agent.py

import os

from langchain_openai import ChatOpenAI
from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage, ToolMessage
from langgraph.graph.message import add_messages
from langgraph.graph import MessagesState
from langgraph.graph import StateGraph, START, END
from typing import TypedDict, Annotated, Literal

from langchain_community.tools import BraveSearch  # web search
from langchain_experimental.tools.python.tool import PythonAstREPLTool  # for logic/math problems

from tools import (calculator_basic, datetime_tools, transcribe_audio, transcribe_youtube, query_image, webpage_content, read_excel)
from prompt import system_prompt

# gpt-4.1-nano (cheaper for debugging) with temperature 0 for less randomness
# for benchmarking use 04-mini (better reasoning) or gpt-4o-mini

llm = ChatOpenAI(
    model="gpt-4o-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=0
)

python_tool = PythonAstREPLTool()
search_tool = BraveSearch.from_api_key(
                    api_key=os.getenv("BRAVE_SEARCH_API"),
                    search_kwargs={"count": 4},  # returns the 4 best results and their URL
                    description="Web search using Brave"
)

community_tools = [search_tool, python_tool]
custom_tools = calculator_basic + datetime_tools + [transcribe_audio, transcribe_youtube, query_image, webpage_content, read_excel]

tools = community_tools + custom_tools
llm_with_tools = llm.bind_tools(tools)

# Prepare tools by name
tools_by_name = {tool.name: tool for tool in tools}

class MessagesState(TypedDict):  # creates the state (is like the agent's memory at any moment)
    messages: Annotated[list[AnyMessage], add_messages]

# LLM node
def llm_call(state: MessagesState):
    return {
        "messages": [
            llm_with_tools.invoke(
                [SystemMessage(content=system_prompt)] + state["messages"]
            )
        ]
    }

# Tool node
def tool_node(state: MessagesState):
    """Executes the tools"""
    
    result = []
    for tool_call in state["messages"][-1].tool_calls:   # gives a list of the tools the LLM decided to call
        tool = tools_by_name[tool_call["name"]]   # look up the actual tool function using a dictionary
        observation = tool.invoke(tool_call["args"])   # executes the tool
        result.append(ToolMessage(content=observation, tool_call_id=tool_call["id"]))   # the result from the tool is added to the memory
    return {"messages": result}   # thanks to add_messages, LangGraph will automatically append the result to the agent's message history

# Conditional edge function to route to the tool node or end based upon whether the LLM made a tool call
def should_continue(state: MessagesState) -> Literal["Action", END]:
    """Decide if we should continue the loop or stop based upon whether the LLM made a tool call"""

    last_message = state["messages"][-1]  # looks at the last message (usually from the LLM)
    
    # If the LLM makes a tool call, then perform an action
    if last_message.tool_calls:
        return "Action"
    # Otherwise, we stop (reply to the user)
    return END

# Build workflow
builder = StateGraph(MessagesState)

# Add nodes
builder.add_node("llm_call", llm_call)
builder.add_node("environment", tool_node)

# Add edges to connect nodes
builder.add_edge(START, "llm_call")
builder.add_conditional_edges(
    "llm_call",
    should_continue,
    {"Action": "environment",  # name returned by should_continue : Name of the next node
     END: END}
)
    # If tool calls -> "Action" -> environment (executes the tool)
    # If no tool calls -> END

builder.add_edge("environment", "llm_call")  # after running the tools go back to the LLM for another round of reasoning

gaia_agent = builder.compile()  # converts my builder into a runnable agent by using gaia_agent.invoke()

# Wrapper class to initialize and call the LangGraph agent with a user question
class LangGraphAgent:
    def __init__(self):
        print("LangGraphAgent initialized.")

    def __call__(self, question: str) -> str:
        input_state = {"messages": [HumanMessage(content=question)]}  # prepare the initial user message
        print(f"Running LangGraphAgent with input: {question[:150]}...")
        result = gaia_agent.invoke(input_state, {"recursion_limit": 30})  # prevents infinite looping when the LLM keeps calling tools over and over
        final_response = result["messages"][-1].content
        
        try:
            return final_response.split("FINAL ANSWER:")[-1].strip()  #  parse out only what's after "FINAL ANSWER:"
        except Exception:
            print("Could not split on 'FINAL ANSWER:'")
            return final_response