Build Financial Analysis into Your Hermes AI Agent with the FMP Skill

The Problem

Building AI agents that can answer real-time financial questions is tough. You need reliable data sources, API integrations, and the right abstractions to make it seamless. That's why the Hermes community just shipped something powerful: the Financial Modeling Prep (FMP) skill.

Whether you're building a financial chatbot, an investment research assistant, or an autonomous trading dashboard, you now have a plug-and-play skill that gives your Hermes AI agent instant access to professional-grade financial data.

What Can You Do?

The FMP skill connects Hermes to real-time market data across multiple asset classes:

• 
  Stock quotes & fundamentals — real-time prices, company profiles, income statements


• 
  Financial ratios — P/E ratios, dividend yields, debt-to-equity metrics


• 
  Stock screeners — filter stocks by market cap, PE ratio, or custom criteria


• 
  Crypto & forex prices — Bitcoin, Ethereum, EUR/USD, and more


• 
  Market indices — CAC40, S&P 500, Nasdaq snapshots


• 
  News & movers — trending stocks and market sentiment

How It Works

The skill is built on a clean abstraction over Financial Modeling Prep's stable API (v2025+):

def fmp(path, **params):

    params["apikey"] = os.environ["FMP_API_KEY"]

    qs = "&".join(f"{k}={v}" for k, v in params.items())

    url = f"https://financialmodelingprep.com/stable/{path}?{qs}"

    return json.loads(urllib.request.urlopen(url).read())

With this helper, querying data is one line:

# Get Apple's current stock price

price = fmp("quote", symbol="AAPL")

print(f"AAPL: ${price[0]['price']} ({price[0]['changesPercentage']:+.2f}%)")

Using It with Hermes

Setup is straightforward:

1. 
   Get an API key from repository. If you're building financial AI agents, check it out and give the repo a ⭐ to support the project.
   
   
   

   Have ideas for other financial endpoints? PRs welcome—let's build the financial data layer for AI agents together.

   Vollständiger Original-Artikel

   Den kompletten Beitrag mit allen Details direkt auf dev.to lesen.

   ↗ Original-Artikel auf dev.to lesen