Scavio Python SDK

The official Python SDK for the Scavio Search API. Access real-time data from Google, Amazon, Walmart, YouTube, Reddit, X, TikTok, TikTok Shop, Instagram, and LinkedIn with a single API key. Built for AI agents, LLM applications, and data pipelines.

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

Why Scavio

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']}")

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

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 0.14.0

• 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
• 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, 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.