ProvidersMoreLog10On this pageLog10This page covers how to use the Log10 within LangChain.What is Log10?​Log10 is an open source proxiless LLM data management and application development platform that lets you log, debug and tag your Langchain calls.Quick start​Create your free account at log10.ioAdd your LOG10_TOKEN and LOG10_ORG_ID from the Settings and Organization tabs respectively as environment variables.Also add LOG10_URL=https://log10.io and your usual LLM API key: for e.g. OPENAI_API_KEY or ANTHROPIC_API_KEY to your environmentHow to enable Log10 data management for Langchain​Integration with log10 is a simple one-line log10_callback integration as shown below:from langchain.chat_models import ChatOpenAIfrom langchain.schema import HumanMessagefrom log10.langchain import Log10Callbackfrom log10.llm import Log10Configlog10_callback = Log10Callback(log10_config=Log10Config())messages = [    HumanMessage(content="You are a ping pong machine"),    HumanMessage(content="Ping?"),]llm = ChatOpenAI(model_name="gpt-3.5-turbo", callbacks=[log10_callback])Log10 + Langchain + Logs docsMore details + screenshots including instructions for self-hosting logsHow to use tags with Log10​from langchain.llms import OpenAIfrom langchain.chat_models import ChatAnthropicfrom langchain.chat_models import ChatOpenAIfrom langchain.schema import HumanMessagefrom log10.langchain import Log10Callbackfrom log10.llm import Log10Configlog10_callback = Log10Callback(log10_config=Log10Config())messages = [    HumanMessage(content="You are a ping pong machine"),    HumanMessage(content="Ping?"),]llm = ChatOpenAI(model_name="gpt-3.5-turbo", callbacks=[log10_callback], temperature=0.5, tags=["test"])completion = llm.predict_messages(messages, tags=["foobar"])print(completion)llm = ChatAnthropic(model="claude-2", callbacks=[log10_callback], temperature=0.7, tags=["baz"])llm.predict_messages(messages)print(completion)llm = OpenAI(model_name="text-davinci-003", callbacks=[log10_callback], temperature=0.5)completion = llm.predict("You are a ping pong machine.\nPing?\n")print(completion)You can also intermix direct OpenAI calls and Langchain LLM calls:import osfrom log10.load import log10, log10_sessionimport openaifrom langchain.llms import OpenAIlog10(openai)with log10_session(tags=["foo", "bar"]):    # Log a direct OpenAI call    response = openai.Completion.create(        model="text-ada-001",        prompt="Where is the Eiffel Tower?",        temperature=0,        max_tokens=1024,        top_p=1,        frequency_penalty=0,        presence_penalty=0,    )    print(response)    # Log a call via Langchain    llm = OpenAI(model_name="text-ada-001", temperature=0.5)    response = llm.predict("You are a ping pong machine.\nPing?\n")    print(response)How to debug Langchain calls​Example of debuggingMore Langchain examplesPreviousLlama.cppNextMarqoWhat is Log10?Quick startHow to enable Log10 data management for LangchainHow to use tags with Log10How to debug Langchain calls