What problem does it solve? Coordinating multiple AI agents across complex tasks like research, full-stack development, and testing requires manual orchestration, topology selection, and state management that is error-prone and time-consuming. ## Core Features & Use Cases - Swarm Topology Patterns: Configure mesh, hierarchical, star, and ring topologies matched to research, development, testing, or pipeline workflows. - Parallel Task Orchestration: Spawn specialized agents (researchers, coders, testers, analysts) and execute tasks in parallel with monitoring and fault tolerance. - Memory and State Management: Persist findings across sessions with namespaced memory, snapshots, backups, and neural pattern learning. - Use Case: Spin up a hierarchical development swarm with an architect, backend and frontend developers, testers, and a DevOps engineer to design, implement, test, and deploy a REST API in coordinated phases. ## Quick Start Ask the AI to initialize a Claude Flow swarm with a chosen topology and spawn specialized agents to research a topic or build and test an application in parallel.