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. English · 中文 · 日本語 ▶ Live demo · Try one command · Open in Codespaces · 100% local Three reports,…
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.
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.
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.
Prefer Docker with a cloud model? Run ./deploy.sh without a local model profile, then paste your own model key into the printed setup link. The app and records stay on your host; the model service processes your documents and questions.
Chat calculations take the original queried data directly. A cloud model’s
code runs as JavaScript inside the app, with nothing to set up; a model on your
own hardware computes in Python in a local Docker container when the app can
reach a Docker daemon. Setup and data flow cover
both. Query views provide their built-in statistics without either.
No container, and nothing pulled from a registry. On Debian or Ubuntu, PostgreSQL 16 with pgvector comes from the distribution’s own packages and the app runs straight from the checkout. It is the same install the Codespaces path performs:
git clone --depth 1 https://github.com/thetahealth/mirobody.git && cd mirobody
scripts/install_native.sh # PostgreSQL, pgvector, the Python package, a .env
scripts/start_native.sh # the worker and the app on port 18060
Open the link it prints and sign in as [email protected], code 111111: the demo record is seeded already. A model key from the printed setup link adds document extraction and answers. install_native.sh installs system packages with sudo and writes one sudoers rule for the postgres account, and says so as it runs.
The local models belong to the Docker path, so this one answers through a model service. Everything else matches the row above it in what stays on your machine: the records live in the PostgreSQL you just installed, and a name to a code never leaves the machine.
What you can do
Compare reports across labs. PDFs, phone photos and spreadsheets, 23 file types in all, land in one history. Every reading links to the page it was read from.
Bring in everyday data. Import an Apple Health export, or connect Garmin, Oura and Whoop.
Look after your family. Invite someone who shares their own record, or keep one for a parent who never signs in.
Write a health journal.headache since last night, BP 150/95, no fever becomes a coded complaint and two coded readings; “no fever” is not logged as a fever.
Explore genetic data. Upload a 23andMe, AncestryDNA or WeGene export, or a VCF, and ask by rsID, gene or region. Drug questions get the matching CPIC guideline coverage, never a change of medication.
Use your own agent. Connect Claude Code, Codex or Cursor over MCP, or embed the offline engine in Python.
Mirobody does not diagnose or prescribe, and its answers are not medical advice.
Collect · Standardize · Agent
Stage
What it does
Where
① Collect
Lab reports, devices, photos, genetic files and journal entries come in from every source; each reading keeps the file it came from.
A1c, HbA1c and Glycated Hemoglobin resolve to one code, units to one standard, offline: readings from scattered sources land as clean rows in one table. A name it cannot place stays unresolved.
Answers come from tools that query that table, not from the model’s memory: trends, comparisons across labs and devices, a chart. Every number traces to its file, so you can check it instead of taking it on trust.
Joining a care circle shares nothing by itself. Each member decides whether others may view or edit their own record. A parent who never signs in can be managed by you until they claim the record and choose what you keep. See it in the walkthrough.
What stays on your machine
Where the app runs and where the model runs are separate choices. Mirobody does not report usage anywhere.
Codespaces + a model key
Local Docker + a model key
100% on this machine
App and stored records
in your codespace on GitHub’s cloud
on your machine
on your machine
Documents and questions sent for model processing
sent to your model vendor, under its terms
sent to your model vendor, under its terms
stay on your machine
A name to a code, a unit to UCUM (② Standardize)
in the codespace, from the bundled vocabulary
on your machine, with no network
on your machine, with no network
Model weights
managed by the model vendor
managed by the model vendor
downloaded once from Hugging Face
The command-line resolver also runs on your machine without a model or network access after its initial download.
Device vendors see data only after you link one. Before this reaches a network you do not control, read SECURITY.md: it lists every address the server can call.
Build on Mirobody
You want
Start here
Your record in Claude Code, Codex, Cursor or Gemini CLI
In the running app, Settings → MCP link (valid for 10 days by default), then one line per client
The offline engine as tools in any MCP client, with no app
uvx --python 3.12 mirobody mcp over stdio (client setup)
Names and units resolved in your own code
pip install mirobody: offline, no key, numpy the only dependency (library guide)
Over MCP the stack serves tools scoped to the signed-in person: four read your record (readings, medications, genotypes, pharmacogenomics), three resolve names and units, and two search medical reference material when the deployment has a knowledge source.
After pip install mirobody, use the same engine from Python:
from mirobody.engine import resolve, resolve_reading
resolve("血红蛋白").loinc # '718-7' any language, one code
resolve_reading("total cholesterol", "5.0", "mmol/L").loinc # '14647-2' the unit picks the code...
resolve_reading("total cholesterol", "193", "mg/dL").loinc # '2093-3' ...mass, not moles
resolve("中性粒细胞百分比").loinc # '26511-6' Neutrophils/Leukocytes
resolve("血脂").resolved # False a category, not one test
Numbers you can check
Claim
Check it
347/347 on the tests an ordinary checkup prints, in English, Chinese, Japanese, Russian, Estonian and Turkish
63,416 LOINC 2.83 codes cut by rule and 1,023,273 names pointing at them, including LOINC’s own 21 languages; report spellings the index misses are added one reviewed row at a time
23andMe, AncestryDNA, WeGene, MyHeritage and FamilyTreeDNA exports and VCF on GRCh37 and GRCh38 decode to the same 13 calls across 12 genes, tested on public 1000 Genomes samples shipped in the package
Mirobody is the health data engine behind Theta, a live consumer health app on the web and mobile, with these users today:
Contributing
The most useful contribution is a term the resolver gets wrong. Run mirobody resolve "<term>"; if the answer is wrong or empty, report it or send a fix with a test case. That is how the vocabulary grows into new languages: Russian panel and Estonian vitamin terms arrived in #88 and Turkish lab-report spellings in #140, each one row of resolver_overrides.tsv and one test case per term. CONTRIBUTING.md has the setup and the checks.
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