diff --git a/core/agent.py b/core/agent.py
deleted file mode 100644
index ed73e4d..0000000
--- a/core/agent.py
+++ /dev/null
@@ -1,119 +0,0 @@
-from langchain_openai import ChatOpenAI
-from graphstate import GraphState
-from tools.tools import get_tools
-from langgraph.graph import StateGraph, START, END
-from langgraph.prebuilt import ToolNode, tools_condition
-from langchain_core.messages import BaseMessage, AIMessageChunk, HumanMessage
-from models import Model
-import json
-from config import OPENAI_API_KEY
-
-
-
-class Agent1:
-    llm = ChatOpenAI(
-        model = Model.gpt_4o,
-        temperature=0,
-        max_tokens=512,
-        #streaming=True, #Can't use because of metadata
-    )
-
-
-class Agent:
-    def __init__(self, model_type) -> None:
-        #Langsmith Tracing
-        LANGCHAIN_TRACING_V2: str = "true"
-
-        self.llm = ChatOpenAI(
-            model=model_type,
-            temperature=0,
-            max_tokens=512,
-        )
-
-        self.llm_with_tools = self.llm.bind_tools(get_tools())
-        
-        self.workflow = StateGraph(GraphState)
-
-        # Adding nodes to the workflow
-        self.workflow.add_node("chatbot", self.chatbot)
-        self.workflow.add_node("tools", ToolNode(get_tools()))
-        # TODO: Visualize these tools
-
-        # Defining edges between nodes
-        self.workflow.add_edge(START, "chatbot")
-        self.workflow.add_edge("tools", "chatbot")
-        self.workflow.add_edge("chatbot", END)
-
-        # Defining conditional edges
-        self.workflow.add_conditional_edges(
-            "chatbot",
-            tools_condition
-        )
-
-        self.graph = self.workflow.compile() # Compiles the workflow in a graph.
-
-        #Saving image of graph node comment in and out as needed
-        #with open("core/graph_node_network.png", 'wb') as f:
-        #    f.write(self.graph.get_graph().draw_mermaid_png())
-
-    def chatbot(self, state: GraphState):
-        """
-        Simple bot that invokes the list of previous messages
-        and returns the result which will be added to the list of messages.
-        """
-        #state_of_chatbot = self.llm_with_tools.invoke(state["messages"]).tool_calls
-        #print("Tools called: " + state_of_chatbot["name"][-1].content)
-        
-        return {"messages": [self.llm_with_tools.invoke(state["messages"])]}
-
-
-# UNFINISHED
-    def run_stream_only(self, user_prompt: str):
-        """
-        Run the agent, returning a token stream.
-        """
-        print('Running stream...')
-        print(user_prompt)
-        print(type(user_prompt))
-        for chunk in self.llm.stream(user_prompt):
-            yield chunk.content
-
-    #for running the agent comment out for testing in terminal
-    def run(self, user_prompt: str) -> tuple[str, int]:
-        """
-        Run the agent with a user prompt and return a tuple containing the llm 
-        response and the total amount of tokens used.
-        """
-        first = True
-        for event in self.graph.stream("tell me about orcas?"):
-            for value in event.values():
-                messages = value["messages"][-1]
-                gathered = ""
-                # if messages.content and not isinstance(messages, HumanMessage):
-                #     print(messages.content, end="|", flush=True)
-
-                gathered += messages
-
-                if isinstance(messages, BaseMessage):
-                    if hasattr(messages, 'usage_metadata'):
-                        total_tokens = messages.usage_metadata.get('total_tokens', 0)
-                        gathered += messages.content
-                    else:
-                        print(f"Warning: Message of type {type(messages)} does not have usage_metadata")
-                    
-        return gathered, total_tokens
-                
-    #for testing in terminal
-    """ def run(self, user_prompt: str):
-        for event in self.graph.stream({"messages": [("user", user_prompt)]}):
-            for value in event.values():
-                if isinstance(value["messages"][-1], BaseMessage):
-                    print("Assistant:", value["messages"][-1].content)
-
-if __name__ == "__main__":
-    agent = Agent("gpt-4o-mini")
-    while True:
-        user_prompt = input("User: ")
-        agent.run(user_prompt) """
-    
-# Counting tokens: https://python.langchain.com/docs/how_to/llm_token_usage_tracking/
\ No newline at end of file