Phoenix has first-class support for LangChain applications.
To begin, you will have to start a Phoenix server. You can do this by running:
import phoenix as px
session = px.launch_app()Once you have started a Phoenix server, You will instrument LangChain so that spans are created whenever you run a chain.
{% tabs %} {% tab title="Python" %}
from phoenix.trace.langchain import LangChainInstrumentor
# By default, the traces will be exported to the locally running Phoenix
# server. If a different endpoint is desired, change the environment
# variable os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = ...
LangChainInstrumentor().instrument()
# Initialize your LangChain application
# Note that we do not have to pass in the tracer as a callback here
# since the above instrumented LangChain in it's entirety.
response = chain.run(query){% endtab %} {% endtabs %}
By instrumenting LangChain, spans will be created whenevera a chain is run and will be sent to the Phoenix server for collection.
To view the traces in Phoenix, simply open the UI in your browser.
px.active_session().urlFor a fully working example of tracing with LangChain, checkout our colab notebook.
{% embed url="https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/tracing/langchain_tracing_tutorial.ipynb" %} Troubleshooting an LLM application using the OpenInferenceTracer {% endembed %}