Track your training and your nutrition. Own your data.
LogIt is an open-source, offline-first training and nutrition tracker. Log workouts and meals, run your own progression and macro algorithms, and see everything on the web — all without an account.
What you get
Training
Log it your way
Log sessions freeform or from a split you define. Plate math, supersets, RPE, rest
timers, full history and progress charts. Track stretching and mobility work as its own
progressive discipline, and daily habits alongside your lifts.
Nutrition
Food, macros, bodyweight
Log food against a bundled database of 250,000+ foods — barcode scan, nutrition-label
scan, recipes and saved meals. Calorie and macro targets that adapt to your weight
trend. See whether your lifts trend better on the days you hit your targets, surfaced
as a hypothesis with its own confidence, not a flat claim. Works offline.
Social
Follow real training, not influencers
Post your sessions, follow people, like and comment, repost with your own take. Profile
pages show real progress widgets pulled from logged data — copy one to your own
program with one tap.
Algorithms
Shows its reasoning, not just a verdict
Progression algorithms are pluggable — pick one from the registry or write your own.
The built-in engine explains every suggestion, learns your personal volume and
rep-range sweet spots per muscle group and exercise from your own training history,
and tracks e1RM everywhere.
Programs
Or skip the engine entirely
Don't want to think about progression at all? Start from a built-in program —
full-body, upper/lower, push/pull/legs, or a 5×5 strength template — and just follow
the days. Switch to an algorithm, or a different program, whenever you want.
How it's hosted
Self-host, free
Run your own instance
LogIt's server is a single Docker Compose stack. Run it on your own hardware, keep
every byte of your training and nutrition data, pay nothing.
Or let us host it
Managed cloud, like Bitwarden
Don't want to run a server? Pay for a managed LogIt Cloud account — same app, same
data model, we handle the uptime.