context-management

To prevent goal drift in long conversations, use structured compaction workflows to save accumulated context, clear heavy token history, and reorient the AI to your main objective. This preserves essential project knowledge while starting a fresh session.

The best way to preserve session context is by maintaining a knowledge base and a persistent project-level context file like AIBO.md. This stores project goals, decisions, and constraints, loading them automatically at every new session start.

You can compact conversation history without losing knowledge by running a compaction workflow that saves critical information, creates a fresh session, migrates the knowledge base, and displays a summary of exactly what was preserved.

Conversational AI slows down because long conversations accumulate excessive tokens in the active window. Compacting this history clears the heavy token load, restoring processing speed while keeping essential knowledge intact for future use.

You should use context management compaction when your conversations grow lengthy, risk processing slowdowns, or begin to drift from the main objective. It clears heavy history while preserving essential knowledge and reorienting the AI.