context-and-continuity

You can preserve agent memory across context compaction by using explicit checkpoint operations to save named findings and configuring persistent memory tiers with a database path. This ensures intermediate discoveries survive session restarts and context window shrinkage.

AI agents lose intermediate findings when context windows shrink from compaction or sessions restart without persistent memory. Without explicit checkpointing, valuable discoveries vanish, causing rework and missed insights during multi-iteration workflows.

Set up cross-session persistence by configuring an agent with the checkpoint tool enabled and enhanced persistent memory tiers. Providing a configurable database path supports semantic recall, allowing research agents to retrieve confirmed findings across multiple sessions.

Checkpoint agent context when token utilization reaches built-in auto-checkpoint thresholds. Applying recommended iteration budgets and windowing guidance surfaces exactly when to persist observations, helping avoid context compaction during iterative extraction workflows.

Context windowing manages active message windows without a database, but cross-session persistence requires a configurable database path. Explicit checkpointing saves named findings during the run, while enhanced memory tiers with a database survive full agent restarts.

The best way to manage context compaction is combining explicit checkpoint APIs with auto-checkpoint thresholds tied to token utilization tiers. This approach saves critical findings before context pressure forces compaction and supports retrieving them by key later.