OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
from langchain_openai import ChatOpenAI

def ask_gpt(prompt, temperature, max_tokens):

    """
    Sends a prompt to the GPT-3.5 Turbo model and returns the AI response.

    Parameters:
    prompt (str): The input prompt to send to the GPT-3.5 Turbo model.
    temperature (float): The temperature parameter controls the randomness of the output. Higher values (e.g., 0.8) make the output more random, while lower values (e.g., 0.2) make it more focused and deterministic.
    max_tokens (int): The maximum number of tokens in the response. This parameter can be used to limit the length of the generated text.

    Returns:
    str: The AI response generated by the GPT-3.5 Turbo model.
    """

    llm = ChatOpenAI(api_key=OPENAI_API_KEY, temperature=temperature, max_tokens=max_tokens, model="gpt-3.5-turbo")
    AI_Response = llm.invoke(prompt)
    return AI_Response.content

If you check the source code of load_tools method, you can actually see that the allowed input names are defined inside the following dictionaries:

• _LLM_TOOLS
• _EXTRA_LLM_TOOLS
• _EXTRA_OPTIONAL_TOOLS
• DANGEROUS_TOOLS

It's correct that python_repl name is not recognized. However, you can actually use it as tool like the following:

import os

from langchain_openai import AzureChatOpenAI
from langchain_experimental.tools import PythonREPLTool
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent

llm = AzureChatOpenAI(
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
    azure_deployment=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"],
    model_version=os.environ["AZURE_OPENAI_MODEL_VERSION"]
)

tools = [PythonREPLTool()]

instructions = """You are an agent designed to write and execute python code to answer questions.
You have access to a python REPL, which you can use to execute python code.
If you get an error, debug your code and try again.
Only use the output of your code to answer the question. 
You might know the answer without running any code, but you should still run the code to get the answer.
If it does not seem like you can write code to answer the question, just return "I don't know" as the answer.
"""

base_prompt = hub.pull("langchain-ai/react-agent-template")
prompt = base_prompt.partial(instructions=instructions)

agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

agent_executor.invoke({"input": "What is the 10th fibonacci number?"})

Giving as output something like:

Entering new AgentExecutor chain...
Thought: Do I need to use a tool? Yes
Action: Python_REPL
Action Input:
```
def fibonacci(n):
    if n<=1:
        return n
    else:
        return fibonacci(n-1)+fibonacci(n-2)

print(fibonacci(9))
```Python REPL can execute arbitrary code. Use with caution.
34
Do I need to use a tool? No
Final Answer: The 10th fibonacci number is 55.

Finished chain.