Best open-source alternatives to Fitbod

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.

Workout.cool is a self-hosted fitness coaching platform built to help users create workout plans, monitor progress, and explore an extensive exercise library. It is presented as a modern replacement for an earlier abandoned project, with an emphasis on community ownership and ongoing maintenance. The project appears aimed at developers and fitness enthusiasts who want a customizable coaching app they can run locally or deploy themselves. The README describes a Next.js-based application organized with Feature-Sliced Design, and it includes a workflow for importing exercise data from CSV files so the database can be populated with sample or custom exercises. The installation instructions support both Docker-based setup and a manual development path using Node.js, pnpm, and PostgreSQL.

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.

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.