Build an Autonomous Pokemon Card Trading Agent with AI Grading & Monte Carlo Pricing

#ai #python #langchain #pokemon

Give your AI agent real-time market intelligence across 187K+ trading card products. Search, price, and grade cards -- all from Python.

If you're building an AI agent that deals with trading cards -- whether it's a personal shopping bot, a portfolio tracker, or an autonomous collector -- you need real-time market data. Not mockups. Not CSV dumps. Live prices.

I built TCG Oracle, an API that tracks 187,689 products across 13 card games (Pokemon, Magic, Yu-Gi-Oh, Lorcana, One Piece, and more). Today I'm open-sourcing the tool wrappers so you can drop TCG intelligence into any AI agent in under 60 seconds.

Install

pip install tcg-oracle-tools

That's it. No API keys. No signup. Free tier gives you search and market data.

Quick Start

from tcg_oracle import TCGOracleClient

client = TCGOracleClient()

results = client.search("Charizard", limit=5)
for card in results.results:
    print(f"{card.name}: ${card.market_price}")

Output:

Mega Charizard Y ex - 294/217: $439.69
Mega Charizard Y ex - 022/217: $6.97

Market Snapshot

market = client.market("Pokemon")
print(f"Tracking {market.total_products} products")
for card in market.top_cards[:5]:
    print(f"  {card.name}: ${card.market_price}")

Drop Into a LangChain Agent

from langchain.agents import initialize_agent, AgentType
from langchain_openai import ChatOpenAI
from tcg_oracle.langchain import TCGSearchTool, TCGMarketTool

tools = [TCGSearchTool(), TCGMarketTool()]
agent = initialize_agent(tools, ChatOpenAI(model="gpt-4o-mini"), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)
agent.run("What's the most expensive Pokemon card right now?")

OpenAI Function Calling

from tcg_oracle.openai_spec import tools, handle_tool_call
response = openai.chat.completions.create(model="gpt-4o", messages=[{"role": "user", "content": "Find Pikachu cards"}], tools=tools())