scavio 0.1.1

Python SDK for the Scavio Search API - real-time Google, Amazon, Walmart, YouTube, Reddit, and TikTok data

Scavio Python SDK

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

One API key, six 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

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 Search	Yes	No	No	No
TikTok Data (11 endpoints)	Yes	No	No	No
Data Sources	6	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
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 SDK with 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["results"]:
    print(r["title"], r["url"])

Examples

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['url']})\n{r['content']}"
    for r in results["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, domain="com")
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

from scavio import ScavioClient

client = ScavioClient()

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

print(f"Brand:   {data['brand']}")
print(f"Title:   {data['title']}")
print(f"Rating:  {data['rating']} ({data['reviews_count']} reviews)")
print(f"Price:   ${data['buybox'][0]['price']}")

4. SEO Competitor Analysis

from scavio import ScavioClient

client = ScavioClient()

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

domains = {}
for r in results["results"]:
    domain = r["domain"]
    domains[domain] = domains.get(domain, 0) + 1

print("Domains ranking for this keyword:")
for domain, count in sorted(domains.items(), key=lambda x: -x[1]):
    print(f"  {domain}: {count} result(s)")

5. News Aggregation

from scavio import ScavioClient

client = ScavioClient()

news = client.google.search("AI startups", search_type="news")

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]:
    title = v["title"]["runs"][0]["text"]
    views = v.get("viewCountText", {}).get("simpleText", "N/A")
    print(f"{title} ({views})")
    print(f"  https://youtube.com/watch?v={v['videoId']}")

# Get detailed metadata for a specific video
meta = client.youtube.metadata("dQw4w9WgXcQ")
print(f"\n{meta['data']['title']}")
print(f"  {meta['data']['view_count']:,} views, {meta['data']['like_count']:,} likes")

7. Reddit Market Research

from scavio import ScavioClient

client = ScavioClient()

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

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

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. Social Media Monitoring

from scavio import ScavioClient

client = ScavioClient()

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

print(f"Reddit mentions ({len(reddit['data']['posts'])}):")
for post in reddit["data"]["posts"][: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]}")

10. 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}")

11. 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", domain="com")

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

        for r in google["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())

12. 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,
)

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")

Configuration

client = ScavioClient(
    api_key="sk_...",
    base_url="https://api.scavio.dev",  # custom base URL
    timeout=30,                          # request timeout in seconds
    max_requests_per_second=1,           # rate limiting (1-10)
)

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	1-2
Amazon	search, product	1 each
Walmart	search, product	1 each
YouTube	search, metadata	1 each
Reddit	search, post	2 each
TikTok	profile, user_posts, video, video_comments, comment_replies, search_videos, search_users, hashtag, hashtag_videos, user_followers, user_followings	1 each

Links

• Website
• Documentation
• Dashboard & API Keys
• API Reference

License

MIT