LangGraph for Workflows
Master stateful, graph-based agent workflows with cycles, branching, and human-in-the-loop patterns
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You've mastered LangGraph for building stateful, cyclic agent workflows! Check off each concept below to track your understanding. When you've mastered all 15 takeaways, you'll unlock the next module.
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0%LangGraph extends LangChain with stateful, cyclic graphs for complex agent workflows
Unlike linear chains, LangGraph supports loops, branches, and conditional routing
Graphs consist of nodes (functions) and edges (connections defining execution flow)
Nodes can be LLM calls, tool executions, or any custom Python/TypeScript function
Conditional edges enable dynamic routing based on state (e.g., route to different tools)
Cycles allow retry logic, iterative refinement, and looping until conditions are met
State is a shared object that flows through every node, persisting across execution
Define state schema using TypedDict (Python) or interfaces (TypeScript) for type safety
State reducers control merge behavior: append to lists vs replace values
Human-in-the-loop pattern pauses execution for approval before critical actions
Nested subgraphs enable modular, reusable workflows composed inside main graphs
Streaming provides real-time updates as each node completes (better UX)
Checkpointing saves state to database, enabling workflow resumption after crashes
Use LangGraph for complex workflows with cycles, state, or human approval needs
Stick with LangChain for simple, linear, one-shot agent interactions
Complete all 15 takeaways to finish this module and unlock the next one!