Arize Phoenix

AI Observability and Evaluation

Phoenix is an open-source observability library designed for experimentation, evaluation, and troubleshooting. It allows AI Engineers and Data Scientists to quickly visualize their data, evaluate performance, track down issues, and export data to improve. Phoenix is built by Arize AI, the company behind the the industry-leading AI observability platform, and a set of core contributors.

Install Phoenix

Using pip

In your Jupyter or Colab environment, run the following command to install.

pip install arize-phoenix

For full details on how to run phoenix in various environments such as Databricks, consult our environments guide.

Using conda

conda install -c conda-forge arize-phoenix[evals]

Container

Phoenix can also run via a container. The image can be found at:

Docker

Images for phoenix are published to dockerhub

Checkout the environments section and deployment guide for details.

Phoenix works with OpenTelemetry and OpenInference instrumentation. If you are looking to deploy phoenix as a service rather than a library, see Self-hosting

Quickstarts

Running Phoenix for the first time? Select a quickstart below.

Tracing

Evaluation

Inferences

Datasets and Experiments

Available Packages

The main Phoenix package is arize-phoenix. We offer several helper packages below for specific use cases.

Package	What It's For	Pypi
arize-phoenix	Running and connecting to the Phoenix client. Used: - Self-hosting Phoenix - Connecting to a Phoenix client (either Phoenix Developer Edition or self-hosted) to query spans, run evaluations, generate datasets, etc. *arize-phoenix automatically includes arize-phoenix-otel and arize-phoenix evals
arize-phoenix-otel	Sending OpenTelemetry traces to a Phoenix instance
arize-phoenix-evals	Running evaluations in your environment
openinference-semantic-conventions	Our semantic layer to add LLM telemetry to OpenTelemetry
openinference-instrumentation-xxxx	Automatically instrumenting popular packages.	See Integrations: Tracing

Next Steps

Try our Tutorials

Check out a comprehensive list of example notebooks for LLM Traces, Evals, RAG Analysis, and more.

Community

Join the Phoenix Slack community to ask questions, share findings, provide feedback, and connect with other developers.