arize-phoenix

What it is and what it does

Phoenix is a web-based observability and evaluation platform for LLM applications. It captures execution traces using OpenTelemetry instrumentation, stores them in a local or remote database, and provides a UI for inspecting traces, running LLM-based evaluations, managing versioned datasets, and experimenting with prompts and model parameters. The platform is vendor-agnostic and integrates with popular frameworks like LangGraph, LlamaIndex, OpenAI Agents, and Claude Agent SDK, as well as major LLM providers.

You run Phoenix as a server (locally via `phoenix serve`, in Docker, or in Kubernetes) and instrument your application to send traces to it. The platform then lets you replay traces, benchmark performance, version and organize test datasets, and iterate on prompts and retrieval strategies. It also includes an MCP server endpoint for integration with coding agents like Claude Code and Cursor.

Use it for:

• Trace and inspect LLM application execution to debug unexpected outputs or performance issues
• Run LLM-based evaluations on application responses and retrieval quality across versioned datasets
• Organize and version datasets for experimentation, evaluation, and fine-tuning workflows
• Compare prompt variations and model parameters side-by-side using the playground and experiment tracking
• Deploy a self-hosted observability backend for teams that need on-premises or air-gapped LLM monitoring

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Phoenix is an open-source AI observability platform that traces LLM application runtime, evaluates performance, manages datasets and experiments, and provides a web-based playground for prompt optimization and debugging.

Yes, if you are building or maintaining LLM applications and need observability. The platform is actively maintained, has strong community adoption (11052 stars), and offers a comprehensive feature set for tracing, evaluation, and experimentation. The 51 dependencies and unclear license (Elastic-2.0) warrant review before production use; verify license terms and whether your deployment model (local, cloud, or self-hosted) aligns with your requirements.

Install

arize-phoenix on PyPI

pip install arize-phoenix

uv add arize-phoenix

poetry add arize-phoenix

Installing arize-phoenix

Before you install

Low install friction with a pure-Python wheel. Actively maintained with a release 1 day old and 11052 GitHub stars. Depends on 51 runtime packages including FastAPI, OpenTelemetry, and specialized Phoenix sub-packages, which may add setup complexity despite the wheel distribution.

License in practice

Licensed under Elastic-2.0, which is not a standard SPDX identifier and its treatment is unclear. Verify the license terms before use in proprietary or commercial contexts, as Elastic licenses can carry specific restrictions.

Quickstart

pip install arize-phoenix
phoenix serve

# In your application:
from openinference.instrumentation.openai import OpenAIInstrumentation
from arize_phoenix.otel import register

register()
OpenAIInstrumentation().instrument()

Requires Python 3.10 or later. The platform runs as a server process; tracing requires OpenTelemetry instrumentation setup in your application code.

Verify before relying

• Whether Elastic-2.0 is a typo or variant of a known license; clarify commercial use restrictions
• Whether the 51 runtime dependencies can be selectively installed or if all are required
• Whether the platform requires external services or can run fully self-contained locally

Package facts

Evidence: arize_phoenix-20.2.0-py3-none-any.whl

Keywords: Explainability, Monitoring, Observability

Tags

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Further reading