hive-mind-advanced

Multi-agent AI systems are coordinated through queen-led orchestration, which manages worker specialization across roles like researchers, coders, and analysts to achieve strategic objectives with collective memory and configurable consensus algorithms.

Queen-led orchestration is a coordination mechanism where a central queen agent manages task distribution and monitors consensus-driven progress across specialized worker agents, ensuring adaptive decision making and collaborative problem solving within a swarm.

Yes, Byzantine fault tolerance is supported as a configurable consensus mechanism alongside majority and weighted algorithms, ensuring robust and reliable decisions across distributed autonomous agents even with conflicting inputs.

Collective memory persistence uses a shared knowledge base with LRU caching and SQLite WAL mode, ensuring durable state retention and session management across multiple swarms and complex workflow orchestration.

Distributed workflow orchestration scales across multiple swarms using session management and task distribution features, allowing you to initialize a Hive Mind, spawn swarms with clear objectives, and monitor consensus-driven progress.