Scavio Python SDK

The official Python SDK for the Scavio Search API. Access real-time data from 31 sources -- Google, Amazon, Walmart, eBay, Target, Home Depot, YouTube, Reddit, X, TikTok, TikTok Shop, Instagram, LinkedIn, Threads, Kuaishou, Zillow, Redfin, Booking.com, Tripadvisor, Airbnb, Yelp, Indeed, Glassdoor, the Apple App Store, Google Play, SEC EDGAR, Companies House, G2, Capterra, Google Ads Transparency and the Meta Ad Library -- plus extract() for reading any URL, all with a single API key. Built for AI agents, LLM applications, and data pipelines.

One API key, 31 data sources, 195 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
Amazon Products	Yes	No	Yes	No
Walmart Products	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 (11 endpoints)	Yes	No	No	No
TikTok Shop Data (8 endpoints)	Yes	No	No	No
Instagram Data (12 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, 6 endpoints)	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	31	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 pass the upstream provider's shape through, 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",
)

# 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']}")

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 = client.instagram.profile(username="instagram")
user = profile["data"]["user"]
print(f"@{user['username']} - {user['edge_followed_by']['count']:,} followers")

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

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 upstream provider withdrew the datasets behind person_contact, company_people, company_jobs, search_people and search_posts. 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
# (upstream 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  # no detail data upstream 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, so it is a top-level method: client.extract(url), never client.extract.extract(). 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, plain datacenter fetch. 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", a full browser render
# (1 credit) -> "ultra", the hardest-target tier (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.

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.

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 (1-10)
    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`, `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`, `reviews`, `category`, `offers`, `seller`, `seller_products`	1, except `search`/`category` on `domain="com.mx"`, 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`	`search`/`shorts` 2, `transcript` 8, `streams` 3, rest 1 each
Reddit	`search`, `search_suggestions`, `post`, `post_comments`, `comment_replies`, `subreddit`, `subreddit_posts`, `user`, `user_posts`, `user_comments`, `popular`, `trending`	1 each
X	`search`, `tweet`, `tweet_comments`, `tweet_retweeters`, `user`, `user_tweets`, `user_replies`, `user_media`, `user_followers`, `user_followings`, `trending`	1 each
TikTok	`profile`, `user_posts`, `video`, `video_comments`, `comment_replies`, `search_videos`, `search_users`, `hashtag`, `hashtag_videos`, `user_followers`, `user_followings`	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_posts` 2, `post`/`comment_replies` 8, the other nine 10 each
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`	1 each
Home Depot	`search`, `product`, `reviews`	2 each
Zillow	`search`, `property`, `agent_reviews`	1 each
Redfin	`search`, `property`, `market`	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
Extract (core, not a namespace)	`client.extract(url)`	1 for `mode="normal"` or `"advanced"`, 2 for `"ultra"`; only a successful extraction is billed

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 0.15.0

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)

Amazon moved to a new upstream and the API now returns a normalized shape instead of the previous raw provider 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.

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License

MIT

About Scavio

Scavio is a unified search API built for AI agents — one API key, structured JSON, no scraping or proxies. 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.

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pyproject.toml		pyproject.toml

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