state management across turns

Article explicitly emphasizes 'maintaining memory' and 'state management' as reasons agent frameworks exist—this is state management across multi-turn interactions

Paper explicitly identifies 'intermediate state, execution feedback, changing environments' as core dimensions—these are state management across long trajectories, which is identical to context preser

JSON contracts for state and correction loops demonstrate how to preserve and pass information between agent turns

The values/updates dictionary pattern explicitly shows state persisting within a session across multiple agent invocations, demonstrating context preservation within a conversation.

Explicitly mentions 'Each agent has a state that can be updated with information during each flow' - shows state persistence pattern

Agents require persistent state management; guide likely addresses how frameworks handle memory and context between turns

Mentions checkpoints, validation steps, and recovery patterns that relate to preserving agent state across multiple turns/iterations.