What problem does it solve?
Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. Effective optimization can double or triple effective context capacity without requiring larger models or longer windows — but only when applied with discipline. The techniques below are ordered by impact and risk.
Core Features & Use Cases
- KV-cache optimization — Reorder and stabilize prompt structure so the inference engine reuses cached Key/Value tensors. This is the cheapest optimization: zero quality risk, immediate cost and latency savings. Apply it first and unconditionally.
- Observation masking — Replace verbose tool outputs with compact references once their purpose has been served. Tool outputs consume 80%+ of tokens in typical agent trajectories, so masking them yields the largest capacity gains. The original content remains retrievable if needed downstream.
- Compaction & Context partitioning — Summarize accumulated context when utilization exceeds 70%, then reinitialize with the summary; partition work across sub-agents when necessary to avoid exceeding window limits.
- Context budgeting — Monitor token usage and trigger optimization when any category approaches its allocation or total utilization crosses 70%.
Quick Start
Provide your current context and let Context Optimization apply KV-cache, masking, and compaction to maximize token efficiency.
