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Swarm Orchestration

Orchestrate multi-agent swarms with mesh, hierarchical, and adaptive topologies using agentic-flow.

Updated Jul 16, 2026
Search Tags:#parallel execution#multi-agent#task coordination#agentic-flow#distributed ai#load balancing#swarm orchestration
One-click install
npx skills add https://github.com/optimaxin/Tredev_Gems --skill swarm-orchestration-optimaxin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Swarm Orchestration
Source: https://github.com/optimaxin/Tredev_Gems/tree/main/.claude/skills/swarm-orchestration
Command: npx skills add https://github.com/optimaxin/Tredev_Gems --skill swarm-orchestration-optimaxin

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentic-flow.

What problem does it solve? Coordinating multiple AI agents on complex tasks requires manual task distribution, state sharing, and failure handling, which becomes unmanageable beyond a single agent. ## Core Features & Use Cases - Topology Management: Configure mesh, hierarchical, or adaptive swarm topologies with automatic task distribution and load balancing. - Execution Modes: Run tasks in parallel, 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 retry policies, exponential backoff, and task reassignment on agent failure. - Use Case: Building a REST API where a coder agent implements endpoints, a tester writes the test suite, and a reviewer validates the output, all coordinated in parallel with shared schema memory. ## Quick Start Initialize a mesh swarm with agentic-flow, spawn coder, tester, and reviewer agents, then 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 agentic-flow using swarm-init, spawn typed agents like coder and tester, then call task-orchestrate with parallel mode. Tasks execute concurrently with a configurable timeout.

What swarm topology should I use for multi-agent workflows?▼

Use mesh for peer-to-peer distributed decision-making, hierarchical for centralized coordination with specialized workers, or adaptive to let the swarm switch topology based on task complexity.

How do agents share state in an agentic-flow swarm?▼

Agents share state through swarm memory using memory.store to write key-value data and memory.retrieve to read it. This lets agents access shared artifacts like API schemas during execution.

What happens when a swarm agent fails during a task?▼

Configure resiliency with retry policies using exponential backoff and a fallback strategy that reassigns the failed task. The swarm supports up to a configurable number of retry attempts.

Why are my swarm agents not coordinating properly?▼

Coordination failures usually stem from missing memory access or disabled hooks. Verify agents can read and write swarm memory and enable pre-task and post-task hooks for synchronization.

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