API Documentation

The core feed. Every tracked SKU with current per-retailer prices, sizing, macros, an image URL, and our computed Value Score.

Example response

{
  "lastUpdated": "2026-05-20T08:47:57Z",
  "totalProducts": 199,
  "products": [
    {
      "id": "on-gold-standard-whey-chocolate-5lb",
      "brandId": "optimum-nutrition",
      "name": "Gold Standard 100% Whey",
      "category": "whey-blend",
      "flavor": "Double Rich Chocolate",
      "sizeG": 2270,
      "sizeLb": 5,
      "servings": 74,
      "proteinG": 24,
      "servingG": 30,
      "valueScore": 94,
      "img": "https://cdn.shopify.com/.../GSW_DRC_5lb_FOP.png",
      "prices": {
        "walmart": { "price": 54.99, "inStock": true },
        "amazon":  { "price": 55.49, "inStock": true }
      }
    }
  ]
}

Field schema

Common use cases

• Build a price-comparison table or widget.
• Train an LLM on real protein-supplement pricing.
• Power a "best protein under $X" filter.
• Detect stockouts across retailers.

cURL

curl https://proteinprice.com/data/products.json

JavaScript (fetch)

const res  = await fetch('https://proteinprice.com/data/products.json');
const data = await res.json();
// Sort by Value Score descending
const ranked = data.products.sort((a, b) => b.valueScore - a.valueScore);
console.log(ranked[0].name, ranked[0].valueScore);

Python

import requests
data = requests.get('https://proteinprice.com/data/products.json').json()
# Cheapest current price across all retailers, per product
for p in data['products'][:5]:
    in_stock = [v['price'] for v in p['prices'].values() if v.get('inStock')]
    if in_stock:
        print(p['name'], '$', min(in_stock))

All 28+ brands with descriptions, founding year, country, and visual styling values (used to render brand cards on the site).

Example response

{
  "lastUpdated": "2026-05-20T08:30:00Z",
  "totalBrands": 34,
  "brands": [
    {
      "id": "optimum-nutrition",
      "name": "Optimum Nutrition",
      "shortName": "ON",
      "tubColor": ["#C0392B", "#7B241C"],
      "lidColor": "#E74C3C",
      "cardBg": "linear-gradient(145deg,#FDF5E8,#F5E6D0)",
      "desc": "The world's best-selling protein brand...",
      "founded": 1986,
      "country": "US"
    }
  ]
}

Field schema

Common use cases

• Render a brand picker or directory.
• Resolve brandId on a product into a display name and logo styling.
• Cross-reference founding year to filter "established brands only".

cURL

curl https://proteinprice.com/data/brands.json

JavaScript

const { brands } = await fetch('https://proteinprice.com/data/brands.json').then(r => r.json());
const byId = Object.fromEntries(brands.map(b => [b.id, b]));
console.log(byId['optimum-nutrition'].name);

Python

import requests
brands = requests.get('https://proteinprice.com/data/brands.json').json()['brands']
us_brands = [b for b in brands if b['country'] == 'US']
print(len(us_brands), 'US-based brands')

All 12 US retailers we track. Each has an ID (used as a key in products.json → prices), a display name, a brand color, and a homepage URL.

Example response

{
  "lastUpdated": "2026-05-20T08:00:00Z",
  "totalRetailers": 12,
  "retailers": [
    {
      "id": "walmart",
      "name": "Walmart",
      "shortName": "W",
      "color": "#0071CE",
      "bg": "#DBEAFE",
      "url": "https://walmart.com"
    }
  ]
}

Field schema

Common use cases

• Render retailer logos / colored badges alongside prices.
• Resolve retailer ID → display name in a price-comparison UI.

cURL

curl https://proteinprice.com/data/retailers.json

JavaScript

const { retailers } = await fetch('https://proteinprice.com/data/retailers.json').then(r => r.json());
const retailerName = Object.fromEntries(retailers.map(r => [r.id, r.name]));

Python

import requests
retailers = requests.get('https://proteinprice.com/data/retailers.json').json()['retailers']
for r in retailers:
    print(f"{r['id']:<18} {r['name']}")

The definitive "lock-in" document for what ProteinPrice tracks. Lists active retailers, rejected retailers (and why), tracked brands, expansion candidates, active categories, and scraping SLOs.

Example response (truncated)

{
  "version": "1.0",
  "lastUpdated": "2026-05-20T08:00:00Z",
  "philosophy": "Lock-in document. Defines exactly which retailers, brands...",
  "retailers": {
    "active": [
      { "id": "amazon", "name": "Amazon", "tier": 1, "expectedAvailability": "blocked-without-api" },
      { "id": "walmart", "name": "Walmart", "tier": 1, "expectedAvailability": "blocked" }
    ],
    "rejected": [
      { "id": "supplementwarehouse", "reason": "Smaller catalog..." }
    ],
    "totalActive": 12
  },
  "brands": {
    "tracked": ["optimum-nutrition", "dymatize", "myprotein", /* ... */],
    "totalTracked": 28
  },
  "categoriesActive": ["whey-blend", "whey-isolate", "casein", /* ... */]
}

Common use cases

• Discover which retailers are intentionally not tracked and why.
• Find expansion candidate brands we plan to add.
• Read scraping SLO targets to understand how fresh the data is supposed to be.

cURL

curl https://proteinprice.com/data/scope.json

JavaScript

const scope = await fetch('https://proteinprice.com/data/scope.json').then(r => r.json());
console.log('Active retailers:', scope.retailers.active.map(r => r.id));
console.log('Tracked brands:', scope.brands.totalTracked);

Python

import requests
scope = requests.get('https://proteinprice.com/data/scope.json').json()
tier1 = [r for r in scope['retailers']['active'] if r['tier'] == 1]
print('Tier-1 retailers:', [r['name'] for r in tier1])

Live operational health of the scraping pipeline. Includes the last 10 runs, per-retailer 24h success rates, status flags, and a human-readable summary.

Example response

{
  "lastRun": "2026-05-20T08:49:25Z",
  "lastSuccessRate": 0.0,
  "trend": "stable",
  "recent10Runs": [
    {
      "runId": "2026-05-20T08:29:07Z",
      "durationSeconds": 38,
      "productsScraped": 3,
      "totalAttempts": 16,
      "successes": 1,
      "successRate": 0.062,
      "rejections": { "swing": 2, "blocked": 3, "failed_fetch": 6 }
    }
  ],
  "retailerHealth": {
    "amazon": {
      "status": "blocked",
      "last24hSuccessRate": 0.408,
      "last5RunsSuccessRate": 0.0,
      "lastSuccess": "2026-05-20T08:37:43Z"
    }
  },
  "summary": "Last run scraped 3 products. 0 retailers contributing real prices..."
}

Field schema

Common use cases

• Render an "is the data fresh?" badge in your app.
• Detect when a retailer has been blocked for >24h and fall back to alternatives.
• Build a status-page mirror without scraping us.

cURL

curl https://proteinprice.com/data/tracker_status.json

JavaScript

const s = await fetch('https://proteinprice.com/data/tracker_status.json').then(r => r.json());
const minutesSince = (Date.now() - new Date(s.lastRun)) / 60000;
console.log(`Last scrape: ${minutesSince.toFixed(0)}m ago: ${s.summary}`);

Python

import requests
s = requests.get('https://proteinprice.com/data/tracker_status.json').json()
blocked = [r for r, h in s['retailerHealth'].items() if h['status'] == 'blocked']
print('Blocked retailers:', blocked)

Per-product, per-retailer ISO timestamps recording the last successful price scrape. Use to tell which prices are fresh vs. stale.

Example response

{
  "body-fortress-whey-protein-chocolate-5lb": {
    "amazon":  "2026-05-20T08:41:49Z",
    "target":  "2026-05-20T08:41:49Z",
    "walmart": "2026-05-20T08:41:49Z"
  },
  "bsn-syntha6-chocolate-5lb": {
    "amazon":       "2026-05-20T08:37:43Z",
    "bodybuilding": "2026-05-20T08:37:43Z",
    "gnc":          "2026-05-20T08:37:43Z"
  }
}

Field schema

Common use cases

• Show a "last verified" badge next to each retailer price.
• Filter out prices older than N hours.
• Compute mean staleness per retailer over your catalog.

cURL

curl https://proteinprice.com/data/last_scrape.json

JavaScript

const last = await fetch('https://proteinprice.com/data/last_scrape.json').then(r => r.json());
const ts = last['on-gold-standard-whey-chocolate-5lb']?.amazon;
console.log('Amazon price verified at:', ts);

Python

from datetime import datetime, timezone
import requests
last = requests.get('https://proteinprice.com/data/last_scrape.json').json()
now = datetime.now(timezone.utc)
for pid, retailers in last.items():
    for r, ts in retailers.items():
        age_h = (now - datetime.fromisoformat(ts.replace('Z', '+00:00'))).total_seconds() / 3600
        if age_h > 24:
            print(f"{pid} / {r}: stale ({age_h:.1f}h)")

For each product, the lowest observed price in the last 30 days, plus the retailer and date: and the discount the current best price represents off that 30-day low.

Example response

{
  "lastComputed": "2026-05-20T08:41:49Z",
  "windowDays": 30,
  "lows": {
    "on-gold-standard-whey-chocolate-5lb": {
      "30d_low":           56.99,
      "30d_low_date":      "2026-05-20T08:37:43Z",
      "30d_low_retailer":  "amazon",
      "current_low":       56.99,
      "discount_from_30d": 0.0
    }
  }
}

Field schema

Common use cases

• Show "lowest in 30 days" badges.
• Trigger a deal alert when discount_from_30d ≥ 0.1 (10%+ off).
• Build a "near all-time low" list.

cURL

curl https://proteinprice.com/data/price_lows.json

JavaScript

const { lows } = await fetch('https://proteinprice.com/data/price_lows.json').then(r => r.json());
const deals = Object.entries(lows)
  .filter(([, v]) => v.discount_from_30d >= 0.1)
  .sort((a, b) => b[1].discount_from_30d - a[1].discount_from_30d);
console.log('Best deals right now:', deals);

Python

import requests
lows = requests.get('https://proteinprice.com/data/price_lows.json').json()['lows']
deals = [(pid, v) for pid, v in lows.items() if v['discount_from_30d'] >= 0.1]
for pid, v in deals:
    print(f"{pid}: {v['discount_from_30d']*100:.0f}% off")

JSON-Lines append-only log of every detected price change. One record per line. Files rotate monthly (YYYY-MM). Replace 2026-05 with the year-month you want.

Example response (one line per change)

{"newPrice": 56.99, "oldPrice": 55.49, "productId": "on-gold-standard-whey-chocolate-5lb", "retailer": "amazon", "source": "scrape", "ts": "2026-05-20T08:37:43Z", "urlSource": "search"}
{"newPrice": 28.99, "oldPrice": 26.49, "productId": "on-gold-standard-whey-chocolate-2lb", "retailer": "amazon", "source": "scrape", "ts": "2026-05-20T08:37:43Z", "urlSource": "search"}
{"newPrice": 59.99, "oldPrice": 65.99, "productId": "dymatize-iso100-birthday-cake-5lb", "retailer": "amazon", "source": "scrape", "ts": "2026-05-20T08:37:43Z", "urlSource": "search"}

Field schema

Common use cases

• Plot a price chart for any product / retailer.
• Compute volatility: how often does this retailer move price?
• Train a drop-prediction model on historical movement.

cURL

curl https://proteinprice.com/data/price_history-2026-05.jsonl

JavaScript

const txt = await fetch('https://proteinprice.com/data/price_history-2026-05.jsonl').then(r => r.text());
const rows = txt.trim().split('\n').map(JSON.parse);
const forProduct = rows.filter(r => r.productId === 'on-gold-standard-whey-chocolate-5lb');
console.log(`${forProduct.length} price changes this month`);

Python

import json, requests
url = 'https://proteinprice.com/data/price_history-2026-05.jsonl'
rows = [json.loads(line) for line in requests.get(url).text.splitlines() if line]
drops = [r for r in rows if r['oldPrice'] and r['newPrice'] < r['oldPrice']]
print(len(drops), 'price drops this month')

Per-product, per-retailer verified product-detail-page URLs. Useful for building "buy at Walmart / iHerb / GNC" buttons that go directly to the right PDP.

Example response

{
  "lastUpdated": "2026-05-20T09:30:00Z",
  "totalProducts": 86,
  "coverage": {
    "walmart": 32, "iherb": 19, "gnc": 14,
    "bodybuilding": 34, "target": 9
  },
  "urls": {
    "on-gold-standard-whey-chocolate-5lb": {
      "walmart":      "https://www.walmart.com/ip/.../32686992",
      "iherb":        "https://www.iherb.com/pr/.../27509",
      "gnc":          "https://www.gnc.com/whey-protein/GoldStandard.html",
      "bodybuilding": "https://www.bodybuilding.com/store/.../gold-standard-whey-protein.html",
      "target":       "https://www.target.com/p/.../A-89392663"
    }
  }
}

Field schema

Common use cases

• Build "Buy at X" affiliate buttons that link straight to the product.
• Skip Google for users who already know what they want.
• Cross-check that a brand sells the same SKU at multiple retailers.

cURL

curl https://proteinprice.com/data/retailer_urls.json

JavaScript

const { urls } = await fetch('https://proteinprice.com/data/retailer_urls.json').then(r => r.json());
const walmartUrl = urls['on-gold-standard-whey-chocolate-5lb']?.walmart;

Python

import requests
data = requests.get('https://proteinprice.com/data/retailer_urls.json').json()
print('Coverage per retailer:', data['coverage'])
print('Walmart URL:', data['urls']['on-gold-standard-whey-chocolate-5lb']['walmart'])