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, AgentTypefrom langchain.llms import OpenAIfrom langchain.callbacks import LLMonitorCallbackHandlerhandler = 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 nameagent.run(    "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?",    callbacks=[handler],)User Tracking‚ÄãUser tracking allows you to identify your users, track their cost, conversations and more.from langchain.callbacks.llmonitor_callback import LLMonitorCallbackHandler, identifywith 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‚ÄãFor any question or issue with integration you can reach out to the LLMonitor team on Discord or via email.PreviousLabel StudioNextPromptLayerSetupUsage with LLM/Chat modelsUsage with chains and agentsUser TrackingSupportCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
LLMonitor is an open-source observability platform that provides cost and usage analytics, user tracking, tracing and evaluation tools.
LLMonitor is an open-source observability platform that provides cost and usage analytics, user tracking, tracing and evaluation tools. ->: 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, AgentTypefrom langchain.llms import OpenAIfrom langchain.callbacks import LLMonitorCallbackHandlerhandler = 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 nameagent.run(    "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?",    callbacks=[handler],)User Tracking‚ÄãUser tracking allows you to identify your users, track their cost, conversations and more.from langchain.callbacks.llmonitor_callback import LLMonitorCallbackHandler, identifywith 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‚ÄãFor any question or issue with integration you can reach out to the LLMonitor team on Discord or via email.PreviousLabel StudioNextPromptLayerSetupUsage with LLM/Chat modelsUsage with chains and agentsUser TrackingSupportCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.