Open-source PDF construction takeoff engine driven natively by AI agents via MCP.
Measure construction plans in your browser or hand the engine to your AI agent.
OpenTakeoff is an open-source PDF takeoff and estimation engine built for preconstruction. It provides both a browser canvas for human estimators and 58 native Model Context Protocol (MCP) tools for AI agents.
It parses construction PDF plan sets, lets you set drawing scales, trace rooms on wall faces, execute symbol sweeps, and calculate material quantities. The engine exports marked-up PDFs, reports, DXF files, and structured cost estimates.
It suits construction estimators, preconstruction teams, and developers building AI workflows for blueprint analysis. It does not suit casual users looking for general document readers or non-technical estimators who want a fully managed cloud SaaS.
Use the browser canvas yourself or let an AI agent drive the exact same math and scale gate over MCP.
Agent work lands as dashed proposals and cannot become approved ink until a human reviews and accepts it.
The engine reads scale notes but never applies them automatically, refusing to measure unscaled sheets.
Burns measurement layers and a legend cover with totals directly into the original plan drawings.
The software is free and open source under its license. The only cost is running it yourself locally or hosting your own static copy.
870 open remote AI-training roles, updated November 14, 2026; most pay $20–$170 an hour. Zekai lists them free; the application goes to the employer’s platform.
These reviewed AI tools do the same job instantly — from our scored directory.
No. The browser canvas and engine run entirely locally in your browser, and nothing phones home.
Agents connect via the Model Context Protocol using standard I/O, utilizing 58 specialized tools to measure, check, and propose shapes.
No. All agent work stays a dashed proposal until a human reviewer manually applies the approved seal on the canvas.
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