Q‑Branch: My OpenClaw‑Powered Agent Stack (and Why It’s Actually Fun)

I kept seeing people build wild stuff with OpenClaw - agent swarms, automated research, specs that write themselves, etc. So after a lot of back and forth, I had to try it. I spun up my own setup, named it Q, and started wiring it into a real project.

So here’s a quick tour of how the system actually works, why it’s useful, and how the pieces talk to each other. If you’re building with OpenClaw or thinking about it, this should give you a clean mental model without drowning you in details.

The Big Idea: Agents + Memory = Leverage

Agents are cool, but agents with memory are dangerous (in the good way). OpenClaw gives you:

  • Persistent memory (so your agents don’t wake up clueless every time)
  • Clear task state (so you can run a team, not a chaos blob)
  • Artifacts (so output is real, durable, and easy to reuse)

That combo turns “clever bots” into a legit project team.

What Q‑Branch Actually Is

At its core, Q‑Branch is:

1) Convex as Mission Control
Convex is the system of record. Tasks live there. Statuses live there. Artifacts are registered there. It’s the source of truth that keeps everything honest.

2) A Structured Artifact Folder
Every agent has a clean place to ship work. The structure is boring on purpose:

products/project_name/agents/<agent>/<type>/<name>.md

This keeps work discoverable for humans and other agents. No hunting around random folders.

3) CLI‑First Everything
No magic, no black boxes. The agents call:

  • npx convex run … for task ops
  • tasks:addArtifact to register deliverables
  • messages:create to post updates

It’s boring in the best way: transparent, repeatable, easy to debug.

How the Handoff Works (the part that matters)

The flow is simple and consistent:

  1. Task lands in Convex.
  2. Agent grabs it and marks it in_progress.
  3. Agent produces an artifact (spec, research, architecture, etc.).
  4. Artifact gets registered back to the task.
  5. Agent posts an update in the task thread.
  6. Task moves to review or done.

That’s it. Simple loop, clean output, no lost work (or at least that’s what I’m striving for).

Cross‑Agent Handoffs

Handoffs are just artifacts + task threads.

  • Everything lives at a known path.
  • It’s registered in Convex.
  • Other agents read it and keep moving.

That’s the whole “team” effect: artifacts are the handoff contract.

Why This Works

  • Parallel work without stepping on each other
  • Zero guessing where outputs are
  • Real durability (artifacts don’t vanish after a session)
  • Human oversight without micromanaging every step

It’s basically a lightweight OS for teams - just with agents doing most of the work.

Where We Can Make It Even Better

I’m still looking to add some stuff and make it a near‑autonomous team:

1) Auto‑Register Artifacts

Right now, artifacts are registered manually. Add frontmatter (taskId/agentId/type), and a script can auto‑link everything in Convex. No more “forgot to register.”

Integrate a local doc search engine (like qmd) so agents pull only the right snippets, not whole files. This cuts token use and makes everything faster.

3) Lightweight Handoff Contracts

Even a short “what I expect / what I deliver” doc per agent type would reduce rework and misalignment.

4) Dependency Awareness

Let tasks declare dependencies so agents can block/unblock each other automatically instead of guessing.

5) Quality Gates

Add simple checks before a task goes to review. Not bureaucratic—just enough to keep output sharp.

6) Project Dashboards

A quick dashboard for:

  • task status
  • recent artifacts
  • blockers
  • next actions

So you can steer the swarm without chaos.

Closing Thought

Q‑Branch feels already like a solid base: structured tasks, clean handoffs, real memory. But the real win is this: you can treat agents like teammates, not toys. And once you do that, the whole thing scales fast.

If you’re using OpenClaw and haven’t wired up something like this yet, give it a try. Once you play around with it…it get’s pretty addictive.

Full disclosure: This article was 90% AI-generated and human-edited.

work AI