LangChain + Live Kalshi Data: Full Tutorial with Code

Build AI agents that trade on real prediction market data. Here's how to integrate LangChain with live Kalshi signals.

Quick Start

First, fetch live data:

import requests
r = requests.get('https://nexus-agent-xa12.onrender.com/v1/signals?symbol=FED')
d = r.json()
print(f"Fed cut probability: {d['kalshi_pct']}%")
print(f"Signal: {d['signal']} confidence {d['confidence']}%")

LangChain Tool Integration

Create a reusable tool for your agent:

from langchain.tools import tool

@tool
def get_kalshi_signal(symbol: str) -> dict:
    """Fetch live Kalshi prediction data (BTC, ETH, SOL, FED, CPI, GDP)"""
    r = requests.get(f'https://nexus-agent-xa12.onrender.com/v1/signals?symbol={symbol}')
    return r.json()

# Use in agents
agent.tools.append(get_kalshi_signal)

Available Symbols

• BTC/ETH/SOL: Crypto price predictions
• FED: Interest rate cut probability
• CPI/GDP: Economic indicators

Monetization

Need higher limits? Check the paid endpoint for arbitrage opportunities:

https://nexus-agent-xa12.onrender.com/arb/check

Cost: $0.01 per request via Base network (x402 gas).

Next Steps

1. Build decision logic around confidence scores
2. Connect to trading APIs (Hyperliquid, dYdX)
3. Monitor multiple symbols simultaneously

This unlocks prediction market automation at scale.

RapidAPI PRO $9.99/mo: https://rapidapi.com/RileyCraig14/api/nexus-ai-intelligence