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

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

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)

# Start Phoenix server
phoenix serve

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

Basic tracing