agent.py

import os
from dotenv import load_dotenv
from langchain_core.messages import SystemMessage
from langchain_google_genai import ChatGoogleGenerativeAI
from langgraph.prebuilt import create_react_agent
from tools import all_tools

# Load the API keys from your .env file
load_dotenv()

# Verify the Gemini API key is mapped correctly
if "GEMINI_API_KEY" not in os.environ:
    print("Warning: Please set your GEMINI_API_KEY environment variable.")

# Initialize the Gemini LLM engine
# temperature=0 ensures strict, objective tool choices
# Initialize a stable free-tier model with broader token windows
llm = ChatGoogleGenerativeAI(model="gemini-3.1-flash-lite", temperature=0)


# GAIA exact-match rules to block conversational fluff
GAIA_SYSTEM_PROMPT = (
    "You are an objective, precise AI assistant evaluating GAIA tasks.\n"
    "Your final response must contain ONLY the raw, objective final answer.\n"
    "Strict Rules:\n"
    "- Do NOT include conversational text (e.g., 'The answer is...').\n"
    "- Do NOT use formatting prefixes like 'Answer:' or 'FINAL ANSWER:'.\n"
    "- Output only the exact words, numbers, or list requested, and absolutely nothing else."
)

# Compile the LangGraph ReAct agent workflow using Gemini
agent_executor = create_react_agent(
    model=llm,
    tools=all_tools,
    prompt=GAIA_SYSTEM_PROMPT
)