Overview

Plain English

Open-source observability platform for LLM tracing, evaluation, and RAG debugging with in-process deployment.

Technical

Open-source observability platform for LLM tracing, evaluation, and RAG debugging with in-process deployment.

Technical scorecard

License Apache 2.0

Commercial use Yes

OpenAI-compatible API No

REST API No

Fine-tuning support No

Quantization support No

Docker available No

GUI / no-code available No

Telemetry None

Offline after setup Yes

Data & Privacy

After setup, this listing is marked as usable offline. Confirm network behavior against the upstream project before regulated deployment.

Not verified in this directory yet. Review the upstream docs for persistence, logs, and workspace storage.

Commercial use is marked as allowed or likely allowed by the listed license.

None

Last verified: May 15, 2026. Maintainer verification should be treated as directory guidance, not legal advice.

Setup & Installation

Medium

A developer can usually get this running with standard docs.

Python, TypeScript / JavaScript, Docker, Bare Metal

# Start with the official project documentation
# https://github.com/Arize-ai/phoenix

Hardware Requirements

RAM8 GB minimum / 16 GB recommended

Hardware tagsCPU Only

Model formatsNot specified

Primary languagePython, TypeScript / JavaScript

Works Well With

You might also evaluate