> ## Documentation Index
> Fetch the complete documentation index at: https://arizeai-433a7140-claude-llms-txt-2026-08-12.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

export const projectName_0 = "my-llm-app"

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

## Install

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

## Setup

Connect your application to Phoenix with the `register` function:

<CodeBlock language="python">
  {`from phoenix.otel import register

    # configure the Phoenix tracer
    tracer_provider = register(
    project_name="${projectName_0}", # Default is 'default'
    auto_instrument=True # Auto-instrument your app based on installed OI dependencies
    )`}
</CodeBlock>

## 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)