
A tiny town that ships.
Quick Start · How It Works · AGENTS.md
Goosetown coordinates flocks of AI agents — researchers, writers, workers, reviewers — so you describe what to build and the town builds it. Research-first, parallel by default, with crossfire reviews across multiple models.
bash
git clone https://github.com/block/goosetown.git
cd goosetownbash
./gooseSay hello — goose already knows its way around town.
[!NOTE] On first run, goose may ask you to set a few environment variables. Follow its instructions and restart.
Here's what coordination looks like on the Town Wall:
[16:21:41] <orchestrator> Spawning research flock...
[16:22:06] <researcher-local> 💡 Found existing patterns in GUIDES/
[16:22:19] <researcher-github> 🎬 Scanning issues and PRs
[16:23:46] <orchestrator> Research complete. Dispatching workers...
[16:24:11] <worker-auth> 🎬 Claiming src/auth/mod.rs
[16:25:02] <reviewer-gpt5> ✅ APPROVE (9/10)
The orchestrator decomposes your request into phases — research, build, review — and dispatches parallel delegates called flocks that coordinate through the Town Wall.
Orchestrator
│ spawns
┌──────┼──────┐
▼ ▼ ▼
Researchers (flock) ← share findings via gtwall
│ synthesize
┌──────┼──────┐
▼ ▼ ▼
Workers + Writers ← parallel execution
│ review
┌──────┼──────┐
▼ ▼ ▼
Reviewers (crossfire) ← multi-model adversarial QA
│
Final deliverable
When three or more delegates share a task and coordinate via gtwall, that's a flock.
There's a real-time dashboard for watching your flock work (yes, they're actual geese on a map) — just ask goose to launch it.

Learn more in AGENTS.md.
Goosetown was inspired by Gas Town, the multi-agent coordination framework created by Steve Yegge. His blog post announcing Gas Town laid out the vision of orchestrating flocks of AI agents — researchers, workers, reviewers — that Goosetown builds on.
$ claude mcp add goosetown \
-- python -m otcore.mcp_server <graph>