Memory Degradation Detection

Long-session LLM memory degradation (entropy) is the silent killer of complex coding projects. Models like Gemini, GPT-4, and Claude all suffer from it, leading to hallucinations and lost context.I've developed an open-source protocol that temporarily "fixes" this issue by structuring

Unique: The detection system is designed to work seamlessly with the LLM's internal metrics, providing insights without requiring extensive external instrumentation.

vs others: Offers more granular detection capabilities compared to generic monitoring tools, allowing for targeted interventions.

via “memory lifecycle management with temporal tracking”

Domain-driven memory engine with graph storage, embeddings, and semantic search

Unique: Integrates temporal tracking as a domain concern rather than a storage concern, allowing domain aggregates to define custom decay functions and lifecycle policies that are independent of the storage backend

vs others: More flexible than TTL-based expiration (Redis, DynamoDB) because it supports custom decay functions and lifecycle hooks; simpler than time-series databases (InfluxDB, TimescaleDB) for memory-specific workloads

via “memory safety vulnerability detection”