> ## 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.

# LangGraph Tracing

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

<Info>
  LangGraph is supported by our LangChain instrumentor. If you've already set up instrumentation with LangChain, you don't need to complete the set up below
</Info>

## Install

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

Install the OpenInference Langchain library before your application code. Our LangChainInstrumentor works for both standard LangChain applications and for LangGraph agents.

## 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 LangGraph

By instrumenting LangGraph, spans will be created whenever an agent is invoked and will be sent to the Phoenix server for collection.

## 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://github.com/Arize-ai/phoenix/blob/main/tutorials/tracing/langgraph_agent_tracing_tutorial.ipynb)

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

* [Blog walkthrough](https://arize.com/blog/langgraph/)