observability-phoenix

Topics in the source

• Phoenix - AI Observability Platform
• When to use Phoenix
• Quick start
• Installation
• Launch Phoenix server
• Command-line server (production)
• Basic tracing
• Core concepts

Open-source AI observability and evaluation platform for LLM applications with tracing, evaluation, datasets, experiments, and real-time monitoring. 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 A trace represents a complete execution flow, while spans are individual operations within that trace. Projects organize related traces: | Variable | Description | Defaul

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