swarm-advanced

You can orchestrate distributed multi-agent workflows by configuring swarm topologies such as Mesh, Hierarchical, Star, or Ring. This allows specialized agents to coordinate complex research, development, testing, and analysis tasks based on your specific structural requirements.

Available execution strategies for distributed agent swarms include Adaptive, Balanced, Specialized, and Parallel. These approaches define how agents coordinate tasks, allowing you to optimize workflows for complex research, development, testing, and analysis operations.

Yes, you can manage multi-agent swarms using MCP tools and CLI commands. This integration provides comprehensive control over distributed workflows, enabling the execution of advanced techniques like fault tolerance, memory management, and neural learning.

Agent swarms handle errors and memory using advanced techniques like fault tolerance and memory management during execution. These mechanisms ensure distributed workflows remain resilient, allowing specialized agents to operate continuously without losing critical data.

Choose a mesh topology for agentic workflows requiring decentralized peer-to-peer coordination among agents, or a hierarchical topology for structured, top-down execution. The selection depends on whether your distributed workflow needs egalitarian collaboration or centralized task delegation.