checkpoint

To prevent context overflow, you can create a persistent checkpoint of the current AI session. This proactively saves session state into a Markdown file, allowing you to resume long-running, multi-turn workflows seamlessly.

You need to create a checkpoint at key milestones during long-running, multi-turn tasks where session continuity is critical. This prevents the loss of context and enables quick restoration of your work.

You need a writable .claude/checkpoints directory in your environment. The checkpoint is saved as a standardized Markdown file containing metadata for status, date, and context usage.

To resume work after reaching a context limit, reload the last saved checkpoint from the .claude/checkpoints directory. This restores the previous session state so you can continue exactly where you left off.

The best way to organize session checkpoints is to save them as standardized Markdown files within a dedicated .claude/checkpoints directory. Including clear metadata for status, date, and context usage ensures quick restoration.

Your AI loses context in long-running workflows due to context overflow limits. Automatically creating persistent checkpoints at key milestones prevents this loss by saving session state externally.