Hermes as Brain, OpenClaw as Arms: The Chief-of-Staff Multi-Agent Setup

Key Takeaways

• Hermes solves OpenClaw's memory problem. OpenClaw's context resets per session and can't compound knowledge over time. Hermes was designed for long-running persistent memory — routing all AI work through it means your agent gets smarter with every interaction rather than starting fresh each time.
• OpenClaw sub-agents get specialized skills from GitHub repos. Point a new OpenClaw instance to a skill repo (e.g., Corey Haynes's marketing-skills repo) and it becomes a domain expert in seconds — no manual configuration, just one instruction to "get this repo and implement these skills."
• Deploy both agents on a VPS for 24/7 availability. A Hostinger KVM 2 plan ($9/month) handles both Hermes and OpenClaw in Docker containers. Power outages or travel no longer kill your agent setup. One-click deploys through the Hostinger dashboard — no terminal required.
• The "Chief of Staff" system prompt is the key architectural piece. Craig uses a free GitHub repo (linked in description) that gives Hermes a structured role: personal priorities, delegation patterns, skill awareness, and communication preferences. Without this, Hermes is a blank slate.
• OpenClaw is never user-facing in this setup — it's a sub-agent only. Users communicate with Hermes via Telegram. Hermes decides when to invoke an OpenClaw sub-agent and packages the brief it sends. OpenClaw focuses entirely on execution, not conversation.

How the Live Demo Works

Craig shows a real end-to-end run: he asks Hermes (via Telegram) to call Gary, his email/copywriting OpenClaw sub-agent, and create an email newsletter about smart model switching for AI agents. Here's the sequence:

1. Hermes pulls its chief-of-staff skill and identifies that this request requires the Gary sub-agent.
2. Hermes researches model-switching best practices using its web search tools (Minimax M2.7 via OpenRouter).
3. Hermes prepares a structured brief with audience, objective, key points, and tone instructions.
4. Hermes calls the Gary OpenClaw sub-agent with the brief via an "agent call."
5. Gary returns the completed newsletter copy to Hermes.
6. Hermes presents the output to Craig in Telegram with full context about what was done.

Total time from "call Gary" to newsletter draft: a few minutes. The compounding value: Hermes's knowledge of Craig's audience, preferences, and previous campaigns informs the brief — knowledge that Gary doesn't have and doesn't need.

Why This Architecture Works

The video articulates a key principle: route as much work as possible through one place (Hermes) because compounding context is where the real leverage lives. Every piece of work Hermes sees — your emails, your creative decisions, your feedback on Gary's copy — becomes context for the next task. OpenClaw sub-agents are powerful but stateless between calls. Hermes provides the continuity layer.

Think of it this way: Hermes is your COO. Gary is a specialist contractor. The contractor is excellent at their job but has no institutional knowledge. Your COO briefs them with everything they need, evaluates their output, and files away what was learned. That's the architecture Craig is running.

Related on OpenClawDatabase

• Hermes Agent Guide — full guide to Hermes setup, memory, tasks, and MCP tools
• OpenClaw Skills Guide — how to give OpenClaw sub-agents specialized capabilities
• OpenClaw + Hermes Multi-Agent Workflow — Alex Finn's complementary take on the same architecture