# 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: **58 MCP tools** for an agent, and a browser canvas for a person. Agents and people share the takeoff document and quantity calculations. Each sheet carries its calibration; measurements carry geometry, method and authorship. Recalibration updates quantities together, incompatible imports report scale conflicts, and agent measurements carry an explicit review status. See the [Phase 1 test guide](docs/PHASE_1_TESTING.md). [![License: AGPL-3.0](https://img.shields.io/badge/license-AGPL--3.0-blue.svg)](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://union.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) · [Fork it](#fork-it) · [Contribute](#contributing) **The two manuals:** [agent manual](docs/AGENT_GUIDE.md) · [user manual](docs/USER_GUIDE.md) **Protocol work:** [Takeoff Protocol draft and compatibility status](docs/TAKEOFF_PROTOCOL.md)—formalizing existing records; current takeoff behavior is unchanged. **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) > **One-Click Area is temporarily gated.** The flood engine is being re-validated against a wider plan corpus. Until that finishes the One-Click tool is off the canvas rail (`O` reports the gate) and the `one_click` / `detect_rooms` MCP verbs are **not registered** (a default build ships 58 tools). Trace rooms with **Area** (`A`) in the canvas and `measure_polygon` over MCP; every other tool, sweep and derivation is unchanged. A build lifts the gate with `VITE_ONE_CLICK=1` (canvas) / `OPENTAKEOFF_ONE_CLICK=1` (server). Sections and videos below that show One-Click describe the engine as it returns — see [`docs/design/ONE_CLICK_GATE.md`](docs/design/ONE_CLICK_GATE.md).
OpenTakeoff — a real takeoff on a floor finish plan, driven the same way by a person or by an AI agent over MCP, with the scale and origin of every measurement
--- The **Premium workspace** offers compact controls, searchable actions and sheets, personal panel arrangements, adjustable surfaces and larger sheet previews. It is the default when this browser has no saved layout choice; **Classic layout** remains available. [See the workspace design and research](docs/design/PERSONAL_WORKSPACE.md). Sheet navigation releases closed PDFs and embedded fonts from the tile workers; see the [30-flip rendering check](docs/review/508/README.md). Beyond the PDFs in open tabs, at most four idle PDFs stay loaded (two on a phone or tablet, or a browser that reports 4 GB of memory or less), least recently used out first; see the [12-PDF check](docs/review/302-doc-lru/README.md). ## 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. | | **A crew that wants its own copy** | [**Fork it**](#fork-it)—your own instance on your own URL in a few minutes, AGPL-3.0, nothing phones home. Same path if you're going to send a pull request. | ### 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 sharing geometry and quantity modules: - **Review cleanup**—agents can edit annotation text with undo while preserving geometry and review; RFI-linked notes require browser review. Scope warnings distinguish numeric edge residue from real small overlaps. - **A stdio MCP server**—`npx -y opentakeoff-mcp`, 58 tools, on the [official MCP registry](https://registry.modelcontextprotocol.io). An agent opens a plan, reads the title block, sets the scale, traces the rooms to their wall faces (the flood engine stays gated), 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 shared web modules, so quantity math and takeoff records are compatible. Browser and MCP room-detection paths currently differ; shared code does not guarantee identical boundaries on every plan. One-Click remains gated while that boundary is re-validated. **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. This includes agent shapes drawn with manual measurement tools; the actor and drawing method are separate fields. 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 - **Tracing rules an agent actually reads**—the room-boundary conventions an estimator uses (innermost wall face, door openings crossed on the wall centerline, columns and chases wrapped, never a hatch edge or a door leaf, a tight overlay check per ring) now ship inside the server: `takeoff://wiki/workflows`, the initialize instructions and the `measure_polygon` description. On a task with no conventions in it, one unprompted run went from 0 of 6 rooms with door notches to 6 of 6 ([#423](https://github.com/Kentucky-ai/opentakeoff/pull/423), MCP 0.9.84) - **A plan set with reference rings, and blind runs against it**—three public floor plans (a VA healthcare finish plan, a VA clinic floor plan, a city public-domain ADU) with estimator-trace references, a scorer profile for concave rooms and finish splits, and published blind agent runs with frozen exports, scores and overlays. The runs found defects in the references first; the references are v2 and still unreviewed by a person, and the open estimator questions are listed ([#421](https://github.com/Kentucky-ai/opentakeoff/pull/421), [#422](https://github.com/Kentucky-ai/opentakeoff/pull/422), [`evals/mcp-workflow-bench/plan-set/`](evals/mcp-workflow-bench/plan-set/README.md)) - **The human review loop, start to finish**—import an agent's takeoff, correct a ring, accept the batch, export, and reopen the archive on a clean machine with geometry and review state intact, with screenshots of the real app in the [user manual](docs/USER_GUIDE.md#reviewing-an-agents-takeoff-start-to-finish) ([#424](https://github.com/Kentucky-ai/opentakeoff/pull/424)) - **The Takeoff Protocol draft, one shared wiki, and a scripted workflow benchmark**—the existing record fields inventoried and formalized as draft schemas with an executable compatibility matrix (10 conform, 1 needs an adapter, 5 unsupported and named); one wiki for people and agents, packaged as MCP resources and checked against source in CI; and a workflow benchmark that drives the built server flat and staged, scores geometry separately from totals, and reopens the export in a fresh process ([#406](https://github.com/Kentucky-ai/opentakeoff/pull/406)–[#419](https://github.com/Kentucky-ai/opentakeoff/pull/419), [roadmap](docs/ROADMAP.md)) - **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** (gated off the toolbar by default; `VITE_COMMAND_BOX=1` builds it back)—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"] } } } ``` If you carry a GitHub identity, [fork the repository](https://github.com/Kentucky-ai/opentakeoff/fork) as well: that's where an engine change you make gets tested—every pull request from a fork runs the full CI with a read-only token—and tested pull requests from agents merge under the same bar as anyone else's. The [contributor path](#fork-it) is the same one. **New here? [Connect OpenTakeoff to your AI](docs/CONNECT.md)** walks Claude Code, Codex, Cursor and Claude Desktop step by step and runs a first command on the demo plan. 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, real time at 3×: an AI agent in a terminal one-clicks three patient rooms on a VA medical center finish plan over MCP; each export lands in the web app as dashed pencil proposals, and the operator accepts them — 743.64 SF, pencil to ink *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 tools | Group | Tools | |---|---| | **Open and orient** | `load_plan` · `sheet_info` · `sheet_context` · `get_sheet_vectors` · `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` | | **Ask** | `create_rfi` · `list_rfis` · `resolve_rfi` · `delete_rfi` | | **Hand off** | `takeoff_summary` · `export_takeoff` · `export_report` · `export_marked_pdf` · `export_dxf` · `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 wall network produced from a seed point you name. A model cannot hand back a polygon it imagined and have it counted. While One-Click is gated, a model draws the ring itself with `measure_polygon`, and the packaged rules say where it must sit: on the innermost wall-face strokes it read with `get_sheet_vectors`, doors crossed on the wall centerline, checked on a tight overlay before the next room. How well unprompted agents follow that is measured, not assumed: see the [plan set](evals/mcp-workflow-bench/plan-set/README.md). 4. **Every record carries how it was made.** Method, seed point, whether hatch filtering engaged, whether it came off scan pixels, 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 / Commonwealth Agent Union [**Commonwealth Agent Union**](https://union.kentucky-ai.com) is the home of OpenTakeoff Academy, its construction-agent evaluation and certification arm. 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, press **`A`** (Area) and click the room's corners. (One-Click Area is temporarily gated — see the note at the top.) 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. **Trace** the floors room by room — Area (`A`) while One-Click is gated. 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 — **temporarily gated** while the flood engine is re-validated (see the note at the top); this is what it does when it is on: 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.
One-Click Area on a real finish plan: one click inside a patient room and the whole room traces itself wall to wall — 240.7 SF, committed on Enter
### 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** reads the architect's finish or material table off the sheet (keyed by CODE, TAG, MARK or SYMBOL) into conditions behind a verify dialog—grouped by category, with a category guessed from a row's words flagged for review, rows the schedule marks NOT USED left unticked, and, on a box with a text layer, a four- or five-letter code it couldn't read named in a notice—and you approve what becomes a condition. The product spec, the schedule's remarks included, rides along as report columns. Parenthesized codes such as `G-01(C)` keep their suffix; re-import holds a possible older `G-01C` condition for review. On raster imports, every scanned code starts unchecked and requires individual review or correction; two OCR reads can agree on the same wrong identity. Short codes also get a second read, with disagreements shown as **Check code**. A numeric misread recovered inside a recognized finish table appears as **set code**; you must enter its code before creating it. See the [public OCR evidence](web/bench/ocr-codes/README.md). **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.
OpenTakeoff report — per-condition breakdown and materials buy list
### Markups, seals, and RFIs The annotation toolbar offers editable arrows, three highlighter modes, callouts, cloud notes, saved tool favorites, and a numbered Sweep confirmation checklist before batch annotations are applied. **Pin** keeps a cropped drawing reference visible while you change sheets. See the [user guide](docs/USER_GUIDE.md) and [review evidence](docs/reviews/premium-annotations/README.md). A separate layer the totals never count: revision clouds, callouts, text notes, highlighter ink, **images** (upload a PNG/JPEG, or marquee a region of the plan to drop it back as a floating screenshot—move, resize, and it burns into the marked set), 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 Choose **Work** to inspect canvas and imported agent measurements together: search across sheets, locate boundaries, inspect provenance, and record an undoable review. Its **Agent** tab runs the browser agent. Imported takeoffs need no model connection to review. 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 includes evidence fields for review and cannot set a scale. Inspect proposed boundaries and their evidence before accepting them. 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.
Optional: team cloud mode (Google sign-in + Drive)
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).
Optional: bring your own vision model
The in-canvas takeoff agent runs on a vision model **you** provide: type a goal and it aims the app's own tools at it. Open the **Agent** panel and its AI settings, 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 while a run you started is going:** your goal, the prompts, and what the agent's tools return, to *your* endpoint. That includes sheet names (which include the PDF file name), each sheet's size and scale status, the text and schedule rows it reads, and images of the sheet regions it looks at (up to a whole sheet, at most 1,024 px a side). It also includes your conditions (finish tag, hatch, waste %) and the areas and perimeters it measures. This list isn't complete. Never the whole plan file. - **Nothing configured = nothing sent.** Unconfigured builds make zero AI network calls; the Agent panel only offers AI settings. No telemetry either way. - Shapes the agent finds are **proposals**: each one waits, dashed, until you accept it. Conditions it creates are added to your list right away. - 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.
## 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 — temporarily gated), 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** | Sheets tree with title-block sheet numbers, sheet gallery, tabs and side-by-side groups, Regroup, levels, **stitching across a match line**, PDF layer roles | | **Search and copy** | Search every sheet's text from the gallery, **Copy text** from a box or a whole sheet, and read a scanned sheet on-device (OCR, labelled wherever it's used) | | **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, **images** (upload or marquee screenshot), 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 — gated off the toolbar by default (`VITE_COMMAND_BOX=1`) | | **View** | Light or **dark (negative print)**—sheet pixels inverted at draw time, exports follow | | **Storage** | IndexedDB + localStorage—client-only, nothing uploaded | | **MCP server** | 58 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" ``` The capture server allows the public app and local development origins by default. For another host, pass `--allow-origin https://takeoff.example.com`; see the [capture setup](capture/README.md#run-it) for the exact defaults and repeatable option. 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 AGPL-3.0 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://union.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). ## Fork it AGPL-3.0: fork it, change it, ship it—for your own crew or as the base of your own product. If you run a modified copy for other people, including as a hosted service, publish your changes under the same license. A fork is the unit of ownership here, and it's the unit of contribution: the same three steps give you a private instance and a branch to send back. 1. **[Fork on GitHub](https://github.com/Kentucky-ai/opentakeoff/fork)**, then clone your fork. 2. **Run it:** `cd web && npm ci && npm run dev` — the canvas is at `localhost:5173`, and `npm run check` is the exact CI gate (typecheck, lint, test, build). 3. **Put it on your own URL:** the repo carries its [`netlify.toml`](netlify.toml) (base `web`, publish `dist`), so importing your fork into Netlify deploys with no settings; any static host works, and [`docs/SELF_HOSTING.md`](docs/SELF_HOSTING.md) names the one nginx gotcha. Your instance keeps every plan local exactly as the public one does. Pull requests from a fork run the full CI with no secrets and a read-only token ([`docs/DEPLOYMENT.md`](docs/DEPLOYMENT.md)), so a green check on your fork is a green check here. 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. The [public OCR code fixtures](web/bench/ocr-codes/README.md) provide invented schedules, per-cell expected codes and native/browser measurement commands. 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. What is measured about agents driving it, and what is still open, is in one table: the [Phase 3 completion gate](docs/ROADMAP.md#phase-3-completion-gate-2026-09-12). 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) NVIDIA Inception Program member badge Kentucky AI is a member of the NVIDIA Inception program. **Contact:** research collaborations, data questions, press, or anything that is not a bug — [research@kentucky-ai.com](mailto:research@kentucky-ai.com). Bugs and feature requests go in [issues](https://github.com/Kentucky-ai/opentakeoff/issues); security reports follow [SECURITY.md](SECURITY.md). ### Contributor credit Thanks to **[@knmurphy](https://github.com/knmurphy)** (Kevin Murphy) for [image captures](https://github.com/Kentucky-ai/opentakeoff/pull/346), [drawing styles](https://github.com/Kentucky-ai/opentakeoff/pull/337), and [sharper overlays](https://github.com/Kentucky-ai/opentakeoff/pull/329). The **Pin** reference tool builds on his image-capture work. ## License [GNU Affero General Public License v3.0](LICENSE) (AGPL-3.0-only)—use it, [fork it](#fork-it), ship it, build on top of it, and share your changes when you run a modified copy for others. Through commit `84c7f48e` (tag `apache-2.0-final`) OpenTakeoff was Apache-2.0, and that code stays Apache-2.0 for everyone who already has it. See [NOTICE](NOTICE) for attribution and the relicensing note and the section 7(b) attribution term; contributions are accepted under the [CLA](CLA.md). For commercial terms outside the AGPL, contact [Kentucky AI](https://kentucky-ai.com). For an agent measurement workflow focused on accurate geometry, see [Geometry from source to review](docs/GEOMETRY_WORKFLOW.md). ## Shared knowledge for people and agents The [wiki](docs/wiki/README.md) routes architecture, protocol, human/agent workflows, MCP tool selection and domain knowledge. MCP clients read the same packaged pages at `takeoff://wiki` and `takeoff://wiki/{page}` before loading a plan. [AGENTS.md](AGENTS.md) is the contributor router; detailed guidance lives in the wiki. CI checks packaged wiki content, tool counts/inventory, schema references and links against source. ## Privacy and terms [Privacy Policy](https://opentakeoff.kentucky-ai.com/privacy/) · [Terms of Service](https://opentakeoff.kentucky-ai.com/terms/). Both are also linked in the in-app guide (`?`). The software license (AGPL-3.0) is separate. © 2026 NVIDIA, the NVIDIA logo, and NVIDIA Inception are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries.