bodylog

A workout and food log for personal agents. You tell your agent your sets and your meals in chat, the way you would say them out loud. It stores them, counts calories and macros from real nutrition data, keeps your streaks, and sends back shareable cards.

One package for any agent: an MCP server (bodylog-mcp), a CLI (bodylog), a Python library and a Claude skill. Everything lives in one local SQLite file. No accounts, no cloud, no social features.

Food day	Story	Workout

More: food, light, food + training on one day (full), workout story, 8-exercise workout, two pages, mixed kg and lb, sticker for a photo.

Install

Python 3.10+.

pip install bodylog          # CLI + library + MCP server
uvx --from bodylog bodylog-mcp   # run the MCP server without installing

Claude Code, as a plugin (skill and MCP server together):

/plugin marketplace add abhaymettu/bodylog
/plugin install bodylog@bodylog

Claude Code, MCP server only:

claude mcp add bodylog -- uvx --from bodylog bodylog-mcp

Any other MCP client:

{
  "mcpServers": {
    "bodylog": {
      "command": "uvx",
      "args": ["--from", "bodylog", "bodylog-mcp"],
      "env": { "FDC_API_KEY": "optional, free at https://api.data.gov/signup/" }
    }
  }
}

Settings, all optional:

The skill is skills/bodylog/SKILL.md. Outside the plugin, copy or symlink skills/bodylog/ into your agent's skills folder.

Food

The CLI prints what the MCP tools return (bodylog eat is log_food). A real run, offline:

$ bodylog eat "2 eggs, toast and a protein shake"
#1 breakfast: 2 × egg, 100 g: 143 kcal, P 12.6 C 0.7 F 9.5
#2 breakfast: toast, 22 g: 64 kcal, P 2 C 12 F 0.9
#3 breakfast: whey protein, 26 g: 92 kcal, P 20.3 C 1.6 F 0.4
$ bodylog eat "greek yogurt with blueberries and a handful of almonds"
#4 breakfast: greek yogurt, 170 g: 100 kcal, P 17.3 C 6.1 F 0.7
#5 breakfast: blueberries, 148 g: 84 kcal, P 1.1 C 21.4 F 0.5
#6 breakfast: 1 handful almonds: not counted (no weight for a handful of almonds; give grams or one of: almond, cup, oz)
$ bodylog food-fix 6 --grams 28
#6 breakfast: 28 g almonds: 162 kcal, P 5.9 C 6 F 14

A bare count uses USDA's portion for that food: a large egg (50 g), a slice of toast, a scoop of whey (the FNDDS "1 scoop, NFS", 26 g). Through MCP, the agent asks one short question about item 6 instead of guessing.

How a food gets its numbers:

1. Bundled table. 62 common foods from USDA FoodData Central (SR Legacy and FNDDS), each with its FDC id, per-100 g values and USDA's own portion weights (a large egg, a slice of bread, a cup of rice). Works offline. Rebuilt from the USDA downloads by scripts/build_common_foods.py.
2. Cache. Anything looked up before, stored in the SQLite file.
3. USDA FoodData Central search (POST /fdc/v1/foods/search, survey, SR Legacy and Foundation foods), with FDC_API_KEY or DEMO_KEY.
4. Open Food Facts search for branded and packaged foods, and product lookup by barcode (GET /api/v2/product/<barcode>.json, no key).

A search result is used only when its name contains every word you said. Otherwise the item is kept as unknown with the closest candidates, so the agent can ask which one you meant. An item whose amount cannot become grams (a handful, or millilitres of something with no USDA volume weight) is kept as needs_amount. Neither counts toward totals until it is fixed, and neither is ever filled with made-up numbers. Numbers you read off a label are stored as given (manual).

Also: daily goals for kcal, protein, carbs and fat; a logging streak in days (a day you have not logged yet does not break it) and a training streak in weeks; and a day card in dark, light, clear or story format. On a day you also trained, the card adds a Training block.

Workouts

30-second demo

The chat below comes from a real run through log_set(text=...). The right column shows what the store recorded for each message.

you:   push day                                  -> title: Push Day
you:   bench 20 kg x 10 warm up                  -> Bench Press: 20 kg x 10 (warmup)
you:   60kg x 10                                 -> Bench Press: 60 kg x 10
you:   67.5 kg x 4                               -> Bench Press: 67.5 kg x 4
you:   62.5 kg x 8 rpe 9                         -> Bench Press: 62.5 kg x 8
you:   next incline db press 55 lb x 10          -> Incline Dumbbell Press: 55 lb x 10
you:   same                                      -> Incline Dumbbell Press: 55 lb x 10
you:   55lb x 9                                  -> Incline Dumbbell Press: 55 lb x 9
you:   12.5 kg x 12                              -> Incline Dumbbell Press: 12.5 kg x 12
you:   that was triceps pushdowns on the cable   -> named 1 set(s) Triceps Cable Pushdown
you:   12.5 kg x 11                              -> Triceps Cable Pushdown: 12.5 kg x 11
you:   lateral raise 15 lb x 14                  -> Lateral Raise: 15 lb x 14
you:   15 lb x 12                                -> Lateral Raise: 15 lb x 12
you:   one more                                  -> Lateral Raise: 15 lb x 12
you:   wrapped up, workout took 1h 3m            -> ended session 4

**Push Day**
Thursday, Sep 17 · 6:05 PM
1h 3m · 2,640 kg · 11 sets · 12 PRs
Muscles: Chest 55%, Shoulders 27%, Triceps 18%

**Bench Press** · Chest · 3 sets · best 60 kg x 10 · PR: Heaviest weight, Best 1RM, Best set volume, Most reps
  W. 20 kg x 10 (warm-up)
  1. 60 kg x 10 🏆 Best 1RM, Best set volume, Most reps
  2. 67.5 kg x 4 🏆 Heaviest weight
  3. 62.5 kg x 8 @ RPE 9
**Incline Dumbbell Press** · Chest · 3 sets · best 55 lb x 10 · PR: Best 1RM, Best set volume, Most reps
  1. 55 lb x 10 🏆 Best 1RM, Best set volume, Most reps
  2. 55 lb x 10
  3. 55 lb x 9
**Triceps Cable Pushdown** · Triceps · 2 sets · best 12.5 kg x 12 · PR: Heaviest weight, Best 1RM
  1. 12.5 kg x 12 🏆 Heaviest weight, Best 1RM
  2. 12.5 kg x 11
**Lateral Raise** · Shoulders · 3 sets · best 15 lb x 14 · PR: Best 1RM, Best set volume, Most reps
  1. 15 lb x 14 🏆 Best 1RM, Best set volume, Most reps
  2. 15 lb x 12
  3. 15 lb x 12

This week: 2 workouts, 4,687 kg (−14% vs last week to date)

The last message ends the workout, so the tool returns this card along with the workout PNG at the top of this page (end_session does the same). PRs are counted against the three earlier sessions in tests/fixtures/chat.txt.

How it reads chat

• Sets: 60kg x 8, 60 x 8, 50lbs for 9, 8 reps at 60kg, 3x10 @ 90lb, 60kg 3x8, 12 reps (bodyweight). Flags: warmup, drop set, to failure, rpe 9.
• Exercises: named at the start of a message (incline db press 55 lb x 10), announced (now squats), or named after the fact (this is tricep cable pushdowns btw). A set without a name continues the last exercise.
• Naming after the fact takes the sets that have no exercise yet. If every set already has one, it takes the trailing run of carried-over sets at the last set's weight. That covers the usual case where you move to a new machine and name it a set or two later. If you changed weight before naming it, use edit_last_set(exercise=...) for the earlier sets.
• Names are folded to one exercise: case, plurals, db/bb shorthand, word order, filler (on the), and muscle words that don't change the movement. So tricep cable pushdowns, cable pushdown and triceps pushdowns on the cable are one exercise, while triceps curl and biceps curl stay apart. bodylog alias teaches any other name.
• Commands: same / one more repeats the last set; done, 1h 5m / workout took 45 min ends the workout. Agent replies in a pasted log (Agent:, Assistant:, Claude:) are skipped.

What the numbers mean

• Volume is weight x reps over working sets. Warmups are listed on the card but excluded from volume, set counts and PRs. Each set shows the unit it was logged in. Totals convert every set exactly into one unit: the unit you pass, else $BODYLOG_UNIT, else whichever unit most sets used.
• Muscle split is the share of working sets per primary muscle group, from a name map in muscles.py that covers the common lifts (unknown names count as Other). Percentages always add to 100.
• Estimated 1RM uses the Epley formula, weight x (1 + reps / 30).
• PRs are counted against sessions that started earlier: heaviest weight, best estimated 1RM, best set volume (weight x reps in one set), and most reps at a weight you have lifted before (one per exercise, the largest gain). The first time you do an exercise sets no PRs, so a first workout does not show a PR on every set.
• The week line compares this week with last week up to the same weekday and time, so a Monday workout is not shown as a 90% drop.
• Duration is what you say at the end. The session start is set to end time minus that duration.

MCP tools

CLI and library

bodylog log "bench 60kg x 8"
bodylog end "1h 5m" --title "Push Day"
bodylog eat "chicken breast 180g, 1.5 cups rice and broccoli"
bodylog food-fix 12 --grams 28
bodylog goals --kcal 2400 --protein 160
bodylog food-card --style story
bodylog --help

from bodylog import Store, log_text, log_food, day_summary, summary, render_png
from bodylog.foodcard import render_png as food_png

store = Store()                                  # ~/.bodylog/log.db
log_text(store, "bench 60kg x 8")
log_food(store, "2 eggs, toast and a protein shake")
food_png(day_summary(store), "today.png", style="story")

Why Python

The MCP Python SDK needs a few lines per tool, Pillow draws the cards with no browser, and sqlite3 and urllib ship with Python. Runtime dependencies are Pillow and mcp. Cards use Inter (SIL Open Font License, bundled in src/bodylog/fonts/), so they look the same everywhere.

Development

uv sync
uv run pytest                                            # offline: API calls replay recorded responses
uv run python scripts/render_examples.py   # regenerate examples/ (offline)

Tests never touch the network or the repo: tests/fixtures/http/ holds real USDA and Open Food Facts responses, and every file a test writes goes to a temp dir.

Layout: store.py (SQLite), names.py, chatlog.py, stats.py, muscles.py, card.py (workouts), food.py (meal parsing, matching, totals, streaks), sources.py (USDA and Open Food Facts), foodcard.py (day card), cli.py, server.py (MCP), data/common_foods.json (bundled USDA table).

Credits

Food data: USDA FoodData Central (public domain) and Open Food Facts (Open Database License; product data is attributed to Open Food Facts contributors).