All 226 Dependencies
Every package Arize Phoenix depends on, ranked by repo health score.
Arize Phoenix is an open-source AI observability platform that gives AI engineers complete visibility into LLM applications from trace collection through evaluation and optimization. Built on OpenTelemetry and the OpenInference semantic conventions, it auto-instruments popular frameworks and LLM providers so you get detailed traces without rewriting your code.
Phoenix combines runtime observability with structured experimentation: you can capture traces in production, slice them into versioned datasets, run prompt or model swap experiments against those datasets, and score every result with LLM-based evaluators—all in one platform. The Prompt Playground lets you replay real traced calls with modified parameters before committing changes, shortening the feedback loop from days to minutes.
As of mid-2026, Phoenix ships with Phoenix Intelligence (PXI), an AI engineering agent embedded directly in the UI that can debug traces, suggest prompt improvements, and navigate the product on your behalf. It also exposes an MCP server (`@arizeai/phoenix-mcp`) so external tools like Cursor and Claude Desktop can query your traces, prompts, datasets, and experiments through the Model Context Protocol.
Deployment is flexible: install as a Python package (`pip install arize-phoenix`), pull the Docker image from Docker Hub, deploy via Helm on Kubernetes, or use Arize's managed cloud at `app.phoenix.arize.com`. Sub-packages (`arize-phoenix-otel`, `arize-phoenix-client`, `arize-phoenix-evals`) let lightweight agents ship telemetry without pulling in the full platform.