hive-mind-advanced

Multi-agent coordination uses a hierarchical structure with a queen-led orchestration system that directs specialized workers and maintains shared memory. This approach enables agents to collaborate, reach consensus on decisions, and learn collectively across tasks like research, coding, analysis, and testing.

Byzantine consensus is a fault-tolerant decision-making mechanism that ensures reliable collective intelligence even when some agents fail or provide incorrect information. Hive Mind implements majority, weighted, and Byzantine consensus protocols to guarantee robust agreement across distributed agent systems.

Collective memory is a shared knowledge base where all agents store and retrieve information with LRU caching and SQLite persistence. This allows agents to learn from previous work, consolidate knowledge, and avoid redundant computation across sessions.

Yes. Queen-led orchestration coordinates specialized workers across the full development lifecycle—from microservices architecture design and coding through testing and optimization—with strategic, tactical, and adaptive queens directing workers by role and task priority.

Hive Mind depends on claude-flow for workflow execution. It includes scripts and references as core components and manages session state, task distribution, and memory persistence internally without additional external dependencies.

Byzantine consensus handles adversarial or faulty agent conditions where majority voting alone is insufficient. Use weighted consensus for agents with different reliability or expertise levels, and Byzantine protocols when system resilience against unpredictable agent failures is critical.