Master LangGraph — state machines, multi-agent orchestration, human-in-the-loop, streaming, checkpointing, and supervised agent teams. The framework behind production AI agents.
from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver
# Production agent with checkpointing
graph = StateGraph(AgentState)
graph.add_node("plan", planner)
graph.add_node("execute", executor)
graph.add_node("review", reviewer)
graph.add_conditional_edges(
"review", should_continue,
{"retry": "execute", "done": END}
)
app = graph.compile(checkpointer=MemorySaver())
# ✓ Stateful, recoverable, production-gradeReal projects, not toy demos. Every module leads to something you can ship.
Design agents as state machines with conditional routing, cycles, error recovery, and human approval gates.
Build supervised agent teams — a planner, researcher, writer, and reviewer collaborating on complex tasks.
Add approval steps, interrupt points, and human feedback loops to any agent graph.
Deploy with checkpointing, persistence, streaming, and graceful error recovery.
16 modules · 192+ lessons · 35+ hours of content
You've built basic chains/agents and need production patterns — state, memory, multi-agent, human oversight.
Your team needs reliable agent systems. This teaches the architecture patterns that survive production.
You know LangChain and want to build complex, stateful agent systems with LangGraph.
“The MCP and A2A lessons are incredibly current. My team is implementing these right now.”
“As a developer but needed AI for product decisions. The n8n module let me build workflows without code. Mind-blown.”
“Built a multi-agent research assistant using exactly the patterns from this course. Shipped it to prod in 3 days.”