langchain/docs/docs/integrations/callbacks/llmonitor.md

LLMonitor

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

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.callbacks import LLMonitorCallbackHandler

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

Usage with LLM/Chat models

from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.callbacks import LLMonitorCallbackHandler

handler = LLMonitorCallbackHandler()

llm = OpenAI(
    callbacks=[handler],
)

chat = ChatOpenAI(callbacks=[handler])

llm("Tell me a joke")

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.chat_models import ChatOpenAI
from langchain.schema import SystemMessage, HumanMessage
from langchain.agents import OpenAIFunctionsAgent, AgentExecutor, tool
from langchain.callbacks import LLMonitorCallbackHandler

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:

from langchain.agents import load_tools, initialize_agent, AgentType
from langchain.llms import OpenAI
from langchain.callbacks import LLMonitorCallbackHandler

handler = LLMonitorCallbackHandler()

llm = OpenAI(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, metadata={ "agent_name": "GirlfriendAgeFinder" })  # <- recommended, assign a custom name

agent.run(
    "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?",
    callbacks=[handler],
)

User Tracking

User tracking allows you to identify your users, track their cost, conversations and more.

from langchain.callbacks.llmonitor_callback import LLMonitorCallbackHandler, identify

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

with identify("user-456", user_props={"email": "user456@test.com"}):
    agen.run("Who is Leo DiCaprio's girlfriend?")

Support

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