ProvidersMoreMLflowOn this pageMLflowMLflow is a versatile, expandable, open-source platform for managing workflows and artifacts across the machine learning lifecycle. It has built-in integrations with many popular ML libraries, but can be used with any library, algorithm, or deployment tool. It is designed to be extensible, so you can write plugins to support new workflows, libraries, and tools.This notebook goes over how to track your LangChain experiments into your MLflow ServerExternal examples​MLflow provides several examples for the LangChain integration:simple_chainsimple_agentretriever_chainretrieval_qa_chainExample​pip install azureml-mlflowpip install pandaspip install textstatpip install spacypip install openaipip install google-search-resultspython -m spacy download en_core_web_smimport osos.environ["MLFLOW_TRACKING_URI"] = ""os.environ["OPENAI_API_KEY"] = ""os.environ["SERPAPI_API_KEY"] = ""from langchain.callbacks import MlflowCallbackHandlerfrom langchain.llms import OpenAI"""Main function.This function is used to try the callback handler.Scenarios:1. OpenAI LLM2. Chain with multiple SubChains on multiple generations3. Agent with Tools"""mlflow_callback = MlflowCallbackHandler()llm = OpenAI(    model_name="gpt-3.5-turbo", temperature=0, callbacks=[mlflow_callback], verbose=True)# SCENARIO 1 - LLMllm_result = llm.generate(["Tell me a joke"])mlflow_callback.flush_tracker(llm)from langchain.prompts import PromptTemplatefrom langchain.chains import LLMChain# SCENARIO 2 - Chaintemplate = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.Title: {title}Playwright: This is a synopsis for the above play:"""prompt_template = PromptTemplate(input_variables=["title"], template=template)synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=[mlflow_callback])test_prompts = [    {        "title": "documentary about good video games that push the boundary of game design"    },]synopsis_chain.apply(test_prompts)mlflow_callback.flush_tracker(synopsis_chain)from langchain.agents import initialize_agent, load_toolsfrom langchain.agents import AgentType# SCENARIO 3 - Agent with Toolstools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=[mlflow_callback])agent = initialize_agent(    tools,    llm,    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,    callbacks=[mlflow_callback],    verbose=True,)agent.run(    "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")mlflow_callback.flush_tracker(agent, finish=True)PreviousMLflow AI GatewayNextModalExternal examplesExample