hive-mind-advanced

Multi-agent coordination is managed by a queen-led hierarchy where a strategic queen directs specialized workers like coders, analysts, and testers. This structure enables scalable, resilient decision-making and hierarchical task orchestration for complex software projects.

Multi-agent decision making supports configurable consensus algorithms including majority voting, weighted voting leveraging queen power, and Byzantine fault-tolerant mechanisms. These options ensure reliable collective decisions across various agent types and organizational workflows.

Collective memory operates through a shared knowledge base utilizing an LRU cache and SQLite persistence with WAL mode. It features memory consolidation, association tracking, and session management to maintain durable shared context across agent tasks.

Yes, queen-led swarm coordination integrates with Claude Code, SPARC workflows, and GitHub repo analysis to manage large-scale development efforts. It orchestrates code-review pipelines by dynamically distributing tasks among researcher, coder, and reviewer agents.

Multi-agent coordination provides session tracking with automatic checkpointing and dynamic task distribution featuring auto-scaling. This ensures resilient, continuous execution and adaptive task management across many specialized agent types.

Byzantine fault-tolerant consensus should be used for AI agent orchestration when managing large-scale software projects or organizational workflows where agents may fail or act unreliably. It ensures resilient, collective decision-making despite adversarial or faulty conditions.