hive-mind-advanced

Queen-led multi-agent orchestration uses hierarchical strategic, tactical, and adaptive queens to coordinate diverse agent roles like researchers and coders. It manages high-level planning and execution across complex projects using specialized worker agents.

You coordinate diverse AI agents by implementing consensus protocols like majority, weighted, or Byzantine alongside a collective memory layer. This architecture uses SQLite persistence and an LRU cache to maintain shared knowledge across different agent roles.

Yes, SQLite provides persistent memory for multi-agent coordination through WAL persistence. This collective memory system retains context across sessions, enabling memory consolidation and a shared knowledge base for all coordinated agents.

The best way to manage large-scale AI agent coordination is using a hierarchical architecture with specialized workers like analysts, testers, architects, and reviewers. This structure allows strategic queens to plan while workers execute and validate projects.

Byzantine consensus protocols are needed in multi-agent orchestration when handling complex, large-scale tasks requiring fault-tolerant agreement across diverse agents. The system also supports majority and weighted consensus for varying coordination requirements.

Hive-mind-advanced supports memory consolidation for AI agents through a collective memory layer using SQLite persistence. This architecture ensures shared knowledge is retained and accessible across different coordination sessions.