phoenix-observability

npx skills add Orchestra-Research/AI-Research-SKILLs --skill phoenix --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# 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 ### Installa

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
Start Phoenix server
phoenix serve
With PostgreSQL
export PHOENIX_SQL_DATABASE_URL="postgresql://user:pass@host/db"