How to Set Up Hermes to Supervise OpenClaw
If you already run OpenClaw, you probably know where it starts to feel thin. It can execute well, reply across channels, and run scheduled jobs, but it does not naturally learn from what just happened. It will not wake up one day with better judgment because yesterday went badly.
That is where Hermes Agent fits.
Hermes adds the layer OpenClaw is missing: persistent memory, skill synthesis, failure review, and a feedback loop that can improve how your setup works over time. Put them together and you get a split that makes sense. OpenClaw does the work. Hermes watches, reviews, and helps the system improve. If you want background on either side first, start with the OpenClaw docs and the Hermes docs.
This guide shows how to set that up.
Why the Pairing Works
OpenClaw is good at operational work. It handles messaging, scheduled jobs, skills, and reliable task execution.
Hermes is better suited for the meta layer. It can learn from previous runs, turn repeated behavior into reusable skills, compress memory, and act more like a supervisor than a worker.
That split is the whole appeal. You let OpenClaw stay focused on execution while Hermes handles oversight.
What You Need
- A working OpenClaw installation with your
~/.openclawconfig, skills, and gateway already running - Linux, macOS, or WSL2
- Python 3.11 or newer
- Access to at least one model provider such as OpenRouter, Anthropic, OpenAI, or Ollama
- A VPS or dedicated machine if you want both systems running full-time
Install Hermes Agent
Run this:
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
Then reload your shell:
source ~/.bashrc # or ~/.zshrc
Check that it installed correctly:
hermes --version
If you want the official walkthrough, use the Hermes docs.
Migrate Your OpenClaw Setup
Hermes includes a migration path for OpenClaw. If you want to inspect the project itself first, check the Hermes GitHub repo.
hermes claw migrate
Run it with --dry-run first if you want to see what will be imported.
If you would rather start fresh, skip the migration and go straight to hermes setup.
Run the Setup Wizard
hermes setup
A few choices matter more than the others.
Provider: Use the same model family you trust for OpenClaw. Keeping both systems reasonably aligned makes behavior more predictable.
Messaging adapters: Turn on the same channels OpenClaw already uses, such as Telegram or Discord. That makes it easier for Hermes to observe what OpenClaw is doing in shared spaces.
Gateway: Enable the Hermes gateway service so it can stay available in the background.
Profiles: Create a dedicated supervisor profile with:
hermes profile create supervisor
This keeps your oversight instance separate from any other Hermes profiles you may want to run.
Decide How Hermes Should Supervise OpenClaw
There are three practical ways to do it.
Chat-Based Supervision
This is the simplest setup.
Put Hermes and OpenClaw in the same Telegram group, Discord server, or private control channel. Then give Hermes a standing instruction like this:
You are my OpenClaw supervisor. Monitor @openclawbot. Review every task it completes. Respond with [ACK] to approve, [REJECT] plus a reason to stop, or [IMPROVE] plus suggestions. Log everything to memory and synthesize new skills when patterns emerge.
That gives Hermes a clear role. OpenClaw does the task. Hermes reviews the result.
Programmatic Control Through MCP or API
If you want tighter control, Hermes also ships with MCP support and an OpenAI-compatible API server.
Start Hermes with MCP enabled:
hermes gateway start --mcp
Then expose OpenClaw through its gateway, API, or Mission Control dashboard. From there, you can give Hermes a tool that calls OpenClaw directly.
A typical delegation prompt looks like this:
Use your OpenClaw control tool to delegate this task to the OpenClaw instance, then review the output and learn from it.
This setup is better if you want Hermes acting like an actual manager instead of just a second bot reading a group chat.
Hybrid Group Orchestration
You can also keep things simple:
- Hermes acts as the manager
- OpenClaw acts as the worker
- Hermes reviews failures, stores what it learns, and suggests updates to your OpenClaw skills
That model is less formal, but it works well if most of your workflow already lives in chat.
Turn on Learning and Monitoring
Hermes usually enables its learning loop during setup. Once it is running, a few commands are worth checking:
hermes skills listto see what Hermes has synthesizedhermes memory search "openclaw review"to inspect what it has learned from reviewing OpenClaw activity
For long-running setups, keep Hermes in the background with profiles and use either hermes cron or OpenClaw’s scheduler for recurring checks.
A Simple Example
User: Research the latest AI papers and summarize them. Hermes: Delegates the task to OpenClaw, reviews the result, responds with approval or feedback, and turns the workflow into a reusable skill if the pattern repeats.
A Few Things to Watch For
Resource usage: Hermes can get heavy during active learning. Give it enough memory, or use model compression if the machine is tight on RAM.
Security: Hermes creates filesystem snapshots before risky changes. That is useful when you are letting an agent modify files or settings. If you plan to expose either system beyond your laptop or LAN, spend a few minutes reading the deployment and security sections in the docs first.
Migration confusion: If Hermes behaves like it is still OpenClaw after migration, reset the profile and run the migration again.
Multiple instances: Run hermes -p supervisor to keep the oversight profile separate from your main Hermes environment.
Final Thought
This setup does not magically make either agent smarter on day one. What it gives you is a cleaner division of labor. OpenClaw executes. Hermes watches what happened, keeps track of patterns, and helps improve the system over time.
If that is what you want, the pairing is worth setting up.