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Agent Mesh Coordinator

This coordinator sets up a peer-to-peer mesh network where agents communicate directly without a single point of control. It uses gossip protocols for information sharing, Byzantine fault tolerance for consensus, and supports work stealing plus DHT-based task distribution. You'd reach for this when you need a swarm that can lose a third of its nodes and keep running, or when centralized coordination becomes a bottleneck. The implementation hooks into claude-flow's DAA tools for peer messaging and consensus voting. Fair warning, the complexity overhead is real. Most tasks don't need this level of resilience, but for long-running distributed work where reliability matters more than simplicity, the tradeoff makes sense.

skills.sh installs
1.1K
GitHub stars
81.4K
last updated
Aug 21, 2026

Install

$ npx -y skills add ruvnet/ruflo --skill agent-mesh-coordinator --agent claude-code

Installs into .claude/skills of the current project.

Files


name: mesh-coordinator type: coordinator
color: "#00BCD4" description: Peer-to-peer mesh network swarm with distributed decision making and fault tolerance capabilities:

  • distributed_coordination
  • peer_communication
  • fault_tolerance
  • consensus_building
  • load_balancing
  • network_resilience priority: high hooks: pre: | echo "🌐 Mesh Coordinator establishing peer network: $TASK"

    Initialize mesh topology

    mcp__claude-flow__swarm_init mesh --maxAgents=12 --strategy=distributed

    Set up peer discovery and communication

    mcp__claude-flow__daa_communication --from="mesh-coordinator" --to="all" --message="{"type":"network_init","topology":"mesh"}"

    Initialize consensus mechanisms

    mcp__claude-flow__daa_consensus --agents="all" --proposal="{"coordination_protocol":"gossip","consensus_threshold":0.67}"

    Store network state

    mcp__claude-flow__memory_usage store "mesh:network:${TASK_ID}" "$(date): Mesh network initialized" --namespace=mesh post: | echo "✨ Mesh coordination complete - network resilient"

    Generate network analysis

    mcp__claude-flow__performance_report --format=json --timeframe=24h

Sponsors

Details

Repository
github.com/ruvnet/ruflo
Category
AI & Agent Building
First seen
Jun 3, 2026

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