# Phoenix Observability

Open-source AI observability platform for LLM tracing, evaluation, and monitoring.

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Testing
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill phoenix-observability -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill phoenix-observability -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill phoenix-observability -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill phoenix-observability -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Phoenix - AI Observability Platform

Open-source AI observability and evaluation platform for LLM applications with tracing, evaluation, datasets, experiments, and real-time monitoring.

## When to use Phoenix

**Use Phoenix when:**
- Debugging LLM application issues with detailed traces
- Running systematic evaluations on datasets
- Monitoring production LLM systems in real-time
- Building experiment pipelines for prompt/model comparison
- Self-hosted observability without vendor lock-in

**Key features:**
- **Tracing**: OpenTelemetry-based trace collection for any LLM framework
- **Evaluation**: LLM-as-judge evaluators for quality assessment
- **Datasets**: Versioned test sets for regression testing
- **Experiments**: Compare prompts, models, and configurations
- **Playground**: Interactive prompt testing with multiple models
- **Open-source**: Self-hosted with PostgreSQL or SQLite

**Use alternatives instead:**
- **LangSmith**: Managed platform with LangChain-first integration
- **Weights & Biases**: Deep learning experiment tracking focus
- **Arize Cloud**: Managed Phoenix with enterprise features
- **MLflow**: General ML lifecycle, model registry focus

## Quick start

### Installation

```bash
pip install arize-phoenix

# With specific backends
pip install arize-phoenix[embeddings]  # Embedding analysis
pip install arize-phoenix-otel         # OpenTelemetry config
pip install arize-phoenix-evals        # Evaluation framework
pip install arize-phoenix-client       # Lightweight REST client
```

### Launch Phoenix server

```python
import phoenix as px

# Launch in notebook (ThreadServer mode)
session = px.launch_app()

# View UI
session.view()  # Embedded iframe
print(session.url)  # http://localhost:6006
```

### Command-line server (production)

```bash
# Start Phoenix server
phoenix serve

# With PostgreSQL
export PHOENIX_SQL_DATABASE_URL="postgresql://user:pass@host/db"
phoenix serve --port 6006
```

### Basic tracing

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/17-observability/phoenix/SKILL.md