context-optimization

Context optimization compresses and partitions long conversations to extend effective context window. Techniques include summarizing near-limit content, masking verbose tool outputs with concise references, reusing stable context blocks via KV-cache, and splitting work across sub-agents to maintain small, efficient contexts in each thread.

KV-cache optimization reuses stable context blocks across requests to reduce recomputation and latency. The included Python scripts demonstrate caching patterns that preserve critical signals while cutting redundant processing overhead in long-running agent workflows.

Yes. Compaction summarizes near-limit content to preserve decisions and state, while observation masking replaces verbose tool outputs with concise retrievable references. This approach maintains signal fidelity while freeing token capacity for new reasoning.

Context partitioning divides work across sub-agents to keep each thread's context small and efficient. This reduces per-request token load and latency while enabling complex workflows that would exceed a single agent's context window.

Optimization is preferred for production workflows where cost and latency matter. It works within existing model limits to maximize usable context, reduce recomputation, and maintain performance without upgrading model capacity or incurring higher inference costs.