> ## 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.

# LangGraph Tracing

export const projectName_0 = "my-llm-app"

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

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 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/)