Swarm Orchestration

Orchestrate multi-agent swarms with dynamic topologies and fault tolerance.

Updated Aug 23, 2026

Search Tags:#orchestration #memory #swarm #topology

Ready to run

npx skills add https://github.com/wedosoft/project-a --skill swarm-orchestration-wedosoft

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Swarm Orchestration
Source: https://github.com/wedosoft/project-a/tree/main/.claude/skills/swarm-orchestration
Command: npx skills add https://github.com/wedosoft/project-a --skill swarm-orchestration-wedosoft

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Orchestrates multi-agent swarms using advanced coordination for parallel tasks, dynamic topologies, and fault tolerance.

Core Features & Use Cases

Topology patterns: Mesh, Hierarchical, Ring, Star.
Adaptive strategies and memory coordination for robust tasks.
Memory and monitoring for observability.

Quick Start

Initialize a swarm with topology and agents; orchestrate tasks with parallel/pipeline modes.

Frequently Asked Questions about Swarm Orchestration

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

FAQPage Schema

How do I coordinate multiple agents working on distributed tasks in parallel?▼

Multi-agent swarm orchestration distributes tasks across agents using parallel or pipeline execution modes. Initialize agents with a topology pattern (mesh, hierarchical, ring, or star), then submit tasks to the orchestrator, which handles automatic task distribution, inter-agent communication, and load balancing across your swarm.

What topology patterns are available for organizing agent swarms?▼

Swarm orchestration supports four core topology patterns: mesh (all agents connected), hierarchical (parent-child relationships), ring (agents in a loop), and star (central coordinator). Choose based on your communication needs, latency sensitivity, and fault tolerance requirements for distributed task coordination.

How does memory coordination work across agents in a swarm?▼

Memory coordination synchronizes state and context across distributed agents, enabling them to share decisions and outcomes. The orchestrator manages memory consistency during parallel execution, ensuring agents can access shared data and coordinate on complex workflows without race conditions or data conflicts.

Can I handle agent failures and retries automatically in a swarm?▼

Yes, swarm orchestration provides fault tolerance through automatic retries and fallback mechanisms. If an agent fails or a task times out, the orchestrator can reassign work to healthy agents, reroute through alternative paths in the topology, and recover distributed workflows without manual intervention.

How do I monitor and observe what's happening inside a multi-agent swarm?▼

Monitoring integrates with agentic-flow hooks for real-time observability. You can track task distribution, inter-agent communication, memory state changes, and topology events. This visibility helps debug distributed workflows, identify bottlenecks, and verify that adaptive strategies are responding correctly.

When should I use swarm orchestration instead of a single agent?▼

Use swarm orchestration when tasks require parallel execution, dynamic workload distribution, or resilience across multiple agents. It's essential for scaling beyond single-agent limits, implementing complex multi-step workflows, and building systems that need automatic load balancing and fault tolerance in distributed environments.