health-os

io.github.andronaft由 GitHub 账号 andronaft 发布v0.3.0更新于 Oct 24, 2026

Local-first personal health record: labs, meds, wearables, food log, deterministic safety checks

发布方自己的服务:Health OS

概览

AI 生成的概览

本地优先的个人健康记录 MCP 服务器:化验、用药、穿戴设备与饮食记录存于自建 Postgres,并带确定性安全检查。

可以做什么
  • 从 PDF 或照片导入化验单,规范化名称与单位,并逐行批准或拒绝待审记录
  • 读取与更新健康记录:化验、诊断、用药、穿戴设备导入与饮食日志
  • 生成趋势、异常检测、筛查日历、周报与就诊简报
  • 对危急值给出确定性告警,并提供固定的危机与药物相互作用回应
  • 记录餐食的 41 项营养画像、RDA 百分比与缺乏或过量标记
适用场景
适合希望助手在本机查询和维护个人健康数据的人,例如询问 LDL 趋势、待审化验结果或本周营养缺口,同时把数据库留在自己机器上。
运行要求
以本地 Docker 容器通过 stdio 运行。完整安装需要 Docker 与 Python 3.12+、一个 PostgreSQL 16 + pgvector 数据库(需先执行迁移与种子数据),以及 DATABASE_URL 连接串;建议另设 READONLY_DATABASE_URL 用于只读 SQL。另提供含虚构患者的一键演示。
安装前请注意
涉及敏感医疗数据:数据库文件在磁盘上为明文,建议启用磁盘加密。连接云端助手时,工具返回的内容会发送给该服务商。服务器可写入和更新记录,但暂存的化验行需显式批准。它不是医疗器械,也不提供医疗建议。DATABASE_URL 与 READONLY_DATABASE_URL 均为机密信息。

安装

在 SourceWeft 中

  1. 打开 控制台中的 health-os,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

health-os

[tests] [Listed on mcpservers.org]

Local-first personal health record, exposed over MCP. Your labs, diagnoses, medications, wearable data and food log live in your own Postgres; any MCP client — one running a local model or a cloud assistant — can read and update them through guarded tools. Critical values, drug-safety rules and screening schedules are deterministic code, not LLM judgement.

[An MCP session with the demo patient: LDL trend and the pending-review queue]

Medical disclaimer. This is not a medical device and does not give medical advice. Critical-value alerts and screening reminders are only a signal to contact a doctor — never a diagnosis and never a reason to delay care. Use at your own risk.

What it does

  • Lab results — drop a PDF or photo into your MCP client; the model extracts the values, health-os normalizes names (uk/ru/en/Latin synonyms) and units, and stages the panel as pending. Nothing counts as fact until you approve it.
  • Safety net in code — critical values alert immediately (log, macOS notification, optional Telegram); critical findings in narrative reports are flagged; drug-interaction questions are refused and redirected to a doctor/pharmacist (only deterministic checks run: total daily paracetamol across products, biotin before lab tests); a crisis tool returns a fixed response with hotlines, independent of the model.
  • Trends and analytics — Mann-Kendall trends with a per-year slope, anomalies against your own baseline (get_anomalies), a screening calendar, a weekly report, a doctor-visit brief. Risk calculators (SCORE2, ASCVD, FRAX) so far only enforce their validated age range; the formulas aren't implemented yet.
  • Food log — meals with a 41-nutrient profile, %RDA, deficiency/excess flags, meal templates.
  • Devices — Apple Health export and Garmin import.
  • 38 MCP tools + server instructions — the safety rules are sent to every client on connect; see mcp_server/README.md.

Try it in one command

Only Docker needed. Starts a throwaway database with a fictional patient — two years of labs (LDL creeping up), blood pressure, medications, a food log and a lab panel awaiting approval:

bash
git clone https://github.com/andronaft/health-os && cd health-osmake demo                             # or: cd demo && docker compose up -d --build

Point your MCP client at it:

json
{  "mcpServers": {    "health-os-demo": {      "command": "docker",      "args": ["run", "-i", "--rm", "--network", "health-os-demo",               "-e", "DATABASE_URL=postgresql+psycopg://health:demo@db:5432/health_os",               "-e", "READONLY_DATABASE_URL=postgresql+psycopg://health_readonly:demo_ro@db:5432/health_os",               "health-os:local"]    }  }}

Ask "show my health summary", "is my LDL trending up?", "what's pending review?", "what am I short on nutritionally?". Remove it all with make demo-down.

Install for your own data

Requires Docker and Python 3.12+.

bash
git clone https://github.com/andronaft/health-os && cd health-oscp .env.example .env                  # set the passwordsdocker compose up -d db               # Postgres 16 + pgvectorpython3 -m venv .venv && .venv/bin/pip install -e ".[dev]".venv/bin/alembic upgrade head        # schema.venv/bin/python -m seed.load         # marker catalog, synonyms, units, nutrients.venv/bin/python -m seed.demo         # optional: a fictional demo patient to play with

Then connect an MCP client — config for LM Studio, Open WebUI, Ollama CLI and Claude is in mcp_server/README.md. Try: "show my health summary", "LDL trend", "what am I short on nutritionally this week?".

Local models: the server speaks standard MCP over stdio, so any MCP client that runs a local model can use it. Verified so far: the server itself with the official MCP Python client (CI + the Docker demo). Not yet verified end-to-end with a local model — see #9; reports welcome.

Browser UI (preview)

bash
make ui                               # or: .venv/bin/python -m web [--port N] [--no-browser]

Starts a local, read-only web page on 127.0.0.1:8765 (port: UI_PORT in .env) and opens it with this run's session link. Only this machine can reach it: the server listens on 127.0.0.1, refuses requests with a foreign Host, and its API answers only GET requests that carry the session token. So far the page only shows whether it is connected to the server and the health-os version — the data pages come in 0.4.0 (milestone).

How it works

[Architecture: MCP client over stdio to mcp_server; read tools query approved views, write tools go through core/services into pending; safety, analytics, PostgreSQL with pgvector, ingestion]

A lab result, from report to fact

Every row of a staged panel ends up pending — nothing becomes a fact without an explicit approve. A row the catalog can't place is kept, not dropped:

[How a lab result row moves from stage_lab_panel through pending states to approved]

Unmapped and pending rows show up in list_pending_reviews and in the health summary's "awaiting review" block; a learned name maps automatically on the next panel.

DirectoryWhat's inside
core/config, DB, normalization, services, dedup, health summary
mcp_server/MCP server, read and write tools
safety/deterministic safety rules and alert delivery
analytics/trends, baselines, calculators, screening, nutrition, reports
ingestion/extraction schema, confidence scoring, device importers, embeddings
migrations/Alembic schema
seed/reference catalog + the demo patient
evals/red-team scenarios (injections, hidden critical values, unit tricks)
scripts/backup/restore (restic; install_launchd.sh schedules them on macOS), read-only role setup, importers

Privacy / local-first

  • Your data stays in your own database. Postgres runs locally in Docker; data/ and .env are outside git. Nothing is sent anywhere by health-os itself.
  • What leaves the machine depends on the MCP client you connect. With a local model (LM Studio, Open WebUI + Ollama, …) nothing does. With a cloud assistant, whatever the tools return is sent to that provider — use one whose terms fit medical data (no training on your data, zero/short retention).
  • The goal is fully local: local models for chat and extraction, local embeddings for search (already supported via fastembed). Cloud clients remain optional.
  • Optional alert channel (Telegram) sends only a generic "check your health system" text, never values.
  • Encrypt the disk (FileVault / LUKS / BitLocker) — the database files are plaintext at rest.
  • Never put real medical data in issues, PRs or tests — synthetic data only.

Tests

bash
make test              # everything (needs Postgres for the integration part)make test-unit         # pure unit tests — no database neededmake test-integration  # only tests marked `integration`

Integration tests never touch the working database: tests/conftest.py drops and recreates <POSTGRES_DB>_test on the same server (migrations + seed) on every run. Override with TEST_DATABASE_URL (the name must end in _test). Without Postgres, integration tests are skipped locally; CI sets REQUIRE_DB=1 so they fail instead.

Development history: PROGRESS.md.

License

AGPL-3.0-or-later. You may use, modify and fork health-os; if you distribute it or run a modified version as a network service, you must publish your source under the same license.

Want to use it in a closed-source or commercial product without those obligations? A separate commercial license is available from the author — reach out via GitHub (@andronaft).

Contributions are welcome — see CONTRIBUTING.md (includes a short CLA).

来源:README.md,提交 fbcdf5c

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

3
  1. v0.3.0最新Oct 18, 2026
  2. v0.2.1Oct 1, 2026
  3. v0.2.0Sep 30, 2026

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