What problem does it solve? Coordinating multiple AI agents on complex tasks requires manual management of task distribution, communication, and failure handling, which becomes unmanageable as agent counts grow. ## Core Features & Use Cases - Multiple Topologies: Configure mesh, hierarchical, or adaptive swarm structures to match task complexity and coordination needs. - Flexible Execution Modes: Run tasks in parallel, as sequential pipelines with dependencies, or let the swarm auto-orchestrate based on goals and constraints. - Shared Memory & Resilience: Share state across agents via swarm memory, with load balancing, retry policies, and fault-tolerant task reassignment. - Use Case: Building a REST API with a team of agents—spawn a coder, tester, and reviewer, orchestrate them in parallel, and share the API schema through swarm memory so every agent works from the same context. ## Quick Start Initialize a mesh swarm with five agents and orchestrate the task of building a REST API with tests in parallel mode.