Every engineering team deploying production-grade LLM agents or RAG pipelines faces the exact same architectural bottleneck: truncated network data streams causing broken JSON schemas and triggering unhandled parsing exceptions (JSONDecodeError) downstream.
To fix this friction, our team at Kylik Daniels Labs engineered and benchmarked a local, zero-cloud-overhead Python middleware framework designed to intercept, heal, and stabilize malformed AI streaming outputs before they compromise data layers.
Evaluated under asynchronous massive parallel load loops simulating high-frequency corporate traffic variables (i7-14700F / 32GB RAM Architecture):
Throughput Capacity: 10,000 Simultaneous Streaming Anomalies Processed
Total Execution Speed: 0.1215 Seconds (Net)
