agent.py

import os
from typing import Optional, TypedDict, Literal
from langgraph.graph import MessagesState, StateGraph, START, END
from langgraph.prebuilt import ToolNode
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage
from logging_config import logger
from tools import (
    python_tool,
    reverse_tool,
    excel_file_to_markdown,
    sum_numbers,
    web_search,
    get_wikipedia_info,
    ask_audio_model
    )
from chess_tool import chess_tool

# MODEL_PROVIDER = "gemini"
MODEL_PROVIDER = "openai" 

MAX_ITERATIONS = 5

SYSTEM_PROMPT = \
"""You are a general AI assistant. This is a GAIA problem to solve, be succinct in your answer.
YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. 
If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless 
specified otherwise. 
If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits 
in plain text unless specified otherwise. 
If you need to access a file, use the provided task_id as a parameter to the corresponding tool, unless a url is provided.
If you are asked for a comma separated list, apply the above rules depending of whether the element to be put 
in the list is a number or a string.
"""

llm_gemini = ChatGoogleGenerativeAI(
    model="gemini-2.5-flash",
    include_thoughts=False,
    temperature=0,
    max_output_tokens=None,
    timeout=60,  # The maximum number of seconds to wait for a response.
    max_retries=2,
)

llm_openai = ChatOpenAI(
    # model="openai/gpt-oss-120b:together",
    model="openai/gpt-oss-120b:fireworks-ai",
    temperature=0,
    max_tokens=None, # type: ignore
    timeout=60,
    max_retries=2,
    api_key=os.getenv("HF_TOKEN"),
    base_url="https://router.huggingface.co/v1",
)

if MODEL_PROVIDER == "gemini":
    llm = llm_gemini
elif MODEL_PROVIDER == "openai":
    llm = llm_openai
else:
    raise ValueError(f"Unsupported MODEL_PROVIDER: {MODEL_PROVIDER}")

tools = [python_tool,
    reverse_tool,
    excel_file_to_markdown,
    sum_numbers,
    web_search,
    get_wikipedia_info,
    ask_audio_model,
    chess_tool]

llm_with_tools = llm.bind_tools(tools)

class InputState(TypedDict):
    question: str
    task_id: str

# Define the state type with annotations
class AgentState(MessagesState):
    system_message: str
    question: str
    task_id: str
    final_answer: str
    iterations: int
    error: Optional[str]

class OutputState(TypedDict):
    final_answer: str
    error: Optional[str]

def input(state: InputState) -> AgentState:
    question = state["question"]
    messages = [
        SystemMessage(content=SYSTEM_PROMPT),
        HumanMessage(content=question)
    ]
    return {"messages": messages, # type: ignore
            "iterations": 0} 

def agent(state: AgentState) -> AgentState:
    logger.info(f"LLM invoked: {state['question'][:50]=}{state['task_id']=}")
    question = state["question"]
    try:
        result = llm_with_tools.invoke(state["messages"])
        logger.info(f"model metadata = {result.usage_metadata}") # type: ignore
        logger.info(f"LLM answer: {result.content}")
        # Append the new message to the messages list
        messages = state["messages"] + [result]
        return {"messages": messages} # type: ignore
    except Exception as e:
        logger.error(f"LLM invocation failed: {e}")
        return {"error": str(e)} # type: ignore

def increment_iterations(state: AgentState) -> AgentState:
    # Additional node to increment the iteration count
    iterations = state.get("iterations", 0) + 1
    return {"iterations": iterations} #type: ignore

def route_tools(state: AgentState) -> Literal["tools", "final_output"]:
    """
    Decide if we should continue execution or stop.
    """
    messages = state["messages"]
    ai_message = messages[-1]
    iterations = state["iterations"]
    
    if iterations > MAX_ITERATIONS:
        return "final_output"  # Stop execution if max iterations are reached
    if hasattr(ai_message, "tool_calls") and len(ai_message.tool_calls) > 0: # type: ignore
        return "tools"
    return "final_output"  # Stop execution if no tool calls are present

def final_output(state: AgentState) -> OutputState:
    try:
        messages = state["messages"]
        ai_message = messages[-1]
        return {"final_answer": ai_message.content} # type: ignore
    except Exception as e:
        return {"error": e} # type: ignore

builder = StateGraph(AgentState)
tool_node = ToolNode(tools=tools)
builder.add_node("input", input)
builder.add_node("agent", agent)
builder.add_node("increase", increment_iterations)
builder.add_node("tools", tool_node)
builder.add_node("final_output", final_output)
# Define edges for the standard flow
builder.add_edge(START, "input")
builder.add_edge("input", "agent")
builder.add_conditional_edges("agent", 
                              route_tools,
                              {"tools": "increase",
                               "final_output": "final_output"}
                            )
builder.add_edge("increase", "tools")
builder.add_edge("tools", "agent")
builder.add_edge("final_output", END)
builder.compile()
graph = builder.compile()