context-optimization

What problem does it solve?

Context optimization extends the effective capacity of limited context windows through compaction, masking, KV-cache optimization, and context partitioning. The goal is not to magically increase context windows but to make better use of available capacity. Effective optimization can double or triple effective context capacity without requiring larger models or longer contexts.

Core Features & Use Cases

• Compaction: Summarize context near limits and refresh context with a concise summary.
• Observation Masking: Replace verbose outputs with compact references.
• KV-Cache Optimization: Reuse cached computations to avoid recomputation.
• Context Partitioning: Split work across isolated contexts for scalability.
• Budget Management: Allocate and monitor context budgets to trigger optimizations.

Quick Start

Enable compaction when context usage approaches limits; mask older observations and reuse cached results to improve throughput in long-running agent flows.