hive-mind-advanced

Multi-agent orchestration with shared memory uses a queen-led coordination model to spawn worker agents, distribute tasks dynamically, and apply consensus algorithms like majority, weighted, or Byzantine for robust collective decision-making.

Multi-agent coordination supports majority, weighted, and Byzantine consensus algorithms to ensure robust decision-making across distributed agents, allowing you to choose the validation strictness based on your task requirements.

Hive-mind orchestration is built for complex, collaborative projects such as architecture planning, system design experiments, and large-scale simulations that require dynamic task distribution and consensus across specialist agents.

Spawning a multi-agent swarm requires initializing the hive mind, then spawning workers with a designated queen type, a maximum worker count, and a chosen consensus method to dynamically distribute and execute tasks.

Multi-agent orchestration provides session management with checkpoints and persistent collective memory storage, retaining shared knowledge across long-running experiments and sessions to prevent coordination drift.

A centralized queen-led coordination model is not ideal for fully decentralized peer-to-peer agent networks, as it relies on hierarchical oversight and a single coordinator to distribute tasks and manage collective memory.