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 (500 credits/month, no credit card required) and scale to paid plans when you need higher volume.