Private workout and nutrition tracking for Android and iOS.
Train Libre is an open-source, offline-first fitness app for logging workouts, calories, macros, bodyweight, and recovery — without ads, mandatory accounts, or analytics SDKs.
Designed for people who want serious tracking without social feeds, gamification, or subscription pressure, Train Libre prioritizes privacy, local data ownership, and transparent analytics.
 Diary |
 Workout |
 Nutrition |
 AI Meal Capture |
 Recovery |
 Measurements |
 Data Insights |
App Store Release |
Android (via Obtainium) |
Android (via F-Droid) |
Google Play release is currently not available.
Train Libre is built with Flutter and supports:
- iOS (Active)
- Android (Active)
- Workout Tracker: Log sets (warm-up, failure, dropsets), routines, and session history.
- Calorie & Macro Tracker: Track nutrition, hydration, and supplements with adaptive weekly guidance.
- Bodyweight & Recovery Analytics: Deep insights into muscle readiness, volume trends, and body measurements.
- Next-Gen AI Meal Capture: Capture meals from photos or text via BYOK (Bring Your Own Key) setup. Fully integrated with a holistic culinary anchor (
mealContext) and a state-aware "Top-N Fuzzy Alternatives" SQLite matching system that prevents hallucinations. Always reviewable and self-repairing before saving. - Privacy & Local-First: Data stays on device. Optional one-way health export to Apple Health and Google Health Connect.
- No Ads. No Mandatory Account. No Analytics SDKs.
- Offline-First: Your data stays local unless you explicitly choose otherwise.
- Open-Source Transparency: Trust through public code and understandable data flows.
- User-Controlled AI: Optional AI features require your own API key; no data is sent to providers without opt-in.
This project features a comprehensive, modular documentation suite split by target audience and component. Use the links below to access the technical resources:
- Developer Overview: Technical vision, key architectural pillars, technology stack, and testing philosophy.
- Architecture & SQLite Lifecycle: Clean Architecture layering and database connection lifecycle pattern.
- Data Flow & State Lifecycle: Reactive reads, imperative writes, subscription cancellation, and UI concurrency guards.
- Smart Features Overview: Overview of algorithmic features and architectural privacy invariants.
- Bayesian TDEE Estimator: Comprehensive mathematical and statistical formulation of the Kalman filter-based adaptive energy expenditure engine.
- BYOK AI Meal Validation: AI meal capture pipeline details, fuzzy validation scoring, and the 3-pass self-repair verification loop.
- Native Health Sync & Export: Bidirectional vital synchronization (Steps, Sleep), outbound manual log export pipelines, SQLite-backed idempotency tracking, and fault-tolerance patterns.
For the full interlinked documentation map, see the main Documentation Entry Point.
The long-term vision, future modules, and planned features are maintained in the ROADMAP.md file.
- Open Food Facts for food database coverage.
- wger for the workout database foundation.