Best open-source alternatives to Strong

wger is a free and open source workout and fitness manager aimed at people who want to organize training, nutrition, and progress tracking in one system. It combines routine planning with tools for logging body weight, diet plans, measurements, and photos, and it also includes a collaborative exercise wiki and multilingual support. The project is designed for both individual use and shared environments, as indicated by its multi-user gym management features. It can be self-hosted with Docker Compose and offers a REST API for integrations and automation. In addition to the web application, it is available through mobile and desktop distribution channels such as Android, iOS, F-Droid, and Flathub.

Wingfit is a minimalist fitness application designed for people who want to plan training sessions, keep track of personal records, and incorporate smartwatch data into their routine. It is presented as a privacy-first, self-hostable project, with the README emphasizing that users keep control of their data and can inspect, modify, and contribute to the code. The project appears aimed at self-hosters and fitness enthusiasts who want a lightweight web app rather than a full commercial platform. It is deployed with Docker or Docker Compose, uses a FastAPI backend with SQLModel and SQLite, and includes configuration guidance for OIDC authentication. A public demo is available, and the README shows screenshots of planning, statistics, programs, and other core UI views.

openGym is a self-hosted fitness tracker focused on gym training and body-weight progress. It is designed for people who want to keep workout data on infrastructure they control rather than on a third-party platform. The README positions it as a phone-friendly, installable web app that supports passkey sign-in, syncing across devices, and offline use. The project centers on planning weekly routines and logging workouts in detail. It supports guided sessions, exercise libraries with demos, custom exercises with media, workout history, progress tracking, and statistics such as charts and PRs. It also includes training features like supersets, warm-up sets, timed exercises, rest timers, plate math, and editable past sessions, making it useful for lifters who want both workout planning and long-term record keeping.

Lyftr is a self-hosted fitness tracking application aimed at people who want to manage workout and nutrition data on their own infrastructure. It focuses on mobile use while still providing a web-based experience, and it emphasizes ownership of data rather than subscription-based access. The project supports workout logging, reusable training programs, a guided gym session mode with a rest timer, and tracking for nutrition and bodyweight. It stores data in a single SQLite file on the user’s server and offers a demo instance, Android APK downloads, and documentation for deployment, configuration, HTTPS, backups, and troubleshooting.

LiftTrace is a self-hosted weightlifting tracker designed for athletes, coaches, and gym users who want to keep workout data on their own hardware. It runs in a single Docker container and provides a browser-based PWA plus a native Android app. The project emphasizes local ownership of data, with workouts, photos, and other records stored in a SQLite database and uploads directory on the user's server. The application supports multiple user roles and OIDC-based single sign-on, making it suitable for shared environments such as teams or coaching setups. Users can log sets and reps, follow training programs, review statistics and personal records, store progress photos, import history from other fitness apps, and optionally use an AI coach. It also includes integrations for exercise libraries and music/radio services, while remaining self-hostable and free of telemetry or subscriptions.

GymCoach is a self-hosted training tracker designed for people who want detailed workout logging, progress analysis, and coaching features without sending their training data to a hosted SaaS. It stores training history in the user’s own Postgres database and supports multi-user use with per-user data isolation. The product also emphasizes local control over data and a bring-your-own-model approach for AI assistance. The app combines workout logging with training analytics, program management, and an AI coach. Users can log sets, track strength and volume trends, manage exercises and equipment, import cardio and wearable data, and generate or discuss training plans through Anthropic or OpenRouter models. The README also describes a live demo, PWA offline logging, and an MCP connector for external agents that can inspect or modify training data with user permission.