context-memory

To persist context and memory across AI agent sessions, use a multi-tier architecture with daily logs, long-term memory files, and a searchable conversation archive to ensure continuity. This structure loads and merges historical data on startup.

An AI agent learns from user satisfaction by gathering feedback signals to generate behavioral insights. These insights are automatically compiled into a learning file to update and refine future interactions.

The best way to structure long-term memory for an AI agent is using a multi-tier storage system. This combines daily markdown logs, a central long-term memory file, and an organized conversation archive for searchable recall.

Yes, you need to set up a specific workspace structure for AI session continuity. Create directories for daily memory, conversations, and satisfaction insights, then load configuration files on startup to apply context.

Yes, you can archive AI conversations and search them later. The system uses scripts to move conversations into an organized archive directory, making historical sessions searchable for future context retrieval.

To maintain AI session continuity, the agent loads configuration files including SOUL.md, USER.md, LEARNING.md, and recent daily memory files. This merges startup context with long-term memory.