context-optimization

You can optimize context usage by enabling compaction and observation masking to summarize verbose outputs, which frees up token space and extends the effective capacity of limited context windows.

Observation masking is a technique that replaces verbose outputs with compact references, keeping context lean and preventing large outputs from consuming your token budget during multi-turn planning.

You can maximize KV-cache hits by stabilizing prefixes and reusing blocks. This approach reduces token cost and improves processing efficiency for memory-constrained agents.

Yes, context partitioning splits tasks across sub-contexts to prevent overflow in any single window, allowing complex tasks to be processed without hitting hard token limits.

Yes, explicit token budgeting manages your context window and triggers compaction automatically, ensuring optimization activates before hard limits are reached.

Avoid context compaction when critical details cannot be safely summarized. If compacting content risks losing essential signal for decisions, keep the full context to preserve accuracy.