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Build stateful agents with LangGraph by defining your state, adding nodes and edges, and compiling the graph. This approach manages state across multi-step workflows using explicit state machines for complex tasks.

Add human-in-the-loop review by constructing your agent as an explicit state machine. This workflow design allows you to incorporate human oversight and approval directly into the required processing steps.

Yes, LangGraph supports crash recovery and conversation persistence through checkpointing. Checkpointing ensures conversation continuity by explicitly managing and saving stateful agent data across execution steps.

Yes, you can use LangGraph to create a debugging agent. You define your state, add nodes and edges for the required debugging steps, and compile the graph to manage the complex, multi-step workflow.

Yes, you need Python and the LangGraph library to implement multi-actor state machines. These dependencies are required for building and managing complex agent workflows with control flow.

Explicit state machines provide better control flow for complex tasks. They enable stateful agent building, support multi-step workflows, and allow crash recovery with checkpointing, which standard agents lack.