How does checkpointing work in LangGraph?

LangGraph's checkpointing system saves the full graph state after every node execution to a persistent store. This enables resuming interrupted runs, time-travel debugging (replay from any past state), and human-in-the-loop workflows (pause, inspect, modify state, then continue).

To enable checkpointing, pass a checkpointer to graph.compile() and provide a thread_id in the config on each invocation. The thread_id is the key that groups checkpoints belonging to the same conversation or workflow run:

from langgraph.checkpoint.memory import MemorySaver

memory = MemorySaver()  # in-memory, for development
graph = graph_builder.compile(checkpointer=memory)

config = {"configurable": {"thread_id": "user-123-session-1"}}

# First invocation
graph.invoke({"messages": [HumanMessage("Hello")]}, config)

# Second invocation — LangGraph automatically loads the previous state
graph.invoke({"messages": [HumanMessage("What did I say?")]}, config)

Checkpointer options: MemorySaver (in-process, ephemeral), SqliteSaver (persistent SQLite file, single-process), AsyncSqliteSaver (async SQLite), PostgresSaver / AsyncPostgresSaver (production multi-process). All implement the BaseCheckpointSaver interface, so switching backends requires only changing the checkpointer passed to compile().

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