TL;DR: Paperclip is an open source control plane for teams of AI agents: you define a company with a goal, hire agents into an org chart, give them budgets and approval gates, and they wake on a schedule to pick up tickets, do the work and report back. It is now packaged for Cloudron, with the coding agents it runs contained as a separate unprivileged user. Built and tested on Cloudron 10; unofficial and community maintained.

Headline features

• Run Claude Code, Codex and OpenCode agents inside the app as a separate, unprivileged user that cannot read Paperclip's secrets or reach your other apps and databases.
• An org chart for agents, with per-agent budgets, approval gates and an audit log of everything they do.
• Tickets, recurring routines and cost tracking built in, so agents pick up work on a schedule rather than waiting to be prompted.
• Bring your own model: a Claude or ChatGPT subscription, an API key, or any OpenAI-compatible endpoint, including one you host yourself.
• Agents can hire other agents to build out a team, with an optional board sign-off for each new hire.

Links

• Project homepage: https://paperclip.ing
• Documentation: https://docs.paperclip.ing
• Upstream repo: https://github.com/paperclipai/paperclip
• 🧱 Cloudron package repo: https://github.com/OrcVole/paperclip-cloudron

Once installed, the whole thing is a web UI: a dashboard of your agents and their work, a ticket board, and a settings area to connect models and tools.

How to install

The easiest route is the dashboard. In the App Store, click the Add custom app dropdown (top right) and choose Community app, then paste the CloudronVersions.json URL below into the box that pops up. Apps installed this way receive automatic updates.

https://ca.cloudron.io/app/io.github.orcvole.paperclip

Or with the CLI:

cloudron install \
  --versions-url https://raw.githubusercontent.com/OrcVole/paperclip-cloudron/main/CloudronVersions.json \
  --location paperclip.example.com

Minimums: a 4 GiB memory limit (the app idles at about 1.3 GiB, and each agent running at the same time adds roughly 0.5 GiB), and the postgresql and localstorage addons. The package asks for the net_admin capability, which it uses only to firewall the agent user; Cloudron grants it with the install, and the app's log confirms the firewall at every start. The image is large, because it bundles the coding-agent tools, so the first install takes several minutes to download.

First run: there is no single sign-on. Read the app's Logs for a one-time first-admin invite link; whoever opens it first becomes the administrator, so treat it like a password. Sign-up is open only until the first admin has claimed the instance, and closes automatically at the next start, so restart the app once after claiming it. To connect a Claude or ChatGPT subscription, open the app's Web Terminal, run claude (or codex), then /login, and follow the prompt. If an agent fails with "ACP agent reported a terminal access failure", that is this step not having been done: the onboarding wizard's subscription button does not store a login by itself. API keys are simpler and are entered in the agent's settings.

For users

Why try it: if you already use AI coding agents one prompt at a time, Paperclip turns them into something closer to a small team that works through a backlog on its own, within the budgets and approvals you set. Cloudron wins: automatic backups of your agents, tickets and settings, one-click updates, and the agents kept apart from the rest of your server. Good fit if you want agents doing routine work on a schedule; probably not if you only ever want a single interactive chat.

🧰 For packagers: what we learned

What helped: the postgresql addon covered the database cleanly, and Paperclip runs its own migrations as a non-superuser without complaint.

What was tricky: containing the agents was the real work. A Cloudron app is one container, so a local coding agent would normally share the server's user and could read its secrets. The package gives agents their own user, reached only through a small wrapper and one narrow sudo rule, with an iptables firewall (IPv4 and IPv6) that lets them reach the internet, their workspace and Paperclip's own API, and nothing on the internal network. Paperclip also ships its own bundled copies of the agent tools for its ACP engine, which would sidestep a wrapper; every one of those is routed back through the contained path.

Still rough: the first image download is large. Agent workspaces are git checkouts kept outside the backup on purpose, so anything an agent has not committed and pushed is not backed up.

️ For the Cloudron team

Maintenance burden: upstream releases often, so the package pins the exact agent-tool versions upstream tests with and moves them together on each bump. Why it suits the App Store: agent orchestration is in demand, the upstream is MIT, and it pairs naturally with the AI and automation apps already here. Friction worth knowing: containing a second, less-trusted user inside one app container needs net_admin and a careful sudo design; a per-app way to run a sandboxed sub-process would make packages like this simpler.

For Paperclip's developers

A setting to move the workspace and project roots out of the instance directory would help any packager that splits persistent storage. Package source and pull requests welcome at https://github.com/OrcVole/paperclip-cloudron. Happy to co-maintain.

Unlocks

Now you can run a small, budgeted team of AI agents on your own Cloudron that picks up tickets, works through recurring chores and reports back, without handing your server's secrets to the agents doing the work.

Synergies

• Paperclip + vLLM or Ollama: point agents at a model you host yourself on the same Cloudron, through its public URL, and keep every prompt on your own hardware.
• Paperclip + Forgejo: give agents a repository to clone, commit to and push, so their work lands in version control you own.
• Paperclip + Uptime Kuma: watch /api/health so you know the moment the agent control plane goes down.
• Paperclip + Langfuse: trace what your agents actually send to their models, for cost and quality review.

Feedback, bug reports, and "works on my install" confirmations would be great.