05_agent_langgraph.py

import json
import os
import subprocess

from langchain_core.tools import tool
from langchain_ollama import ChatOllama
from langgraph.prebuilt import create_react_agent

OLLAMA_URL = os.environ.get("OLLAMA_URL", "http://localhost:11434")
model = os.environ.get("MODEL", "qwen3:4b")
llm = ChatOllama(model=model, base_url=OLLAMA_URL)


@tool
def list_transactions(email: str) -> list[dict]:
    """List user transaction by email"""
    try:
        # Executa o comando da CLI
        result = subprocess.run(
            ["dundie", "list", "--email", email, "--asjson"],
            capture_output=True,
            text=True,
            check=True,
        )
        return json.loads(result.stdout.strip())
    except subprocess.CalledProcessError as e:
        print(f"Error listing transactions: {e}")
        return []


@tool
def sum_transactions(transactions: list[dict]) -> float:
    """
    Sum the values of a list of transactions.
    """
    print("sum_transactions", len(transactions))
    return sum([t["value"] for t in transactions])


# Create the agent
agent = create_react_agent(
    llm,
    tools=[list_transactions, sum_transactions],
)


def invoke_agent(prompt: str) -> dict:
    return agent.invoke({"messages": [{"role": "user", "content": prompt}]})


def main():
    while True:
        prompt = input("Enter your prompt: ")
        if not prompt:
            print("Prompt cannot be empty.")
            continue
        if prompt.strip() in ["exit", "quit", "q"]:
            print("Exiting...")
            break
        result = invoke_agent(prompt)
        print("-" * 50)
        print(result)


if __name__ == "__main__":
    main()