context-tiering

Prevent context window overflow by organizing long-running conversation context into tiered memory with working, reference, and archival tiers. This approach promotes and demotes elements as the window fills to preserve critical data and reduce token churn.

Tiered memory management for multi-file sessions works by classifying context into working, reference, and archival tiers. It enforces proactive promotion and demotion of elements as the window fills, retrieving from indexed tiers on demand.

The best way to maintain cross-session continuity is using a tiered memory system that compresses infrequently used material into an archival tier. It balances performance, recoverability, and cost via lossy compression and an indexed reference store.

You need to compress and archive infrequently used context when the working window approaches capacity. Proactive tiering moves essential context to the reference tier and applies lossy archival compression to optimize memory usage during long sessions.

Yes, tiered context memory supports multi-turn conversations across multiple files or modules. It applies during multi-turn sessions by enforcing proactive tiering and an indexed reference store to prevent context loss.

An LLM agent loses critical data during long sessions due to context-window overflow without tiered memory. Organizing information into working, reference, and archival tiers with proactive promotion and demotion preserves data and reduces token churn.