Purple Flea for LiteLLM

import litellm import json import requests PURPLEFLEA_API_KEY = "YOUR_API_KEY" BASE = "https://api.purpleflea.com/v1" # Tool definitions (OpenAI function calling format) tools = [ { "type": "function", "function": { "name": "get_crypto_price", "description": "Get current price for any cryptocurrency", "parameters": { "type": "object", "properties": { "symbol": {"type": "string", "description": "e.g. BTC, ETH, SOL"} }, "required": ["symbol"] } } }, { "type": "function", "function": { "name": "open_trade", "description": "Open a perpetual futures trade", "parameters": { "type": "object", "properties": { "symbol": {"type": "string"}, "side": {"type": "string", "enum": ["long", "short"]}, "size": {"type": "number"}, "leverage": {"type": "integer", "default": 1} }, "required": ["symbol", "side", "size"] } } } ]

def execute_tool(tool_call): name = tool_call.function.name args = json.loads(tool_call.function.arguments) if name == "get_crypto_price": r = requests.get( f"{BASE}/trading/price/{args['symbol']}", headers={"X-API-Key": PURPLEFLEA_API_KEY} ) return r.json() elif name == "open_trade": r = requests.post( f"{BASE}/trading/open", json=args, headers={"X-API-Key": PURPLEFLEA_API_KEY} ) return r.json() # Works with GPT-4o response = litellm.completion( model="gpt-4o", messages=[{"role": "user", "content": "Is BTC bullish? Open a long if yes."}], tools=tools ) # Switch to Claude with ZERO code changes response = litellm.completion( model="claude-3-5-sonnet-20241022", messages=[{"role": "user", "content": "Is BTC bullish? Open a long if yes."}], tools=tools ) # Or Llama via Ollama response = litellm.completion( model="ollama/llama3.1:70b", messages=[{"role": "user", "content": "Is BTC bullish? Open a long if yes."}], tools=tools )

def run_agent(model: str, task: str): messages = [{"role": "user", "content": task}] while True: response = litellm.completion( model=model, messages=messages, tools=tools ) msg = response.choices[0].message if not msg.tool_calls: return msg.content # Execute all tool calls messages.append(msg) for tc in msg.tool_calls: result = execute_tool(tc) messages.append({ "role": "tool", "tool_call_id": tc.id, "content": json.dumps(result) }) # Run the SAME agent across different models task = "Check BTC price, evaluate market conditions, and trade accordingly" print(run_agent("gpt-4o", task)) print(run_agent("claude-3-5-sonnet-20241022", task)) print(run_agent("gemini/gemini-1.5-pro", task))

from litellm import Router # Route expensive tasks to capable models # Route cheap tasks to efficient models router = Router(model_list=[ { "model_name": "analysis", "litellm_params": {"model": "gpt-4o"} }, { "model_name": "execution", "litellm_params": {"model": "gpt-4o-mini"} } ]) # Use the powerful model for market analysis analysis = router.completion( model="analysis", messages=[{"role": "user", "content": "Analyze BTC technicals in depth"}], tools=[get_crypto_price_tool] ) # Use cheaper model for trade execution execution = router.completion( model="execution", messages=[{"role": "user", "content": f"Execute: {analysis.choices[0].message.content}"}], tools=[open_trade_tool] )