Scavio for LangChain Tool Runtime Audit

The Problem

LangChain agents can call tools with unexpected inputs, make dangerous system calls, or exhaust API credits without limits. Runtime governance catches these issues before execution, not after.

How Scavio Helps

• Block dangerous tool calls before execution
• Track tool usage per session for cost control
• Validate search tool inputs and outputs at runtime
• Zero LLM calls -- deterministic policy enforcement
• Works as a wrapper on any LangChain tool

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "A production LangChain agent has 8 tools including web search. The ShadowAudit wrapper enforces: max 10 search calls per session, no queries containing PII patterns, results must contain at least 3 organic results before passing to the agent. When the agent tries an 11th search call, the wrapper blocks it and returns a cached result. Monthly tool audit logs show the agent attempts 2.3 blocked calls per 100 sessions.":

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 LangChain developers deploying agents to production, security teams reviewing agent tool access, platform teams managing multi-tool agents

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your langchain tool runtime audit 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.