangGraph" for building AI workflows, targeted at beginners and hosted on KodeKloud. The page includes course details, such as duration, modules, and an instructor photo. 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. The image presents a course's learning objectives in a list format, including explaining concepts, building paths, implementing memory, integrating controls, and using time travel for debugging. A person is pictured in the bottom right corner. ## 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. The image illustrates a flowchart explaining cyclical graphs related to a delivery process, involving two houses and a condition for recipient clarification. It includes labeled nodes and decision paths, with profile icons representing delivery personnel and possibly a video presenter. ## 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. The image explains how LangGraph handles persistence by bookmarking a state, allowing resumption from the saved point. It illustrates the process and the result with visuals of a robot, bookmarking, rehydrating state, and continuing. ## 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. The image shows a flowchart labeled "Why Edit State Mid-Execution?" with three steps linked together. There's an emphasis on inspecting and correcting at "Step 2," alongside an icon of a person, and a video call frame in the bottom right corner with a person speaking. ## 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. ## Verify installation Before you begin the labs, confirm the LangGraph package is installed: ```bash theme={null} pip show langgraph ``` 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. ## Prerequisites & environment checklist | Item | Minimum / Recommendation | Purpose | | ------------------------ | ------------------------- | ---------------------------- | | Python | 3.9+ | Run LangGraph and examples | | pip | Latest | Install dependencies | | OpenAI / LLM credentials | N/A (as required by labs) | Connect to model providers | | Git | Latest | Clone example repos and labs | | Editor | VS Code or similar | Edit code and view logs | Tip: Store API keys securely (e.g., environment variables or a secrets manager). See the LangGraph docs for provider-specific setup and configuration. ## Links and references * LangGraph documentation: [https://langgraph.dev](https://langgraph.dev) (refer to provider docs for setup) * Best practices for prompt engineering and context management: [https://platform.openai.com/docs/guides](https://platform.openai.com/docs/guides) * General orchestration patterns and state management: [https://en.wikipedia.org/wiki/State\_machine](https://en.wikipedia.org/wiki/State_machine) 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.