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Swarm Orchestration

Orchestrates multi-agent swarms with mesh, hierarchical, and adaptive topologies for parallel task execution.

11|Updated Jan 20, 2026
Search Tags:#parallel execution#multi-agent#coordination#agentic-flow#topology#swarm orchestration#task distribution
One-click install
npx skills add https://github.com/ishandutta2007/claude-agent-orchestration --skill swarm-orchestration-ishandutta2007
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Swarm Orchestration
Source: https://github.com/ishandutta2007/claude-agent-orchestration/tree/main/.claude/skills/swarm-orchestration
Command: npx skills add https://github.com/ishandutta2007/claude-agent-orchestration --skill swarm-orchestration-ishandutta2007

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentic-flow.

What problem does it solve? Coordinating multiple AI agents on complex tasks requires manual management of task distribution, topology selection, shared state, and failure handling, which becomes unmanageable beyond a single agent. ## Core Features & Use Cases - Topology Management: Configure mesh, hierarchical (queen-worker), or adaptive topologies that switch based on task complexity. - Task Orchestration: Execute tasks in parallel, sequential pipelines with dependencies, or fully automatic orchestration with time and agent 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, then orchestrate them in parallel while sharing the API schema through swarm memory. ## Quick Start Initialize a mesh swarm with five agents and orchestrate the task of building a REST API with tests in parallel mode.

Frequently Asked Questions about Swarm Orchestration

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate multiple AI agents in parallel?▼

Initialize a swarm with a topology, spawn typed agents like coder and tester, then call task orchestration in parallel mode. With agentic-flow, run swarm-init, agent-spawn for each agent, and task-orchestrate with the parallel flag.

What is the difference between mesh and hierarchical agent topologies?▼

Mesh topology treats all agents as equal peers using broadcast communication and distributed decisions. Hierarchical topology uses a central queen agent that coordinates specialized workers, suiting tasks needing centralized control.

How do AI agents share state in a swarm?▼

Agents share state through swarm memory, a key-value store accessible to all agents. Store artifacts like API schemas with memory.store and let other agents retrieve them with memory.retrieve during execution.

What happens when an agent fails during swarm execution?▼

The swarm supports resiliency configuration with retry policies using exponential backoff and fallback strategies that reassign failed tasks. You can set a maximum number of retry attempts per task.

What are the requirements for running agentic-flow swarms?▼

You need agentic-flow version 1.5.11 or higher and Node.js 18 or higher. Familiarity with distributed systems concepts is helpful but not required.

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