text: EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerCometOn this pageCometIn this guide we will demonstrate how to track your Langchain Experiments, Evaluation Metrics, and LLM Sessions with Comet.  Example Project: Comet with LangChainInstall Comet and Dependenciesâ€‹import sys{sys.executable} -m spacy download en_core_web_smInitialize Comet and Set your Credentialsâ€‹You can grab your Comet API Key here or click the link after initializing Cometimport comet_mlcomet_ml.init(project_name="comet-example-langchain")Set OpenAI and SerpAPI credentialsâ€‹You will need an OpenAI API Key and a SerpAPI API Key to run the following examplesimport osos.environ["OPENAI_API_KEY"] = "..."# os.environ["OPENAI_ORGANIZATION"] = "..."os.environ["SERPAPI_API_KEY"] = "..."Scenario 1: Using just an LLMâ€‹from datetime import datetimefrom langchain.callbacks import CometCallbackHandler, StdOutCallbackHandlerfrom langchain.llms import OpenAIcomet_callback = CometCallbackHandler(    project_name="comet-example-langchain",    complexity_metrics=True,    stream_logs=True,    tags=["llm"],    visualizations=["dep"],)callbacks = [StdOutCallbackHandler(), comet_callback]llm = OpenAI(temperature=0.9, callbacks=callbacks, verbose=True)llm_result = llm.generate(["Tell me a