Your medical history, fragmented across a dozen patient portals, in one place on your server: MediKeep (formerly Personal Medical Records Keeper) is a self-hosted health record system. Built with a React frontend and FastAPI backend over PostgreSQL, it organizes 14 categories of medical data - medications with dosages and schedules, conditions, procedures, allergies, immunizations, symptoms, injuries, doctor visits and encounters, treatments, lab results, and even medical equipment with service dates and supplier info. Treatment management is genuinely sophisticated: an advanced mode links treatments to their medications with per-medication overrides for prescriber, pharmacy, and effective dates, and reverse lookup shows which treatments use a given medication. A dashboard summarizes records and recent activity, file uploads attach documents to records, and tagging works across categories. When a new specialist asks for your history, the report builder assembles custom reports by category and exports to PDF, JSON, or CSV - a curated, portable summary instead of a folder of photocopies. Authentication supports Google and GitHub SSO with OIDC providers like Keycloak and Authelia expected to work, and the built-in backup system protects the archive. Health data is exactly what should never live in someone else's cloud.

Garmin, Oura, Whoop, and Apple Health each speak a different data dialect, and Open Wearables translates all of them into one consistent REST API that you host yourself. Users authorize through a connection link or the connect widget, history backfills on the first sync, and heart rate, HRV, SpO2, sleep, workouts, and meals land in one normalized schema. When two devices record the same night, configurable data priorities decide which provider and device type wins. Native iOS, Android, Flutter, and React Native SDKs push Apple Health, Samsung Health, and Health Connect data from the phone, and full Apple Health XML exports can be imported too. Outgoing webhooks announce new data with signed payloads, test events, and delivery logs, while a live sync stream shows every pull as it happens. The developer portal adds a dashboard, user profiles with sleep, meals, scores, and women's health tabs, sync history, data coverage, API keys, team invitations, retention policies, and a seed data generator. Sleep and resilience scores ship with open algorithms you can audit and tune. An MCP server lets Claude Desktop or Cursor answer questions such as how a user slept last week. Use it for AI health coaches, fitness coaching apps, healthcare platforms, research cohorts, or a personal health archive with n8n automations on top. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Stop handing your daily food diary, body weight history, and biometric sleep data to ad-funded fitness clouds: NutriTrace brings complete calorie, macro, micronutrient, and wellness tracking to your own server with an offline-capable web PWA and native Android app. Log meals across customizable slots using live Open Food Facts and USDA FoodData Central lookups, barcode scanning, AI nutrition label OCR, or direct recipe imports from Mealie and CookTrace. Calorie and macro targets adjust automatically through three goal engines, including an inspectable Adaptive TDEE algorithm that regresses 35 days of intake against scale weight at 7,700 kcal per kilogram. Connect Fitbit via Google Health, Garmin, Withings, and Android Health Connect to pull steps, heart rate variability, sleep stages, and body composition into unified Sleep, Readiness, and Resilience scores. An integrated Trace AI assistant and Smart Log let you log meals by voice, text, or meal photo, while the opt-in Model Context Protocol server exposes 20 read, write, and management tools to Claude Desktop, Cursor, and Codex. Multi-user households get OpenID Connect SSO and one-step migration from MyFitnessPal, Cronometer, Lose It, and Waistline. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Lyftr delivers a self-hosted workout and nutrition tracker that runs on a small VPS — no subscriptions, no vendor lock-in, no "export is a Pro feature" paywalls. The Go backend with Gin framework provides JWT authentication and a REST API while storing everything in a single SQLite database file you can back up with one copy command. The exercise library ships with 800+ movements auto-seeded on first run, letting you build reusable workout programs with target sets, reps, and weights from day one. Active workout mode provides a guided set-by-set flow through your session while Gym Mode offers a full-screen card layout showing one exercise at a time with an integrated rest timer for focused training. The React frontend built with TypeScript and Tailwind delivers a mobile-first interface featuring a dashboard with weekly session KPIs, daily calorie and protein tracking, 12-week consistency heatmap, volume trend charts, and muscle balance visualization with sparklines. Nutrition tracking connects to Open Food Facts for food search with camera-based barcode scanning, logging calories and macros across customizable meal sections. Bodyweight logging with trend graphs supports both lbs and kg units across all data. An Android APK connects to your self-hosted instance for native mobile access. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

A personal Strava on your own server: Endurain is a self-hosted fitness platform that keeps your complete workout history, GPS routes, and health data out of a vendor's cloud. It ingests the standard device formats (.gpx, .tcx, and preferred .fit with full sensor data) via manual or bulk upload, and syncs directly with Strava and Garmin Connect so migrating years of history is straightforward - Garmin sync covers activities, gear, and body composition. The dashboard shows activity feeds with weekly and monthly statistics, routes on maps, and distance, speed, and training-volume trends over time, with definable goals that update automatically. Gear tracking is notably deep: log wetsuits, bicycles, shoes, racquets, skis, and snowboards, assign default gear per activity type, and track individual components like bike chains against replacement mileage. Multi-user support with admin and user roles, follower features, per-activity privacy settings, and configurable sign-up (email verification, admin approval) make it usable for clubs and coaches as well as individuals. Auth is serious for a fitness app: MFA TOTP, OIDC/SAML SSO, and email-based password resets via Apprise. The stack is Vue.js over a Python FastAPI backend with PostgreSQL, plus weight, steps, and sleep logging, imperial/metric units, multi-language support, and third-party app integration.

Baby Buddy is a private self-hosted child care tracker that centralizes feeding sessions, sleep cycles, diaper changes, and developmental health milestones into a collaborative dashboard for parents and caregivers. Caregivers can start quick timers on mobile devices to measure nursing intervals, bottle feedings, naps, and tummy time sessions with single-tap precision. The dashboard automatically calculates daily averages, feeding amounts, and awake windows to help families establish consistent daily sleep and feeding routines. Parents can track diaper changes by recording wetness, stool consistency, and color anomalies to spot digestive health changes early. Physical measurements including weight, height, and head circumference plot directly against World Health Organization growth curves to visualize percentile progression between pediatric visits. Multi-child profiles allow families with twins or multiple young children to switch contexts instantly and monitor individual growth timelines. Granular user permissions let parents invite nannies, babysitters, and relatives with restricted logging permissions while keeping administrative settings private. The built-in REST API and webhook system enable physical smart button clicks and home automation routines to log events instantly without opening a browser. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. BSD-2-Clause licensed.

openGym is a self-hosted gym and body-weight tracker that plans your weekly routines, guides every set with pre-filled weights and rest timers, and keeps your training data on a server you control instead of inside a subscription app. Over 5,600 exercises come with animated demos, searchable by body part and the equipment you own, and you can add custom ones with your own photo or video. Progression follows a rule you pick per routine (linear, Greyskull LP, double, triple, or added time), and each target explains where it came from. Supersets, warm-ups, drop sets, rest-pause, timed planks, and cardio by time and speed all log properly, and plate math shows what goes on each side of the bar. A muscle map shows where your volume went, what is still recovering, and what went untrained, beside estimated one-rep maxes, a year-long heatmap, and body weight charted against your goal. Imports from FitNotes, Strong, and Hevy carry your history over. Several people can share one server, each with a private passkey profile, and an optional admin dashboard handles invite-only signup. An optional AI coach, off by default, drafts routines and suggests changes through your own Anthropic, OpenAI, Gemini, or Ollama endpoint, and nothing applies without your approval. The interface speaks 19 languages. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Cyclists, runners, hikers, and kayakers can log every GPS route in FitTrackee, a workout tracker where each workout stays private until shared. Upload a GPX, FIT, KML, KMZ, or TCX file and the workout appears with a map and charts. Zip archives and manual entry work too. Charts plot speed or pace, elevation, heart rate, cadence, and power on one graph or several. Each workout holds 20 photos at most, and photos with GPS coordinates appear on the map. Twenty-seven sports are available, including cycling, running, kayaking, and swimrun. Shoes, bikes, and boards each tally distance, duration, and workouts, and sports can have default equipment. The dashboard shows a monthly chart and a calendar of workouts and records, and statistics group totals by day, week, month, year, or sport. Filters narrow the workouts list by date, sport, equipment, location and radius, distance, or speed, and results can be mapped. A global map plots workouts, filtered by date and sport. Workout data, charts, map, and photos each get a visibility setting: private, followers only, or public. Follow requests need approval by default, and unwanted users can be blocked. Comments accept limited Markdown, and the five latest public workouts publish as an Atom or RSS feed. Data export bundles an account's workouts and uploaded files. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

Every ride, run, hike, and swim you have ever recorded finds a home in Dreeve, a private fitness dashboard that lives on your own server rather than in a social feed. Import FIT, TCX, or GPX files from a watch folder, sync Strava, or add connectors for Garmin Connect, Wahoo, Polar Flow, Hammerhead, and Zepp/Amazfit. Weekly charts follow distance, time, and elevation beside training goals, heart rate zones, and training load analysis, and monthly stats pair per-sport totals with an interactive calendar. A heatmap paints every kilometer you have covered. Best efforts rank your fastest 5k, 10k, 50k, and 100k across every sport, while Eddington numbers, a milestones timeline, and a yearly Rewind mark the long game. Strava sync also brings segments, segment efforts, and challenges. Gear pages total the distance on each bike and shoe, and maintenance tasks send alerts through ntfy, Gotify, Discord, Telegram, Slack, or email when an interval comes due. Automation rules assign gear, flag commutes, and set sport types by device, distance, weekday, time of day, or location, so new imports arrive organized. Ask the AI workout assistant about your training, using cloud models, OpenRouter, or a local Ollama model. An installable mobile app and embeddable badges carry your stats to phones and blogs, and a password-protected admin panel keeps the settings. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.

The deep end of self-hosted recipe management: Tandoor Recipes is a Django/Vue application that replaces Paprika, AnyList, and ad-choked recipe sites with a database you control. Import is where most people start: paste any URL and Tandoor scrapes schema.org markup for ingredients, instructions, images, times, and nutrition; bulk-paste URLs for batch import; or migrate wholesale from Mealie, Paprika, Nextcloud Cookbook, Pepperplate, and other managers with built-in importers. The structured recipe editor tracks ingredients individually, which is what powers everything downstream: full-text search with trigram similarity, a "what can I make with what's in the fridge" ingredient search, automatic nutrition via OpenFoodFacts, and shopping lists that merge duplicate ingredients across recipes, convert units, scale by servings, and sort by your supermarket's actual aisle layout. The weekly meal planner is drag-and-drop, feeds shopping lists automatically, exports to your calendar, and shows a nutritional summary for the week. Multi-user support comes with a granular permission system - shared household cookbooks, private recipes, even secret ones - and shopping lists sync in real time so two people can shop simultaneously. A full REST API integrates with Home Assistant and Grocy. For households serious about cooking, Tandoor's depth is unmatched.

Large language models get a polished front end that can run fully offline: Open WebUI is the self-hosted front end of choice. It talks to local model runners, primarily Ollama, and to any OpenAI-compatible API, so LM Studio, vLLM, Groq, Mistral, OpenRouter, and cloud providers all plug into the same chat interface and can be mixed per conversation. RAG is built in: upload files to knowledge bases or reference them in chat with the # command, backed by a choice of nine vector databases (ChromaDB and PGVector officially maintained) and multiple extraction engines including Tika and Docling, with hybrid BM25-plus-vector search and cross-encoder reranking. Web search results from providers like SearXNG, Brave, and Tavily inject directly into conversations. Extensibility comes from Python tools and functions that run inside the chat, a Pipelines plugin framework, and native MCP support. Multi-user features include RBAC, SSO, and group permissions, and the instance itself exposes an OpenAI-compatible API your own apps can call.

Every major model provider behind one ChatGPT-style interface: LibreChat spans OpenAI, Anthropic, Google, Azure, AWS Bedrock, Vertex AI, Groq, Mistral, OpenRouter, DeepSeek, and any OpenAI-compatible endpoint including local Ollama. You can switch models mid-conversation and compare providers without changing tools. Its Agents framework builds no-code custom assistants with tool access via Model Context Protocol servers, file search over uploaded documents through an optional pgvector-backed RAG service, and a sandboxed Code Interpreter that executes Python, JavaScript, Go, C++, Java, PHP, and Rust. Artifacts render React components, HTML, and Mermaid diagrams directly in chat, and image generation works through DALL-E and other configured providers. Multi-user support is enterprise-grade, with OAuth, SAML, LDAP, and two-factor authentication, per-user conversation history in MongoDB, and Meilisearch-powered search across all messages and files, plus reusable presets, forkable threads, and persistent memory across conversations. The economics favor teams: instead of a ChatGPT Plus seat per person, everyone shares one instance billed per API token, with access to every provider rather than one - and providers see individual API calls, not your accumulated organizational knowledge. Deployment is Docker Compose; API keys and endpoints are configured through .env and librechat.yaml.