memory-system

Persistent memory for agents is maintained by storing user preferences, project conventions, and past decisions in structured Markdown files. This allows agents to recall previously learned information without needing to re-ask, ensuring context retention across sessions.

Agents lose context because they lack a durable memory system that outlives individual sessions. Without persistent memory storage, agents cannot recall past decisions or project conventions, leading to context drift and the need to repeatedly ask for the same information.

Yes, enabling agent memory retention requires structured Markdown files for storing memory. The system uses a slim index file to manage this context retention, handling user preferences and past decisions to reduce drift.

The best way to store user preferences for context management is using a dedicated memory system that writes to a slim index file. This approach ensures agents retain context and recall specific preferences efficiently without re-asking.

Yes, because the memory system relies on structured Markdown files, you can manually edit the slim index file to update agent training context. This directly modifies the stored user preferences and project conventions available for recall.