phoenix-observability

# 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 # Li...