How to logger when calling a query through a agent

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from langchain import OpenAI
from langchain.agents import initialize_agent
from gpt_index import GPTListIndex, GPTIndexMemory

index = GPTListIndex([])
memory = GPTIndexMemory(index=index, memory_key="chat_history", query_kwargs={"response_mode": "compact"})
llm = OpenAI(temperature=0)
agent_chain = initialize_agent([], llm, agent="conversational-react-description", memory=memory)

agent_chain.run(input="what's my name?")

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from langchain.agents import Tool
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain import OpenAI
from langchain.agents import initialize_agent
from gpt_index import GPTSimpleVectorIndex

index = GPTSimpleVectorIndex.load_from_disk('../vector_indices/index_simple.json')

tools = [
    Tool(
        name="GPT Index",
        func=lambda q: str(index.query(q)),
        description="useful for when you want to answer questions about the author. The input to this tool should be a complete English sentence.",
        return_direct=True
    ),
]

memory = ConversationBufferMemory(memory_key="chat_history")
llm = OpenAI(temperature=0)
agent_chain = initialize_agent(tools, llm, agent="conversational-react-description", memory=memory)

agent_chain.run(input="What did the author do growing up?")

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from langchain.agents import Tool
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain import OpenAI
from langchain.agents import initialize_agent
from gpt_index import GPTSimpleVectorIndex

index = GPTSimpleVectorIndex.load_from_disk('../vector_indices/index_simple.json')

tools = [
    Tool(
        name="GPT Index",
        func=lambda q: str(index.query(q)),
        description="useful for when you want to answer questions about the author. The input to this tool should be a complete English sentence.",
        return_direct=True
    ),
]

memory = ConversationBufferMemory(memory_key="chat_history")
llm = OpenAI(temperature=0)
agent_chain = initialize_agent(tools, llm, agent="conversational-react-description", memory=memory)

def log_input_output(agent, input_text):
    logging.info(f"Input: {input_text}")
    output = agent.run(input=input_text)
    logging.info(f"Output: {output}")
    return output

log_input_output(agent_chain, "What did the author do growing up?")

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from langchain.agents import Tool
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain import OpenAI
from langchain.agents import initialize_agent
from gpt_index import GPTSimpleVectorIndex

index = GPTSimpleVectorIndex.load_from_disk('../vector_indices/index_simple.json')

tools = [
    Tool(
        name="GPT Index",
        func=lambda q: str(index.query(q)),
        description="useful for when you want to answer questions about the author. The input to this tool should be a complete English sentence.",
        return_direct=True
    ),
]

memory = ConversationBufferMemory(memory_key="chat_history")
llm = OpenAI(temperature=0)
agent_chain = initialize_agent(tools, llm, agent="conversational-react-description", memory=memory)

agent_chain.run(input="What did the author do growing up?")

from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader, ServiceContext
from llama_index.logger import LlamaLogger

docs = SimpleDirectoryReader('./data').load_data()

service_context = ServiceContext.from_defaults(llama_logger=llama_logger)

index = GPTSimpleVectorIndex.from_documents(docs, service_context=service_context)

response = index.query("What did the author do growing up?", service_context=service_context)

llama_logger.get_logs()

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))