Purple Flea
for IBM watsonx

pip install ibm-watsonx-ai purpleflea

# .env
WATSONX_API_KEY="ibm_api_key_here"
WATSONX_PROJECT_ID="your_project_id"
PURPLE_FLEA_API_KEY="pf_live_..."

import purpleflea as pf

# Purple Flea provides pre-built tool schemas in watsonx/OpenAI format
tools = pf.get_tool_schemas(format="openai")

# Or define specific tools manually:
tools = [
    {
        "type": "function",
        "function": {
            "name": "open_trading_position",
            "description": "Open a long or short perpetual futures position. 275 markets available.",
            "parameters": {
                "type": "object",
                "properties": {
                    "symbol": {"type": "string", "description": "e.g. BTC-USD, ETH-USD"},
                    "side": {"type": "string", "enum": ["long", "short"]},
                    "size_usd": {"type": "number"},
                    "leverage": {"type": "integer", "minimum": 1, "maximum": 50}
                },
                "required": ["symbol", "side", "size_usd"]
            }
        }
    }
]

import json, os
from ibm_watsonx_ai import Credentials
from ibm_watsonx_ai.foundation_models import ModelInference
import purpleflea as pf

credentials = Credentials(
    url="https://us-south.ml.cloud.ibm.com",
    api_key=os.environ["WATSONX_API_KEY"]
)
pf_client = pf.Client(os.environ["PURPLE_FLEA_API_KEY"])

model = ModelInference(
    model_id="meta-llama/llama-3-3-70b-instruct",
    credentials=credentials,
    project_id=os.environ["WATSONX_PROJECT_ID"],
    params={"max_new_tokens": 1024}
)

messages = [{"role": "user", "content": "Create a new wallet and check my ETH balance"}]

while True:
    response = model.chat(messages=messages, tools=tools, tool_choice="auto")
    msg = response["choices"][0]["message"]
    messages.append(msg)

    if not msg.get("tool_calls"):
        print("Agent:", msg["content"])
        break

    for tc in msg["tool_calls"]:
        fn_name = tc["function"]["name"]
        args = json.loads(tc["function"]["arguments"])
        result = pf_client.call(fn_name, **args)
        messages.append({
            "role": "tool",
            "tool_call_id": tc["id"],
            "content": json.dumps(result)
        })