What is LangGraph and how does it enable complex agent workflows?

LangGraph is a framework for building stateful, multi-actor agent applications using a graph-based architecture. Core concepts: nodes represent processing steps (LLM calls, tool execution, human input), edges define transitions between nodes (including conditional routing), and state is a shared data structure updated by each node. Advantages over linear chains: support for cycles (agent loops), conditional branching, parallel execution, and human-in-the-loop interrupts. Implementation: define a StateGraph with typed state, add nodes as functions that process and update state, add edges (including conditional edges based on state), compile the graph, and invoke it. Example pattern: a research agent graph with nodes for queryanalysis, websearch, documentretrieval, synthesis, and qualitycheck, with conditional edges that loop back to search if quality is insufficient. Key features: persistence (checkpoint state to resume later), streaming (stream intermediate results to the user), breakpoints (pause for human approval at critical decision points), and time travel (replay from any previous state). LangGraph Studio provides visual debugging of agent execution. Production deployment: use LangGraph Platform for managed hosting with built-in persistence, fault tolerance, and scaling. Compared to CrewAI or AutoGen, LangGraph provides more fine-grained control over agent behavior at the cost of more implementation effort.