Scavio Python SDK

The official Python SDK for the Scavio Search API. Access real-time data from 64 sources -- Google, ChatGPT, Gemini, Amazon, Walmart, Costco, Best Buy, TCGplayer, Vinted, Nordstrom, Nordstrom Rack, Lowe's, Sam's Club, Canadian Tire, Dollar General, Sephora, Ulta Beauty, ALDI, Micro Center, Walgreens, CVS, Whole Foods Market, Harbor Freight, PetSmart, Petco, iHerb, Chewy, Menards, HEB, Kroger, Spec's, Decathlon, Mercado Libre, eBay, Target, Home Depot, YouTube, Reddit, X, TikTok, TikTok Shop, Instagram, LinkedIn, Threads, Kuaishou, Zillow, Redfin, Realtor.com, Rightmove, Booking.com, Tripadvisor, Airbnb, Yelp, Indeed, Glassdoor, the Apple App Store, Google Play, SEC EDGAR, Companies House, G2, Capterra, Trustpilot, Google Ads Transparency and the Meta Ad Library -- plus extract for reading any URL and for mapping, crawling or batch-extracting whole websites, all with a single API key. Built for AI agents, LLM applications, and data pipelines.

One API key, 60 data sources, 355 endpoints, structured JSON with knowledge graphs. A powerful alternative to Tavily, SerpAPI, and ScraperAPI for developers who need more than just web search.

Why Scavio

Feature	Scavio	Tavily	SerpAPI	ScraperAPI
Google Search	Yes	Yes	Yes	Yes
ChatGPT and Gemini Answers with Cited Sources	Yes	No	No	No
Amazon Products	Yes	No	Yes	No
Walmart Products	Yes	No	No	No
Costco Warehouse Prices, Stock, Gas and Coupons (13 endpoints)	Yes	No	No	No
Store-Level Prices and Stock (Lowe's, Sam's Club, Canadian Tire, Dollar General; 21 endpoints)	Yes	No	No	No
Beauty, Pharmacy, Grocery and Electronics Stores (Sephora, Ulta Beauty, ALDI, Micro Center, Walgreens, CVS; 20 endpoints)	Yes	No	No	No
Grocery, Pet, Supplement and Hardware Stores (Whole Foods Market, Harbor Freight, PetSmart, Petco, iHerb, Chewy, Menards; 33 endpoints)	Yes	No	No	No
Nordstrom Products, Reviews, Categories, Brands, Store Pickup and Store Locator (8 endpoints)	Yes	No	No	No
Nordstrom Rack Clearance, Flash Events, Products, Reviews, Store Pickup and Store Locator (8 endpoints)	Yes	No	No	No
Best Buy Products, Open-Box Prices, Store Pickup, Deal of the Day, Reviews and Batch Prices (9 endpoints)	Yes	No	No	No
TCGplayer Card Market Prices, Price History, Recent Sales, Listings, Set Price Guides and Sellers (13 endpoints)	Yes	No	No	No
Vinted Listings, Prices, Sellers, Brands and Categories across 27 country sites (9 endpoints)	Yes	No	No	No
Trustpilot Businesses and Reviews (6 endpoints)	Yes	No	No	No
Mercado Libre Listings, Reviews and Best Sellers (MX, BR, AR, CO; 5 endpoints)	Yes	No	No	No
YouTube Search	Yes	No	Yes	No
Reddit Data (12 endpoints)	Yes	No	No	No
X Data (11 endpoints)	Yes	No	No	No
TikTok Data (24 endpoints)	Yes	No	No	No
TikTok Shop Data (8 endpoints)	Yes	No	No	No
Instagram Data (20 endpoints)	Yes	No	No	No
LinkedIn Data (9 endpoints)	Yes	No	No	No
eBay Sold-Listing Price History	Yes	No	No	No
Real Estate (Zillow + Redfin + Realtor.com + Rightmove, 19 endpoints)	Yes	No	No	No
UK Sold House Prices since 1995 (Rightmove, England and Wales)	Yes	No	No	No
Travel (Booking + Airbnb + Tripadvisor, 10 endpoints)	Yes	No	No	No
Jobs & Employers (Indeed + Glassdoor, 8 endpoints)	Yes	No	No	No
Ad Transparency (Meta + Google, 6 endpoints)	Yes	No	No	No
Filings (SEC EDGAR + Companies House, 10 endpoints)	Yes	No	No	No
Read Any URL (extract)	Yes	Yes	No	Yes
Data Sources	60	1	1 per plan	1
Structured JSON	Yes	Yes	Yes	Raw HTML
Knowledge Graphs	Yes	No	Yes	No
Async Client	Yes	Yes	No	No
Single API Key	Yes	Yes	No	No
Rate Limiting Built-in	Yes	No	No	No
Automatic Retries + Backoff	Yes	No	No	No
Fully Typed Parameters	Yes	No	No	No
Type Hints (PEP 561)	Yes	Yes	No	No

Tavily focuses on AI-optimized web search. SerpAPI offers SERP parsing across search engines with separate plans. ScraperAPI provides raw web scraping with proxy rotation. Scavio combines multi-source structured data in a single search API for AI agents with one SDK and one API key.

Installation

pip install scavio

Quick Start

Get your free API key at dashboard.scavio.dev.

from scavio import ScavioClient

client = ScavioClient(api_key="sk_...")  # or set SCAVIO_API_KEY env var

results = client.search("best noise cancelling headphones 2026")
for r in results["organic_results"]:
    print(r["title"], r["link"])

Every method returns the API response as a plain dict. Amazon responses are normalized to a stable, documented shape; the other endpoints keep each platform's own field names, so fields vary by endpoint.

Fully typed parameters

Every endpoint exposes all of its parameters as explicit, documented, autocomplete-friendly keyword arguments with Literal types for enums. Your editor shows the full parameter set, allowed enum values, and defaults inline.

# Google web search with the full parameter surface
results = client.google.search(
    "electric cars",
    gl="us",                 # country of the search
    hl="en",                 # UI language
    location="Austin, Texas, United States",
    time_period="last_month",
    device="mobile",
)

# Local rank tracking: web_only=True returns Google's Web results (organic
# only, ranked for location). The standard page localizes only the local pack;
# call again without web_only when you also need local_results.
local = client.google.search("emergency plumber", location="Austin,Texas,United States", web_only=True)

# YouTube filters. The digit-named API fields (4k, 360, 3d) are exposed as
# valid Python identifiers: four_k, video_360, video_3d.
client.youtube.search("drone footage", four_k=True, hdr=True, duration="long")

# Amazon product lookup: pass the ASIN (sent to the API as `query`).
# `country` is the marketplace, as an ISO 3166-1 alpha-2 code.
client.amazon.product("B09XS7JWHH", country="gb")

Forward-compatible passthrough

Any parameter the API adds in the future can be passed via **extra and is sent verbatim, so you never have to wait for an SDK release:

client.google.search("openai", **{"some_new_param": "value"})

Retries and resilience

The client automatically retries transient failures (HTTP 429 and 5xx, plus network/timeout errors) with exponential backoff, jitter, and Retry-After support. Configure or disable it with max_retries.

1. AI Web Research -- Feed Search Results to an LLM

from scavio import ScavioClient

client = ScavioClient()

results = client.search("latest advances in quantum computing 2026")

context = "\n\n".join(
    f"[{r['title']}]({r['link']})\n{r.get('snippet', '')}"
    for r in results["organic_results"]
)

prompt = f"Based on these search results, summarize the latest advances:\n\n{context}"
# Pass `prompt` to your LLM of choice (OpenAI, Anthropic, etc.)
print(prompt[:500])

2. Price Comparison -- Amazon vs Walmart

from scavio import ScavioClient

client = ScavioClient()

query = "sony wh-1000xm5"
amazon = client.amazon.search(query, country="us")
walmart = client.walmart.search(query)

print("Amazon:")
for p in amazon["data"]["products"][:3]:
    print(f"  ${p['price']} - {p['title'][:60]}")

print("\nWalmart:")
for p in walmart["data"]["products"][:3]:
    print(f"  ${p['price']} - {p['title'][:60]}")

3. Product Lookup by ASIN, plus every seller offer

from scavio import ScavioClient

client = ScavioClient()

data = client.amazon.product("B0BS1PRC4L")["data"]

print(f"Brand:   {data['brand']}")
print(f"Title:   {data['title']}")
print(f"Rating:  {data['rating']} ({data['reviews_count']} reviews)")
print(f"Price:   {data['price']} {data['currency']}")

# Same ASIN, every seller: price, condition, and who holds the buy box.
offers = client.amazon.offers("B0BS1PRC4L")["data"]
print(f"{offers['total_offers']} offers")
for o in offers["offers"][:5]:
    tag = " (buy box)" if o["is_buy_box_winner"] else ""
    print(f"  {o['price']} {o['currency']} - {o['seller_name']} [{o['condition']}]{tag}")

4. SEO Competitor Analysis

from scavio import ScavioClient

client = ScavioClient()

results = client.search("best project management software", gl="us")

for r in results["organic_results"]:
    print(f"{r['position']}. {r['title']}")
    print(f"   {r['link']}")

4b. Migrating from the Google Custom Search JSON API -- google.custom_search

google.custom_search takes the Custom Search JSON API (cse.list) parameters and returns the same customsearch#search response: items[] with title, link, snippet and displayLink, searchInformation.totalResults, and queries.nextPage. Parsing code written for the Custom Search JSON API keeps working. 1 credit per call.

results = client.google.custom_search(
    "abortcontroller fetch timeout",
    num=5,
    site_search="stackoverflow.com",   # enforced on the returned results
    date_restrict="y1",
)
for item in results.get("items", []):
    print(item["displayLink"], item["title"], item["link"])
next_page = results["queries"].get("nextPage", [{}])[0].get("startIndex")

Not using this SDK? Keep your existing Custom Search code and change the host: https://api.scavio.dev/customsearch/v1?key=YOUR_SCAVIO_KEY&q=.... cx is accepted and ignored (use site_search to restrict). Parameters that cannot be honoured (image search, sort, rights) raise BadRequestError instead of being silently dropped. For the full SERP (knowledge graph, AI overview, People Also Ask) use google.search. Docs.

4c. AI Visibility -- What ChatGPT and Gemini Recommend -- chatgpt.prompt / gemini.prompt

Send the prompt a buyer would ask and get the answer the assistant gives: the full Markdown answer, the pages it cites, the web searches it ran (ChatGPT) and the products it recommended. Both return the same shape under data, so one parser tracks both. ChatGPT costs 2 credits and Gemini 5 credits per answered prompt; a failed prompt is not charged. Answers take 10-40 seconds, so both methods raise the request timeout to 120s when your client's is shorter.

prompt = "What are the best CRM tools for small agencies?"

for res in (client.chatgpt.prompt(prompt, country="us"), client.gemini.prompt(prompt)):
    data = res["data"]
    answer = data["answer"]["markdown"]
    print(data["platform"], "mentions HubSpot:", "HubSpot" in answer)
    print("  cited:", [s["url"] for s in data["sources"]][:5])
    print("  products:", [p["title"] for p in data["shopping"]])

# ChatGPT also reports the web searches it ran and each source's title and publisher
data = client.chatgpt.prompt(prompt)["data"]
print(data["search_queries"])
print([(s["source"], s["title"]) for s in data["sources"]][:5])

ChatGPT docs | Gemini docs.

5. News Aggregation

from scavio import ScavioClient

client = ScavioClient()

news = client.google.news("AI startups")

for article in news["news_results"][:5]:
    print(f"[{article['source']}] {article['title']}")
    print(f"  {article['link']}")
    print()

6. YouTube Content Discovery

from scavio import ScavioClient

client = ScavioClient()

videos = client.youtube.search("python tutorial", sort_by="view_count")

for v in videos["data"]["results"][:5]:
    print(f"{v['title']} ({v['view_count']:,} views)")
    print(f"  {v['url']}")

# Full details for a specific video (metadata() is a deprecated alias of video())
video = client.youtube.video("dQw4w9WgXcQ")
print(f"\n{video['data']['title']}")
print(f"  {video['data']['view_count']:,} views")

# Transcript, related videos, comments, channel, and streams
transcript = client.youtube.transcript("dQw4w9WgXcQ", format="text")
related = client.youtube.related("dQw4w9WgXcQ")
comments = client.youtube.comments("dQw4w9WgXcQ")
channel_id = client.youtube.channel_resolve("@mkbhd")["data"]["channel_id"]
channel = client.youtube.channel(channel_id)
streams = client.youtube.streams("dQw4w9WgXcQ")

7. Reddit Market Research

from scavio import ScavioClient

client = ScavioClient()

posts = client.reddit.search("best mechanical keyboard")

for post in posts["data"]["results"]:
    print(f"r/{post['subreddit']} - {post['title']}")
    print(f"  {post['url']}")
    print()

# Drill into a subreddit, a single post, or a redditor. reddit.post() takes a
# url or a post_id and returns the post alone -- comments are a separate call.
feed = client.reddit.subreddit_posts("MechanicalKeyboards", sort="TOP")
detail = client.reddit.post(post_id="t3_1v6ngaf")
comments = client.reddit.post_comments("t3_1v6ngaf", sort="TOP")
history = client.reddit.user_posts("spez")
popular = client.reddit.popular()
trending = client.reddit.trending()

8. TikTok Hashtag Analysis

from scavio import ScavioClient

client = ScavioClient()

hashtag = client.tiktok.hashtag(hashtag_name="python")
info = hashtag["data"]["challengeInfo"]

print(f"#{info['challenge']['title']}")
print(f"  Views: {int(info['statsV2']['viewCount']):,}")
print(f"  Videos: {int(info['statsV2']['videoCount']):,}")

9. Instagram Profile and Posts

from scavio import ScavioClient

client = ScavioClient()

# Profile and reels: typically 1 credit each; most other calls 2
profile = client.instagram.profile(username="nike")
user = profile["data"]
print(f"@{user['username']} - {user['follower_count']:,} followers")

posts = client.instagram.user_posts(username="nike", count=12)
reels = client.instagram.user_reels(username="nike")
hashtags = client.instagram.search_hashtags("fashion")

# Highlights, similar accounts, likers, and Reels / audio / place search
highlights = client.instagram.user_highlights(username="natgeo")
reels_found = client.instagram.search_reels("latte art")
tracks = client.instagram.search_music("espresso")
places = client.instagram.search_locations("blue bottle coffee")

10. X Search and Profiles

from scavio import ScavioClient

client = ScavioClient()

tweets = client.x.search("AI agents", search_type="Latest")
for t in tweets["data"]["timeline"][:5]:
    print(f"@{t['screen_name']}: {t['text'][:80]}")

# Profile, a user's tweets, followers, and a single tweet's replies
profile = client.x.user("elonmusk")
timeline = client.x.user_tweets("elonmusk")
followers = client.x.user_followers("elonmusk")
replies = client.x.tweet_comments("1808168603721650364", rank="top")
trending = client.x.trending(country="UnitedStates")

11. LinkedIn People and Companies

from scavio import ScavioClient

client = ScavioClient()

# Member profile (1 credit) and their recent posts (10 credits per page). A
# handle or a full LinkedIn URL works anywhere.
person = client.linkedin.person(username="williamhgates")
person_posts = client.linkedin.person_posts(url="https://www.linkedin.com/in/williamhgates/")

# Company profile and its recent posts
company = client.linkedin.company(company="microsoft")
company_posts = client.linkedin.company_posts(company="microsoft")

# Jobs: search, then pull full detail for one listing
job_results = client.linkedin.search_jobs("software engineer", location="United States")
job = client.linkedin.job(job_id=job_results["data"]["data"][0]["id"])

# A post and its comments (10 per page)
post = client.linkedin.post(post_id="7488618410256523265")
comments = client.linkedin.post_comments(post_id="7488618410256523265", page=1)

Retired endpoints. The LinkedIn data behind person_contact, company_people, company_jobs, search_people and search_posts is no longer available. They remain callable but always return HTTP 410 and are never billed. company() still returns featured_employees (a small sample of staff), and search_jobs() with a company name substitutes for company_jobs.

12. TikTok Shop Product Research

from scavio import ScavioClient

client = ScavioClient()

# Listings carry exact prices
results = client.tiktok_shop.search("phone case")
for p in results["data"]["products"][:5]:
    print(p["title"], p["price"]["current"], p["shop"]["shop_name"])

# Detail adds description, variants, stock and shipping -- but NOT a price
# (TikTok masks it), and it resolves only about 44% of the ids search returns.
# A 404 there is a normal outcome, not an error: skip the item, do not retry.
from scavio import NotFoundError

product_id = results["data"]["products"][0]["product_id"]
try:
    detail = client.tiktok_shop.product(product_id)
    print(detail["data"]["title"], len(detail["data"]["variants"]), "variants")
except NotFoundError:
    pass  # TikTok Shop has no detail data for this product; skip it, do not retry

reviews = client.tiktok_shop.product_reviews(product_id, page_size=200, sort="relevant")
catalog = client.tiktok_shop.shop_products("7495514739648989419")   # exact prices
tree = client.tiktok_shop.categories()
resolved = client.tiktok_shop.resolve("https://vt.tiktok.com/ZT2AHoGsE/")

13. Social Media Monitoring

from scavio import ScavioClient

client = ScavioClient()

brand = "scavio"
reddit = client.reddit.search(brand)
tiktok = client.tiktok.search_videos(brand, count=5)

print(f"Reddit mentions ({len(reddit['data']['results'])}):")
for post in reddit["data"]["results"][:3]:
    print(f"  r/{post['subreddit']}: {post['title']}")

tiktok_videos = tiktok["data"].get("search_item_list", [])
print(f"\nTikTok mentions ({len(tiktok_videos)}):")
for v in tiktok_videos[:3]:
    desc = v["aweme_info"].get("desc", "No description")
    print(f"  {desc[:80]}")

14. Price Drop Alert

from scavio import ScavioClient

client = ScavioClient()

product = client.walmart.product("123456789")
price = product["data"]["price"]
title = product["data"]["title"]

threshold = 50.00
if price and price < threshold:
    print(f"PRICE DROP: {title[:60]}")
    print(f"  Now ${price} (threshold: ${threshold})")
else:
    print(f"{title[:60]}: ${price}")

15. Async Multi-Source Search

import asyncio
from scavio import AsyncScavioClient

async def main():
    async with AsyncScavioClient() as client:
        google = await client.search("mechanical keyboard")
        amazon = await client.amazon.search("mechanical keyboard", country="us")

        print(f"Google: {len(google['organic_results'])} results")
        print(f"Amazon: {len(amazon['data']['products'])} products")

        for r in google["organic_results"][:3]:
            print(f"  Web: {r['title'][:60]}")
        for p in amazon["data"]["products"][:3]:
            print(f"  Amazon: ${p['price']} - {p['title'][:50]}")

asyncio.run(main())

16. Check API Usage

from scavio import ScavioClient

client = ScavioClient()

usage = client.get_usage()
print(f"Plan: {usage['plan']}")
print(f"Credits remaining: {usage['credit_balance']}")

17. Read Any URL -- extract()

extract is a core capability, not a platform. client.extract(url) reads one page; the same client.extract object also maps, crawls and batch-extracts whole sites (example 17b). It is the "read this page" primitive an agent reaches for when the page is not on a site Scavio has a dedicated namespace for.

from scavio import ScavioClient

client = ScavioClient()

# Default: readability Markdown, normal mode. 1 credit.
page = client.extract("https://example.com/blog/post")["data"]
print(page["content_length"], "chars of", page["format"])
print(page["content"][:500])

# format="html" is the raw page; format="text" is the Markdown flattened.
raw = client.extract("https://example.com/blog/post", format="html")

# mode is the price-bearing parameter, and the only knob that matters on a
# hard target: "normal" (1 credit) -> "advanced" for JavaScript-built pages
# (1 credit) -> "ultra" for the hardest pages (2 credits).
hard = client.extract("https://example.com/spa", format="markdown", mode="ultra")

# Billing is charged only on a successful extraction: a dead link, a bot wall
# or a timeout costs nothing, so retrying up a tier is safe.

17b. Map, Crawl and Batch-Extract Whole Sites -- extract.map / crawl / batch

Three multi-page operations hang off client.extract:

Call	What it returns	Price
extract.map(url, ...)	A site's page URLs from its sitemaps and start page, deduplicated and path-filtered (synchronous)	1 credit on the normal tier (up to 2 on advanced, up to 10 on ultra)
extract.crawl(url, ...)	A job that discovers and extracts same-site pages breadth-first, each as Markdown, text or HTML	Per successful page at extract's price: 1 (normal, advanced) or 2 (ultra)
extract.batch(urls, ...)	A job that extracts a list of URLs, results in input order	Same per-page price; invalid URLs are reported and never billed
extract.crawl_status(job_id) / extract.batch_status(job_id)	Progress (status, pages, credits_used, stopped_reason) and one page of results with next_cursor	Free
extract.cancel_crawl(job_id) / extract.cancel_batch(job_id)	Stops a queued or running job; results so far stay available (409 if it already finished)	Free

Failed and skipped pages cost nothing, starting a job costs nothing, and credits are taken page by page: if your balance runs out the job stops cleanly with stopped_reason="insufficient_credits". robots.txt is always obeyed for map and crawl. A crawl stays on the start URL's site (subdomains optional via allow_subdomains=True) and visits URLs under the start path first. Results are kept for 3 days after a job finishes (up to 2 MB per page, 250 MB per job); a job runs for at most 6 hours.

Plan	Max pages per crawl	Active jobs	URLs per batch
Free	10	1	10
PAYG	100	1	50
Project	500	2	100
Bootstrap	2,000	3	500
Startup	5,000	5	1,000
Growth	10,000	10	1,000

Starting a job beyond your active-job limit raises RateLimitError (429).

from scavio import ScavioClient

client = ScavioClient()

# Map: every URL on a site, filtered. Synchronous, 1 credit.
site = client.extract.map("https://scavio.dev", include_paths=["/docs/**"], limit=200)
for link in site["data"]["links"]:
    print(link["url"], link["source"], link.get("lastmod"))

# Crawl and wait: polls until the job finishes, then pages through every
# result for you. Two pages here, so 2 credits on the normal tier.
crawl = client.extract.crawl("https://scavio.dev/docs", max_pages=2, wait=True)
for page in crawl["data"]["results"]:
    print(page["url"], page["status"], page["content_length"])

# Or start it and poll yourself (better for large crawls).
job = client.extract.crawl(
    "https://example.com/blog",
    max_pages=500,
    max_depth=2,
    exclude_paths=["/blog/tag/**"],
    format="markdown",
)["data"]
status = client.extract.crawl_status(job["id"], include_content=False)
print(status["data"]["status"], status["data"]["pages"])

# Page through results with next_cursor (1-100 per call, default 25).
cursor = None
while True:
    page = client.extract.crawl_status(job["id"], cursor=cursor, limit=100)["data"]
    for result in page["results"]:
        ...  # result["url"], result["status"], result["content"]
    cursor = page["next_cursor"]
    if not cursor:
        break

# Batch: a list of URLs in one job, results in input order.
batch = client.extract.batch(
    ["https://example.com/a", "https://example.com/b"], format="text", wait=True
)
print(batch["data"]["pages"])

wait=True is a convenience: it polls every poll_interval seconds (default 2) and returns {"data": {...final status..., "results": [...every page...], "next_cursor": None}, "created": {...the job as created...}}. Pass wait_timeout= (seconds) to bound it; on expiry it raises ScavioJobTimeoutError, whose job_id you can keep polling, while the job keeps running on the server. Without wait you get the queued job back at once (id, status, status_url, credits_per_page, and for batch urls_count and invalid_urls).

Every crawl and batch POST carries an Idempotency-Key header. Pass your own with idempotency_key= to make a retried request return the original job within 24 hours; when you pass none, the SDK generates one per call so its own automatic retries never start a second job. The async client has the same surface: await client.extract.crawl(...).

Completion webhooks

Pass webhook_url="https://..." to crawl or batch and Scavio POSTs the final job status there when it completes, fails or is cancelled (retried after 30 seconds and 5 minutes on failure). The job creation response carries a webhook_secret, returned only once; store it. Each delivery is signed with Scavio-Signature: t=<unix>,v1=<hex>, an HMAC-SHA256 of "<t>.<raw body>". Verify it with scavio.verify_webhook, passing the RAW request body:

import scavio

job = client.extract.crawl(
    "https://example.com/docs", webhook_url="https://hooks.example.com/scavio"
)["data"]
secret = job["webhook_secret"]  # store it: it is returned only once

# In your webhook handler (Flask shown):
from flask import Flask, abort, request

app = Flask(__name__)

@app.post("/scavio")
def scavio_webhook():
    if not scavio.verify_webhook(
        secret, request.headers.get("Scavio-Signature"), request.get_data()
    ):
        abort(401)
    event = request.get_json()
    print(event["id"], event["status"])
    return "", 204

verify_webhook(secret, header, body, tolerance=300) returns False for a missing, malformed, tampered or stale (older than tolerance seconds) signature, and compares in constant time.

18. eBay Price Research -- Sold Listings, Not Asking Prices

Live listings tell you what sellers want. sold=True searches completed listings that actually sold, which is what a pricing model needs.

from scavio import ScavioClient

client = ScavioClient()

sold = client.ebay.search(
    query="airpods pro 2",
    sold=True,
    condition="used",
    per_page=240,          # 60, 120 or 240 only; anything else falls back to 60
    min_price=50,          # prices are numbers, so 49.99 is legal too
)["data"]

prices = sorted(p["price"] for p in sold["products"] if p.get("price"))
print("median sold price:", prices[len(prices) // 2])

# On the sold view eBay publishes no headline count, so total_results is null.
# Page with `page` until a page comes back empty rather than trusting a total.
assert sold["total_results"] is None

# On the LIVE view total_results is eBay's own estimate and an unstable one --
# four identical requests minutes apart reported 28k, 140k, 170k and 27k.
# Treat it as an order of magnitude, never as a count.

# `seller` works with no query at all, which is how you page a whole catalogue.
# ebay.seller() is a profile card only -- it cannot enumerate inventory.
catalogue = client.ebay.search(seller="mytechstore", page=1)["data"]
profile = client.ebay.seller("mytechstore")["data"]

19. Meta Ad Library -- Walking a Competitor's Whole Ad Set

Meta pages the ad library by cursor, and the first page is a different size from the rest: 30 ads on page 1, then 10 per cursor page.

from scavio import ScavioClient

client = ScavioClient()

ads, cursor = [], None
while True:
    # page_id is the advertiser's numeric Facebook Page id, as a string.
    page = client.meta_ads.advertiser("20531316728", cursor=cursor)["data"]
    ads.extend(page["ads"])
    if not page["has_next_page"]:
        break
    cursor = page["next_cursor"]

print(len(ads), "ads")

# Every page is 1 credit, and past the first 30 ads a page is 10 ads, so a deep
# walk costs roughly one credit per ten ads. Budget for depth.

# Keyword search pages the same way. total_results caps at 50000 with
# total_is_capped true, because Meta itself only reports ">50,000" -- never
# present it as an exact count.
first = client.meta_ads.search(
    "black friday", country="US", active_status="active"
)["data"]
if first["total_is_capped"]:
    print("more than", first["total_results"], "ads match")

# Spend, reach, impressions and the paid-for-by disclosure are political-only.
# On commercial ads they are null by design, not a bug.
political = client.meta_ads.search("vote", ad_type="political_and_issue_ads")["data"]

# The cursor is a self-contained blob: every other filter is ignored while it
# is set, so do not "change the filter and keep paging" -- start a new walk.

20. Costco -- Warehouse Prices, Stock, Gas, Coupons and Clearance

costco covers costco.com and costco.ca (country="ca"); search, product and warehouses also take the international sites (uk, au, mx, jp, kr, tw). Warehouse numbers come from warehouses(); pass one as warehouse_id to add that warehouse's in-store price, stock status and price code to every search, category or deals result.

from scavio import ScavioClient

client = ScavioClient()

# Nearest warehouses to a US zip code, with hours, services and gas prices.
near = client.costco.warehouses(zip="10025", limit=3)["data"]

# Search with one warehouse's in-store price alongside the online price.
oil = client.costco.search("olive oil", warehouse_id="1062", page_size=5)["data"]
for item in oil["results"]:
    print(item["title"], item["price"], item["warehouse"]["price"])

# Online vs in-warehouse price at up to 10 warehouses, with discount, final
# price and promotion dates. 1 credit for up to 9 warehouses, 2 for 10.
prices = client.costco.prices(item_id="1492456", warehouse_ids=["1062", "1107"])["data"]
for row in prices["items"][0]["warehouses"]:
    print(row["warehouse_id"], row["price"], row["final_price"])

# In-warehouse stock status (a status, never a quantity). 1 credit per 5 warehouses.
stock = client.costco.availability("1492456", ["1062"])["data"]

# Regular, premium and diesel prices near a location, or at given warehouses.
gas = client.costco.gas(zip="90001")["data"]
for station in gas["stations"]:
    print(station["name"], station["prices"]["regular"], station["prices"]["premium"])

# The current member coupon book (US), and the deal feeds.
book = client.costco.coupons()["data"]
hunt = client.costco.deals("treasure_hunt")["data"]

# Markdown finder at one warehouse: .97 clearance and .00/.88 manager markdowns
# by default; add special_buy for .49/.79/.89 endings. The codes are the
# member-community decode -- Costco does not publish them.
marked_down = client.costco.clearance("1062", query="snacks")["data"]
print(marked_down["count"], "of", marked_down["products_scanned"], "items marked down")

21. Trustpilot -- Business Search, Profiles, Reviews and Categories

trustpilot finds businesses by keyword or category, reads a full profile (TrustScore, rating distribution, reply rate and days to reply on negative reviews, AI summary and topics when Trustpilot has them), and pages filtered reviews. Every call costs 2 credits. business() and reviews() take exactly one of domain or url.

from scavio import ScavioClient

client = ScavioClient()

# Businesses matching a keyword, up to 100 per page. Email and phone appear
# only when the business published them.
found = client.trustpilot.search("vpn", country="US", page_size=10)["data"]
for biz in found["businesses"]:
    print(biz["name"], biz["domain"], biz["trust_score"], biz["review_count"], biz["email"])

# One profile with the rating distribution, reply behavior and 20 newest reviews.
profile = client.trustpilot.business(domain="nordvpn.com")["data"]
print(profile["trust_score"], profile["rating_distribution"])
print(profile["reply_behavior"]["reply_rate"], profile["reply_behavior"]["avg_days_to_reply"])
topic_ids = [t["id"] for t in profile["topics"]]  # feed these to reviews(topics=...)

# Filtered reviews, 20 per page. Trustpilot serves up to 10 pages (200 reviews)
# per filter combination; max_page says how far this one goes. Slice by stars,
# language, date_range, topics or search to reach more. total_filtered counts
# every match, so a date range doubles as review velocity.
bad = client.trustpilot.reviews(
    domain="nordvpn.com", stars=[1, 2], language="en", date_range="last30days"
)["data"]
print(bad["total_filtered"], "1-2 star reviews in 30 days; pages:", bad["max_page"])
for review in bad["reviews"]:
    print(review["rating"], review["title"], (review["reply"] or {}).get("text"))

# The category tree, then ranked businesses in one category. min_trust_score is
# a star-rating floor: Trustpilot rounds TrustScore to stars, so 4 includes 3.8+.
tree = client.trustpilot.categories()["data"]
print(tree["count"], "top-level,", tree["subcategory_count"], "subcategories")
ranked = client.trustpilot.category(
    "vpn_service", sort="reviews_count", min_trust_score=4, claimed_only=True
)["data"]
for biz in ranked["businesses"]:
    print(biz["name"], biz["trust_score"], biz["website"])

# One review by id, with the business it is about.
one = client.trustpilot.review("6aca2504b52cc4b518bf4b75")["data"]
print(one["review"]["text"], one["business"]["name"])

An unknown business, review or category returns a 404 (NotFoundError) and is billed 2 credits. Trustpilot keeps no TrustScore history, so none is returned.

22. Realtor.com and Rightmove -- Listings, Sold Comps and Agents

realtor covers US listings (for sale, pending, for rent and sold) with up to 200 rows per call, full property detail, market stats and agents. rightmove covers UK listings, England and Wales sold house prices since 1995 and agent branches. Every call on both costs 1 credit.

Docs: Realtor.com search, property, locations, market, agents, agent; overview at the Realtor.com API page. Rightmove search, property, sold, sold property, locations, agents, agent; overview at the Rightmove API page.

from scavio import ScavioClient

client = ScavioClient()

# Sold land comps: 200 rows per call, filtered by lot size, price and recency.
# Realtor.com serves the first 10,000 results of a search; slice by price band,
# property type or area to go further. Sold prices are null in US non-disclosure
# states (Texas, Utah, Wyoming and others); sold dates still come back.
comps = client.realtor.search(
    location="Maricopa County, AZ",
    listing_status="sold",
    property_types=["land"],
    sold_within_days=365,
    lot_sqft_min=43560,
    page_size=200,
)["data"]
print(comps["total"], "sold; pages:", comps["max_page"])
for row in comps["listings"]:
    print(row["sold_price"], row["sold_date"], row["lot_sqft"], row["address"]["line"])

# Listing-text keyword watch, then one property in full. A listing must contain
# ALL the keywords you pass, so each extra keyword narrows the results.
shops = client.realtor.search(location="Austin, TX", keywords=["workshop"])["data"]
detail = client.realtor.property(property_id=shops["listings"][0]["property_id"])["data"]
print(detail["list_price"], detail["risk"]["flood_factor"], len(detail["property_history"]))

# Autocomplete -> market stats, and agents by ZIP code.
slug = client.realtor.locations("Austin")["data"]["locations"][0]["slug_id"]
market = client.realtor.market(slug)["data"]
print(market["median_listing_price"], market["median_days_on_market"])
agents = client.realtor.agents(postal_code="78744")["data"]
profile = client.realtor.agent(agents["agents"][0]["agent_id"])["data"]
print(profile["name"], profile["rating"], len(profile["reviews"]))

# Rightmove: resolve a place, search for sale (24-25 per page, up to 42 pages).
# There is no keyword filter; match on summary and key_features yourself.
loc = client.rightmove.locations("Bristol")["data"]["locations"][0]["location_id"]
homes = client.rightmove.search(location_id=loc, property_types=["terraced"], max_price=350000)
for home in homes["data"]["listings"]:
    print(home["price_display"], home["address"], home["tenure"])

# Sold prices since 1995 for an outcode (England and Wales only), then one
# address's full history.
sold = client.rightmove.sold("BS3", sold_within_years=5)["data"]
print(sold["summary"]["average_price_last_year"])
history = client.rightmove.sold_property(sold_id=sold["properties"][0]["sold_id"])["data"]
for sale in history["transactions"]:
    print(sale["date"], sale["price_display"], sale["tenure"], sale["percent_change"])

# Agent branches in an area, then one branch.
branches = client.rightmove.agents(location="Bristol")["data"]
branch = client.rightmove.agent(branch_id=branches["agents"][0]["branch_id"])["data"]
print(branch["name"], branch["listings"]["for_sale_count"], branch["listings"]["to_rent_count"])

An unknown property, listing, sold id, agent or branch returns a 404 (NotFoundError) and is billed 1 credit; a removed Rightmove listing is also a 404. Rightmove serves 42 search pages, 40 sold pages and 50 agent pages per query.

23. Mercado Libre -- Listings, Products, Reviews, Questions and Best Sellers

mercadolibre covers Mercado Libre in Mexico, Argentina and Colombia and Mercado Livre in Brazil (country="mx", "br", "ar" or "co"; prices come back in MXN, BRL, ARS or COP). reviews costs 1 credit; search, product, questions and best_sellers cost 2. Chile is not supported.

Docs: Mercado Libre search, product, reviews, questions, best sellers; overview at the Mercado Libre API page.

from scavio import ScavioClient

client = ScavioClient()

# Search by keyword (about 48 rows per page, up to 42 pages, so about 2,000
# results per query; slice by price range, condition or seller to reach more).
found = client.mercadolibre.search(
    "iphone 15",
    country="mx",
    free_shipping=True,
    sort="price_asc",
)["data"]
print(found["total"], found["auto_applied_filters"])
for row in found["results"]:
    # sold_quantity_min is a lower bound ("+1000 vendidos"), never an exact count.
    # review_count is null when the page shows none (most sponsored rows).
    print(row["price"], row["brand"], row["rating"], row["review_count"], row["sold_quantity_min"], row["title"])

# One catalog product: price, buy-box seller with reputation, star distribution.
product = client.mercadolibre.product(product_id="MLM1027172667")["data"]
print(product["price"], product["seller"]["nickname"], product["rating"]["distribution"])

# A seller's catalogue: the buy-box seller_id feeds a filtered search.
catalogue = client.mercadolibre.search(
    country="mx", seller_id=str(product["seller"]["seller_id"]), condition="new"
)["data"]

# Reviews (15 per page, 1 credit), e.g. only 1-star; original_text keeps the
# untranslated review and country says where it was written.
reviews = client.mercadolibre.reviews(product_id="MLM1027172667", rating=1)["data"]
for review in reviews["reviews"]:
    print(review["rating"], review["country"], review["text"])

# Buyer questions with the seller's answers; page with next_offset.
qa = client.mercadolibre.questions(product["item_id"])["data"]
for q in qa["questions"]:
    print(q["text"], "->", q["answer"]["text"] if q["answer"] else None)

# Top 20 best sellers in a category (mx, ar or co; not Brazil). The category
# prefix must match the country.
top = client.mercadolibre.best_sellers(product["category_id"], country="mx")["data"]
for row in top["results"]:
    print(row["rank"], row["price"], row["sold_label"], row["title"])

A zero-result search returns an empty page and is billed. An unknown product, item or seller nickname returns a 404 (NotFoundError) and is billed, as does a listing with no public questions. Only the buy-box seller is returned per product, and Brazil and Argentina may auto-apply a "new" condition (listed in auto_applied_filters).

24. Store-Level Retail -- Lowe's, Sam's Club, Canadian Tire and Dollar General

Four retailers whose prices and stock are per store. Each answers for ONE store you choose: find its number with the locator (lowes.stores, sams_club.clubs, canadian_tire.stores, dollar_general.stores) and pass it on every call. Lowe's search, product and stores cost 2 credits and reviews 1; every Sam's Club, Canadian Tire and Dollar General method costs 1.

from scavio import ScavioClient

client = ScavioClient()

# --- Lowe's: store_id AND that store's own ZIP are required on search/product.
store = client.lowes.stores("28117", limit=1)["data"]["stores"][0]
sid, zip_code = store["store_id"], store["address"]["postal_code"]

drills = client.lowes.search(sid, zip_code, query="cordless drill")["data"]
for p in drills["products"]:
    print(p["item_id"], p["price"], p["original_price"], p["stock_quantity"], p["title"])

# Deals are search(on_sale=True); there is no clearance endpoint.
deals = client.lowes.search(sid, zip_code, query="cordless drill", on_sale=True)["data"]

item = client.lowes.product("5016412045", sid, zip_code)["data"]
print(item["price"], item["stock_quantity"], item["aisle_locations"])
for near in item["pickup"]["nearby_stores"]:
    print(near["store_id"], near["distance_miles"], near["quantity"])

reviews = client.lowes.reviews("5016412045", sort="most_helpful")["data"]  # 1 credit
for r in reviews["reviews"]:
    print(r["rating"], r["verified_purchase"], r["title"])

# --- Sam's Club: search, savings and category answer for a club picked by
# location (echoed in data.club). For one specific club, use product(club_id=).
club = client.sams_club.clubs("75201", limit=1)["data"]["clubs"][0]
found = client.sams_club.search("paper towels", sort="price_low")["data"]
for p in found["products"]:
    print(p["item_id"], p["price"], p["was_price"], p["availability_status"], p["title"])

product = client.sams_club.product("15325822107", club_id=club["club_id"])["data"]
print(product["club_price"]["price"], product["in_stock_at_club"], product["aisle"])
for option in product["fulfillment"]["options"]:
    # available_quantity is capped at 8 by the site: 8 means 8 or more.
    print(option["method"], option["availability_status"], option["available_quantity"])

savings = client.sams_club.savings(sort="top_selling")["data"]        # Instant Savings
clearance = client.sams_club.category("1150109")["data"]              # clearance
reviews = client.sams_club.reviews("15325822107", sort="lowest")["data"]

# --- Canadian Tire: prices are in CAD and differ by store and province.
stores = client.canadian_tire.stores(postal_code="M5V 2T6", sku="0438858")["data"]
for s in stores["stores"]:
    print(s["store_id"], s["name"], s["distance_km"], s["quantity"], s["aisle"])

found = client.canadian_tire.search("600", query="dewalt drill")["data"]
for p in found["products"]:
    print(p["code"], p["sku"], p["price"], p["was_price"], p["store_quantity"], p["title"])

detail = client.canadian_tire.product("0547273P", "600")["data"]
stock = client.canadian_tire.availability("600", ["0438858", "0547273"])["data"]  # up to 50 SKUs
for i in stock["items"]:
    print(i["sku"], i["price"], i["is_clearance"], i["quantity"], i["aisle"])
print("unknown:", stock["not_found"])

clearance = client.canadian_tire.clearance("600")["data"]             # in-stock only by default
sale = client.canadian_tire.deals("600", "sale")["data"]              # or instant_rebate, special_buy, loyalty

# --- Dollar General: one call gives a product's unit count at up to 25 stores.
nearby = client.dollar_general.stores(zip="75203", upc="030772156629")["data"]
for s in nearby["stores"]:
    print(s["store_id"], s["address"], s["city"], s["distance_miles"], s["quantity"], s["in_stock"])

found = client.dollar_general.search("paper towels", "12629", in_stock_only=True)["data"]
for p in found["products"]:
    print(p["upc"], p["price"], p["original_price"], p["available_quantity"], p["title"])

item = client.dollar_general.product("030772156629", "12629")["data"]
print(item["price"], item["available_quantity"], item["nearby_stores_with_stock"])

# Clearance items carry the current price only (no was-price).
on_sale = client.dollar_general.deals("12629", "on_sale")["data"]
clearance = client.dollar_general.deals("12629", "clearance")["data"]
for c in client.dollar_general.coupons()["data"]["coupons"]:
    print(c["summary"], c["brand"], c["expiration_date"])

Canadian Tire and Dollar General return a rating and review count but no review text. Sam's Club's search, savings and category results reflect the club in data.club, not a club you pick.

25. US Retail -- Sephora, Ulta Beauty, ALDI, Micro Center, Walgreens and CVS

Six US retailers. Sephora and Ulta Beauty carry per-shade prices and stock; ALDI prices are per store and per shopping mode; Micro Center stock is per store; Walgreens and CVS prices are national while stock is per store. Every method costs 1 credit except Micro Center search and product (2), Walgreens search, product and stores (2) and Walgreens stock (4).

from scavio import ScavioClient

client = ScavioClient()

# --- Sephora: search, then one product with every shade and the review summary.
found = client.sephora.search("lipstick", sort="best_selling", zip="10001")["data"]
for p in found["products"]:
    print(p["product_id"], p["brand"], p["price"], p["rating"], p["same_day_eligible"], p["name"])

lips = client.sephora.category("lipstick")["data"]                  # same product shape
item = client.sephora.product(url=found["products"][0]["url"])["data"]  # url is faster than product_id
print(item["review_summary"])                                       # no review text
for s in item["review_sentiments"]:
    print(s["type"], s["sentiment"], s["count"])                    # pros / cons with counts
for sku in item["skus"]:
    print(sku["sku_id"], sku["variation_value"], sku["price"], sku["in_stock"], sku["only_few_left"])

# --- Ulta Beauty: stores() is in stock / out of stock at the 10 stores nearest a ZIP.
found = client.ulta.search("lipstick")["data"]
p = found["products"][0]
print(p["product_id"], p["sku_id"], p["list_price"], p["sale_price"], p["variant_label"])

detail = client.ulta.product(p["url"])["data"]
print(detail["availability"], detail["gtin"], [v["sku_id"] for v in detail["variants"]])
stores = client.ulta.stores(p["product_id"], p["sku_id"], "10001")["data"]
for s in stores["stores"]:
    print(s["store_id"], s["name"], s["distance_miles"], s["in_stock"], s["availability"])

# --- ALDI: prices are one store's and one shopping mode's. service_type is
# instore (default, the shelf price), pickup or delivery (often higher).
store = client.aldi.stores("33131", limit=1)["data"]["stores"][0]
milk = client.aldi.search("milk", store_id=store["store_id"])["data"]
for p in milk["products"]:
    print(p["product_id"], p["price"], p["was_price"], p["unit_price"], p["in_store_location"], p["name"])

online = client.aldi.search("milk", zip="33131", service_type="delivery")["data"]  # nearest store
item = client.aldi.product("16902710", store_id=store["store_id"])["data"]
print(item["price"], item["in_stock"], item["ingredients"], item["warnings"])

# --- Micro Center: stock is per 3-digit store_id; omit it for the web store
# (029, shippable items only). Counts are capped at 25+ (quantity_capped).
dallas = [s for s in client.micro_center.stores(state="TX")["data"]["stores"] if s["name"] == "Dallas"][0]
gpus = client.micro_center.search(query="rtx 5070", store_id=dallas["store_id"])["data"]   # 2 credits
for p in gpus["products"]:
    print(p["product_id"], p["price"], p["stock"]["label"], p["stock"]["quantity"], p["open_box"])

gpu = client.micro_center.product("691231", store_id=dallas["store_id"])["data"]          # 2 credits
print(gpu["selected_store"]["stock"], gpu["selected_store"]["open_box"])
print([s["store_id"] for s in gpu["stores_in_stock"] if s["in_stock"]])                  # yes/no per store

# --- Walgreens: prices are national; stock() checks up to 20 stores in one call.
nearby = client.walgreens.stores(zip="60614", limit=5)["data"]                # 2 credits
numbers = [s["store_number"] for s in nearby["stores"]]
found = client.walgreens.search("shampoo", sort="top_sellers", store_id=numbers[0])["data"]
p = found["products"][0]
print(p["product_id"], p["price"], p["sale_price"], p["availability"]["text"])

stock = client.walgreens.stock(p["product_id"], numbers)["data"]              # 4 credits
for s in stock["stores"]:
    print(s["store_number"], s["status"], s["quantity"], s["in_stock"])
print("unknown:", stock["unknown_store_ids"])

# --- CVS: there is no store locator; the store number is on every cvs.com
# store page ("Store ID: #7019"). search defaults to store 7019 (New York).
found = client.cvs.search("toothpaste", store_id="7019")["data"]
for p in found["products"]:
    print(p["sku_id"], p["price"], p["sale_price"], p["stock"]["in_store"], p["stock"]["pickup"])

# product() can take 10-30+ seconds; the SDK raises its timeout to 120s for it.
item = client.cvs.product(product_id="832677")["data"]
print(item["upc"], item["promo"], item["promo_end_date"], [v["sku_id"] for v in item["variants"]])

units = client.cvs.stock(["832677", "789787"], "7019")["data"]                 # up to 20 SKUs
for i in units["items"]:
    print(i["sku_id"], i["in_store"], i["pickup"], i["ship"])

Walgreens and CVS cover retail (front-store) products, not the pharmacy. An unknown CVS SKU or store number reads as 0 units rather than an error.

26. US Retail -- Whole Foods Market, Harbor Freight, PetSmart, Petco, iHerb, Chewy and Menards

Seven more US retailers. Whole Foods prices are per store; Harbor Freight, PetSmart, Petco, iHerb and Chewy prices are national; Menards prices carry the mail-in rebate. Every method costs 1 credit except iHerb (2 each), Chewy (10 each) and Menards product with a store_id (2).

from scavio import ScavioClient

client = ScavioClient()

# --- Whole Foods Market: prices are one store's (5-digit store_id, default 10145,
# Lamar in Austin TX): regular, sale and Prime member price. No per-store stock.
store = client.whole_foods.stores("10019")["data"]["stores"][0]
milk = client.whole_foods.search("milk", store_id=store["store_id"], sort="price_asc")["data"]
for p in milk["products"]:
    print(p["asin"], p["regular_price"], p["sale_price"], p["prime_price"], p["price_unit"], p["name"])

deals = client.whole_foods.deals(store_id=store["store_id"], category="cheese")["data"]
item = client.whole_foods.product(asin="B078WVT2BC", store_id=store["store_id"])["data"]
print(item["in_store"]["regular_price"], item["online"]["price"], item["online"]["availability"])

flyer = client.whole_foods.sales_flyer(store["store_id"])["data"]
for promo in flyer["promotions"]:
    print(promo["headline"], promo["sale_price"], promo["prime_price"], promo["end_date"], promo["asins"])

# --- Harbor Freight: national prices with sale, public coupon, Inside Track Club
# member price and the competitor price it quotes (compare_at).
prices = client.harbor_freight.prices(["73216", "59716"])["data"]
for p in prices["products"]:
    print(p["sku"], p["price"], p["regular_price"], p["sale"], p["coupon"], p["member_deal"], p["compare_at"])

found = client.harbor_freight.search(query="cordless drill")["data"]
print(found["redirect"])                       # set when a keyword maps to a category page
drills = client.harbor_freight.search(category_id="1827", sort="price_low")["data"]

# Stock is a status (in_stock, limited, out_of_stock, not_carried) plus the
# aisle, from a daily snapshot -- not a unit count.
stock = client.harbor_freight.availability(["73216"], zip="90012", limit=5)["data"]
print(stock["inventory_updated_at"])
for s in stock["stores"]:
    for i in s["items"]:
        print(s["store_id"], s["name"], i["sku"], i["stock_status"], i["aisle"])

# --- PetSmart: national prices with the Autoship first-order and recurring
# price; stock is a unit count per store.
found = client.petsmart.search("dog food", store_id="1658")["data"]   # in stock at that store
p = found["products"][0]
item = client.petsmart.product(web_style_id=p["web_style_id"])["data"]
for v in item["variants"]:
    print(v["sku"], v["options"], v["price"], v["sale_price"], v["autoship"])  # autoship: first_order_price, recurring_price

stock = client.petsmart.store_stock("1658", [v["sku"] for v in item["variants"]])["data"]
for i in stock["items"]:
    print(i["sku"], i["store_quantity"], i["ship_to_home_quantity"])
pairs = client.petsmart.availability([{"store_id": "1658", "sku": "5252900"}, {"store_id": "2528", "sku": "5252900"}])
sale = client.petsmart.deals(pet="dog")["data"]
print([p["name"] for p in sale["products"] if p["on_sale"]])   # the Sale section also lists non-price promotions

# --- Petco: national price plus the Repeat Delivery (autoship) price. Store
# inventory takes cat_entry_id (from search or product), not the part number.
found = client.petco.search("dog food")["data"]
p = found["products"][0]
print(p["part_number"], p["cat_entry_id"], p["price"], p["list_price"], p["autoship_price"])

# Petco calls can take 10-30 seconds; the SDK raises their timeout to 120s.
item = client.petco.product(url=p["url"])["data"]
for v in item["variants"]:
    print(v["cat_entry_id"], v["price"], v["autoship_price"], v["first_autoship_price"], v["online_quantity"])

store = client.petco.stores("10001", limit=1)["data"]["stores"][0]
inv = client.petco.store_inventory([v["cat_entry_id"] for v in item["variants"]], store_id=store["store_id"])["data"]
for i in inv["items"]:
    print(i["cat_entry_id"], i["qty_available"], i["alert_level"], i["in_stock"])

# --- iHerb: online only (no stores), US store in USD. 2 credits per call.
# search() and product() can take 10-25 seconds; the SDK raises their timeout to 120s.
found = client.iherb.search("vitamin d", sort="best_selling")["data"]
ids = [p["product_id"] for p in found["products"][:10]]
for row in client.iherb.prices(ids)["data"]["products"]:          # up to 50 ids
    print(row["product_id"], row["found"], row["price"], row["list_price"], row["in_stock"])

summary = client.iherb.reviews(product_id=ids[0])["data"]        # summary, not a review list
print(summary["average_rating"], summary["rating_histogram"]["five"], summary["top_positive_review"])

# --- Chewy: one-time vs Autoship vs first-Autoship price, MAP fields. 10 credits per call.
found = client.chewy.search(query="kong classic dog toy", sort="bestselling")["data"]
print(found["redirect"])                       # set when Chewy sends the query to a category
item = client.chewy.product(product_id=found["products"][0]["product_id"])["data"]
print(item["price"], item["autoship_price"], item["first_autoship_price"], item["map_price"], item["map_enforced"])

# --- Menards: search one store (2 credits), then open a product. Prices carry
# the mail-in rebate and the final price after it. product's store_id adds that
# store's stock and aisle, costs 2 credits and can take 10-40 seconds (timeout
# raised to 120s).
store = client.menards.stores(zip="55401")["data"]["stores"][0]
hits = client.menards.search("sledge hammer", store["store_id"], sort="price_asc")["data"]
print(hits["total_results"], [(p["name"], p["final_price"], p["stock_quantity"]) for p in hits["products"][:3]])
item = client.menards.product(item_id=hits["products"][0]["item_id"], store_id=store["store_id"])["data"]
print(item["everyday_price"], item["sale_price"], item["rebates"], item["final_price"], item["you_save"])
print(item["store"]["stock_quantity"], item["store"]["locations"])

Whole Foods returns no ratings, reviews or UPCs. iHerb and Chewy have no store endpoints. A Petco store_inventory call with one unknown cat_entry_id is a 404 for the whole batch.

27. US Retail -- Nordstrom

nordstrom.com (US site only). search, category and brand return 24 products a page with current and original price ranges, percent off and on-sale flag (category and brand add the filter sidebar with product counts); product lists every color and size as a sku with UPC, price, price type and the quantity available to ship; reviews pages a product's full review history with sort and filters; availability adds the stores that can do in-store pickup per sku; stores is the store locator; suggestions is the search-box autocomplete. 5 credits per call, availability and reviews 10. Every call can take several seconds, so the SDK raises their request timeout to 120s; reviews takes 20-60s (up to about 100s at 200 reviews), so its floor is 150s.

from scavio import ScavioClient

client = ScavioClient()

# --- Search, deepest markdowns first. sort: relevance (default), rating,
# price_high, price_low, newest, percent_off.
found = client.nordstrom.search("dress", sort="percent_off")["data"]
print(found["total_results"], found["page_count"], found["count"])
for p in found["products"][:5]:
    print(p["style_id"], p["brand"], p["name"], p["price"], p["original_price_range"], p["percent_off"], p["badges"])

# --- A category (/browse path or URL, incl. sale/women and /browse/new) or a
# brand page: same product rows plus breadcrumbs, subcategories and filters.
cat = client.nordstrom.category("/browse/women/clothing/dresses", sort="price_low")["data"]
print(cat["category"]["title"], cat["total_results"], [s["path"] for s in cat["category"]["subcategories"][:3]])
for f in cat["filters"][:3]:
    print(f["name"], f["option_count"], [(o["label"], o["count"]) for o in f["options"][:3]])
brand = client.nordstrom.brand("/brands/astr-the-label--53360")["data"]
print(brand["brand"]["name"], brand["total_results"])

# --- One product by url or style_id: per-size UPC, price and online stock.
item = client.nordstrom.product(style_id="7737947")["data"]
print(item["price_range"], item["clearance_price_range"], item["rating"], item["review_count"])
for color in item["colors"]:
    for sku in color["skus"]:
        print(color["name"], sku["size"], sku["upc"], sku["price"], sku["price_type"], sku["in_stock"], sku["quantity"])
print(item["size_fit"]["average"], item["rating_histogram"])
for r in item["reviews"]:
    print(r["rating"], r["size_rating"], r["title"], r["verified_purchase"])

# --- Full review history (10 credits): up to 200 per call, default 60.
# sort: most_helpful (default), most_recent, highest_rated, lowest_rated.
# Filters: stars, verified_only, with_photos, search (not combinable with sort).
rev = client.nordstrom.reviews(style_id="7737947", sort="most_recent", max_reviews=100)["data"]
s = rev["summary"]
print(s["average_rating"], s["total_reviews"], s["ratings_histogram"], s["size_rating"], s["ratings_only_count"])
print([(f["name"], f["mentions"], f["quote"]) for f in s["best_features"][:3]], rev["ai_summary"])
print(rev["total_results"], rev["returned"], rev["has_more"])
for r in rev["reviews"][:5]:
    print(r["date"], r["rating"], r["title"], r["text"], r["size_purchased"], r["helpful_votes"], r["badges"], len(r["photos"]))
critical = client.nordstrom.reviews(style_id="7737947", stars=[1, 2], verified_only=True)["data"]
print(critical["total_results"], [r["text"] for r in critical["reviews"][:3]])

# --- Ship and pickup availability per sku, with the pickup stores (10 credits).
avail = client.nordstrom.availability(style_id="7737947")["data"]
print(avail["sku_count"], avail["in_stock_count"], avail["pickup_store_count"])
for sku in avail["skus"][:3]:
    print(sku["size"], sku["color"], sku["ship_quantity"], sku["pick_quantity"], [s["name"] for s in sku["pickup_stores"]])

# --- Stores near a ZIP (or latitude/longitude, or store_numbers), up to 100 miles.
for store in client.nordstrom.stores(zip="10001")["data"]["stores"][:3]:
    print(store["store_number"], store["name"], store["type"], store["distance_miles"], store["hours"], store["curbside_pickup"])

# --- Autocomplete.
print(client.nordstrom.suggestions("nike air")["data"]["suggestions"])

product reviews are the ones shown on the product page (up to about 13), alongside the total review_count and rating_histogram; reviews() returns the rest. Rating-only reviews come back with title and text null. If the upstream page still fails after the API's own retries, the call is an unbilled 502. quantity and ship_quantity are units available to ship online; in_stock is true when a sku can be shipped or picked up, so a store-only sku reads in stock with quantity 0. pick_quantity is chain-wide: per store Nordstrom only says whether pickup is available, not how many. A ZIP with no store in range returns an empty list (billed). An unknown style id, sku id, category or brand is a 404. For nordstromrack.com use client.nordstrom_rack (next section).

28. US Retail -- Nordstrom Rack

nordstromrack.com (US site only), its own namespace nordstrom_rack, separate from nordstrom but with the same methods, parameters and credit costs: 5 per call, availability and reviews 10. category takes Rack's /shop and /clearance paths, including Clear the Rack (/clearance) and flash events (/shop/flash lists the live events as subcategories, /shop/flash/event-N opens one); there is no /browse. brand takes /brands/<slug> with no id. Every product row carries clearance_price_range, and during Clear the Rack a clearance item can carry an extra percent off on top: promotion_price_range is the price after it and promotion_percent_off the extra percent (all three are null when not set). stores and availability cover Rack stores only. Rack has its own review pool, separate from nordstrom.com's, so the same style can show a different rating and review count on each. Same timeout floors as nordstrom: 120s, reviews 150s (15-60s typical, up to about 100s at 200 reviews).

from scavio import ScavioClient

client = ScavioClient()
rack = client.nordstrom_rack

# --- Clear the Rack: clearance listings, deepest markdowns first.
clr = rack.category("/clearance/women", sort="percent_off")["data"]
print(clr["category"]["title"], clr["total_results"], [s["path"] for s in clr["category"]["subcategories"][:3]])
for p in clr["products"][:5]:
    print(p["style_id"], p["brand"], p["name"], p["price"], p["original_price_range"],
          p["clearance_price_range"], p["promotion_price_range"], p["promotion_percent_off"])

# --- Flash events: /shop/flash lists them, each one is a listing.
events = rack.category("/shop/flash")["data"]["category"]["subcategories"]
print([(e["name"], e["path"]) for e in events[:5]])

# --- Search, a brand page, one product.
found = rack.search("dress", sort="percent_off")["data"]
print(found["total_results"], found["page_count"], found["count"])
brand = rack.brand("/brands/adidas")["data"]
print(brand["brand"]["name"], brand["total_results"])
item = rack.product(style_id="8816500")["data"]
print(item["price_range"], item["original_price_range"], item["percent_off"], item["rating"], item["review_count"])
for color in item["colors"]:
    for sku in color["skus"]:
        print(color["name"], sku["size"], sku["upc"], sku["price"], sku["price_type"], sku["in_stock"], sku["quantity"])

# --- Rack's own reviews (10 credits), same sort and filters as nordstrom.
rev = rack.reviews(style_id="7737947", max_reviews=50)["data"]
print(rev["summary"]["average_rating"], rev["summary"]["total_reviews"], rev["has_more"])

# --- In-store pickup per sku at Rack stores (10 credits), and the store locator.
avail = rack.availability(style_id="7737947")["data"]
for sku in avail["skus"][:3]:
    print(sku["size"], sku["color"], sku["ship_quantity"], sku["pick_quantity"], [s["name"] for s in sku["pickup_stores"]])
for store in rack.stores(zip="10001")["data"]["stores"][:3]:
    print(store["store_number"], store["name"], store["type"], store["distance_miles"])

print(rack.suggestions("dre")["data"]["suggestions"])

pick_quantity is chain-wide: per store the site only says whether pickup is available, not how many. Responses carry no event start or end dates. An unknown style id, sku id, category or brand is a 404.

29. US Retail -- Best Buy

bestbuy.com (US site only), namespace best_buy, 9 endpoints. Credits: search, product, reviews and prices 2 each (prices is per call, up to 15 SKUs); category 2 for an abcat id and 3 for a pcmcat page; deals 3; stores and suggestions 1; availability 2 flat for up to 8 stores. product, reviews and availability take sku or url; availability takes exactly one of zip or store_ids. filters is a dict: brand, on_sale, clearance, open_box, and min_price with max_price (both bounds). Search and category return 18 organic products per page; sponsored tiles only with include_sponsored=True. Every method raises the request timeout to at least 120s; availability typically takes 5-20s.

from scavio import ScavioClient

client = ScavioClient()
bb = client.best_buy

# --- Search with filters, then a category listing.
found = bb.search("tv", sort="best_selling", filters={"brand": "Samsung", "on_sale": True})["data"]
print(found["total_results"], found["total_pages"], found["count"], found["sponsored_count"])
for p in found["products"][:5]:
    print(p["sku"], p["model"], p["name"], p["price"], p["regular_price"], p["savings_percent"], p["badge"])
tvs = bb.category(category_id="abcat0101001", sort="price_low")["data"]
print(tvs["category"]["name"], tvs["total_results"])

# --- One product: open-box offers by condition, UPC, specs.
item = bb.product(sku="6607832")["data"]
print(item["upc"], item["model"], item["price"], item["regular_price"], item["review_summary"])
for ob in item["open_box"]:
    print(ob["condition"], ob["price"], ob["savings_vs_new"], ob["savings_vs_regular"])

# --- Store pickup status at the stores nearest a ZIP (2 credits flat).
avail = bb.availability(sku="6607832", zip="94103", max_stores=5)["data"]
print(avail["in_stock_count"], avail["status_counts"])
for store in avail["stores"]:
    print(store["store_id"], store["name"], store["status"], store["quantity"], store["pickup_date"])

# --- Up to 15 prices in one call (2 credits), Deal of the Day, reviews.
for row in bb.prices(["6607832", "6673112", "6679316"])["data"]["prices"]:
    print(row["sku"], row["found"], row["price"], row["regular_price"], row["on_sale"])
deals = bb.deals()["data"]
print(deals["deal_of_the_day"]["name"], deals["deal_of_the_day"]["price"], deals["countdown_ends"])
rev = bb.reviews(sku="6607832", sort="newest")["data"]
print(rev["average_rating"], rev["recommended_percent"], [(c["feature"], c["mentions"]) for c in rev["pros"]])

# --- Store locator and autocomplete.
for store in bb.stores("94103")["data"]["stores"][:3]:
    print(store["store_id"], store["name"], store["distance_miles"], store["hours"][0])
print([s["term"] for s in bb.suggestions("ps5 con")["data"]["suggestions"]])

Availability is store pickup status, not a unit count: quantity is set only for limited (Best Buy's "only N left") and is null for in_stock. Statuses are in_stock, limited, ship_to_store, out_of_stock, not_available_for_pickup and unknown (with a reason). In prices, a SKU Best Buy does not know comes back found: False; all unknown is a 404. An unknown SKU or category is a 404.

30. Trading Cards -- TCGplayer

tcgplayer.com (US marketplace, USD), namespace tcgplayer, 13 endpoints, 1 credit each: search, product, listings, price_history, sales, games, sets, price_guide, suggestions, trending, seller, seller_feedback, seller_products. Covers every game TCGplayer lists (Pokemon, Magic: The Gathering, Yu-Gi-Oh!, One Piece, Lorcana and 60+ more). product, listings, price_history and sales take product_id or a tcgplayer.com /product/<id>/ url. search needs a query unless game or set is given. Every method raises the request timeout to at least 120s.

from scavio import ScavioClient

client = ScavioClient()
tcg = client.tcgplayer

# --- Search a game, then one product with market price per SKU.
found = tcg.search("charizard", game="pokemon", sort="price_desc", limit=10)["data"]
print(found["total_results"], found["count"], found["has_more"])
for p in found["products"][:5]:
    print(p["product_id"], p["name"], p["set_name"], p["market_price"], p["listing_count"])

card = tcg.product(product_id=219058)["data"]
print(card["name"], card["market_price"], card["median_price"], card["seller_count"])
for sku in card["skus"]:
    # lowest/highest_sale_price and sale_count are recent SALE stats, not listings.
    print(sku["condition"], sku["printing"], sku["language"], sku["market_price"],
          sku["lowest_sale_price"], sku["highest_sale_price"], sku["sale_count"])

# --- Live offers, cheapest price plus shipping first.
offers = tcg.listings(product_id=219058, condition=["Near Mint"], limit=10)["data"]
for row in offers["listings"]:
    s = row["seller"]
    print(row["price"], row["shipping_price"], row["condition"], s["name"], s["rating_percent"])

# --- Sold volume over a month (daily buckets) and the 5 newest sales.
hist = tcg.price_history(product_id=219058, range="month")["data"]
print(hist["totals"]["quantity_sold"], hist["totals"]["transaction_count"])
for b in hist["skus"][0]["buckets"][:7]:
    print(b["date"], b["market_price"], b["quantity_sold"], b["low_sale_price"])
for sale in tcg.sales(product_id=219058)["data"]["sales"]:
    print(sale["date"], sale["condition"], sale["price"], sale["shipping_price"])

# --- Games -> sets -> a whole set's price guide.
games = tcg.games()["data"]["games"]
sets = tcg.sets("pokemon")["data"]["sets"]
guide = tcg.price_guide(604, limit=500)["data"]
print(len(games), sets[0]["set_id"], guide["set_name"], guide["total_results"])
for row in guide["rows"][:5]:
    print(row["name"], row["number"], row["condition"], row["market_price"], row["low_price"])

# --- Autocomplete, trending searches, and sellers.
print([p["name"] for p in tcg.suggestions("charizard")["data"]["products"]])
print([t["term"] for t in tcg.trending(game="pokemon")["data"]["terms"]])
seller = tcg.seller("58f7acae")["data"]
print(seller["name"], seller["rating_percent"], seller["feedback"]["total"])
fb = tcg.seller_feedback("58f7acae", rating=1, with_comment=True)["data"]
stock = tcg.seller_products("58f7acae", game="pokemon")["data"]
print(fb["total_results"], stock["total_results"])

sales returns only the 5 most recent sales (TCGplayer shows no more); use price_history for sold volume, where low_sale_price and high_sale_price are null in a bucket with no sales. Buckets are daily for month, 3-day for quarter (default) and weekly for semi_annual and annual. Seller methods take the 8-character seller.key from a listing; feedback buyers appear only as the site's masked nickname. An unknown product id, game, set or seller key is a 404. No store locator, product reviews or buylist prices.

31. Second-hand Fashion -- Vinted

Vinted on 27 country sites, namespace vinted, 9 endpoints, 1 credit each: search, product, seller, seller_products, seller_feedback, brands, suggestions, filters, categories. Every method takes an optional country: fr (default), be, lu, es, nl, lt, de, at, it, uk, pt, us, cz, sk, pl, se, ro, hu, dk, fi, hr, gr, ie, ee, lv, si, au. Category, condition and filter names come back in that site's language. product takes item_id or a Vinted item url (the URL's own site wins). Every method raises the request timeout to at least 120s.

from scavio import ScavioClient

client = ScavioClient()
v = client.vinted

# --- Search one country site; price is in that site's currency.
found = v.search("barbour jacket", country="uk")["data"]
print(found["listed_results"], found["total_pages"], found["total_is_capped"])
for item in found["items"][:5]:
    # service_fee is the Vinted fee the buyer pays on top of price.
    print(item["id"], item["title"], item["price"], item["currency"],
          item["service_fee"], item["total_price"], item["condition"], item["seller"]["id"])

# --- Resolve ids for filters: brands, conditions/sizes/colors, categories.
brand = v.brands("barbour", country="uk")["data"]["brands"][0]
print(brand["id"], brand["title"], brand["item_count"], brand["is_luxury"])
for opt in v.filters("status", country="uk")["data"]["options"]:
    print(opt["id"], opt["title"], opt["item_count"])
tree = v.categories(country="uk", parent_id=1904)["data"]
print(tree["parent"]["title"], [c["title"] for c in tree["categories"]][:5])
print([s["text"] for s in v.suggestions("barb", country="uk")["data"]["suggestions"]])

# --- One listing, priced in the SELLER's currency.
item = v.product(url="https://www.vinted.co.uk/items/10671938315-nike-air-max-size-uk-75")["data"]
print(item["title"], item["price"], item["currency"], item["condition"], item["is_sold"])
print(item["category"]["path"][-1]["title"], item["seller"]["login"],
      item["seller"]["feedback_reputation"])

# --- A seller: public profile, listings and reviews.
seller = v.seller(65958245, country="uk")["data"]
print(seller["login"], seller["feedback_count"], seller["feedback_reputation"], seller["item_count"])
wardrobe = v.seller_products(65958245, country="uk", limit=10)["data"]
reviews = v.seller_feedback(65958245, country="uk")["data"]
print(wardrobe["total_results"], reviews["total_results"])
for fb in reviews["feedback"][:3]:
    print(fb["rating"], fb["created_at"], fb["is_automatic"], fb["text"])

The EU sites share one cross-border listing pool, priced in the site's currency (EUR, or CZK, PLN, SEK, RON, HUF, DKK); uk (GBP), us (USD) and au (AUD) are separate catalogs. search returns 96 items per page and Vinted serves at most 10 pages (total_is_capped is true past 960 matches); price sorts also surface loosely matching cheap listings, so pair them with a category or price range. search's filters echoes what Vinted actually applied. Live listings only: there is no sold-item search, shipping price, pickup point or similar-items endpoint. Seller data is limited to public, buyer-relevant fields and feedback carries no reviewer identity. An unknown item or member id on product or seller is a 404.

Error Handling

from scavio import (
    ScavioClient,
    InvalidAPIKeyError,
    RateLimitError,
    InsufficientCreditsError,
    NotFoundError,
    BadRequestError,
    ScavioConnectionError,
    ScavioTimeoutError,
    ScavioAPIError,
    ScavioError,
)

client = ScavioClient(api_key="sk_...")

try:
    results = client.search("query")
except InvalidAPIKeyError:
    print("Check your API key")
except RateLimitError:
    print("Too many requests - upgrade your plan")
except InsufficientCreditsError:
    print("Out of credits - purchase more at dashboard.scavio.dev")
except ScavioAPIError as e:
    # Any other non-2xx response; inspect the details:
    print(e.status_code, e.response_body)

All exceptions inherit from ScavioError. HTTP errors (BadRequestError 400, InvalidAPIKeyError 401, InsufficientCreditsError 402, NotFoundError 404, RateLimitError 429, ScavioAPIError for anything else) carry .status_code and .response_body. Network failures raise ScavioConnectionError / ScavioTimeoutError after retries are exhausted.

Configuration

client = ScavioClient(
    api_key="sk_...",
    base_url="https://api.scavio.dev",  # custom base URL
    timeout=30.0,                        # request timeout in seconds
    max_requests_per_second=1,           # client-side rate limit (1-50)
    max_retries=2,                       # retries on 429/5xx/network (0 disables)
)

Async client

The async client mirrors the sync one method-for-method. It keeps a single pooled httpx.AsyncClient alive for its lifetime; close it with await client.aclose() or use the async context manager.

import asyncio
from scavio import AsyncScavioClient

async def main():
    async with AsyncScavioClient(api_key="sk_...") as client:
        return await client.google.search("openai", gl="us")

asyncio.run(main())

Integrations

Scavio works with popular AI/LLM frameworks:

• LangChain -- pip install langchain-scavio
• MCP Server -- for Claude, Cursor, and other MCP clients
• n8n -- no-code workflow automation

API Reference

Service	Endpoints	Credits
Google	search, custom_search, ai_mode, maps_search, maps_place, maps_reviews, shopping, shopping_product, shopping_stores, flights, hotels, hotels_detail, news, trends, trending	1 each
Amazon	search, product, offers, options	1 each (options free)
Walmart	search, product, stores, reviews, category, offers, seller, seller_products	1, except search/category on domain="com.mx" and store-targeted search/product (delivery_zip + store_id), which cost 2
YouTube	search, shorts, suggestions, video, metadata (deprecated alias of video), comments, comment_replies, transcript, related, channel_search, channel, channel_videos, channel_shorts, channel_community, channel_resolve, streams, community_post, community_post_comments	search/shorts 2, transcript 5, streams 3, rest 1 each
Reddit	search, search_suggestions, post, post_comments, comment_replies, subreddit, subreddit_posts, user, user_posts, user_comments, popular, trending, search_comments, search_subreddits	1 each
X	search, tweet, tweet_comments, tweet_retweeters, user, user_tweets, user_replies, user_media, user_followers, user_followings, trending, search_communities, community, community_tweets	1 each
TikTok	profile, user_posts, video, video_transcript, video_comments, comment_replies, search_videos, search_users, hashtag, hashtag_videos, user_followers, user_followings, trending_searches, trending_videos, search_suggestions, music, music_videos, search_music, music_chart, user_similar, user_playlists, playlist_videos, user_live, user_region	1 each
TikTok Shop	search, search_suggestions, product, product_reviews, categories, category_products, shop_products, resolve	1 each
Instagram	profile, user_posts, user_reels, user_tagged, user_stories, post, post_comments, comment_replies, search_users, search_hashtags, user_followers, user_followings, user_highlights, highlight_stories, search_reels, search_music, music_posts, search_locations, user_similar, post_likers	Typically profile/user_reels/user_tagged/music_posts 1 (user_reels/user_tagged 2 the first time a username is looked up), every other method 2; search_users/search_hashtags with a cursor 8. Any call can cost up to 10 when it needs extra work; credits_used is the exact charge
LinkedIn	person, person_about, person_posts, person_contact, company, company_posts, company_people, company_jobs, search_people, search_jobs, search_posts, job, post, post_comments	job 30, person_posts/company_posts/search_jobs/post_comments 10 each, person/person_about/company/post 1 each; the five retired endpoints (person_contact, company_people, company_jobs, search_people, search_posts) return 410 and are never billed
Threads	profile, user_posts, user_replies, post, post_comments, search_users	2, but profile/user_posts/user_replies cost 4 when addressed by username instead of user_id
Kuaishou	profile, user_posts, user_live, user_resolve, video, video_comments, comment_replies, videos_batch, search, search_videos, search_users, search_live, tag_feed, trending	priced per endpoint: videos_batch 40, profile and the four search* 10 each, video 2, the rest 1
eBay	search (live or sold listings), product, seller	1 each
Target	search, category, product, reviews, availability	1 each
Home Depot	search, product, reviews	2 each
Zillow	search, property, agent_reviews	1 each
Redfin	search, property, market	1 each
Realtor.com	search, property, locations, market, agents, agent	1 each
Rightmove	locations (start here), search, property, sold, sold_property, agents, agent	1 each
Booking.com	search, hotel, reviews	1 each
Tripadvisor	locations (start here), search, location, reviews	2 each
Airbnb	search, listing, reviews	1 each
Yelp	search, business, reviews	2 each
Indeed	search, job, company, company_reviews	2 each
Glassdoor	companies (start here), company, reviews, salaries	1 each
Apple App Store	search, app, reviews	1 each
Google Play	search, app, reviews	2 each
SEC EDGAR	lookup (start here), company, filings, concept, facts, search	1 each
Companies House	search (start here), company, officers, filing_history	1 each
G2	search, product, reviews	5 each
Capterra	search, product, reviews	2 each
Google Ads Transparency	advertisers (start here), search, creative	1 each
Meta Ad Library	search, advertiser, ad	1 each
Costco	search, category, categories, product, prices, availability, reviews, warehouses, gas, coupons, deals, clearance, autocomplete	1 each, except prices (2 at 10 warehouses), availability (1 per 5 warehouses), warehouses (2 with country="ca") and gas (2 by location on ca; by warehouse_ids, 1 per 5 on us and 2 per warehouse on ca)
Lowe's	stores (start here), search, product, reviews	reviews 1, the rest 2 each
Sam's Club	clubs, search, product, savings, category, reviews	1 each
Canadian Tire	stores (start here), search, product, availability, clearance, deals	1 each
Dollar General	stores (start here), search, product, deals, coupons	1 each
Sephora	search, category, product	1 each
Ulta Beauty	search, category, product, stores	1 each
ALDI	stores (start here), search, product	1 each
Micro Center	stores (start here), search, product	stores 1, the rest 2 each
Walgreens	stores (start here), search, product, stock	stock 4, the rest 2 each
CVS	search, product, stock	1 each
Whole Foods Market	stores (start here), store, search, category, deals, product, sales_flyer	1 each
Harbor Freight	search, clearance, product, prices, stores, availability, reviews	1 each
PetSmart	search, product, stores, store_stock, availability, deals	1 each
Petco	search, category, product, stores, store_inventory	1 each
iHerb	search, product, prices, reviews	2 each
Chewy	search, product	10 each
Menards	search, stores, product	search 2; stores and product 1 each, product with store_id 2
Nordstrom	search, category, brand, product, reviews, availability, stores, suggestions	availability and reviews 10, the rest 5 each
Nordstrom Rack (nordstrom_rack)	search, category, brand, product, reviews, availability, stores, suggestions	availability and reviews 10, the rest 5 each
Best Buy (best_buy)	search, product, reviews, category, deals, stores, availability, suggestions, prices	stores and suggestions 1; deals 3; category 2, 3 on a pcmcat page; the rest 2 each (prices per call, up to 15 SKUs; availability flat, up to 8 stores)
TCGplayer	search, product, listings, price_history, sales, games, sets, price_guide, suggestions, trending, seller, seller_feedback, seller_products	1 each
Vinted	search, product, seller, seller_products, seller_feedback, brands, suggestions, filters, categories	1 each
Trustpilot	search, business, reviews, categories, category, review	2 each
Mercado Libre	search, product, reviews, questions, best_sellers	reviews 1, the rest 2 each
ChatGPT	prompt	5 per answered prompt
Gemini	prompt	5 per answered prompt
Extract (core, callable)	client.extract(url), extract.map, extract.crawl, extract.crawl_status, extract.cancel_crawl, extract.batch, extract.batch_status, extract.cancel_batch	extract: 1 for mode="normal" or "advanced", 2 for "ultra", only on success. map: 1 on normal. crawl / batch: that same price per successful page. Status and cancel: free

Every method's full parameter list is available inline in your editor (typed keyword arguments with docstrings). See the API docs for field-level details.

Changed in 0.41.0

• New vinted namespace (sync and async) for Vinted on 27 country sites (country, fr default), 9 endpoints at 1 credit each: search, product, seller, seller_products, seller_feedback, brands, suggestions, filters, categories. search and seller_products return the price, the Vinted fee (service_fee) and total_price; the id filters (brand_ids, status_ids, size_ids, color_ids, material_ids) take a list or a comma-separated string. 120s timeout floor on every method.

Changed in 0.40.0

• New tcgplayer namespace (sync and async) for tcgplayer.com (US, USD), 13 endpoints at 1 credit each: search, product, listings, price_history, sales, games, sets, price_guide, suggestions, trending, seller, seller_feedback, seller_products. product returns the market price per SKU (condition x printing x language) with the recent sale range; price_history returns up to a year of sold volume per SKU. 120s timeout floor on every method.

Changed in 0.39.0

• New best_buy namespace (sync and async) for bestbuy.com (US): search, product, reviews, category, deals, stores, availability, suggestions, prices. product returns open-box offers by condition (fair, good, excellent) with savings against new and regular; availability returns per-store pickup status at up to 8 stores; prices takes up to 15 SKUs per call. 120s timeout floor on every method.

Changed in 0.38.0

• New nordstrom_rack namespace (sync and async) for nordstromrack.com (US): search, category, brand, product, reviews, availability, stores, suggestions, with the same parameters, credits (5 each, availability and reviews 10) and timeout floors (120s, reviews 150s) as nordstrom. category takes /shop and /clearance paths, including Clear the Rack and flash events (/shop/flash/event-N); brand takes /brands/<slug>. Product and listing rows carry clearance_price_range, promotion_price_range and promotion_percent_off.

Changed in 0.37.0

• nordstrom.reviews(*, url, style_id, sort, stars, verified_only, with_photos, search, max_reviews) (sync and async), 10 credits: a product's full review history, up to 200 per call (default 60), sorted most_helpful (default), most_recent, highest_rated or lowest_rated, filtered by star ratings, verified purchases, photos or a text search (search cannot be combined with sort). Returns the review summary (average, histogram, size and width rating, praised and criticized features with a quote, rating-only count), the AI review summary, total_results, returned and has_more. Calls take 20-60s (up to about 100s at 200 reviews), so this method's timeout floor is 150s.

Changed in 0.36.0

• nordstrom gains five methods (sync and async), all with the 120s timeout floor:
  • category(category, *, page, sort), 5 credits: a /browse category path or URL (departments, sale/women, /browse/new), 24 products a page plus the category's breadcrumbs, subcategories and the filter sidebar with counts.
  • brand(brand, *, page, sort), 5 credits: a /brands path or URL (a product's brand_url works), same rows and filters.
  • stores(*, zip, latitude, longitude, store_numbers, distance_miles), 5 credits: pass exactly one of zip, latitude + longitude, or store_numbers (list or comma-separated string); radius 1-100 miles, default 100.
  • availability(*, url, style_id, sku_id), 10 credits: per sku ship quantity, pickup quantity (chain-wide) and the stores that can do pickup.
  • suggestions(query, *, limit), 5 credits: search-box autocomplete, 1-20, default 8.

Changed in 0.35.0

• New nordstrom namespace (sync and async), 5 credits per call.
  • search(query, *, page, sort): nordstrom.com (US), 24 products a page. sort: relevance, rating, price_high, price_low, newest, percent_off.
  • product(*, url, style_id): pass one. Every color and size as a sku with sku id, UPC, price, original price, percent off, price type, in_stock and quantity available to ship; star histogram, size-fit distribution and the reviews shown on the product page.
  • Both raise the request timeout to 120s.

Changed in 0.34.0

• menards.search(query, store_id, ...) (sync and async), 2 credits: menards.com search at one store, 40 products a page, with price, sale price, the mail-in rebate, final price and that store's stock count. sort (relevance, price_asc, price_desc, brand_asc, brand_desc; price sorts use the everyday price), in_stock_today, min_price, max_price, page. An unknown store number is a 400.

Changed in 0.33.0

• Seven new US retail namespaces, 33 methods (sync and async).
  • whole_foods: search, category, deals, product, stores, store, sales_flyer (1 credit each). Prices are one store's (5-digit store_id, default 10145): regular, sale and Prime member price. product returns the in-store and online price as separate blocks. Search sorts by relevance or price only.
  • harbor_freight: search, clearance, product, prices (up to 50 item numbers), stores, availability, reviews (1 each). National prices with sale end date, coupon, Inside Track Club member price and compare-at. Store stock is a status plus aisle from a daily snapshot.
  • petsmart: search, product, stores, store_stock, availability, deals (1 each). Autoship first-order and recurring prices; stock is a unit count per store. availability takes pairs of {"store_id", "sku"}.
  • petco: search, category, product, stores, store_inventory (1 each). store_inventory takes cat_entry_ids, not part numbers. category and product can take 10-30 seconds, so their request timeout is raised to 120s.
  • iherb: search, product, prices (up to 50 ids), reviews (2 each). Online only, US/USD; reviews is the rating summary and histogram with the top positive and top critical review.
  • chewy: search, product (10 each). One-time, Autoship and first-Autoship prices and MAP fields; search reports a category redirect.
  • menards: product, stores (1 each; product with store_id 2, adds stock quantity and aisle). No search. product timeout raised to 120s.

Changed in 0.32.1

• cvs.search: same_day_delivery is now always None, as on cvs.stock. CVS reports one same-day count per SKU whichever store is sent, so it was not the store's number.

Changed in 0.32.0

• Six new US retail namespaces, 20 methods (sync and async).
  • sephora: search, category, product (1 credit each). product takes url or product_id and returns a review summary with pro/con sentiment counts (no review text).
  • ulta: search, category, product, stores (1 each). stores is in stock / out of stock for one shade at the 10 stores nearest a ZIP.
  • aldi: search, product, stores (1 each). Pass store_id or zip; service_type picks the in-store (default), pickup or delivery price.
  • micro_center: search, product (2 each), stores (1). Stock is per 3-digit store_id (web store 029 when omitted), capped at 25+.
  • walgreens: search, product, stores (2 each), stock (4, up to 20 stores). Prices are national.
  • cvs: search, product, stock (1 each, stock up to 20 SKUs). No store locator; search defaults to store 7019. product can take 10-30+ seconds, so its request timeout is raised to 120s.

Changed in 0.31.0

• Four new store-level retail namespaces, 21 methods (sync and async). Every method answers for one store or club you choose.
  • lowes: search, product, stores (2 credits each), reviews (1). store_id and the store's zip are required on search and product; search(on_sale=True) returns that store's deals.
  • sams_club: search, product, clubs, savings, category, reviews (1 each). product(club_id=...) prices and stocks one club; clearance is category("1150109").
  • canadian_tire: search, product, stores, availability (up to 50 SKUs), clearance, deals (1 each). store_id is required except on stores.
  • dollar_general: search, product, stores (pass upc for per-store unit counts at up to 25 stores), deals (on_sale or clearance), coupons (1 each).

Changed in 0.27.0

• New target.availability(tcin, zip, radius=None, store_id=None). In-store stock for one product at every Target store near a 5-digit US ZIP: units, in-store, pickup, curbside and ship-to-store status, distance, address and hours, up to 20 stores. store_id narrows the result to one store. 1 credit.

  stock = client.target.availability("85978609", "79407")
  for s in stock["data"]["stores"]:
      print(s["store_id"], s["name"], s["quantity"], s["in_store"], s["pickup"])
  

• store_id on target.search, category, product and reviews sets the store for prices only; its docstring no longer mentions availability. No behaviour change.

Changed in 0.26.2

• chatgpt.prompt now costs 2 credits per answered prompt. Gemini stays at 5.

Changed in 0.26.1

• chatgpt.prompt now costs 1 credit per answered prompt (was 5). Gemini stays at 5.

Changed in 0.26.0

• New chatgpt.prompt and gemini.prompt (sync and async): send a prompt, get the assistant's answer with cited sources, web searches (ChatGPT) and recommended products (POST /api/v1/chatgpt/prompt, POST /api/v1/gemini/prompt). 5 credits per answered prompt at launch.
• Endpoints can now declare a minimum request timeout; these two raise it to 120s when the client's timeout is shorter. A longer client timeout is always kept.

Changed in 0.25.0

• New google.custom_search (sync and async): Google search in the Custom Search JSON API format (POST /api/v1/google/customsearch). Snake_case arguments map to the CSE wire names (site_search -> siteSearch, date_restrict -> dateRestrict, ...). 1 credit.

Changed in 0.24.1

• Docstring wording only: parameter notes describe what each endpoint returns. No price, parameter or behavior changed.

Changed in 0.24.0

• Instagram is cheaper. Typical costs are now 1-2 credits instead of 8-10: profile, user_reels, user_tagged and music_posts 1 (user_reels / user_tagged 2 the first time a username is looked up), everything else 2. search_users / search_hashtags with a cursor cost 8. Any call can cost up to 10 when it needs extra work; credits_used is always the exact charge.
• 8 new Instagram methods, sync and async: user_highlights(*, username=None, user_id=None), highlight_stories(highlight_id), user_similar(*, username=None, user_id=None), post_likers(*, shortcode=None, url=None), search_reels(keyword, *, cursor=None), search_music(keyword), music_posts(music_id, *, cursor=None) and search_locations(keyword).
• user_posts still returns its existing shape: items under data.data.items, next page via data.pagination_token.

hl = client.instagram.user_highlights(username="natgeo")
stories = client.instagram.highlight_stories("highlight:18149897764550869")
track_reels = client.instagram.music_posts("1747512212697470")

Changed in 0.23.0

• client.extract is now a callable namespace. client.extract(url, format=..., mode=...) works exactly as before (same signature, same request on the wire, sync and async), and the same object adds the website map, crawl and batch operations: extract.map, extract.crawl, extract.crawl_status, extract.cancel_crawl, extract.batch, extract.batch_status, extract.cancel_batch. crawl and batch take wait=True to poll until done and return every result. See example 17b.
• scavio.verify_webhook(secret, header, body) verifies the signed completion webhooks crawl and batch jobs send.
• New ScavioJobTimeoutError (a ScavioTimeoutError) when wait_timeout elapses; it carries job_id.
• tiktok.video_transcript(video_id=None, *, url=None, language=None): the spoken-word transcript of a TikTok video as timestamped segments and full text, 1 credit. Returns available: false for videos with no spoken captions.
• 2xx responses other than 200 (a job's 202) are now treated as success.

site = client.extract.map("https://scavio.dev", limit=50)
crawl = client.extract.crawl("https://scavio.dev/docs", max_pages=2, wait=True)
words = client.tiktok.video_transcript("7609177768793787679")

Changed in 0.22.0

• 19 new methods across TikTok, YouTube, Reddit and X, 1 credit each.
  • tiktok: trending_searches(), trending_videos() (about 6 videos per call whatever count says), search_suggestions(keyword), music(music_id), music_videos(music_id) (count is ignored upstream, about 14 per page), search_music(keyword), music_chart() (Top 50 or Viral 50, one global chart), user_similar(sec_user_id), user_playlists(sec_user_id), playlist_videos(playlist_id), user_live(username) (an unknown username is a billed 404) and user_region(username) (an unknown username is an unbilled 404; also returns sec_user_id). These return TikTok's raw payload: large numeric ids can lose precision as JSON numbers, so use id_str / sec_uid.
  • youtube: community_post(post_id), community_post_comments(post_id).
  • reddit: search_comments(query) (sort RELEVANCE, HOT, TOP or NEW), search_subreddits(query).
  • x: search_communities(keyword), community(community_id), community_tweets(community_id) (ranking Relevance or Recency).
• youtube.transcript now costs 5 credits (was 8); the docstring says so.

chart = client.tiktok.music_chart(chart="viral", count=10)
region = client.tiktok.user_region("mrbeast")
similar = client.tiktok.user_similar(region["data"]["sec_user_id"])
threads = client.reddit.search_comments("web scraping api", sort="NEW")
posts = client.x.community_tweets("1508567430188126209", ranking="Recency")

Changed in 0.21.1

• README: Mercado Libre docs and landing page links; wording cleanup. No behavior changes.

Changed in 0.21.0

• New mercadolibre namespace, 5 methods. search (keyword or seller in Mexico, Brazil, Argentina or Colombia; condition, price, discount, free shipping, Full fulfillment, official store and interest-free installment filters; relevance or price sort; 2 credits), product (buy-box seller, variations, star distribution, specs; 2), reviews (15 per page with a star filter; 1), questions (buyer questions with seller answers; 2) and best_sellers (top 20 in a category, not Brazil; 2). country is required on search and best_sellers and is keyword-only. See example 23 above.
• realtor.search(keywords=...) docstring: a listing must contain ALL the keywords, so each extra keyword narrows the results.

Changed in 0.20.1

• README: Realtor.com and Rightmove docs links; Rightmove sold prices cover England and Wales only, and search pages return 24-25 rows (the rightmove.search(page=...) docstring now says so). No behavior changes.

Changed in 0.20.0

• New realtor namespace, 6 methods, 1 credit each. search (for sale, pending, for rent or sold by location, radius or agent; price, beds, baths, sqft, lot, garage, HOA, year built, property type, recency, listing-text keywords and more; up to 200 rows per call), property (price and tax history, valuations, schools, risk, listing agent), locations, market, agents and agent.
• New rightmove namespace, 7 methods, 1 credit each. locations, search (buy or rent by location, place name or branch), property, sold (sale history since 1995 per address plus an area summary), sold_property, agents and agent. See example 22 above.

Changed in 0.19.1

• trustpilot.category(min_trust_score=...) docstring corrected: the filter is a minimum star rating (3, 4 or 4.5). Trustpilot rounds TrustScore to stars, so 4 includes TrustScore 3.8 and up. No code changes.

Changed in 0.19.0

• New trustpilot namespace, 6 methods, 2 credits each. search (businesses by keyword, up to 100 per page), business (full profile by domain or Trustpilot URL), reviews (stars, language, date range, sort, verified-only, with-replies, topics and text filters; up to 200 per filter combination), categories (the tree or a name lookup), category (ranked businesses with sort, minimum star rating and claimed-only) and review (one review by id). See example 21 above.

Changed in 0.18.1

• Documentation links only; no code changes. The client already identifies itself on every call with User-Agent: scavio-python/<version> and X-Client-Source: scavio-python.

Changed in 0.18.0

• New costco namespace, 13 methods. search, category, categories, product (up to 20 ids per call), prices (online vs in-warehouse price at up to 10 warehouses), availability (in-warehouse stock status), reviews, warehouses, gas, coupons (the member coupon book), deals (new, while_supplies_last, treasure_hunt, member_favorites, online_only, on_sale), clearance (price-code markdown finder) and autocomplete. See example 20 above.

Changed in 0.17.0

• Walmart store targeting. walmart.stores(zipcode, domain=...) is new: it returns the Walmart stores near a US ZIP (walmart.com) or a Canadian postal code (domain="ca", walmart.ca), nearest first, each with its store_id. Pass that store_id together with delivery_zip to walmart.search() or walmart.product() on the same domain to get that store's assortment and availability; data.location echoes the store used. Store-targeted calls cost 2 credits and take 10-60 seconds, so give them a generous timeout. walmart.product() also accepts domain="ca", together with a store pair.

  stores = client.walmart.stores("50036")
  store_id = stores["data"]["stores"][0]["store_id"]  # "1389", Boone Supercenter
  milk = client.walmart.search("whole milk", delivery_zip="50036", store_id=store_id)
  print(milk["data"]["location"])  # {"city": "Boone", "state": "IA", "store_id": "1389", ...}
  

• delivery_zip and store_id are no longer retired; device is now the only retired Walmart parameter.

Changed in 0.16.0

• max_requests_per_second now accepts up to 50 (it was capped at 10), matching the raised per-plan request limits -- the top plan allows 50, so the old ceiling rejected a value the plan entitled you to. The default is still 1, and the bound now lives in one place (scavio._ratelimit.MAX_REQUESTS_PER_SECOND) shared by the sync and async clients.

Changed in 0.15.0

• 21 new namespaces with 85 endpoints: Threads, Kuaishou, eBay, Target, Home Depot, Zillow, Redfin, Booking.com, Tripadvisor, Airbnb, Yelp, Indeed, Glassdoor, Apple App Store, Google Play, SEC EDGAR, Companies House, G2, Capterra, Google Ads Transparency and the Meta Ad Library. With the Walmart rebuild and extract below, that is 93 endpoints in this batch and 195 in the SDK. Every method is typed and documented inline, on both the sync and async clients.
• client.extract(url, format=..., mode=...) is new, and is a top-level method, not a namespace. Reading an arbitrary URL is a core capability rather than a platform, so it is client.extract(...), never client.extract.extract(...).
• Walmart grew from 2 endpoints to 7 (reviews, category, offers, seller, seller_products are new) and search/product changed shape. device, delivery_zip and store_id are retired: the API answers 200 with a top-level warnings array if you send them through **extra. domain (com | ca | com.mx) is new on search and category, and it is the price-bearing parameter. page supersedes start_page, which stays as a deprecated alias.
• Walmart min_price / max_price widened from int to float. The backend was always z.number(), so 19.99 was always accepted; the old annotation was simply wrong. No runtime behaviour changed.
• Four surfaces are body-priced and their docstrings say so instead of quoting a flat price: Walmart (com.mx costs 2), Threads (4 credits when addressed by username instead of user_id), Kuaishou (1, 2, 10 or 40 per endpoint) and extract (mode="ultra" costs 2).
• Some caveats worth knowing before you write a loop: eBay sold=True returns total_results: null; Meta Ad Library pages are 30 ads then 10, and the cursor ignores every other filter; Capterra search does not paginate at all; Apple App Store search has no pagination either (raise limit, up to 200); Airbnb prices exist only on search, never on listing.

Changed in 0.14.0

• reddit.post() now takes post_id as well as url -- pass either one. url stays the first positional argument, so existing calls are unaffected. The response is a flat post object and carries no comments; use reddit.post_comments() for those.
• youtube.shorts(sort_by=...) is now typed as relevance | date | view_count | rating instead of a free-form string.
• youtube.search() lost its location flag. It was never part of the backend schema, so it was silently dropped rather than filtering anything.

Amazon changed in 0.12.0 (breaking)

The Amazon API now returns a normalized shape instead of the previous raw payload.

• search returns {query, page, total_results, total_results_text, count, products[], filters[], related_searches[]}. Each product is {asin, title, url, image, price, currency, rating, reviews_count, is_sponsored, position, badge, sales_volume, delivery{is_free, date, fastest_date}}.
• product returns flat fields: price, list_price, currency, rating, reviews_count, features, images, videos, variants, specifications, best_sellers_rank, shipping, and more. The old buybox[] array no longer exists -- use offers for per-seller pricing.
• offers is new: every seller for one ASIN, with price, condition, seller_name, is_buy_box_winner, is_fulfilled_by_amazon, and delivery windows.
• country (ISO 3166-1 alpha-2: us, gb, de) is the marketplace selector and replaces domain. page replaces start_page. The old names still work as deprecated aliases.
• Nine parameters were removed: language, currency, device, sort_by, pages, category_id, merchant_id, zip_code, autoselect_variant. sort_by in particular was verified to be ignored by the marketplace, so result sorting is not available at any layer. Sending one of them anyway (via **extra) still returns 200, with a top-level warnings array explaining what was ignored.
• options still returns domains and countries; languages and currencies are now always empty, because neither is a request parameter any more.

Links

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• Documentation
• Dashboard & API Keys
• API Reference
• Compare Scavio vs alternatives

License

MIT

About Scavio

Scavio is a unified search API built for AI agents — one API key, structured JSON. A real-time Tavily alternative and SerpAPI alternative with data from:

• Google Search API — SERP results, news, images, maps, and knowledge graph
• Amazon Product API and Walmart Product API — product search and details
• YouTube API, TikTok API, and Instagram API — video and social media data
• TikTok Shop API — product search, detail, reviews, categories, and shop catalogs
• Reddit API — posts, comments, subreddits, and trending
• X API and LinkedIn API — tweets, profiles, companies, and jobs

For a detailed head-to-head breakdown, see Tavily vs Scavio.

Get a free API key and explore the documentation.