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LLMonitor is an open-source observability platform that provides cost and usage analytics, user tracking, tracing and evaluation tools.

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Setup

Create an account on llmonitor.com, then copy your new app’s tracking id. Once you have it, set it as an environment variable by running: 
export LLMONITOR_APP_ID="..."
If you’d prefer not to set an environment variable, you can pass the key directly when initializing the callback handler: 
from langchain_community.callbacks.llmonitor_callback import LLMonitorCallbackHandler

handler = LLMonitorCallbackHandler(app_id="...")

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Usage with LLM/Chat models

from langchain_openai import OpenAI
from langchain_openai import ChatOpenAI

handler = LLMonitorCallbackHandler()

llm = OpenAI(
    callbacks=[handler],
)

chat = ChatOpenAI(callbacks=[handler])

llm("Tell me a joke")

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Usage with chains and agents

Make sure to pass the callback handler to the run method so that all related chains and llm calls are correctly tracked. It is also recommended to pass agent_name in the metadata to be able to distinguish between agents in the dashboard. Example: 
from langchain_openai import ChatOpenAI
from langchain_community.callbacks.llmonitor_callback import LLMonitorCallbackHandler
from langchain.messages import SystemMessage, HumanMessage
from langchain.agents import OpenAIFunctionsAgent, AgentExecutor, tool

llm = ChatOpenAI(temperature=0)

handler = LLMonitorCallbackHandler()

@tool
def get_word_length(word: str) -> int:
    """Returns the length of a word."""
    return len(word)

tools = [get_word_length]

prompt = OpenAIFunctionsAgent.create_prompt(
    system_message=SystemMessage(
        content="You are very powerful assistant, but bad at calculating lengths of words."
    )
)

agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt, verbose=True)
agent_executor = AgentExecutor(
    agent=agent, tools=tools, verbose=True, metadata={"agent_name": "WordCount"}  # <- recommended, assign a custom name
)
agent_executor.run("how many letters in the word educa?", callbacks=[handler])
Another example: 
import os

from langchain_community.agent_toolkits.load_tools import load_tools
from langchain_community.callbacks.llmonitor_callback import LLMonitorCallbackHandler
from langchain_openai import ChatOpenAI
from langchain.agents import create_agent


os.environ["LLMONITOR_APP_ID"] = ""
os.environ["OPENAI_API_KEY"] = ""
os.environ["SERPAPI_API_KEY"] = ""

handler = LLMonitorCallbackHandler()
llm = ChatOpenAI(temperature=0, callbacks=[handler])
tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = create_agent("openai:gpt-4.1-mini", tools)

input_message = {
    "role": "user",
    "content": "What's the weather in SF?",
}

agent.invoke({"messages": [input_message]})

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User Tracking

User tracking allows you to identify your users, track their cost, conversations and more. 
from langchain_community.callbacks.llmonitor_callback import LLMonitorCallbackHandler, identify

with identify("user-123"):
    llm.invoke("Tell me a joke")

with identify("user-456", user_props={"email": "[email protected]"}):
    agent.invoke(...)

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Support

For any question or issue with integration you can reach out to the LLMonitor team on Discord or via email.
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