
health-os
io.github.andronaft由 GitHub 帳號 andronaft 發布v0.3.0更新於 Oct 24, 2026
Local-first personal health record: labs, meds, wearables, food log, deterministic safety checks
發布方自己的服務:Health OS
概覽
本地優先的個人健康紀錄 MCP 伺服器:檢驗、用藥、穿戴裝置與飲食紀錄存於自建 Postgres,並附確定性安全檢查。
- 可以做什麼
- 從 PDF 或照片匯入檢驗報告,正規化名稱與單位,並逐列核准或拒絕待審紀錄
- 讀取與更新健康紀錄:檢驗、診斷、用藥、穿戴裝置匯入與飲食日誌
- 產生趨勢、異常偵測、篩檢行事曆、週報與就診摘要
- 對危急值發出確定性警示,並提供固定的危機與藥物交互作用回應
- 記錄餐點的 41 項營養概況、RDA 百分比與缺乏或過量標記
- 適用情境
- 適合希望助理在本機查詢與維護個人健康資料的人,例如詢問 LDL 趨勢、待審檢驗結果或本週營養缺口,同時把資料庫留在自己的機器上。
- 執行需求
- 以本機 Docker 容器透過 stdio 執行。完整安裝需要 Docker 與 Python 3.12+、一個 PostgreSQL 16 + pgvector 資料庫(須先執行遷移與種子資料),以及 DATABASE_URL 連線字串;建議另設 READONLY_DATABASE_URL 供唯讀 SQL 使用。另提供含虛構病患的一鍵示範。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 health-os,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
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:
Point your MCP client at it:
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+.
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)
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
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.
Privacy / local-first
- Your data stays in your own database. Postgres runs locally in Docker;
data/and.envare 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
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).
來源:README.md,提交 fbcdf5c
工具
0版本歷史
3- v0.3.0最新Oct 18, 2026
- v0.2.1Oct 1, 2026
- v0.2.0Sep 30, 2026
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