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Swarm Orchestration

Coordinate parallel AI agents with configurable topologies and shared memory.

1|1|Updated Apr 11, 2026
Search Tags:#parallel execution#multi-agent#distributed AI#fault tolerance#load balancing#swarm orchestration#agent topology
One-click install
npx skills add https://github.com/harshaldhaduk/Lattice --skill swarm-orchestration-harshaldhaduk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Swarm Orchestration
Source: https://github.com/harshaldhaduk/Lattice/tree/main/.claude/skills/swarm-orchestration
Command: npx skills add https://github.com/harshaldhaduk/Lattice --skill swarm-orchestration-harshaldhaduk

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinating multiple AI agents working on parallel tasks often leads to duplicated work, conflicting changes, and inefficient task distribution, as agents operate without shared context or centralized coordination.

Core Features & Use Cases

  • Flexible Topology Support: Configure mesh, hierarchical, or adaptive agent network topologies to match the complexity of your workflow.
  • Automated Task Orchestration: Distribute parallel or sequential tasks across agents with built-in load balancing and fault tolerance for failed agents.
  • Shared Memory Coordination: Enable agents to access shared context to avoid overlapping work and conflicting code changes.
  • Use Case: Use this Skill to coordinate a team of AI agents building a full-stack application, with separate coder, tester, and reviewer agents working in parallel without stepping on each other's work.

Quick Start

Use the Swarm Orchestration skill to initialize a 5-agent mesh topology and orchestrate parallel execution of a full-stack API build task with automatic conflict detection.

Frequently Asked Questions about Swarm Orchestration

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

FAQPage Schema
How do I prevent conflicting code changes when running parallel AI agents?▼

Prevent conflicting code changes in parallel AI agents by using shared memory coordination, which allows agents to access shared context and avoid overlapping work. Pre-execution conflict detection ensures agents do not step on each other's modifications.

What is the best way to distribute tasks across multiple AI agents?▼

Distribute tasks across multiple AI agents using automated task orchestration with built-in load balancing. This allocates parallel or sequential workloads across agents while providing fault tolerance for failed agents in distributed AI workflows.

Can I configure a hierarchical topology for multi-agent AI workflows?▼

Yes, you can configure hierarchical topologies for multi-agent AI workflows. The orchestration supports configurable mesh, hierarchical, or adaptive agent network topologies to match the complexity of your specific distributed AI workflow requirements.

How do I handle fault tolerance when an AI agent fails during parallel execution?▼

Handle fault tolerance when an AI agent fails during parallel execution through automated task orchestration. The system includes built-in load balancing and fault tolerance mechanisms to manage failed agents within distributed AI workflows.

Does swarm orchestration work for full-stack application builds and testing pipelines?▼

Yes, swarm orchestration works for full-stack application builds and testing pipelines. It coordinates separate coder, tester, and reviewer agents working in parallel, satisfying requirements for fault-tolerant agent orchestration and pre-execution conflict detection.

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