LangGraph Agents with Memory & Human-in-the-Loop

A single agent works well on short tasks and gets unpredictable on long ones. It forgets earlier steps, repeats itself, or takes an action nobody approved. LangGraph solves this by turning the agent into a graph of steps with persistent state and checkpoints where a human decides whether to continue. In this course you will build such a graph yourself, node by node.By the end of the course you will be able toBuild a complete LangGraph workflow from scratch, with nodes, edges, and conditional routingApply nine reusable workflow patterns to structure real multi-step tasksManage state and memory so your agent remembers what it did in earlier stepsAdd human-in-the-loop checkpoints that pause the agent before high-stakes actionsDesign approval workflows that keep humans in control without slowing the agent downCourse structureThe course opens with a refresher on agent fundamentals so you know exactly what a graph is improving on. The next section introduces graph-based thinking: why single agents break down, what LangGraph adds, nine patterns coded in one session, and your first end-to-end workflow. The final practical section covers state, memory, and two full builds of approval workflows, from a simple pause-and-confirm to gates for actions like sending, spending, or deleting. Written references follow each section.Who should take this coursePython developers who have built a basic LLM app or agent and want a cleaner way to orchestrate multi-step work. Engineers whose prototype has become hard to control and who need memory and approval gates before trusting it with real actions. Basic familiarity with LangChain is recommended; no LangGraph experience is needed.Why this courseTwo hands-on builds for memory and human-in-the-loop alone, nine patterns you will reuse in every future project, and no filler. You leave with a way of thinking about workflows that transfers to any agent you build.Enroll now and build agents that remember, pause, and ask before they act.