Stateful Graph Orchestration — Durable Execution and Checkpoints
Agent is a state machine; nodes are functions; edges are transitions; state is checkpointed after each node. Resume from any failure at the last successful checkpoint. LangGraph is the 2026 reference for this model of low-level stateful orchestration. Describe LangGraph's core model: state machine with typed state, function nodes, conditional edges, and post-node checkpoints. Name the four capabilities the docs highlight: durable execution, streaming, human-in-the-loop, comprehensive memory. Explain the three orchestration topologies LangGraph supports: supervisor, peer-to-peer (swarm), hierarchical (nested subgraphs). Implement a stdlib state graph with typed state, conditional edges, and a checkpoint/resume cycle. Agents and workflows share a problem: when a 40-step run fails at step 38, you want to resume from step 38, not start over. Second-class state models leave operators hacking retries around a library that assumes fresh runs. LangGraph's design answer: state is a first-class typed object, mutations are explicit, and checkpoints persist after every node. Resume is a loadstate(sessionid) call. A graph is defined by: State type. A typed dict (or Pydantic model) that every node reads and mutates. Nodes. Pure functions (state) -> stateupdate. Updates are merged into state after return. Edges. Conditional or direct transitions between nodes. Entry and exit. START and END sentinel nodes mark the boundary. Example: an agent with classify, refund, bug, sales, done nodes — a routing workflow as…
Stateful Graph Orchestration — Durable Execution and Checkpoints: Agent is a state machine; nodes are functions; edges are transitions; state is checkpointed…
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