The Problem
Agents calling search APIs without token limits consume thousands of tokens per query, quickly exhausting context windows and increasing LLM costs. A single uncontrolled search can use 40% of available context.
How Scavio Helps
- Cap tokens per search call
- Structured JSON results are inherently token-efficient
- Daily budget tracking across all search calls
- Adaptive budgets based on remaining context
- Reduces LLM costs 30-50% without quality loss
Relevant Platforms
Web search with knowledge graph, PAA, and AI overviews
Community, posts & threaded comments from any subreddit
Quick Start: Python Example
Here is a quick example searching Google for "Agent has 8K token budget for search context across a session. Each Google search returns structured JSON capped at 300 tokens (title + snippet + URL for top 5 results). Agent makes 4 searches using 1,200 tokens total instead of 4,000+ from unstructured results. Remaining 6,800 tokens available for reasoning.":
import requests
API_KEY = "your_scavio_api_key"
response = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={
"x-api-key": API_KEY,
"Content-Type": "application/json",
},
json={"query": query},
)
data = response.json()
for result in data.get("organic_results", [])[:5]:
print(f"{result['position']}. {result['title']}")
print(f" {result['link']}\n")Built for Agent developers, LLM cost engineers, teams optimizing agent token usage
Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your agent token budget management solution. The API returns structured JSON that is ready for processing, analysis, or feeding into AI agents.
Start with the free tier (500 credits/month, no credit card required) and scale to paid plans when you need higher volume.