Skills Multi-Agent Deployment Skill for OpenClaw

Name: Multi-Agent Deployment Skill for OpenClaw
Author: openclaw

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.

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/abhinas90/multi-agent-deployment" ~/.claude/skills/openclaw-skills-multi-agent-deployment-skill-for-openclaw && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/abhinas90/multi-agent-deployment" ~/.openclaw/skills/openclaw-skills-multi-agent-deployment-skill-for-openclaw && rm -rf "$T"

manifest: skills/abhinas90/multi-agent-deployment/SKILL.md

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