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[evals]'

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.

Note that arize-phoenix and arize-phoenix-evals work on conjunction with OpenTelemetry and OpenInference instrumentation. If you are looking to deploy phoenix as a service rather than a library, see Deployment

Quickstarts

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

Tracing

Evaluation

Inferences

Deployment

Demo

Overview of Phoenix 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.