hive-mind-advanced

Multi-agent coordination uses a queen-led architecture where a lead agent oversees specialized workers and applies consensus mechanisms—majority voting, weighted consensus, or Byzantine fault tolerance with 2/3 threshold—to reach collective decisions across research, development, and quality assurance tasks.

Yes. Collective memory persists through SQLite with write-ahead logging (WAL), enabling agents to consolidate and retrieve shared knowledge. Sessions support checkpointing, logs, and export/import to preserve decisions and findings across restarts.

Worker specialization includes researchers, coders, analysts, testers, architects, reviewers, optimizers, and documenters. The queen manages strategic and tactical control, delegating tasks and orchestrating these specialized roles to solve complex, multi-domain problems.

Automatic task distribution assigns work based on agent specialization and scales dynamically. The queen coordinates routing, monitors progress through sessions, and uses checkpoints to ensure reliable completion and fault recovery.

Yes. The architecture handles many agents with hierarchical coordination, LRU caching for memory efficiency, and resilient collaboration through session management. It scales across domains including research, development, data processing, and quality assurance.

Multiple consensus algorithms resolve disagreements: majority vote requires >50%, weighted consensus factors agent expertise or confidence, and Byzantine consensus tolerates up to 1/3 faulty or adversarial agents using a 2/3 threshold for safety.