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Open-source (Apache-2.0) PDF takeoff for construction & flooring — the first engine an AI agent drives natively over MCP, not bolted on. One-click room detection, materials + quantities, built for preconstruction. Runs entirely in your browser.

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- - bug_report.md
  - feature_request.md
- - ci.yml
  - publish-mcp.yml
- CODEOWNERS
- FUNDING.yml
- PULL_REQUEST_TEMPLATE.md
- capture_server.py
- README.md
- make_sample_plan.py
- README.md
- sample-finish-plan.pdf
- sample-plan.pdf
- - mcp-live-demo.gif
  - one-click-area.gif
  - report.png
  - social-card.png
  - verify-import-schedule-dialog.png
- AGENT_GUIDE.md
- CONTRIBUTION_SPEC.md
- DEPLOYMENT.md
- ESTIMATING_ROADMAP.md
- GLIDE_INTEGRATION.md
- GOOGLE_SETUP.md
- MCP.md
- SELF_HOSTING.md
- SHEET-GRAPH-EVAL.md
- SYNC_ARCHITECTURE.md
- USER_GUIDE.md
- VOICE.md
- - build-mcpb.mjs
  - build-smithery-mcpb.mjs
  - finish-build.mjs
  - graph-audit.mjs
  - graph-eval.mjs
  - graph-render.mjs
  - make-layered-fixture.mjs
  - make-scan-fixture.mjs
  - make-sheetgraph-fixture.mjs
  - make-symbol-fixture.mjs
  - smoke-dist.mjs
- - format.ts
  - hush.ts
  - importing.ts
  - marked.ts
  - outputs.ts
  - pdf.ts
  - resources.ts
  - safewrite.ts
  - scalewarn.ts
  - session.ts
  - staging.ts
  - tools.ts
  - trace.ts
  - view.ts
- - conformance.test.ts
  - context.test.ts
  - e2e.test.ts
  - labels.test.ts
  - overlap.test.ts
  - parity.test.ts
  - raster.test.ts
  - resources.test.ts
  - safewrite.test.ts
  - scalewarn.test.ts
  - session.test.ts
  - staging.test.ts
  - tools.test.ts
  - transitions.test.ts
  - twins.test.ts
  - view.test.ts
- bin.js
- Dockerfile
- package-lock.json
- package.json
- README.md
- server.json
- server.ts
- tsconfig.json
- check-doc-links.mjs
- mock-agent-server.mjs
- - __init__.py
  - base.py
  - heuristic.py
- - test_api_key_gate.py
- app.py
- Dockerfile
- README.md
- requirements-dev.txt
- requirements.txt
- - batch-results.json
  - batch.mts
  - batch.ts
  - callout-results.json
  - callouts.mts
  - callouts.ts
  - corpus.ts
  - pin-goldens.mts
  - results.json
  - run.mts
  - score.ts
- - _headers
  - favicon.svg
  - googlec4381fb3b0fbe628.html
  - llms.txt
  - og-card.png
  - robots.txt
  - sitemap.xml
  - theme-init.js
- - fetch-voice-model.mjs
  - voice-benchmark.mjs
- - main.jsx
  - pdfTile.worker.ts
  - stt.worker.ts
  - vite-env.d.ts
- - agentLoop.test.ts
  - agentTools.test.ts
  - ai.test.ts
  - approvals.test.ts
  - arc.test.ts
  - authDomain.test.ts
  - benchScore.test.ts
  - branding.test.ts
  - callouts.test.ts
  - canvas-geometry.test.ts
  - canvasBusy.test.ts
  - cloudStore.test.ts
  - composite.test.ts
  - conditionColumns.test.ts
  - confidence.test.ts
  - contribute.test.ts
  - coverage.test.ts
  - csv.test.ts
  - curve.test.ts
  - cutout.test.ts
  - detectLadder.test.ts
  - detectRooms.test.ts
  - doorWedge.test.ts
  - draftInput.test.ts
  - drive.test.ts
  - driveProvider.test.ts
  - fsProvider.test.ts
  - gapBridge.test.ts
  - geometry.test.ts
  - graphDrive.test.ts
  - guideParity.test.ts
  - hatchFamilies.test.ts
  - hdDrift.test.ts
  - identity.test.ts
  - importTakeoff.test.ts
  - ingest.test.ts
  - keys.test.ts
  - labelGroupedRows.test.ts
  - layerExtract.test.ts
  - layerIoU.test.ts
  - layers.test.ts
  - lineStyles.test.ts
  - m365Composite.test.ts
  - markedset.test.ts
  - materials.test.ts
  - merge.test.ts
  - parseScheduleErrors.test.ts
  - plays.test.ts
  - presence.test.ts
  - profile.test.ts
  - projectArchive.test.ts
  - projectHome.test.ts
  - provenance.test.ts
  - rastermask.test.ts
  - renderBudget.test.ts
  - report-csv-golden.test.ts
  - reportColumns.test.ts
  - reportTemplates.test.ts
  - reportTemplatesSync.test.ts
  - reportTheme.test.ts
  - resolutionInvariance.test.ts
  - revisions.test.ts
  - rfi.test.ts
  - ringTidy.test.ts
  - rollgoods.test.ts
  - rollTakeoff.test.ts
  - rules.test.ts
  - scheduleEdit.test.ts
  - scheduleParse.test.ts
  - scheduleScan.test.ts
  - seamLf.test.ts
  - shapeCommands.test.ts
  - shapeLabels.test.ts
  - shapeMetrics.test.ts
  - shapesExport.test.ts
  - sheetgraph.test.ts
  - sheetGroups.test.ts
  - sheetLevels.test.ts
  - sheetNumber.test.ts
  - sheetTotals.test.ts
  - snapshotDiff.test.ts
  - snapshotSync.test.ts
  - stamps.test.ts
  - stitches.test.ts
  - store.test.ts
  - strokeLum.test.ts
  - sttEngine.test.ts
  - svgImport.test.ts
  - svgpath.test.ts
  - sweepCoalesce.test.ts
  - sweepNegative.test.ts
  - symbolLabels.test.ts
  - symbolsweep.test.ts
  - syncStore.test.ts
  - templates.test.ts
  - tiles.test.ts
  - totals.test.ts
  - transitions.test.ts
  - units.test.ts
  - variants.test.ts
  - voiceActions.test.ts
  - voiceCorpus.test.ts
  - voiceIntent.test.ts
  - voicePrivacy.test.ts
  - xlsx.test.ts
  - zone.test.ts
- .env.example
- .nvmrc
- eslint.config.mjs
- index.html
- package-lock.json
- package.json
- tsconfig.json
- vite.config.js
.dockerignore
.gitignore
.mcp.json
.nvmrc
AGENT_BRIEF.md
AGENTS.md
CHANGELOG.md
CODE_OF_CONDUCT.md
CONTRIBUTING.md
Dockerfile
FEATURES.md
glama.json
LICENSE
netlify.toml
NOTICE
README.ja.md
README.ko.md
README.md
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// repository documentation

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# OpenTakeoff

**The measurement engine for building plans—built so an AI agent can drive it, and so an estimator wants to.**

A takeoff is the act of measuring quantities off a construction drawing. OpenTakeoff does it
two ways over one engine: **40 MCP tools** for an agent, and a browser canvas for a person.
Same flood fill, same scale gate, same math, same record. Every measurement stores its
**scale**, its **method**, and **who made it**—which is what makes the output auditable, and
what makes it training data.

[![License: Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://raw.githubusercontent.com/Kentucky-ai/opentakeoff/main/LICENSE)
[![Live demo](https://img.shields.io/badge/demo-opentakeoff.kentucky--ai.com-2ea44f.svg)](https://opentakeoff.kentucky-ai.com)
[![MCP registry](https://img.shields.io/badge/MCP-io.github.Kentucky--ai%2Fopentakeoff-6f42c1.svg)](https://registry.modelcontextprotocol.io)
[![npm](https://img.shields.io/npm/v/opentakeoff-mcp?label=opentakeoff-mcp)](https://www.npmjs.com/package/opentakeoff-mcp)
[![Benchmark](https://img.shields.io/badge/benchmark-OpenTakeoff%20Academy-orange.svg)](https://aec.kentucky-ai.com)
[![OpenArena](https://openarena.to/api/badge/cmsgykvsq0000mkuv7byhlgnl)](https://openarena.to/en/projects/cmsgykvsq0000mkuv7byhlgnl)
[![Sponsor](https://img.shields.io/github/sponsors/Kentucky-ai?logo=githubsponsors&label=sponsor&color=EA4AAA)](https://github.com/sponsors/Kentucky-ai)

[**For agents**](#for-agents--start-here) · [**Try the canvas**](https://opentakeoff.kentucky-ai.com) · [The engine's contract](#the-contract-that-makes-it-drivable) · [For the person at the canvas](#for-the-person-at-the-canvas) · [The data layer](#the-data-layer--why-this-engine-exists) · [Research](#the-research-program) · [Build on it](#build-on-top-of-it) · [Contribute](#contributing)

**The two manuals:** [agent manual](docs/AGENT_GUIDE.md) · [user manual](docs/USER_GUIDE.md)

**Read this in:** [日本語](README.ja.md) · [한국어](README.ko.md) · [简体中文](README.zh-Hans.md)

**Watch it:** [an autonomous agent runs a takeoff, live, no cuts (2:47)](https://youtu.be/e--kXxSGv7Y) · [hospital finish plan → report in about a minute (1:14)](https://youtu.be/cNDpPkTLY1k) · [canvas walkthrough (1:10)](https://youtu.be/aHiW8H2TSBs) · [One-Click Area (0:51)](https://youtu.be/YIjWZ-BAhLE)

<br/>

</div>

---

## Start here

| You are | Go here |
|---|---|
| **An estimator with a bid due** | [Open the canvas](https://opentakeoff.kentucky-ai.com)—drag in a plan, no account, nothing uploads. The [**user manual**](docs/USER_GUIDE.md) gets you from a blank tab to an exported takeoff in five minutes, and its [working order](docs/USER_GUIDE.md#the-working-order-on-a-real-bid) is the sequence to run on a real bid set. |
| **An AI agent**—or the person wiring one up | `npx -y opentakeoff-mcp`, then the [**agent manual**](docs/AGENT_GUIDE.md): the operating model, the standard finish every takeoff ends with, what the engine refuses to guess, and why. Tool-by-tool reference is [`mcp/README.md`](mcp/README.md). |
| **A developer building on the engine** | [`AGENTS.md`](AGENTS.md) is the repo map and the ship discipline; [`FEATURES.md`](FEATURES.md) maps every capability to the code that does it. Apache-2.0—fork it. |

### Windows, macOS, Linux — all of it

OpenTakeoff is a **client-only browser app**, so the canvas runs the same on Windows, macOS,
ChromeOS and Linux in any current Chrome, Edge, Firefox or Safari. Nothing installs, nothing
uploads, and no feature is gated on an operating system.

- **Shortcuts are platform-aware.** The app labels modifiers for the keyboard in front of you —
  `Ctrl` / `Alt` / `Shift` on Windows and Linux, `⌘` / `⌥` / `⇧` on a Mac — and the handlers have
  always treated `⌘` and `Ctrl` as the same key. Press `?` in the canvas for the current list.
- **The MCP server is tested on Windows.** `npx -y opentakeoff-mcp` runs on Windows, macOS and
  Linux, and CI runs the full MCP suite — typecheck, tests, build and the packaged smoke test — on
  `windows-latest` as well as `ubuntu-latest` on every change.
- **Optional extras.** The bundled [capture server](capture/) is stdlib Python 3 and runs anywhere
  Python does — on Windows invoke it with `python capture\capture_server.py selftest` (or the `py`
  launcher) rather than `python3`. Neither it nor the optional [`server/`](server/) AI sandbox is
  needed to use the canvas.
- **Locked-down enterprise fleets** (MSIX packaging, Windows Sandbox, Intune silent deploy) are
  tracked in [#226](https://github.com/Kentucky-ai/opentakeoff/issues/226) and not yet built.

## What this is

Measuring quantities off a plan is the input to every construction bid—how much floor, how
much wall, how many fixtures, at what scale, on which sheet. It happens thousands of times a
day. Until OpenTakeoff there was **no open-source takeoff engine at all**, web-based or
otherwise, and nothing an autonomous agent could call.

OpenTakeoff is that engine, with two front ends over identical geometry:

- **A stdio MCP server**—`npx -y opentakeoff-mcp`, 40 tools, on the
  [official MCP registry](https://registry.modelcontextprotocol.io). An agent opens a plan,
  reads the title block, sets the scale, floods the rooms, checks its own work on a rendered
  overlay, and hands back a marked-up planset PDF.
- **A browser canvas**—no backend, no account, no upload. An estimator drags in a plan set
  and traces it, using One-Click room detection, CAD hatches, roll-goods seam layout, a
  materials buy list, and exports.

Neither is a wrapper around the other. The MCP server imports
`web/src/lib/{oneclick,sheets,geometry,totals}` directly, so a shape committed by an agent is
field-identical to one committed by a hand at the canvas—same flood mask, same corner snap,
same waste math, same refusal messages.

**Provenance is the load-bearing part.** Every shape records the scale it was measured at, the
method that produced it (vector flood, raster trace, hand-drawn, agent-proposed), whether a
human corrected it, and the machine's original boundary frozen beside the correction.
Downstream, that's an audit trail a PM can read. Upstream, it's a labeled
*(geometry → finish)* pair—the training signal takeoff models have never had at scale. That
second use is not a side effect; see [the data layer](#the-data-layer--why-this-engine-exists).

## Recently shipped

- **Stitched sheets**—a floor split across a match line becomes one working surface; a room
  that crosses the seam traces as one shape, One-Click included
  ([#161](https://github.com/Kentucky-ai/opentakeoff/issues/161))
- **PDF layer roles**—CAD-exported sheets *state* what their ink is, so One-Click reads the
  layer tree instead of inferring boundaries from hatch, with a Layers panel on the canvas and
  scored corpus IoU ([#85](https://github.com/Kentucky-ai/opentakeoff/issues/85))
- **The sheet graph**—an agent asks *"what finish is in room 134, and how do you know"* and
  gets the schedule row with a citation per cell, across continuation sheets, rotated headers,
  and multi-building keys: `sheet_graph` / `resolve_tag` / `find_schedule`
  ([#87](https://github.com/Kentucky-ai/opentakeoff/issues/87))
- **Roll goods**—opt a condition into broadloom or sheet material and the engine figures the
  seams: lanes, multi-roll splits, cuts drawn to scale over their rooms in cutting order, a
  to-scale roll diagram with drag-to-reorder, and order footage beside the measured quantities
  ([#136](https://github.com/Kentucky-ai/opentakeoff/issues/136))
- **Transitions, at the canvas**—**⟂ Transitions…** in the Takeoffs panel derives the line where
  two finishes meet from rooms you already measured; `derive_transitions` does the same over MCP.
  Flood-traced rooms don't share edges, so what's actually there is proximity in two flavors that
  mean different things: finishes changing inside one open space commit as a butt joint, while
  rooms parallel across a partition come back **withheld as questions**—the transition is a
  threshold in a doorway, and nothing in a trace record says where the doorway is. Committing
  34 LF of threshold because two rooms share 34 LF of wall would be a wrong bid with a machine's
  confidence behind it ([#202](https://github.com/Kentucky-ai/opentakeoff/issues/202))
- **`symbol_sweep`**—every instance of a repeated symbol from one marqueed example, crossing
  scales only by a *stated* ratio, never a searched one
- **`mark_verdict` / `delete_verdict`**—an agent signs its own work as a graphite `AGENT`
  diamond; only a human hand mints the green `APPROVED` seal
- **One-Click accuracy wave**—face extraction and gap tolerance from
  [RFC #60](https://github.com/Kentucky-ai/opentakeoff/issues/60) (contributed by
  [@knmurphy](https://github.com/knmurphy)), hairline runs shadowing a heavier wall classified
  as annotation rather than boundary, and an in-swing door's sector taken behind the leaf
  instead of the arc
- **Voice takeoff**—hold `M` and say `carpet one, waste seven`; recognition is
  whisper-tiny.en in WebAssembly on your machine, audio never leaves the browser
  ([docs/VOICE.md](docs/VOICE.md))

Full history: [CHANGELOG.md](CHANGELOG.md) · every capability mapped to its code:
[FEATURES.md](FEATURES.md)

---

## For agents — start here

Point any stdio MCP client at the published package. Node 20+, no clone, no build:

```json
{
  "mcpServers": {
    "opentakeoff": {
      "command": "npx",
      "args": ["-y", "opentakeoff-mcp"]
    }
  }
}
```

Claude Code: `claude mcp add opentakeoff -- npx -y opentakeoff-mcp`. Claude Desktop users can
double-click the `opentakeoff-mcp.mcpb` bundle from the
[latest release](https://github.com/Kentucky-ai/opentakeoff/releases) instead—it excludes the
optional native canvas on purpose, so every JSON tool works everywhere and the rendering
surfaces (`view_sheet`, the sheet-image resource) say exactly what's missing where they can't
run. Docker and a local clone are both supported: [`mcp/README.md`](mcp/README.md).

*A real run (3× speed): the agent takes off patient rooms 161–163 on a federal finish plan,
exporting after each commit. Every shape lands in the app as a dashed **pencil proposal** and
becomes ink only when the operator clicks Accept.* The full run, live and uncut, is
[on YouTube (2:47)](https://youtu.be/e--kXxSGv7Y).

### The 40 tools

| Group | Tools |
|---|---|
| **Open and orient** | `load_plan` · `sheet_info` · `sheet_context` · `read_sheet_text` · `find_text` · `view_sheet` |
| **Scale** | `set_scale` |
| **Measure** | `one_click` · `detect_rooms` · `measure_polygon` · `cut_out` · `measure_line` · `measure_surface` · `place_count` |
| **Repeat and derive** | `symbol_sweep` · `sweep_schedule_row` · `derive_base` · `derive_transitions` · `apply_rules` |
| **Read the drawing set** | `sheet_graph` · `resolve_tag` · `find_schedule` |
| **Edit and audit** | `list_shapes` · `edit_shape` · `edit_condition` · `edit_materials` · `duplicate_condition` · `split_condition` · `delete_shape` · `undo_last` |
| **Mark and sign** | `annotate` · `list_annotations` · `link_annotation` · `mark_verdict` · `delete_verdict` |
| **Hand off** | `takeoff_summary` · `export_takeoff` · `export_report` · `export_marked_pdf` · `import_takeoff` |

Plus browsable sheet resources (`takeoff://sheets`) so an agent can *see* the working set, not
only act on it. Multi-document sessions are first-class: a bid set is plans **plus** schedule
**plus** addenda, and `load_plan --merge` adds a document without disturbing existing scales,
conditions, or shapes—the sheet graph then spans the whole set, so a room tag on one file
resolves to a schedule row in another. `edit_condition` reaches the waste %, the ×N multiplier,
and `roll_setup`, so an agent's takeoff doesn't ship with net === gross.

**The agent's manual is [`docs/AGENT_GUIDE.md`](docs/AGENT_GUIDE.md)**—the counterpart to the
estimator's: the operating model in six facts, the standard finish every takeoff ends with, the
withheld-is-the-answer doctrine, what has no agent verb and why, and a refusal-to-next-move table.
Tool-by-tool reference: [`mcp/README.md`](mcp/README.md). The same surface in prose, with the
sheet-graph and sweep behavior in depth: [`docs/MCP.md`](docs/MCP.md).

### The contract that makes it drivable

Most measurement APIs are hostile to an agent because they let it be confidently wrong. These
are the rules that make this one safe to hand a model, and why each one exists:

1. **One coordinate frame, stated everywhere.** Image pixels at render scale 2.0—PDF points
   × 2, origin top-left, y down, the browser canvas's native space. Every sheet payload carries
   dims in both px and pt. No tool takes a coordinate in units it has to guess.
2. **Scale is a gate, not a default.** The drawn scale note is *read* off the sheet but never
   applied silently; adopting it is always an explicit `set_scale`. Measuring an unscaled sheet
   refuses. Pixels × a wrong scale² is every number wrong at once, so the engine would rather
   stop than guess. Disagreeing scale notes inside a measured region raise a warning rather
   than a silent pick.
3. **The engine traces; the model doesn't invent.** `one_click` returns the ring the flood fill
   produced from a seed point you name. A model cannot hand back a polygon it imagined and have
   it counted.
4. **Every record carries how it was made.** Method, seed point, whether hatch filtering
   engaged, whether it came off scan pixels, non-default fill sensitivity, confidence factors,
   and the machine's original ring if a human later moves it.
5. **Agent work is pencil until a person inks it.** Exports land in the canvas as dashed
   proposals. `mark_verdict` lets an agent sign its own work as a graphite `AGENT` diamond; the
   green `APPROVED` seal has exactly one code path and it is the toolbar button under a human
   hand. No MCP call, no import, mints one.
6. **The deliverable is a marked-up planset, not JSON.** `export_marked_pdf` burns the work
   into the drawings as drawn—condition colors, hatches, quantity chips, count markers—behind
   a legend cover with totals and a tally of how much of the set a person has actually
   reviewed. A takeoff nobody can check is not a takeoff.
7. **Refusals are actionable strings.** *"That space isn't enclosed on the plan linework—the
   fill spilled"* tells a model what to do next. A silent zero doesn't. Tools that can't answer
   withhold with a stated reason rather than returning a plausible number.

### Prove it — OpenTakeoff Academy

[**aec.kentucky-ai.com**](https://aec.kentucky-ai.com) is a standalone open benchmark and
certification arena for agents that do takeoff. Bring any model and your own harness; you are
scored on **operating a real takeoff tool** against geometry you don't control—a wrong
calibration yields a wrong area—not on emitting a plausible-looking number. Runs emit a
signed bundle with full provenance of every tool call, scoring is against held-out ground truth
and a human Senior Estimator baseline, and clearing a tier earns a credential that's
independently verifiable. The Certified path drives this engine (`opentakeoff-mcp`) behind the
task tools. Repo:
[Kentucky-ai/opentakeoff-academy](https://github.com/Kentucky-ai/opentakeoff-academy).

---

## For the person at the canvas

The agent path exists because the human path is real. Everything below is the production
measuring engine carved out of a commercial Division 9 estimating system—not a demo
reimplementation.

```bash
cd web
npm install
npm run dev        # http://localhost:5173
```

Or open the [**live demo**](https://opentakeoff.kentucky-ai.com). Drag in
`demo/sample-plan.pdf`, accept the detected scale, choose a condition, click **One-Click Area**,
then click inside a room. Open **Report** for the breakdown and the exports. That whole loop on
video: [walkthrough (1:10)](https://youtu.be/aHiW8H2TSBs) ·
[One-Click Area (0:51)](https://youtu.be/YIjWZ-BAhLE). The complete
zero-to-exported walkthrough is the [**user manual**](docs/USER_GUIDE.md).

**What a real bid looks like on it**, in the order an estimator works one:

1. Drag in the whole `.zip` off the bid platform—plans, finish schedule, addenda.
2. Set the scale on every sheet you'll measure, and **check a dimension** (`K`) on each. Ten
   seconds a sheet, and it's the only mistake that gets every number at once.
3. Pull your conditions off the architect's finish schedule instead of typing them, and set waste
   and materials *before* you trace.
4. Stitch anything split at a match line, align it, and only then start measuring.
5. **One-Click** the floors room by room. Derive base and transitions off the rooms you just
   traced rather than measuring them a second time—and read what the derivation *reports and
   never counts*, because those are doorway thresholds you still owe.
6. Walk the set and look at what landed, fix with the grips, save a **revision**.
7. Export both: the Report for pricing, the **Marked set** PDF for whoever has to check you.

The full version of that sequence, with the section for each step, is the manual's
[working order](docs/USER_GUIDE.md#the-working-order-on-a-real-bid). What every term above means
is in its [glossary](docs/USER_GUIDE.md#18-glossary--what-the-words-mean-here).

### Open anything, instantly
A plan **PDF**, an **image** (scan, screenshot, photo), or a whole **`.zip` plan set** straight
off a bid platform. Zips are unpacked and images wrapped to PDF *in your browser*—multi-page,
multi-file, up to **4 sheets side-by-side**, with hostile-archive guards so a malformed zip
fails cleanly instead of ballooning the tab. No upload step, no conversion service, no account.

### A real measuring engine
**One-Click Area** is the headline: click inside a room, the linework bounds a flood fill, the
polygon traces itself, the vertices snap to true corners. **Hatching and poché don't fool it**—tile
grids, plank lines, and section fills classify as pattern rather than wall, and the
escalation is conservative enough that a misread can never come out worse than the strict fill.
**Scanned sheets work too**: with no vector linework the engine reads rendered pixels—adaptive
thresholding, polarity detection for blueprint negatives, a gap-bridging pass for
faded ink—and badges the result so you verify the edges before committing. On CAD exports
that publish a layer tree, One-Click reads the declared roles instead of inferring them.

Plus the full manual kit—**Area, Rectangle, Linear, Curved Line, Surface Area (walls),
Count**, and **Cut Out** deducts—and a **Zone check** that answers "what's in this wing?"
without touching the takeoff.

**⟂ Transitions** derives the line where two finishes meet, from rooms you already measured.
Finishes changing inside one open space commit as a dashed butt-joint run you accept; rooms
parallel across a wall are **reported and never counted**, because that transition is a
threshold in a doorway no trace can locate—you get its length, the wall thickness, and a link
that puts it on screen.

### Drafting aids that behave like drafting aids
**45°/90° angle lock**: come within a few degrees of square or diagonal and the segment locks
to the axis—the click commits the *exactly* on-axis point, so walls come out dead square
(hold `⇧` to force it at any angle). On the canvas the crosshair **is** the cursor: the OS
pointer hides, a star marks the crossing, in-progress work draws in the instrument's own
cobalt, committed shapes wear their condition color. The lock reads quietly—the star swells,
the preview thickens, a chip shows the locked angle and the live segment length. **Snap**
(beta) pulls onto true PDF endpoints, and a corner beats an axis.

### Scale that matches real plan sets
Auto-detects the drawn scale note, or **calibrate** from any known dimension. Scale is
remembered **per sheet**, because plan sets are never one uniform scale and tools that assume
they are get the numbers wrong. **Check a dimension** (`K`) is calibrate's read-only twin: pick
a printed dimension string, type what the drawing says, and get a graded verdict (green within
1%, amber within 5%, red past it) plus a one-click **Recalibrate to this**. Every scale
acceptance drops an ephemeral calibrated ruler bar on the sheet, so a 2×-off scale is obvious
before anything gets traced. Imperial or metric (m²/m, 1:50-style ratios) is a display toggle—takeoffs
are stored unit-agnostically, so flipping it never changes a measurement.

### Conditions, materials, and the buy list
A **condition** is one finish (LVP, carpet, tile, base…), carrying a line/fill color, a **CAD
hatch pattern** so the canvas reads like the real drawing, a per-condition **waste %**, an
**×N multiplier**, a default wall **height**, and a **thickness** that turns a linear run into
border SF. **Import from schedule** parses the architect's finish table off the sheet into
conditions behind a verify dialog—you approve what becomes a condition, and the product spec
rides along as read-only report columns.

**Supporting Materials** is the layer most takeoff tools punt on: per condition, a labor type
and a subfloor type, plus the consumables that actually go on the order—adhesive, sealer,
thinset, grout, cove-base adhesive—each with a **coverage rate** and a **basis** (floor SF /
linear LF / each / **figured seam LF**). Order quantity derives automatically: measured ÷
coverage, **rounded up** to whole units. Adhesive and mortar lines get coverage presets; grout lines get a calculator that
derives SF/bag from tile size, thickness, joint width, and bag weight. Preset values are
industry-typical round numbers—always verify against the product data sheet.

### Roll goods — the seams, figured
Opt a condition into broadloom or sheet material (material class, roll width, max roll length,
seam and wall allowances, direction, sell unit) and the engine lays out the cuts: lanes, seam
placement, multi-roll splits, and order footage. Cuts draw to scale over their own rooms in
material-true colors, numbered in cutting order, and slide or resize in an edit mode that's on
the undo stack. The docked Roll panel shows those cuts nested **on the roll** with dimensions
and drag-to-reorder re-packing, and **Roll Order LF**, **Rolls**, and **Seam LF** ride the
Report, CSV, and Excel next to the measured quantities. Seam LF is the weld-rod / seam-tape
quantity read straight off that layout—counted between adjacent lanes of the same room, net
of the wall overage, only where two lanes actually face each other—so a supporting-materials
line on the **seam LF** basis prices the rod off where the cuts meet instead of off a share of
the perimeter. A 20-ft-wide room off a 12-ft roll seams once down its length; the same square
footage as two separate 10-ft rooms seams not at all, and no factor on area can tell those
apart. Available headlessly too, through `roll_setup` on `edit_condition`. (The roll-layout engine
was contributed by Michael Hartman.)

### Multi-sheet reality
**Stitching**: a floor split across a match line becomes one working surface—align the joint
by picking the same drawn point on both sheets, then trace straight across the seam.
**Levels** group a multi-floor set. A visual **gallery** (`G`) is where you choose and open sheets, and
**Regroup** restores a side-by-side composition in one click. A trace can't span two *grouped*
sheets—the gap between panels isn't real distance, so the commit refuses and points you at
stitching.

### Reports, exports, and revisions
A per-condition breakdown—**Floor / Wall / Border SF, LF, EA, total SF, SY**, with and
without waste—plus a combined **materials buy list**. Waste applies only in the report's
order quantity, never to the live measured number, so the takeoff and the buy list stay honest
about which is which. Export **CSV**, **JSON**, a real **Excel workbook** (Summary / By-sheet /
Materials / Shapes-audit / **By floor × room**, full-precision cells, formula-shaped names kept
inert text), print,
or **Marked Set PDF**—a distribution-ready planset built entirely in your browser for a GC
who will never install anything.

When the addendum lands, **Revisions** makes it data instead of archaeology: save a named
revision at each bid revision, then compare any two as quantity deltas per condition, per
sheet, and on the buy list, with a compare CSV. The compare is deliberately quantity-level
rather than geometric—it tells you which numbers moved, not which wall did. Restore banks the
live takeoff first, so it's never a one-way door.

### Markups, seals, and RFIs
A separate layer the totals never count: revision clouds, callouts, text notes, highlighter
ink, and reusable **stamps** (plank direction, seam direction, pattern origin—build your own,
or import an `.svg`). **Approval seals** are the estimator's ink: click a committed takeoff to
approve it, and the Marked Set's cover gains a tally line—*N estimator-approved · N
agent-marked* — so a PM knows exactly how much of the set a person has looked at. The **RFI
register** turns any markup into a tracked question with status, priority, ball-in-court, and
cost/schedule impact flags, exporting as CSV/JSON and as an RFI schedule page in the marked set.

### The Agent panel, in the browser
The same proposer/reviewer split as MCP without leaving the canvas: describe a takeoff in a
sentence and a model—**yours**, on your key, from your browser—works the sheet with the
app's own deterministic tools and stages dashed proposals you accept, correct, or reject. It
cannot invent geometry (`propose_shapes` rejects anything uncited) and it cannot set a scale.
To watch the loop with no AI account at all, run the keyless deterministic mock server in
`scripts/`.

### A vector-sharp canvas
Past ~1.15× zoom **times your display's pixel ratio**, the visible region re-renders straight
from the PDF vectors at your current zoom rather than magnifying a fixed bitmap, so fine
callouts and hatching never blur—and it engages after a pause in the gesture, so a continuous
zoom stays on the fast base layer while you're still moving. It overlays only what's on screen,
so there's no full-sheet bitmap to hold. **Dark view** (☾) inverts the sheet pixels
themselves—a true negative print, white linework on black, not a CSS filter—with hatches
retuned, and exports follow it.

### Yours, locally
Every drawing, scale, condition, markup, and RFI autosaves to **your browser** (IndexedDB +
localStorage). Nothing is uploaded, there's no account, and there's no server in the default
build. The flip side is stated plainly in the manual: storage is per browser, per origin, and
clearing site data clears your work.

<details>
<summary><strong>Optional: team cloud mode (Google sign-in + Drive)</strong></summary>

<br/>

Everything above is the default and it's unchanged: open the page and you're an anonymous,
local-only user. A team on Google Workspace can *optionally* sign in to unlock a shared mode
instead: projects live as folders in the team's own Google **Drive**, the project list is
deep-linked from an existing **Glide** app, and material costs come from a synced
`pricing.json`. It's strictly additive—set nothing and it doesn't exist. The security posture
stays honest: still a plain static site, **no secrets in the bundle**, team-only because the
Google OAuth app is **Internal** to your domain, and the data sits in **your own Drive**. See
[`docs/GOOGLE_SETUP.md`](docs/GOOGLE_SETUP.md) and
[`docs/GLIDE_INTEGRATION.md`](docs/GLIDE_INTEGRATION.md). A cloud deployment can also opt into
**local-first sync** (`VITE_CLOUD_SYNC=1`): annotations stay canonical in the browser and sync
to Drive in the background, so the canvas is instant and survives a flaky network—[`docs/SYNC_ARCHITECTURE.md`](docs/SYNC_ARCHITECTURE.md).

</details>

<details>
<summary><strong>Optional: bring your own vision model</strong></summary>

<br/>

OpenTakeoff can ask a vision model **you** provide to read things off the plan—starting with
the drawn scale when a sheet's text doesn't state one (scans, rotated notes, image title
blocks). Click **AI** in the toolbar and point it at an **OpenAI-style** endpoint (the default;
local runtimes on your own machine speak it and need no key) or an **Anthropic-style** one,
plus a vision-capable model id.

- **What's sent, and only when you click an AI button:** one snapshot of the sheet region in
  question, plus the question—to *your* endpoint. Never the whole plan file, file names,
  project names, or your takeoff.
- **Nothing configured = nothing exists.** Unconfigured builds add zero UI beyond the button
  and make zero AI network calls. No telemetry either way.
- The answer is only ever a **suggestion**, landing in the same confirm-to-apply flow as a
  text-detected scale, with the calibrated guide bar shown on acceptance.
- The key is stored in this browser's localStorage—use one you can revoke. Deployers:
  `VITE_AI_ENDPOINT` / `VITE_AI_MODEL` / `VITE_AI_PROVIDER` bake team defaults, but **never set
  `VITE_AI_KEY` on a public deploy**—Vite inlines it into the shipped bundle.

</details>

## What's in the box

| Area | What you get |
|---|---|
| **Ingest** | PDF, image, or `.zip` plan set—unpacked in-browser, multi-page, multi-file, up to 4 sheets side-by-side |
| **Scale** | Auto-detect the drawn note, calibrate from a known dimension, or verify one with a graded check—per sheet |
| **Measure** | One-Click Area (vector flood + raster fallback), Area, Rectangle, Linear, Curved Line, Surface Area, Count, Cut Out deducts, ⟂ Transitions, Zone check—imperial or metric |
| **Drawing aids** | 45°/90° angle lock with `⇧` hard-lock, live angle + segment-length readout at the cursor, endpoint Snap (beta) |
| **Conditions** | Color + CAD hatch per finish, waste %, ×N multiplier, wall height, border thickness, schedule import, browser-wide library |
| **Supporting Materials** | Labor + subfloor type, coverage rate × basis (incl. figured seam LF) → rounded order quantities, trowel/roller presets, grout calculator |
| **Roll goods** | Per-condition roll setup → lanes, seams, multi-roll splits, to-scale cuts with drag-to-reorder nesting, Roll Order LF + Rolls + figured Seam LF on every export |
| **Multi-sheet** | Sheet gallery, tabs and side-by-side groups, Regroup, levels, **stitching across a match line**, PDF layer roles |
| **Report** | Per-condition Floor/Wall/Border SF, LF, EA, SY with and without waste, plus the combined buy list; columns, grouping, saved templates |
| **Export** | CSV, JSON, **Excel (.xlsx)**, print, **Marked Set PDF**, RFI CSV/JSON |
| **Revisions** | Save at each bid revision, compare quantity deltas per condition/sheet/buy list, guarded restore |
| **Markups** | Clouds, callouts, notes, highlighter, stamps, **approval seals**, RFI register—separate layer, never counted |
| **Voice** | Push-to-talk takeoff commands, recognized on-device in WebAssembly; audio never leaves the browser |
| **View** | Light or **dark (negative print)**—sheet pixels inverted at draw time, exports follow |
| **Storage** | IndexedDB + localStorage—client-only, nothing uploaded |
| **MCP server** | 40 tools + browsable sheet resources on stdio, multi-document sessions ([`mcp/`](mcp/README.md)) |
| **Provenance** | Every shape records its scale, its method, its confidence, and whether a person or an agent made it |
| **Capture (opt-in)** | Bundled [capture server](capture/README.md) banks each contributed takeoff as (geometry → label) training rows |
| **Deploy** | One static build—Netlify, Vercel, GitHub Pages, Cloudflare Pages, S3, any static host |

---

## The data layer — why this engine exists

Every finished takeoff is a set of expert decisions: *this* region gets *this* finish, at
*this* waste, yielding *these* quantities. Done once, that's a bid. Recorded every time, it's a
**labeled dataset that does not currently exist**—plan geometry paired with the finish an
expert assigned it, which is the exact raw material for training a model that can do takeoff.
Today that data evaporates the moment the bid goes out.

The thesis, stated so it can be attacked: **markup is label.** Professional takeoff software
already stores every drawn region as vector geometry, and reconstructing those polygons
reproduces the recorded quantities exactly—so two decades of estimating work is an exact,
*verifiable* corpus rather than a noisy one. That claim is what the whole research program
tests, and it's patent pending.

OpenTakeoff is the instrument that produces the corpus, with the collection path opt-in and
auditable:

- The **Contribute** button in the Report builds a derived-only payload—condition labels,
  shape roles, quantities, geometry normalized 0-to-1 against the sheet, and per-shape
  provenance (hand-traced versus machine-proposed, and whether a human corrected it, with the
  machine's original ring beside the fix). The builder is ~150 audited lines
  ([`web/src/lib/contribute.js`](web/src/lib/contribute.js)); the normative wire contract is
  [`docs/CONTRIBUTION_SPEC.md`](docs/CONTRIBUTION_SPEC.md).
- **Never sent**, enforced by a whitelist in the builder: the PDF or any render of it, file or
  sheet names, project/client names, markup text, absolute coordinates, scale *values* (only
  the scale's provenance—calibrated, detected, or standard), and edit timing beyond a
  creation stamp. One linkage is deliberate and disclosed: shapes carry opaque, locally-minted
  IDs so a re-contribution after an addendum supersedes rather than duplicates.
- The bundled **capture server** ([`capture/`](capture/README.md))—one stdlib-only Python
  file, no pip install—receives it on localhost and banks one training row per labeled shape,
  hash-gated so re-contributions never duplicate. v2 rows distinguish what the machine got
  right from what an expert had to fix, which is the signal that actually teaches a takeoff
  model. Point it at a synced folder with `--mirror` and the corpus rides existing company
  storage sync, atomically.

```bash
python3 capture/capture_server.py    # then, in the app's browser console:
# localStorage.opentakeoff_contribute_endpoint = "http://localhost:8787/contribute"
```

Run OpenTakeoff as-is and none of this exists for you—nothing is captured, nothing leaves
your machine. Install it and every takeoff you *choose* to contribute compounds into an asset
you own. This is the open edition of the capture layer inside
[Spline](https://spline.quisutdeus.io), the commercial Division 9 estimating system OpenTakeoff
was carved from, where capture runs ambient on autosave and commit instead of behind a button.
The row schema and the training angle are in [`capture/README.md`](capture/README.md).

## The research program

OpenTakeoff is the open half of an applied-research program run by a working commercial
flooring estimator who builds the AI his own department uses
([Kentucky AI](https://kentucky-ai.com)). The open-core boundary is the same one the better
open scientific software draws: **the measurement engine—rendering, scale, geometry, exports,
the MCP server—is Apache-2.0 and stays open. The models trained on our own estimating archive
are proprietary.** You get a real tool with no seat licenses; the part only our data can build
stays ours.

The research side is run as a lab, and the receipts are the point:

- **Parameter-efficient tuning, not pretraining.** QLoRA adapters on open-weights bases
  (~0.1% of parameters trained), specialized from a verified bid archive—cheap enough to
  retrain when the data says retrain, small enough to ship. The flagship adapter predicts bid
  totals at **12.3% median absolute percentage error on a 51-project temporal holdout**,
  against **62.8%** for the untuned base; full method and honest caveats on the
  [model card](https://huggingface.co/Kentucky-ai/div9-flooring-estimator-gemma4-31b).
- **Verified labels in.** Before a historical bid becomes training data it passes a
  dual-document verification gate: totals must reconcile between the bid workbook and the
  separately filed proposal, change orders only count when corroborated by an actual
  change-order document, and line-item arithmetic is recomputed and forensically checked.
  Unverifiable projects don't train.
- **Verifiable rulers out.** Models are scored against temporally held-out projects—future
  bids, not a random split—with a geometry scorer whose **own error floor is measured
  (0.4%)**, so a number can be attributed to model error versus measurement error.
- **Multi-seed replication.** No result is promoted from a single training run; promotion
  requires seed replication with paired bootstrap confidence intervals, and the cross-seed
  spread gets published alongside the best seed.
- **Negative results are kept.** The experiment ledger records what failed and why—an
  unfreeze recipe that destroyed detection, a vertical-specialist model that lost to the
  generalist's cross-vertical transfer—next to what worked.
- **Leak-audited before release.** Identifiers are replaced *before* training, so the weights
  never see a real name, and every public artifact passes a differential red-team: adversarial
  extraction probes against the tuned model with the untuned base as control.

Sanitized artifacts—model cards, benchmark specs, papers—publish as they clear review:
[Hugging Face](https://huggingface.co/Kentucky-ai) ·
[kentucky-ai.com](https://kentucky-ai.com). The agent-side evaluation lives in
[OpenTakeoff Academy](https://aec.kentucky-ai.com).

---

## Run it / deploy it

To use it, all you need is a browser. To self-host, it's one static build you can drop
anywhere—no backend, no database, no environment to stand up.

```bash
cd web
npm install
npm run build      # → web/dist/  (static; host it anywhere)
```

[![Deploy to Netlify](https://www.netlify.com/img/deploy/button.svg)](https://app.netlify.com/start/deploy?repository=https://github.com/Kentucky-ai/opentakeoff)

The repo ships a root `netlify.toml`, so the button is genuinely one-click. The same
`web/dist/` works on **Vercel, GitHub Pages, Cloudflare Pages, S3**—anywhere that serves
static files. Running your own reverse proxy—nginx, Docker, Tailscale? Check
[`docs/SELF_HOSTING.md`](docs/SELF_HOSTING.md) first—there's one MIME-type gotcha worth
knowing about. Deployment notes and the optional AI backend:
[`docs/DEPLOYMENT.md`](docs/DEPLOYMENT.md).

## Build on top of it

Apache-2.0: fork it, change it, ship it—for your own crew or as the base of your own product.
The codebase is deliberately small and readable, and the geometry libraries are pure so you can
lift them straight out:

| What | Where |
|---|---|
| Flood fill, face extraction, corner snap, raster fallback | [`web/src/lib/oneclick.ts`](web/src/lib/oneclick.ts)—pure TS, tested |
| Scale detection, sheet helpers, polygon area | [`web/src/lib/sheets.ts`](web/src/lib/sheets.ts)—pure TS, tested |
| Waste, square-yard, coverage → order quantity | [`web/src/lib/totals.js`](web/src/lib/totals.js) |
| Roll-goods lane and seam layout | [`web/src/lib/rollgoods.js`](web/src/lib/rollgoods.js)—pure, tested |
| Persistence (IndexedDB + localStorage) | [`web/src/lib/store.js`](web/src/lib/store.js) |
| PDF / image / zip ingest | [`web/src/lib/ingest.js`](web/src/lib/ingest.js) |
| The canvas (one large component, ~90% of the app) | [`web/src/pages/TakeoffCanvas.jsx`](web/src/pages/TakeoffCanvas.jsx) |
| MCP server (imports the same libs) | [`mcp/src/`](mcp/src/) |
| Design tokens—source of truth for color and spacing | [`web/src/styles/tokens.css`](web/src/styles/tokens.css) |

Third-party integrations and downstream forks run on this engine today.

`cd web && npm run check` is the exact CI gate—typecheck, lint, test, build. Keep
`oneclick.ts` and `sheets.ts` free of React and DOM; that purity is what makes them reusable and
testable. Never commit real construction plans. See [CONTRIBUTING.md](CONTRIBUTING.md) and
[AGENTS.md](AGENTS.md)—the repo's own instructions for coding agents—plus the
[user manual](docs/USER_GUIDE.md).

## Contributing

The open work is architectural, and it's posted as RFCs with a stated finish line rather than a
manufactured chore list. Currently open:

- [**RFC #60**—make One-Click Area genuinely great](https://github.com/Kentucky-ai/opentakeoff/issues/60):
  face extraction, gap tolerance, confidence. Partially landed—a first slice merged in
  [#179](https://github.com/Kentucky-ai/opentakeoff/pull/179), contributed by
  [@knmurphy](https://github.com/knmurphy) and credited in the release notes—and the accuracy
  ceiling is still open.
- [**RFC #87**—the sheet graph](https://github.com/Kentucky-ai/opentakeoff/issues/87):
  resolve room tags, schedules, legends, and detail callouts into one queryable graph with a
  citation per answer. Two phases shipped; revision clouds and detail-callout chains are open.
- Anything labeled [`rfc`](https://github.com/Kentucky-ai/opentakeoff/labels/rfc) or
  [`flagship`](https://github.com/Kentucky-ai/opentakeoff/labels/flagship)—a flagship is an
  open design-and-build challenge where multiple entries are welcome and the best one merges
  with credit.
- Smaller, fully-specified entry points are labeled
  [`good first issue`](https://github.com/Kentucky-ai/opentakeoff/labels/good%20first%20issue)—they
  name the exact files. Claim one in a comment and go.

Ground rules are in [CONTRIBUTING.md](CONTRIBUTING.md). The bar is a green `npm run check` plus
a test for anything touching the geometry libraries; tested PRs merge fast. CI also holds two
lines `npm run check` doesn't: every relative link and anchor in the docs must resolve
(`node scripts/check-doc-links.mjs` runs it locally), and `web/bench/results.json` must match
what the engine actually produces—an engine change carries its bench delta in the same PR. External
contributions are credited by name in the commit and the release notes—and because
`opentakeoff-mcp` publishes to npm off a `mcp-v*` tag, engine work you land ships to every
agent that pulls the package.

Found something exploitable? Report it through
[private vulnerability reporting](https://github.com/Kentucky-ai/opentakeoff/security/advisories/new)
rather than a public issue. [SECURITY.md](SECURITY.md) states the threat model up front—worth a
read before reporting, since it explains what the trust boundary actually is for a client-only app
and a local stdio MCP server, and what that does and doesn't make a vulnerability.

## Tech stack

- **Frontend:** React 18 + Vite 6, plain JSX
- **Drawing:** raw HTML5 Canvas + SVG—no charting or canvas frameworks
- **Geometry:** TypeScript (`oneclick.ts`, `sheets.ts`), pure and unit-tested
- **PDF rendering:** [pdf.js](https://github.com/mozilla/pdf.js)
- **Plan-set ingest:** fflate (zip) + pdf-lib (image → PDF), lazy-loaded
- **Speech:** transformers.js, whisper-tiny.en (q8 encoder + uint8 decoder) in a Web Worker—benchmarked
  against the alternatives in [`docs/VOICE.md`](docs/VOICE.md)
- **MCP:** TypeScript stdio server importing the web engine's own libraries
- **Storage:** IndexedDB + localStorage—no backend required
- **Tests:** `node --test` + `tsx`
- **No paid dependencies.** See [THIRD-PARTY-NOTICES.md](THIRD-PARTY-NOTICES.md).

## Status

A working tool used on real commercial bids, not a preview. The measuring engine is the
production engine carved out of a commercial estimating system, and the same engine answers to
a person at the canvas or an agent over MCP with the same math, the same scale gate, and the
same provenance record. Named limits, so you don't find them the hard way: **Snap** is beta,
revision compare is quantity-level rather than geometric, and the translated
READMEs lag the English one. Issues and pull requests are welcome.

## Who's building this

I run estimating for a commercial flooring company and build the AI that runs my department.
OpenTakeoff is the open half of that work: the measuring engine, given to anyone—human or
agent—who needs to read a building. The models trained on our own estimating archive stay
ours, and the boundary is drawn in public so it can be held to account.

What makes the data worth anything is that it comes from bids that were actually submitted, won
or lost, and reconciled against a separately filed proposal. That's also why the engine had to
be free: a corpus is only as good as the number of real takeoffs that flow through the
instrument producing it.

— Michael · [Kentucky AI](https://kentucky-ai.com)

## License

[Apache License 2.0](LICENSE)—use it, fork it, ship it, build on top of it. See
[NOTICE](NOTICE) for attribution.

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