session-close

Automatically summarize conversation history by triggering an end-of-session event, which generates a concise summary of discussions, decisions, and touched files. This creates a structured digest log for memory persistence without manual effort.

The best way to save a digest log for conversational AI memory updates is to trigger an automatic session summary. This persists the conversation history into a structured YAML file, preserving key decisions and actions for later memory consolidation.

Yes, you can chain a session summary workflow to memory consolidation and checklist updates. The session-close process supports deterministic chaining to memory-write, memory-consolidate, evolve-suggest, and checklist-update workflows for continuous memory management.

Yes, session summarization works for long-running sessions without a manual goodbye trigger. The workflow detects long-running sessions automatically and generates a concise history and digest log to preserve key changes and decisions.

Conversation logs are archived using a structured YAML format. The digest log is generated and persisted to a dated YAML file, ensuring the conversation history and memory updates are stored in a readable, structured layout.