text: EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerArthurArthurArthur is a model monitoring and observability platform.The following guide shows how to run a registered chat LLM with the Arthur callback handler to automatically log model inferences to Arthur.If you do not have a model currently onboarded to Arthur, visit our onboarding guide for generative text models. For more information about how to use the Arthur SDK, visit our docs.from langchain.callbacks import ArthurCallbackHandlerfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerfrom langchain.chat_models import ChatOpenAIfrom langchain.schema import HumanMessagePlace Arthur credentials herearthur_url = "https://app.arthur.ai"arthur_login = "your-arthur-login-username-here"arthur_model_id = "your-arthur-model-id-here"Create Langchain LLM with Arthur callback handlerdef make_langchain_chat_llm(chat_model=):    return ChatOpenAI(        streaming=True,        temperature=0.1,        callbacks=[            StreamingStdOutCallbackHandler(),            ArthurCallbackHandler.from_credentials(                arthur_model_id,                 arthur_url=arthur_url,