Mirobody
Mirror your body in data.
One standard, every source, one complete picture of you — building a lasting record to understand where your health is going.
This open-source edition keeps that record on your own machine: lab reports, wearables and genetic files flow into it, and your AI agent can query it over MCP.
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▶ Live demo · Try one command · Open in Codespaces · 100% local
Choose where to start
| You want to | Start here | What you need |
|---|
| Standardize names and units, in a terminal or inside your AI agent | 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 |
| Use it on your own records with nothing to run | Try Mirobody in Theta Web, or in the Theta mobile app | An account |
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.
Give the same engine to your AI agent. In Claude Code:
claude mcp add mirobody -- uvx --python 3.12 mirobody mcp
Your agent can then standardize any name, reading, unit or complaint you paste, on your machine and without a key. Reading a whole report also needs mirobody[parse] and a model key. Other MCP clients take the same command.
→ 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.
- 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.
- 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.
- Sign in as
[email protected], code 111111, on the Email code tab. Ask "How has my cholesterol changed?" The demo holds 2,019 readings across your record and [email protected]'s, shared with you view-only.
- 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.
The app and its records run in GitHub's cloud; the model service receives the documents and questions it processes. Use the included synthetic data to explore. Codespaces has usage limits and billing; model usage is billed by your model provider. Stop the codespace when you finish. For complete control on your own machine, use the local route below.
Run everything 100% locally
The app, records and models all run on your own machine. No model API key needed. Docker with Compose starts the stack and llama.cpp, with a small answering model and a document reader:
git clone --depth 1 https://github.com/thetahealth/mirobody.git && cd mirobody
COMPOSE_PROFILES=local-cpu ./deploy.sh
Allow 16 GB of memory, at least 8 GB of it for Docker. No GPU is required; on Windows, run the commands in a WSL 2 terminal. The first start needs a network connection to download container images and model weights. If the registry is not reachable from your network, deploy.sh says so and retries through a mirror; DOCKER_MIRROR in .env names your own. After those downloads, document reading, standardization and answers run locally. CPU answers can take minutes; Mac, NVIDIA and Windows options explain how to use your GPU.
Open the app link the script prints. The local models are already selected; sign in with the same demo account above, ask a question or upload a sample. Which model to choose compares the local options.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
