How to add pre-trade risk scoring to your LangChain crypto agent in 2 lines

If you're building a crypto trading agent with LangChain, your agent probably makes buy/sell decisions based on price data, sentiment, or technical indicators. But does it check if the token is about to structurally collapse? The problem In 2024-2025, 113 tokens experienced structural collapse (>50% drawdown). Many showed clear warning signs in their on-chain and market microstructure data — but most trading agents don't check for this. The solution: 2 lines from zarq_langchain import ZARQRiskCheck tools = [ ZARQRiskCheck ()] # Add to your agent's tool list Your agent now has access to ZARQ's risk intelligence for 205 tokens: Verdict: SAFE / WARNING / CRITICAL Trust Score: 0-100 (Moody's-style Aaa-D rating) Crash Probability (calibrated on 22 months out-of-sample data) Distance to Default (Merton structural credit model) What's under the hood? ZARQ rates tokens using 7 structural signals: Liquidity stress — is trading volume drying up? Holder concentration — are whales dumping? Resilie