hive-mind-advanced

Multi-agent coordination is achieved using a queen-led architecture that aligns diverse agents through structured decision-making. You can use consensus algorithms like majority, weighted, or Byzantine fault tolerance to ensure reliable decisions across complex workflows.

A queen-led architecture is a governance layer for multi-agent systems that assigns strategic, tactical, and adaptive roles to specialized agents. It is needed for scalable problem solving and orchestrating diverse agents in large-scale research or complex workflows.

Persistent memory management for agent swarms is handled through memory consolidation, retrieval, and context sharing. The system uses an LRU cache and SQLite WAL mode to maintain collective memory across complex workflows and sessions.

Byzantine consensus mechanisms are supported for fault-tolerant decision making in dynamic environments. This allows the multi-agent swarm to continue operating reliably even if some agents fail or provide conflicting information.

To start orchestrating specialized agents, you need to install Claude Flow, initialize a hive mind, and spawn a swarm. This sets up the queen-led coordination layer and enables persistent memory for your multi-agent system.

Session management in multi-agent coordination supports checkpoints and exports. This allows you to save the state of your swarm's collective memory and structured decisions during complex workflows.