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Checkpointer

A checkpointer is a component or object that saves and restores snapshots of state to support persistence, retrieval, or resumption.

How 2 Manning books explain it

AI Agents and Applications

A checkpointer is a component that saves and restores workflow state, including conversational information, across executions or interactions.

How it is used
Maintains conversational state across turns and can also support recovery and human approval workflows.
How deep it goes
Explains snapshots at graph super-steps and demonstrates inspecting and restoring state, without detailing storage internals.
Examples
Uses a travel assistant to resolve “the same town” after restoring a checkpoint from a Penzance weather conversation.
What you can do after
Apply it via LangGraph to persist conversational state and resume a workflow from a selected checkpoint.

Read how AI Agents and Applications explains checkpointer →

Build Python Web Apps with Streamlit

A checkpointer is a component that saves snapshots of graph state during execution so that later invocations can resume or recall prior state.

How it is used
Preserves graph state across invocations so conversational applications can continue a thread instead of restarting.
How deep it goes
Defines in-memory checkpointing and mentions database alternatives, but stops short of implementing durable database storage.
Examples
Uses a chatbot follow-up, MemorySaver, a fixed thread ID, and a LangGraph compilation call.
What you can do after
Apply it via LangGraph to preserve conversational state across repeated graph invocations.

Read how Build Python Web Apps with Streamlit explains checkpointer →

Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.