Ducklab

A self-hosted harness that runs a project's full development cycle with several LLMs in fixed roles, under test gates that only you sign.

In one block: self-hosted development harness (Go engine + CLI + desktop, Linux first) · brief → requirements → spec → plan → build → review → release · verdicts are exit codes, never model opinions · local models first (llama.cpp, vLLM) beside any OpenAI-compatible or Anthropic endpoint · operable by humans or by other agents over MCP with recorded, attributed decisions · Apache-2.0 · develops itself (the run records in .ducklab/ are the receipts). Agents: start at AGENTS.md and llms.txt.

You give it a brief. It writes requirements, a spec and a plan; builds tasks with one model or several arguing; runs your project's real test gate; and stops for you before anything is committed. Every model call is logged. No model ever decides a verdict.

A real council intake, recorded live and sped up: the architect streams the draft, a different model reviews it, the budget ticks in cents — and the run stops at your gate. Total cost of what you just watched: $0.07.

It was built for local models first. Two of the seats that built most of it are a vLLM box on the LAN and a llama.cpp server on localhost, both priced at zero; hosted models sit beside them in the same roster, measured by the same evidence.

Why this exists

Most agentic coding tools assume one strong model and trust it. Ducklab assumes several cheap models and trusts none of them:

• The gate decides, never a model. A verdict is a command's exit code. A test-first run measures a green baseline before any test is written, the red over the new test after, and every accept reproduces the gate from a clean checkout of the committed sha — nothing lands that did not reproduce, and an accept whose reproduction fails takes its own commit back.
• Decorrelation everywhere. A different model reviews; a reviewer never learns who wrote the code (absent from the payload, not hidden in the UI); tournament judges choose blind; council critics read the draft, not each other.
• Work is a contract. A task's deliverables are the implementer's numbered checklist; it reports on each by number, the reviewer checks each against the diff, and an undelivered item summons the rubber duck — an advisor seat that wakes only on measured distress (brake refusals, failure streaks, red gates) and answers none, a note that sends the implementer straight back to work, or stop.
• Seats are chosen on evidence. Every duckling carries a scorecard — in-seat pass rate from your own runs, cost per run, coding index — and the roster board suggests seats from it, with the ranking criteria yours to reorder. Suggestions are rare and justified: pass rates rank by their Wilson lower bound, three runs minimum, locals never win on a $0 price.
• Nothing is unbounded. Turns, tokens, cost, wallclock, tool output, shell commands — every ceiling visible and liftable mid-run, on the record.
• Your documentation is not bounded by the model's window. Attach a wiki to a stage and a big seat reads it whole; a small seat gets each document digested to fit, the full text one ref_read call away, and the gate names any document nobody opened. A 32k local model can be briefed by a quarter-million characters of reference material — the harness carries the working memory.

The record does not round up: every run with its verdict, its cost, and whether its accept reproduced green from a clean checkout.

Ducklab is developed inside ducklab. The plan, the bugs, the releases and the accepted tasks went through its own loop, driven by the same local and hosted models it measures; recent features (per-run worktrees, the merge-proof accept, the acceptance receipts, the governance write guard) were built by the duck and gated by a person. To check the claim yourself:

git clone https://github.com/jrullan/ducklab && cd ducklab
go build -o ducklab-cli ./cmd/ducklab
for r in .ducklab/runs/*/receipt.json; do ./ducklab-cli proof verify "$r"; done

Receipts ship with every accept since v0.7.0: the committed sha, the gate command, its exit code, and the clean-checkout reproduction verdict — facts a third party re-derives, never assessments.

Status

v0.7.0 plus the phase-3 work now on main: every build and test run executes in its own git worktree (your checkout is never touched), acceptance rebases the run branch, re-runs the gate on the rebased commit and merges fast-forward only, and an operator can re-close a finished run as landed when its work reached main outside the engine. Before that: seven stages, five modes, the roster board with measured scorecards, reference documents with automatic digestion, skills managed from the desktop, a seated consultant chat (vision verified before images are sent), bug reports with screenshot evidence, adopt surveys with a deterministic coverage check, provider-aware queueing that states why a run waits, escalation suggestions when a seat measurably hits its ceiling, acceptance receipts (ducklab proof verify), releases, autopilot, a CLI, a desktop app, and an MCP server — in the official MCP registry as io.github.jrullan/ducklab — so another model can operate the loop with recorded, attributed decisions.

docs/status.md tracks all acceptance criteria and does not round up. Where code and spec differ, the difference is recorded in docs/decisions/.

Install

Needs Go 1.25+, Node 22+ for the desktop, and git.

Linux

The CLI and engine are pure Go. The desktop is a Wails v3 app and needs the GTK/WebKit development packages:

sudo apt install libgtk-3-dev libwebkit2gtk-4.1-dev   # Debian/Ubuntu names
make desktop && make install

On Ubuntu 24.04+ the desktop also needs an AppArmor profile — see decision 0003 and packaging/apparmor/.

macOS

xcode-select --install    # the desktop build links against WebKit
brew install go node
make desktop && make install

Honesty note: ducklab is developed and exercised daily on Linux. The CLI and engine compile-check for darwin/arm64 on every make cross, but no desktop build has been verified on a Mac yet — the first person to try it is the test, and make install gives you the CLI and engine either way. Please report whatever breaks.

Both

make install installs to ~/.local/bin — make sure it is on your PATH. It warns when the desktop binary predates frontend/src, because it will happily install a stale one.

Frontend development without the desktop

To exercise the frontend in a browser, run the engine and Vite in separate terminals, then open the browser with its connection details. The fake engine is the quickest option; the same flow can use a real engine with its opt-in CORS flag:

# Fast, scripted data (recommended for UI work)
go run ./cmd/fake-engine --port 8787 --token fake-token
npm run dev --prefix frontend
# open http://localhost:5173/?engine=http://127.0.0.1:8787&token=fake-token

# Or use real engine data (development only; keep the origin explicit)
go run ./cmd/ducklab-engine --allow-origin http://localhost:5173
npm run dev --prefix frontend
# open http://localhost:5173/?engine=http://127.0.0.1:<engine-port>&token=<engine-token>

The real engine remains same-origin restricted by default. --allow-origin enables exactly one browser origin and is intended for local frontend development and visual audits; it does not change authentication or the loopback bind. Without this flag, a browser's cross-origin failure can look like a dead session.

The engine and token query parameters are available only in Vite dev builds. They can also be supplied as VITE_DUCKLAB_ENGINE and VITE_DUCKLAB_TOKEN environment variables. The desktop shell continues to use its injected window.ducklab connection.

Three binaries

Provider keys come from the engine's environment at call time — export them before it starts, or launch the desktop through a wrapper that loads them from your keyring. The app tells you when the engine it adopted is missing a key this app has, with the restart button beside the words.

A cycle, end to end

From the desktop: Projects → New project, then Cycle → Draft it. From a terminal:

cd ~/dev/myproject
git init                                    # ducklab needs a git repo
ducklab project init --name MyProject       # auto-starts the engine if none is running

ducklab intake --from brief.txt             # brief        → requirements
ducklab intent                              # your briefs, verbatim, and what each one changed
ducklab spec                                # requirements → spec
ducklab plan                                # spec         → milestones and tasks

ducklab run T-001                           # build it
ducklab run accept r-20260729-...           # commit it