> ## Documentation Index
> Fetch the complete documentation index at: https://arizeai-433a7140.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# LangChain Tracing

> How to use the python LangChainInstrumentor to trace LangChain

Phoenix has first-class support for [LangChain](https://langchain.com/) applications.

## Install

```bash theme={null}
pip install openinference-instrumentation-langchain langchain_openai
```

## Setup

Use the register function to connect your application to Phoenix:

```python theme={null}
from phoenix.otel import register

# configure the Phoenix tracer
tracer_provider = register(
  project_name="my-llm-app", # Default is 'default'
  auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)
```

## Run LangChain

By instrumenting LangChain, spans will be created whenever a chain is run and will be sent to the Phoenix server for collection.

```python theme={null}
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_template("{x} {y} {z}?").partial(x="why is", z="blue")
chain = prompt | ChatOpenAI(model_name="gpt-3.5-turbo")
chain.invoke(dict(y="sky"))
```

## Observe

Now that you have tracing setup, all invocations of chains will be streamed to your running Phoenix for observability and evaluation.

## Resources

* [Example notebook](https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/tracing/langchain_tracing_tutorial.ipynb)

* [OpenInference package](https://github.com/Arize-ai/openinference/blob/main/python/instrumentation/openinference-instrumentation-langchain)

* [Working examples](https://github.com/Arize-ai/openinference/blob/main/python/instrumentation/openinference-instrumentation-langchain/examples)