The Problem
Deep research agents typically chain Serper for search results, Jina or Firecrawl for page extraction, and E2B or Modal for compute. That is three vendors, three billing models, three failure surfaces, and three sets of rate limits. When the agent needs Amazon prices, add a fourth vendor. YouTube data, a fifth. The orchestration code that manages retries, fallbacks, and error normalization across these tools grows until it is larger than the research logic itself. Each vendor update risks breaking the chain.
The Scavio Solution
Scavio covers search across Google, YouTube, Amazon, Walmart, Reddit, and TikTok in a single API with one billing model: $0.005/credit, 1 credit per request. For page content extraction, the /api/v1/extract endpoint pulls structured text from any URL. The agent calls one base URL with one API key, gets normalized JSON regardless of platform, and the orchestration code shrinks to a simple loop. When you need extraction after search, it is the same key, same error format, same retry logic.
Before
Before Scavio, a deep research agent required Serper ($0.001/search) + Firecrawl ($0.005/page) + separate Amazon API + manual Reddit scraping. Four vendors, four auth systems, four billing cycles, and orchestration code that grew with every platform added.
After
After Scavio, the same agent calls one endpoint for search and one for extraction. One API key, one billing dashboard, one error format. The agent code halves in size and the vendor management overhead drops to zero.
Who It Is For
AI engineers building deep research agents who are tired of managing 3-5 vendor integrations. Teams whose orchestration code has grown larger than their actual research logic.
Key Benefits
- One API key replaces 3-5 vendor integrations
- Uniform JSON schema across all six platforms
- Extract endpoint handles page content retrieval
- Single billing model eliminates cost tracking across vendors
- One error format and retry pattern for the entire pipeline
Python Example
import requests
API_KEY = "your_scavio_api_key"
BASE = "https://api.scavio.dev/api/v1"
def search(query: str, platform: str = "google") -> dict:
res = requests.post(
f"{BASE}/search",
headers={"x-api-key": API_KEY},
json={"platform": platform, "query": query},
timeout=15,
)
res.raise_for_status()
return res.json()
def extract(url: str) -> dict:
res = requests.post(
f"{BASE}/extract",
headers={"x-api-key": API_KEY},
json={"url": url},
timeout=30,
)
res.raise_for_status()
return res.json()
def deep_research(topic: str) -> dict:
# Step 1: Search across platforms
google = search(topic, "google")
reddit = search(topic, "reddit")
youtube = search(topic, "youtube")
# Step 2: Extract top result content
top_urls = [r.get("link") for r in google.get("organic", [])[:3] if r.get("link")]
extracted = [extract(url) for url in top_urls]
# Step 3: Compile findings
return {
"topic": topic,
"google_results": len(google.get("organic", [])),
"reddit_discussions": len(reddit.get("organic", [])),
"youtube_videos": len(youtube.get("organic", [])),
"extracted_pages": len(extracted),
"credits_used": 3 + len(top_urls), # 3 searches + N extractions
}
result = deep_research("best SERP API for AI agents 2026")
print(f"Research complete: {result['credits_used']} credits used")
print(f" Google: {result['google_results']} results")
print(f" Reddit: {result['reddit_discussions']} discussions")
print(f" YouTube: {result['youtube_videos']} videos")JavaScript Example
const API_KEY = "your_scavio_api_key";
const BASE = "https://api.scavio.dev/api/v1";
async function search(query, platform = "google") {
const res = await fetch(`${BASE}/search`, {
method: "POST",
headers: { "x-api-key": API_KEY, "content-type": "application/json" },
body: JSON.stringify({ platform, query }),
});
if (!res.ok) throw new Error(`scavio ${res.status}`);
return res.json();
}
async function extract(url) {
const res = await fetch(`${BASE}/extract`, {
method: "POST",
headers: { "x-api-key": API_KEY, "content-type": "application/json" },
body: JSON.stringify({ url }),
});
if (!res.ok) throw new Error(`scavio ${res.status}`);
return res.json();
}
async function deepResearch(topic) {
const [google, reddit, youtube] = await Promise.all([
search(topic, "google"),
search(topic, "reddit"),
search(topic, "youtube"),
]);
const topUrls = (google.organic ?? []).slice(0, 3).map((r) => r.link).filter(Boolean);
const extracted = await Promise.all(topUrls.map(extract));
return { topic, googleResults: google.organic?.length ?? 0, redditDiscussions: reddit.organic?.length ?? 0, youtubeVideos: youtube.organic?.length ?? 0, extractedPages: extracted.length, creditsUsed: 3 + topUrls.length };
}
const result = await deepResearch("best SERP API for AI agents 2026");
console.log(`Research: ${result.creditsUsed} credits, ${result.googleResults} Google, ${result.redditDiscussions} Reddit, ${result.youtubeVideos} YouTube`);Platforms Used
Web search with knowledge graph, PAA, and AI overviews
YouTube
Video search with transcripts and metadata
Amazon
Product search with prices, ratings, and reviews
Walmart
Product search with pricing and fulfillment data
Community, posts & threaded comments from any subreddit
TikTok
Trending video, creator, and product discovery