Hey everyone!

I just open-sourced an autonomous development pipeline I've been building on OpenClaw for the last few months. It's called Lullabeast. You describe your project idea and it builds it phase by phase against a real git repo, using planner, executor, and reviewer agents.

If you're asking why: cheap open models like Qwen, Kimi, GLM, and MiniMax cost a fraction of frontier prices and they're getting really good, but they still fail in the same predictable ways and I was tired of cleaning up after them manually. They delete files randomly, drift off the spec, and say they're done without ever running the tests. So I built the whole pipeline orchestrator around these failures to make building easier.

Every time one agent hands off to the next, there's a gate in between. The gates are deterministic, no LLM involved. They check the file manifest, the git diff, the test results, and whether anything got deleted that shouldn't have. The gates run the show, so an agent never gets to advance on its own say-so. I added multiple retries so you don't have to babysit it, but once the agents use up all their retries, it escalates instead of spinning endlessly. The agents run inside OpenClaw. Lullabeast is the orchestration layer that sits on top, and it even has a full dashboard too. No frontier models anywhere in the loop, just cheap open and local ones.

To show it isn't a one-off, I had it build a multi-team version of Conway's Game of Life with live analytics, and ran the exact same PRD and the same 11 phase roadmap twice.

**Local** (48GB RTX 4090, modded, running Qwen3.6-27B Q8\_0) Zero retries · 3h27m to complete · no API bill

**Cloud** (GLM-5.2 planning, Kimi-k2.7 Code as executor and reviewer) Two retries · 2h04m to complete · $6.90 in API spend

\_Pro life tip: you can save a lot on API bills if you just buy a regrettably expensive GPU lol.\_

Both builds are live at [https://lullabeast.ai/living-proof](https://lullabeast.ai/living-proof) if you want to check them out. There's also a walkthrough of the actual dashboard if you want a closer look before installing anything.

Honestly speaking, it's an early beta. It does well on small, focused webapps. Push it toward something something too big or complex and more issues can show up. UI-heavy phases are where it struggles the most when you run fully local too. It also executes agent-written code on your host, so I suggest running it in a VM (that's what I do). I'm putting it out now to find where it breaks, so bug reports and pushback are welcome!

What failure modes have you run into with cheap or local models that I didn't list up there? I'm building out the gate coverage and I'd rather catch the ones I haven't hit yet.

Repo: [https://github.com/bigbraingoldfish/lullabeast](https://github.com/bigbraingoldfish/lullabeast)  
Site and live demo: [https://lullabeast.ai/living-proof](https://lullabeast.ai/living-proof)

I built a gated pipeline on OpenClaw that builds MVPs end to end, and ran the same build local vs cloud to compare