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Mirobody

Turn scattered health data into answers you can trace. Self-hosted AI health data engine for lab reports, wearables and genetics, with a built-in MCP server. Run it with Docker…

Install

$git clone https://github.com/thetahealth/mirobody

Install command taken from the project's README or registry entry. Always review code before running it.

Mirobody

Turn scattered health data into answers you can trace.

A self-hosted AI health data engine that brings lab reports, wearables and genetic files into one standardized record. Standardize health data with one command. Try the full app in Codespaces with your own model key, or run the app, the data and the models 100% on your own machine.

English · 中文

▶ Live demo · Try one command · Open in Codespaces · 100% local

Three reports, three names for the same test. One trend, with the file behind every number.

Choose where to start

You want to Start here What you need
Standardize names and units One command uv; no model or API key
Try the full app in your browser GitHub Codespaces A GitHub account and your own model API key
Keep the app, models and records on your machine 100% local with Docker Docker with Compose; 16 GB memory; no API key
Keep the app and records on your machine, without Docker No Docker Debian or Ubuntu; PostgreSQL 16; Python 3.12; your own model API key

Try the engine with one command

With uv installed, paste this into your terminal:

uvx --python 3.12 mirobody resolve "LDL cholesterol" 血红蛋白 ヘモグロビン "Total Kolesterol" 血脂

No repository clone, Docker, model or API key. uv prepares Python and the package for you; the first run needs a network connection to download them. The resolver then works offline from the bundled vocabulary.

血红蛋白 and ヘモグロビン both resolve to 718-7. 血脂 stays unresolved: it names a category, not one test. The same engine standardizes names and units across the full app.

→ Use the engine from Python or MCP

Try the full app in Codespaces

Run the web app in your browser with your own model API key. GitHub prepares the environment; you do not need to install Docker or Python on your computer.

  1. Create the codespace and wait for the automatic setup to finish in its terminal. It starts the app, Postgres, the worker and the synthetic demo data.
  2. Open the setup link printed in the terminal and enter your own model API key: OpenRouter, OpenAI, Gemini, Anthropic, DeepSeek, DashScope, or any OpenAI-compatible gateway. The key is saved only after a real request succeeds. The forwarded app port is private by default.
  3. Sign in as you@mirobody.ai, code 111111, on the Email code tab. Ask "How has my cholesterol changed?" The demo holds 2,019 readings across your record and mom@mirobody.ai's, shared with you view-only.
  4. Upload a synthetic sample from demo/upload/ on the Data page to see a new report join the record, or switch to mom's record and ask again.

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