Scavio for Token-Efficient Search Context for LLM Pipelines

The Problem

Naive search-augmented generation dumps full search results into the LLM context, wasting 40-60% of tokens on metadata, thumbnails, and non-essential fields. At $15/M tokens for GPT-4 class models, this waste adds up.

How Scavio Helps

• 40-60% reduction in search context tokens
• Predictable token budget per search call
• Essential fields only (title, snippet, URL) vs full response
• Budget-aware truncation preserves most relevant results
• Works with any LLM (GPT-4, Claude, open-source)

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "Agent sets 2000-token budget for search context. Full API response would be 5000 tokens. Budget manager extracts title + snippet + URL per result, includes first 8 results within budget, truncates cleanly. LLM receives focused context, generates equally good response, costs 60% less.":

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 AI engineers optimizing LLM costs, teams building search-augmented applications at scale

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your token-efficient search context for llm pipelines solution. The API returns structured JSON that is ready for processing, analysis, or feeding into AI agents.

Start with the free tier (50 credits on signup, no credit card required) and scale to paid plans when you need higher volume.