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 a workspace-{agent}/ folder with SOUL, memory, drafts, skills, and Claude settings scaffolding.
2. Define Each Agent's Role
Edit each workspace's SOUL file:
- 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 a recurring cron job for each agent, for example:
{
"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
Ideal Buyer
This skill is for operators who already know they want multiple agents, but do not want to hand-assemble workspace scaffolding, routing config, memory propagation, and deployment steps from scattered docs.
Example Use Cases
- Build a 4-agent content + research + build + coordinator setup
- Split a single overloaded assistant into role-specific workspaces
- Stand up a VPS-hosted OpenClaw fleet with shared memory conventions
Expected Outcome
By the end of this skill, you should have:
- Generated workspace scaffolding for each agent
- A reusable routing config pattern with model fallbacks
- A repeatable memory sync workflow across agents
- A deployment script you can adapt for your own 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