What problem does it solve?
Context Engineering reduces LLM performance loss caused by oversized or poorly structured context. It helps teams detect attention-related failures (lost-in-middle), context poisoning, and quota exhaustion, then recommends targeted compaction and isolation strategies to preserve task continuity and lower token costs.
Core Features & Use Cases
- Context health analysis: Token utilization, utilization thresholds, attention-distribution heuristics, and poisoning detection.
- Compression evaluation: Probe-based tests, compression ratio calculation, and quality scoring with recommendations.
- Multi-agent and memory guidance: Partitioning, sub-agent handoffs, KV-cache and file-based memory patterns.
- Use Case: Run an analysis on a long-running agent conversation to identify critical items buried in the middle, compute compaction targets, and emit a concise artifact-trail summary for resumption.
Quick Start
Analyze the provided conversation for token utilization, lost-in-middle and poisoning risks, and return compaction thresholds plus actionable next steps.
