Production AI agents need search APIs with predictable uptime, consistent response schemas, and graceful error handling. The 2026 landscape has reliability concerns at every provider: Tavily faces uncertainty after the Nebius acquisition, SerpAPI is in active litigation, Exa is newer with a smaller track record, Scavio is smaller-scale, and Serper only covers Google. Here is what matters for each.
Provider reliability profiles
- Tavily: acquired by Nebius in 2025. Integration uncertainty. Product direction may shift toward Nebius platform needs. AI-summarized results (non-deterministic output)
- SerpAPI: active lawsuits from both Reddit and Google. Existential legal risk. If an injunction is granted, service could be disrupted. Longest track record in the space
- Exa: semantic/neural search, different from traditional SERP. Newer product, less battle-tested at high volume. Websets feature still maturing
- Scavio: smaller company, multi-platform coverage. Credit-based pricing maps well to agent usage. Less public uptime history
- Serper: Google-only. Simple, fast, cheap. Single-platform limitation means you need a second provider for Amazon/Reddit/YouTube
What production agents need
- Schema stability: response JSON structure should not change between versions without notice
- Timeout predictability: agents need to know if a search will take 500ms or 5 seconds
- Error semantics: rate limits, quota exhaustion, and server errors need distinct codes
- Fallback support: the ability to retry on a different provider if the primary fails
Resilient search with fallback
Python
import requests, os, time
PROVIDERS = [
{
"name": "scavio",
"url": "https://api.scavio.dev/api/v1/search",
"headers": {"x-api-key": os.environ.get("SCAVIO_API_KEY", "")},
"body_fn": lambda q: {"query": q, "platform": "google"},
"parse_fn": lambda r: r.get("organic_results", []),
},
{
"name": "serper",
"url": "https://google.serper.dev/search",
"headers": {"X-API-KEY": os.environ.get("SERPER_API_KEY", "")},
"body_fn": lambda q: {"q": q},
"parse_fn": lambda r: r.get("organic", []),
},
]
def resilient_search(query: str, timeout: int = 10):
"""Try providers in order, fall back on failure."""
for provider in PROVIDERS:
try:
resp = requests.post(
provider["url"],
headers=provider["headers"],
json=provider["body_fn"](query),
timeout=timeout)
resp.raise_for_status()
results = provider["parse_fn"](resp.json())
return {"provider": provider["name"], "results": results}
except (requests.RequestException, KeyError) as e:
print(f"{provider['name']} failed: {e}")
continue
return {"provider": None, "results": [], "error": "all providers failed"}
result = resilient_search("best CRM for startups 2026")
print(f"Served by: {result['provider']}, results: {len(result['results'])}")Monitoring provider health
Python
def health_check(providers: list):
"""Run daily health checks on all providers."""
test_query = "python programming language"
results = {}
for p in providers:
start = time.time()
try:
resp = requests.post(p["url"], headers=p["headers"],
json=p["body_fn"](test_query), timeout=15)
latency = round((time.time() - start) * 1000)
results[p["name"]] = {
"status": resp.status_code,
"latency_ms": latency,
"result_count": len(p["parse_fn"](resp.json())),
}
except Exception as e:
results[p["name"]] = {"status": "error", "error": str(e)}
return resultsThe practical recommendation
No single provider is risk-free. The production pattern is: pick a primary provider based on your needs (multi-platform, cost, speed), implement a fallback to a second provider, and monitor both daily. The fallback code is 20 lines. The peace of mind is worth it when your agent handles real traffic.