Edgar B.C., a data science consultant, published a comparison of running Claude Code, OpenClaw, and Hermes Agent on a Raspberry Pi 4. The piece, published May 24, documents what happens when you give three agent frameworks terminal access, filesystem control, and browser automation on hardware that costs under $100. The finding that matters is about containment — specifically, why builders are choosing this path over their primary machines.
The Containment Pattern
The Mac Mini became the unofficial mascot of AI agents earlier this year, partly because OpenClaw demos popularized it and partly because the machines sold out during peak demand. The RPi4 alternative emerged from a simpler motivation: developers did not want autonomous agents running on their primary work machines.
“Once I give an agent terminal access, browser automation, filesystem control, messaging integrations and long-running execution, I was a bit worried about containment,” the author writes. “This is why I really like to start small on a RPi.”
The approach: separate microSD cards per project, fresh Raspbian installs, cloud-backed LLM inference. In the worst case, format the card and start over. The RPi4 handles cloud-backed workflows without issues. It is cheap, low-power, and always-on.
Three Frameworks, Three Architectures
Claude Code was the simplest to set up and understand. The author describes it as “not far away from prompting your LLM via CLI,” with the primary skill being context management. He paired it with an Obsidian vault to organize consulting work, reports, and connections to research papers. The constrained scope worked well: Claude Code reasoning across a structured knowledge base without needing broad system autonomy.
OpenClaw required understanding the gateway concept and network configuration. The framework itself was not the hard part. Consistency over multi-day operation was. The author found that getting OpenClaw to reliably check files for results and produce consistent output across long-running sessions “was not great,” pointing to the heartbeat versus cron job tradeoff documented in OpenClaw’s own guides. The security model “felt very real very quickly” once the agent had full system access.
Hermes Agent felt different. The author attributes some of this to accumulated experience from prior installations, but notes Hermes “felt more stable than OpenClaw” and “less confusing” after a week of continuous operation running cybersecurity check reports via Obsidian.
The Installation Convergence
All three frameworks now use a nearly identical installation method: a curl command pointing to a shell script with terminal-based configuration. The divergence comes in post-install configuration. Claude Code requires minimal setup. OpenClaw and Hermes require decisions about orchestrator models, skills, and long-horizon task management that are not well-explained during first-time setup. The author notes Hermes felt “a bit more baked” in this regard.
Why This Matters
The community pattern of deploying agents on isolated, disposable hardware rather than primary machines is an operational signal. It means builders trust agent capabilities enough to use them for real work but do not trust containment enough to run them alongside personal data. The RPi4 is a $75 air gap.
This mirrors enterprise security practices (staging environments, sandboxed execution) arriving in the personal agent space without formal guidance. No framework vendor recommended the RPi approach. Builders arrived at it independently because the alternative, giving an autonomous agent root-equivalent access to a laptop with email, banking, and source code, felt reckless.
The author’s summary: “I highly recommend to get a RPi and start experimenting.” The recommendation is about isolation, not hardware.