hive-mind-advanced

Multi-agent coordination uses a queen-led hierarchy where a central orchestrator directs specialized workers (researchers, coders, analysts, testers) and applies consensus mechanisms—majority, weighted, or Byzantine fault-tolerant voting—to aggregate their outputs into reliable collective decisions without single points of failure.

Persistent memory combines an LRU cache layer for fast access with SQLite WAL (Write-Ahead Logging) storage for durability, enabling agents to share learnings and context across sessions while maintaining consistency and recovery guarantees.

Yes. Queen-led multi-agent orchestration handles complex architecture projects by distributing specialized tasks—research, implementation, validation, metrics collection, documentation—to workers under centralized guidance, then synthesizing results through consensus mechanisms for validated designs.

Initialize a hive-mind instance, spawn workers with task descriptions, and the queen automatically routes work to specialists based on role expertise, monitors progress in real-time, and collects consensus signals to guide next phases.

A queen-led architecture provides top-down strategic direction and hierarchical oversight, preventing coordination deadlock and ensuring all workers align to shared goals, whereas flat systems require consensus to emerge without centralized guidance.

No. Memory is optional and configurable—use LRU cache alone for stateless tasks, or add SQLite persistence for workflows requiring learnings to survive agent restarts or span multiple sessions.