Paperclip recently caught my attention as it allows you to orchestrate teams of specialised AI agents through a beautiful self-hosted web UI, so I had to give it a try.
What Works Well
The concept is solid: specialized agents for different tasks, all tracked through a ticketing system. The UI looks clean and modern. Navigation feels intuitive. Watching my “CEO” agent hire a UI/UX designer, then a CTO, all coordinating through the interface, was entertaining.
For users who want AI agent orchestration without building everything from scratch, Paperclip provides a polished starting point.
Where It Fell Short for Me
I deployed Paperclip using Docker Compose (my preferred approach for managing services). The setup required some debugging—it always does, of course. Permissions, configs of some utilities not where I was expecting to find them. Probably my fault for trying to optimise too early but, really, I was trying to learn. Nothing insurmountable, but it added complexity not needed at the beginning of a project.
More importantly, I noticed my coding credits depleting steadily while Paperclip was running… with nothing actually happening. My team consisted of a CEO and a designer, happily chatting away, checking tickets in and out, burning credits through heartbeat checks and status polls. The UI showed “agents working” but no real output was being produced.
The pretty interface, it turns out, hides the mechanics. For someone who lives in logs and CLI output, this opacity was a little confusing.
The Simpler Alternative
I stepped back and asked: what am I actually trying to achieve here? Agent orchestration. I then investigated how OpenClaw could solve the same problem and realised I could probably achieve most of what I wanted without the extra software and overhead. Like I said, the UI/dashboard is nice but I can’t just sit there and watch the credits slowly disappear.
If you’re already running OpenClaw, n8n, or similar tools? You’re probably already 80% there with infrastructure you understand end-to-end.
What Paperclip Does Differently
Here’s where Paperclip differs from other harnesses. It’s an orchestrator of harnesses, therefore it can orchestrate multiple different AI harnesses as a single team. As Paperclip puts it “if OpenClaw is an employee, Paperclip is the company”. The employees don’t need to be all of the same type, you could have Claude Code, an OpenClaw and an Hermes Agent all working as part of the same team/organisation.
Paperclip on GitHub — check out the project if you’re curious.
All working together, checking out tickets, assigning work to each other. Paperclip lets you mix and match.
For me, that wasn’t the missing piece. But if you’re building a multi-agent system where different agents need different strengths, it’s worth considering.
If You Want to Try It
The concept shows promise. The project has real potential. If you’re curious about AI agent orchestration and want something with a polished UI, give it a spin.
Learn from my mistakes:
- When deploying with Docker, pin a stable release, not
latest - Watch your credit usage closely until you understand what it’s doing (maybe set some alerts if you can)
- Start with a small project to see results quickly
- Understand that there is some complexity going on which is nicely hidden under the UI
If you, on the other hand, are already using some alternative AI harness and all you want is an easier way to manage an AI team/agents, look more deeply into what your software of choice already supports.
Hope it helps!
Andrea