Editor's Note: What's it really like to set up your own OpenClaw experiment? We interviewed three experimenters at Civic to get you the skinny on how to do it, the best hot tips, examples of work it did, and takeaways for the future.

TL;DR: Experimenters found OpenClaw highly powerful and flexible when set up correctly on dedicated or virtual machines. Top tips include using the one-liner installer, preparing keys ahead of time, limiting permissions, and choosing a strong model. The biggest wins came from OpenClaw acting like a real computer user, autonomously browsing, managing projects, and completing long-running, stateful tasks while users were AFK.

What did you set OpenClaw up on?

Experimenter 1
MacOS. My personal MacBook Air. I then decided to get a Mac mini because OpenClaw would shut down anytime I closed my comp. Haven't set it up on there yet.

Experimenter 2
On a VM inside my M4 Mac mini.

Experimenter 3
I set mine up on virtual machines, using at first a macOS, then a Debian Linux distribution using UTM, the VM tool for Mac. I gave it 12GB of RAM, as well as 100GB of space.

What are your best hot tips for people just beginning to set up OpenClaw?

Experimenter 1
It's extremely capable and might solve tasks in ways you didn't expect. For example I thought I had set up Civic correctly. But when I watched what OpenClaw was doing, it was going to Nexus, opening the browser, taking my text and inputting it into the prompt. Waiting for Nexus to respond and then sending me the response. If it can do that, it can basically do everything you can do on the computer. So be careful with the access you give it.

Experimenter 2

  • Use the one-liner installer.
  • Set up Telegram for interaction — create a dedicated group with topics to organize different projects.
  • If you change Telegram bots, reset the update offset file (echo '{"{"}\"version\":1,\"lastUpdateId\":0{"}"}' > ~/.OpenClaw/telegram/update-offset-*.json) — this tripped us up.

Experimenter 3
Have all the keys and access tokens ready, don't skip install steps, and be on a clean machine so you can give OpenClaw permissions to operate. Don't install random "skills" without checking in detail what they do (with another LLM if you are unsure), since they may contain malware. Use any model you want, but make sure you pick a smarter one, since tool calling and more complex tasks will defeat simpler models.

What projects or experiments have you done so far?

Experimenter 1
Browser use is incredibly powerful. If the agent can surf the web in the same way we do, it opens up doors to so many interesting opportunities. For example, without coding anything, I can ask OpenClaw to go to my X analytics, provide insights, look at content and provide a strategy on how to post. It can keep tabs on personal projects I am building and self-organise research, so I can ask it for information and it will know exactly how to find it.

Experimenter 2

  • Die Forward (Colosseum Hackathon) — a text-based social roguelite game with Solana integration. I helped build the Next.js frontend, deploy an Anchor smart contract for on-chain escrow, create the pitch deck/video, and manage forum engagement. The cool part: the agent can actually play the game via API and engage with the community autonomously.
  • BetweenUs — helped rebrand from therapist-focused to coach-focused, updated all the copy/UI.
  • Email/Calendar monitoring — set up heartbeat-based periodic checks on Gmail and Google Calendar via MCP integrations.
  • Hackathon community engagement — autonomously check and reply to forum posts.

Experimenter 3
Most interesting: having an agent that persists across sessions with real memory, can commit code to GitHub, deploy smart contracts, and engage with communities on my behalf — all while I'm AFK or out and about using the Telegram mobile app.

The recurring tasks to post on Moltbook have been the most useful for me. It was able to score some karma points for my profile after tweaking it and making it less pedantic. This is highly dependent on the model used, though.

What are the most mind-blowing examples of where local agents are taking us?

Experimenter 1
Personal agents are having their moment. I think the best examples are when the agent accomplishes a task in a way you didn't expect. I uploaded a PDF, and the agent realised it didn't have the tools or apps to read it, so it opened it up in Chrome and took screenshots. It then processed and sent the data back to me. Watching it do things like this is mind-blowing because when we think of agents we think of them in a specific vertical. These agents are horizontal — which means they can solve a wide range of tasks.

Experimenter 2
The trajectory points toward agents that can genuinely collaborate on complex multi-day projects.

Experimenter 3
For me, memory management and the ability to run long tasks are the two biggest features in OpenClaw. Since it has the ability to write files and follow through with what you told it to, it opens up more use cases that are state-dependent — like commenting on posts or writing about topics depending on new information from the environment. These were impossible use cases before LLMs and very hard to do well before OpenClaw.

Ready to experiment?

Make sure to check out Civic if you're running an experiment.