Cross-machine AI collaboration — real-time peer-to-peer messaging, chain orchestration, dispatch gates, session-aware task agents, and fleet-wide productivity visibility across multiple Claude Code instances
Delivery confirmation — ACK tracking, retry on timeout, failure alerting
Submit or view multi-step task chains — orchestrate sequential work across fleet nodes
Run the full 7-channel communication test to a target node
HARD-GATE — verify safety before changing fleet configuration or daemon settings
Show the live fleet productivity dashboard — what every machine, agent, and work item is doing right now
Use this agent to coordinate fleet-wide operations across multiple machines. Examples: <example>Context: Multiple machines are available and work needs to be distributed. user: "Run this analysis across all nodes" assistant: "I'll use the fleet-coordinator to dispatch tasks and collect results" <commentary>Work spans multiple machines, so the coordinator dispatches, monitors, and synthesizes.</commentary></example> <example>Context: A node is unhealthy or unreachable. user: "mac3 seems stuck" assistant: "Let me use fleet-coordinator to diagnose and run repair escalation" <commentary>Fleet health issue requires coordinator to probe, diagnose, and escalate repairs.</commentary></example>
Use this agent to handle all fleet communication so the main coding agent stays focused on code. Examples: <example>Context: Fleet messages are arriving while coding is in progress. user: "Handle fleet comms" assistant: "Spawning fleet-liaison to monitor inbox, batch notifications, and escalate only what matters" <commentary>Coding agent delegates all fleet I/O to the liaison — zero distraction.</commentary></example> <example>Context: A rebuttal cycle verdict arrived from mac2. user: "Check fleet" assistant: "Fleet liaison has 1 pending verdict from mac2 (confidence 0.82). Batched as INFO — read when ready." <commentary>Liaison classifies, batches, and presents a summary instead of raw message flood.</commentary></example>
Use this agent for background fleet tasks dispatched by the coordinator. Examples: <example>Context: The coordinator has dispatched a research task to this node. user: "Run codebase analysis on the context-dna module" assistant: "Spawning a fleet-worker to execute this in the background" <commentary>Background task dispatched by coordinator — fleet-worker executes independently and reports results via NATS/seed files.</commentary></example> <example>Context: A worker task arrived via seed file. user: "Process the pending fleet task" assistant: "I'll use fleet-worker to execute the queued task and write results" <commentary>Seed file contained a task — fleet-worker picks it up, executes, and writes results.</commentary></example>
Delivery confirmation protocol — ACK tracking, retry on timeout, failure alerting
Chain orchestration — multi-step task dependencies across fleet nodes with automatic sequencing
Run the full 7-channel communication test — verifies every delivery path to a target node
HARD-GATE — verify safety and blast radius before changing fleet configuration, LaunchAgents, or daemon settings
HARD-GATE — verify target node readiness and task safety before dispatching work to remote fleet nodes
Admin access level
Server config contains admin-level keywords
External network access
Connects to servers outside your machine
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Uses power tools
Uses Bash, Write, or Edit tools
Uses power tools
Uses Bash, Write, or Edit tools
No model invocation
Executes directly as bash, bypassing the AI model
No model invocation
Executes directly as bash, bypassing the AI model
Cross-machine AI collaboration for Claude Code, Cursor, VS Code, Codex, and Gemini.
Real-time peer-to-peer messaging with a 9-priority self-healing fallback chain, session-aware autonomous task agents, HMAC-signed communication, and fleet-wide productivity visibility. Messages always deliver — even when NATS is down, HTTP is blocked, and SSH is your only path.
Quick Start · Architecture · Demo · Install · Docs
A single developer with a coordinated fleet of AI agents will out-ship a team of ten with isolated IDEs.
Multi-Fleet is built on one belief: the bottleneck in software is no longer typing — it's coordination. When you have one AI assistant in one IDE, you're driving a car. When you have a hundred AI assistants running on a hundred machines, all talking to each other, all aware of what the others just shipped — you're not driving anymore. You're conducting an orchestra.
| Scale | What it unlocks |
|---|---|
| 1 machine | Persistent context across sessions, never re-explain your codebase |
| 2 machines | Background agent on machine #2 reviews every PR you push from #1 — instant second opinion |
| 3–5 machines | A team of AI agents you own. One races to fix the bug, one writes the test, one updates the docs. Best result wins. |
| 10+ machines | A swarm. Refactor your entire monorepo overnight. Each agent owns a directory. Failures auto-redistribute. |
| 100+ machines | A coding datacenter. Continuous fleet-wide refactor. AI agents propose changes 24/7. You wake up to a stack of evidence-backed PRs. |
The protocol scales linearly. The architecture scales horizontally. The only ceiling is your imagination.
You have three Macs. Two of them are working on your codebase right now. The third is sleeping. One has the database. One has the GPU. One has your IDE open.
Without Multi-Fleet: You manually ssh into each machine, copy-paste commands, lose context, forget which session knows what. When one machine drops off Wi-Fi, your workflow stops.
With Multi-Fleet: Your AI assistant on mac1 sends a task to mac2, mac2 picks it up in its own Claude Code session, mac3 wakes from sleep to run the GPU job. If NATS goes down mid-conversation, the message reroutes through HTTP. If HTTP fails, it falls through SSH. The message gets there.
"@mac3 train the model overnight"
│
▼
┌──────────────────────────────────────────────────┐
│ mac1 (you) ◀── 9-priority cascade ──▶ mac3 │
│ │
│ P0 Discord/Cloud │
│ P1 NATS pub/sub (clustered, primary) │
│ P2 HTTP direct (both daemons up) │
│ P3 Chief relay (one peer reachable) │
│ P4 Seed file (SSH-write to inbox) │
│ P5 SSH direct (keys configured) │
│ P6 Wake-on-LAN (target asleep) │
│ P7 Git push (last resort, always works) │
│ P8 Local IPC (Superset terminal-host.sock) │
└──────────────────────────────────────────────────┘
npx claudepluginhub supportersimulator/multi-fleet --plugin multi-fleetMulti-model consensus system — 3 LLMs cross-examine each other to catch blind spots on critical decisions
Let local Claude Code sessions talk to each other in natural language.
Repowire mesh usage skills for AI coding agents: cross-agent review and planning, delegate, usage patterns, and install/update. Backend-agnostic and parameterised on the agent you choose.
Multi-agent orchestration for Claude Code. 12 specialized agents working in parallel — planning, building, reviewing, debugging. Plus a Hub for always-alive multi-project sessions controllable from Telegram or Slack.
Enterprise AI agent orchestration plugin with 150+ commands, 74+ specialized agents, SPARC methodology, swarm coordination, GitHub integration, and neural training capabilities
Multi-agent coordination with agent-swarm MCP
Turn any folder of projects into an AI-orchestrated workspace with Slack and Telegram integration. Creates a Project Orchestrator (PO) that routes requests, plans execution with dependency-aware phases, delegates to workspace-level Claude agents, and returns structured results.