Log10 makes it easy to "log" LLM request and responses for tracing, debugging and evaluation.


To turn on logging for a python process, start by setting the following environment variables:

export LOG10_URL=https://log10.io
export LOG10_ORG_ID=... # Find your organization id in the organization settings in your log10.io account.
export LOG10_TOKEN=... # Create or locate an API key in the personal settings in your log10.io account.

Library wrapping

We support OpenAI, Anthropic, Google-Gemini, Mistral, and Lamini via patching their Python SDK. For instance, the simplest way to start logging openai can be done like this:

import openai
from log10.load import log10

This works for both openai v0 and v1.

For openai v1 only, the logging can be done like this:

from log10.load import OpenAI
# from openai import OpenAI
client = OpenAI()
completion = client.completions.create(model='curie', prompt="Once upon a time")
# All completions.create and chat.completions.create calls will be logged

From that point on, all openai calls during the process execution will be logged. Please note that this only works for non-streaming calls at this time.

Anthropic calls can be logged like so:

import os
from log10.load import log10
import anthropic
import os
anthropicClient = anthropic.Anthropic()


To aid debugging across chains and tools, log10 creates a session id when the library is loaded. To create several sessions within the same process, you can use the log10_session context like this:

from log10.load import log10, log10_session
import openai
with log10_session():
    # Will log in session 1
with log10_session():
    # Will log in session 2

Log10 LLM Abstraction

Import Log10 LLM abstraction modules to call closed and open-source LLMs, such as OpenAI, Anthropic, and Llama-2 Inference endpoints on MosaicML and Together.

Create a log10 configuration object to log on Log10 platform. Setting log10_config is optional.

from log10.llm import Log10Config


from log10.openai import OpenAI
llm = OpenAI({"model": "gpt-3.5-turbo"}, log10_config=Log10Config())

Full script here.


from log10.anthropic import Anthropic
llm = Anthropic({"model": "claude-2"}, log10_config=Log10Config())

Full script here.


from log10.mosaicml import MosaicML
llm = MosaicML({"model": "llama2-70b-chat/v1"}, log10_config=Log10Config())

Full script here.

Find all the supported models on MosaicML website (opens in a new tab).


from log10.together import Together
llm = Together({"model": "togethercomputer/llama-2-70b-chat"}, log10_config=Log10Config())

Full script here.

Find all the supported models on Together website (opens in a new tab).


One useful way to organize your LLM logs is using tags. Tags are strings which can be used to filter and select logs in log10.io (opens in a new tab). See here (opens in a new tab) for more information about tags.

You can add tags using the session scope:

import os
from log10.load import log10, log10_session
import openai
from langchain import OpenAI
with log10_session(tags=["foo", "bar"]):
    response = openai.Completion.create(
        prompt="Where is the Eiffel Tower?",

An addition way to add tag with openai v1 API:

from log10.load import OpenAI
client = OpenAI(tags=["foo"])

LangChain logger

If you want to use LLMs which are not natively supported by log10 yet, you can use the log10 logger / callback with langchain's llm abstraction:

from langchain import OpenAI
from langchain.chat_models import ChatAnthropic
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
from log10.langchain import Log10Callback
from log10.llm import Log10Config
log10_callback = Log10Callback(log10_config=Log10Config())
messages = [
    HumanMessage(content="You are a ping pong machine"),
llm = ChatOpenAI(model_name="gpt-3.5-turbo", callbacks=[log10_callback], temperature=0.5, tags=["test"])
completion = llm.predict_messages(messages, tags=["foobar"])
llm = ChatAnthropic(model="claude-2", callbacks=[log10_callback], temperature=0.7, tags=["baz"])
llm = OpenAI(model_name="gpt-3.5-turbo-instruct", callbacks=[log10_callback], temperature=0.5)
completion = llm.predict("You are a ping pong machine.\nPing?\n")

Note that both llm scoped, and individual call's tags are supported.