langchain/docs/versioned_docs/version-0.2.x/integrations/providers/log10.mdx

# Log10

This page covers how to use the [Log10](https://log10.io) within LangChain.

## What is Log10?

Log10 is an [open-source](https://github.com/log10-io/log10) proxiless LLM data management and application development platform that lets you log, debug and tag your Langchain calls.

## Quick start

1. Create your free account at [log10.io](https://log10.io)
2. Add your `LOG10_TOKEN` and `LOG10_ORG_ID` from the Settings and Organization tabs respectively as environment variables.
3. 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 environment

## How to enable Log10 data management for Langchain

Integration with log10 is a simple one-line `log10_callback` integration as shown below:

```python
from langchain_openai import ChatOpenAI
from langchain_core.messages 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"),
    HumanMessage(content="Ping?"),
]

llm = ChatOpenAI(model="gpt-3.5-turbo", callbacks=[log10_callback])
```

[Log10 + Langchain + Logs docs](https://github.com/log10-io/log10/blob/main/logging.md#langchain-logger)

[More details + screenshots](https://log10.io/docs/observability/logs) including instructions for self-hosting logs

## How to use tags with Log10

```python
from langchain_openai import OpenAI
from langchain_community.chat_models import ChatAnthropic
from langchain_openai import ChatOpenAI
from langchain_core.messages 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"),
    HumanMessage(content="Ping?"),
]

llm = ChatOpenAI(model="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="gpt-3.5-turbo-instruct", 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:

```python
import os
from log10.load import log10, log10_session
import openai
from langchain_openai import OpenAI

log10(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 debugging](https://log10.io/docs/observability/prompt_chain_debugging)

[More Langchain examples](https://github.com/log10-io/log10/tree/main/examples#langchain)