SuperLocalMemory: Privacy-Preserving Multi-Agent Memory with Bayesian Trust Defense Against Memory Poisoning

We present SuperLocalMemory, a local-first memory system for multi-agent AI
  that defends against OWASP ASI06 memory poisoning through architectural
  isolation and Bayesian trust scoring, while personalizing retrieval through
  adaptive learning-to-rank—all without cloud dependencies or LLM inference
  calls. As AI agents increasingly rely on persistent memory, cloud-based
  memory systems create centralized attack surfaces where poisoned memories
  propagate across sessions and users—a threat demonstrated in documented
  attacks against production systems. Our architecture combines SQLite-backed
  storage with FTS5 full-text search, Leiden-based knowledge graph clustering,
   an event-driven coordination layer with per-agent provenance, and an
  adaptive re-ranking framework that learns user preferences through
  three-layer behavioral analysis (cross-project technology preferences,
  project context detection, and workflow pattern mining). Evaluation across
  seven benchmark dimensions demonstrates 10.6ms median search latency, zero
  concurrency errors under 10 simultaneous agents, trust separation (gap =
  0.90) with 72% trust degradation for sleeper attacks, and 104% improvement
  in NDCG@5 when adaptive re-ranking is enabled. Behavioral data is isolated
  in a separate database supporting GDPR Article 17 erasure requests via
  one-command deletion. SuperLocalMemory is open-source (MIT) and integrates
  with 17+ development tools via Model Context Protocol.