Multi-Agent Deployment Skill for OpenClaw

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name: Multi-Agent Deployment Skill for OpenClaw slug: multi-agent-deployment version: 1.0.1 description: "Deploy a production-ready multi-agent fleet in OpenClaw. Includes step-by-step setup guide, workspace templates, and Python automation scripts for agent creation, routing config, memory sync, and cloud deployment — based on a real working 4-agent production setup."

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What This Skill Does

Guides you through deploying 3-5 specialized AI agents in OpenClaw that work as a coordinated fleet. Based on a real production setup running on a Hostinger VPS with Docker.

Included Files

| File | Purpose | |------|---------| | agent_setup.py | Creates workspace directory structure for any number of agents | | routing_config.py | Generates openclaw.json agent entries with model routing and fallbacks | | memory_sync.py | Syncs Cross-Agent Intel sections across all agent MEMORY.md files | | deploy.sh | Uploads workspace files to VPS and restarts the container |

Step-by-Step Setup

1. Create Workspace Structure

python3 agent_setup.py --agents pat scout publisher builder --base /data/.openclaw

Creates workspace-{agent}/ with SOUL.md, MEMORY.md, drafts/, skills/, .claude/settings.json, .claudeignore.

2. Define Each Agent's Role

Edit each workspace-{agent}/SOUL.md:

Set the agent's mission and responsibilities

Define which tools it uses

Add hard limits and escalation rules

3. Generate Routing Config

# Preview output
python3 routing_config.py --agents main scout publisher builder
Write directly to openclaw.json
python3 routing_config.py --agents main scout publisher builder \
  --output /data/.openclaw/openclaw.json

Configures model routing with OpenRouter fallbacks (minimax → deepseek → kimi).

4. Set Up Cron Jobs

Add to your cron/jobs.json for each agent:

{
  "name": "Agent: Daily Run",
  "agentId": "scout",
  "schedule": { "expr": "0 10 * * *" },
  "enabled": true
}

5. Deploy to VPS

bash deploy.sh --vps root@your-vps-ip --key ~/.ssh/your_key

6. Sync Agent Memory

Run nightly or manually to propagate cross-agent intelligence:

python3 memory_sync.py --base /data/.openclaw --agents pat scout publisher builder

Architecture Pattern

Coordinator (main) — always-on Telegram, approval queue, briefings
    ├── Scout       — market intel, inbound monitoring, trends
    ├── Publisher   — content drafts for Twitter/LinkedIn/video
    └── Builder     — skill development, marketplace research

Each agent has:

Isolated workspace with its own SOUL.md and memory

Separate cron schedule

Model routing with fallbacks via OpenRouter

Shared memory sync via Cross-Agent Intel

Requirements

OpenClaw running on a VPS (Docker)

OpenRouter API key (for model routing)

SSH access to your VPS

What Makes This Different

Real production patterns — not examples, this is a live setup

Isolation by design — each agent has its own workspace and memory

Fallback routing — agents keep running if a model goes down

Memory persistence — agents remember context across sessions and compaction