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Python SDK for the Scavio Search API - real-time Google, Amazon, Walmart, YouTube, Reddit, TikTok & Instagram data for AI agents and apps

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# Scavio Python SDK

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The official Python SDK for the [Scavio](https://scavio.dev) 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](https://scavio.dev/search-api-for-ai-agents) with one SDK and one API key.

## Installation

```bash
pip install scavio
```

## Quick Start

Get your free API key at [dashboard.scavio.dev](https://dashboard.scavio.dev).

```python
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.

```python
# 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:

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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.

```python
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.

```python
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.

```python
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

```python
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

```python
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.

```python
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](https://github.com/scavio-ai/langchain-scavio) -- `pip install langchain-scavio`
- [MCP Server](https://www.npmjs.com/package/@scavio/mcp-server) -- for Claude, Cursor, and other MCP clients
- [n8n](https://www.npmjs.com/package/n8n-nodes-scavio) -- 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](https://scavio.dev/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.

## Links

- [Website](https://scavio.dev)
- [Documentation](https://docs.scavio.dev)
- [Dashboard & API Keys](https://dashboard.scavio.dev)
- [API Reference](https://docs.scavio.dev/api-reference)
- [Compare Scavio vs alternatives](https://scavio.dev/compare)

## License

MIT

## About Scavio

[Scavio](https://scavio.dev) is a unified [search API](https://scavio.dev/docs/search-api) built for AI agents — one API key, structured JSON, no scraping or proxies. A real-time [Tavily alternative](https://scavio.dev/alternatives/tavily) and [SerpAPI alternative](https://scavio.dev/alternatives/serpapi) with data from:

- [Google Search API](https://scavio.dev/google-search-api) — SERP results, news, images, maps, and knowledge graph
- [Amazon Product API](https://scavio.dev/amazon-product-api) and [Walmart Product API](https://scavio.dev/walmart-product-api) — product search and details
- [YouTube API](https://scavio.dev/youtube-transcript-api), [TikTok API](https://scavio.dev/tiktok-api), and [Instagram API](https://scavio.dev/instagram-api) — video and social media data
- [TikTok Shop API](https://scavio.dev/docs/tiktok-shop-search) — product search, detail, reviews, categories, and shop catalogs
- [Reddit API](https://scavio.dev/reddit-api) — posts, comments, subreddits, and trending
- [X API](https://scavio.dev/docs/x-search) and [LinkedIn API](https://scavio.dev/docs/linkedin-person) — tweets, profiles, companies, and jobs

For a detailed head-to-head breakdown, see [Tavily vs Scavio](https://scavio.dev/compare/tavily/vs-scavio).

Get a free [API key](https://dashboard.scavio.dev) and explore the [documentation](https://scavio.dev/docs/introduction).

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