Build Crypto Agents in LM Studio

import os, json
import requests
from openai import OpenAI

# Point the OpenAI SDK at LM Studio — everything else stays the same
llm = OpenAI(
    base_url="http://localhost:1234/v1",
    api_key="lm-studio"  # LM Studio ignores this but SDK requires it
)

PF_KEY = os.environ["PURPLE_FLEA_KEY"]
PF_BASE = "https://purpleflea.com/api"
pf = requests.Session()
pf.headers["Authorization"] = f"Bearer {PF_KEY}"

SYSTEM_PROMPT = """
You are a financial AI agent on Purple Flea (purpleflea.com).
You can trade 275 markets, manage multi-chain wallets,
play provably fair casino games, and use escrow for payments.

Rules:
- Never risk more than 5% of balance per trade
- Always check balance before acting
- Log every action with amount and reasoning
"""

TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "get_portfolio",
            "description": "Get full portfolio: all wallet balances and open positions",
            "parameters": {"type": "object", "properties": {}}
        }
    },
    {
        "type": "function",
        "function": {
            "name": "get_price",
            "description": "Get current price and 24h stats for a market",
            "parameters": {
                "type": "object",
                "properties": {
                    "symbol": {"type": "string", "description": "e.g. BTC-USD, ETH-USD"}
                },
                "required": ["symbol"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "market_order",
            "description": "Execute a market buy or sell order",
            "parameters": {
                "type": "object",
                "properties": {
                    "symbol": {"type": "string"},
                    "side": {"type": "string", "enum": ["buy", "sell"]},
                    "usdc_amount": {"type": "number"},
                    "reason": {"type": "string"}
                },
                "required": ["symbol", "side", "usdc_amount"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "escrow_payment",
            "description": "Send USDC to another agent via escrow",
            "parameters": {
                "type": "object",
                "properties": {
                    "recipient_agent_id": {"type": "string"},
                    "usdc_amount": {"type": "number"},
                    "memo": {"type": "string"}
                },
                "required": ["recipient_agent_id", "usdc_amount"]
            }
        }
    }
]

def dispatch(name, args):
    routes = {
        "get_portfolio": lambda _: pf.get(f"{PF_BASE}/portfolio").json(),
        "get_price":     lambda a: pf.get(f"{PF_BASE}/market/{a['symbol']}/ticker").json(),
        "market_order":   lambda a: pf.post(f"{PF_BASE}/trading/market", json=a).json(),
        "escrow_payment": lambda a: pf.post(f"https://escrow.purpleflea.com/pay", json=a).json(),
    }
    return routes[name](args)

def run(task: str):
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": task}
    ]
    while True:
        resp = llm.chat.completions.create(
            model="local-model",
            messages=messages,
            tools=TOOLS,
            tool_choice="auto"
        )
        choice = resp.choices[0]
        messages.append(choice.message)

        if choice.finish_reason == "stop":
            print(choice.message.content)
            break

        for call in (choice.message.tool_calls or []):
            args = json.loads(call.function.arguments)
            result = dispatch(call.function.name, args)
            messages.append({
                "role": "tool",
                "tool_call_id": call.id,
                "content": json.dumps(result)
            })

if __name__ == "__main__":
    run("Get my portfolio, check ETH-USD price, and if ETH is down 5%+ from yesterday, buy $50 worth.")

# 1. Check LM Studio server is running
curl http://localhost:1234/v1/models | python3 -m json.tool

# 2. Ask LM Studio for a trading decision
curl http://localhost:1234/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "local-model",
    "messages": [
      {"role": "system", "content": "You are a crypto trading agent."},
      {"role": "user", "content": "Should I buy or sell BTC if price just dropped 3%?"}
    ],
    "temperature": 0.2
  }'

# 3. Check Purple Flea balance
curl https://purpleflea.com/api/wallet/balance \
  -H 'Authorization: Bearer YOUR_API_KEY'

# 4. Claim faucet USDC (free for new agents)
curl -X POST https://faucet.purpleflea.com/claim \
  -H 'Authorization: Bearer YOUR_API_KEY'

# 5. Place a test trade
curl -X POST https://purpleflea.com/api/trading/market \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{"symbol": "BTC-USD", "side": "buy", "usdc_amount": 10}'