langgraph/docs/docs/reference/checkpoints.md

Checkpoints

You can compile any LangGraph workflow with a CheckPointer to give your agent "memory" by persisting its state. This permits things like:

• Remembering things across multiple interactions
• Interrupting to wait for user input
• Resilience for long-running, error-prone agents
• Time travel retry and branch from a previous checkpoint

Checkpoint

::: langgraph.checkpoint.Checkpoint

BaseCheckpointSaver

::: langgraph.checkpoint.base.BaseCheckpointSaver handler: python

SerializerProtocol

::: langgraph.checkpoint.SerializerProtocol handler: python

Implementations

LangGraph also natively provides the following checkpoint implementations.

MemorySaver

::: langgraph.checkpoint.memory.MemorySaver handler: python

AsyncSqliteSaver

::: langgraph.checkpoint.aiosqlite.AsyncSqliteSaver handler: python

SqliteSaver

::: langgraph.checkpoint.sqlite.SqliteSaver handler: python