{"categories":[{"label":"Monitoring","url":"https://skillfed.io/packages/category/system-monitoring/2"}],"enrichment":{"capability":"Phoenix is an open-source AI observability platform that traces LLM application runtime, evaluates performance, manages datasets and experiments, and provides a web-based playground for prompt optimization and debugging.","skillfed_tags":["llm-observability","opentelemetry","prompt-engineering"],"use_cases":["Trace and inspect LLM application execution to debug unexpected outputs or performance issues","Run LLM-based evaluations on application responses and retrieval quality across versioned datasets","Organize and version datasets for experimentation, evaluation, and fine-tuning workflows","Compare prompt variations and model parameters side-by-side using the playground and experiment tracking","Deploy a self-hosted observability backend for teams that need on-premises or air-gapped LLM monitoring"],"what_it_does":"Phoenix is a web-based observability and evaluation platform for LLM applications. It captures execution traces using OpenTelemetry instrumentation, stores them in a local or remote database, and provides a UI for inspecting traces, running LLM-based evaluations, managing versioned datasets, and experimenting with prompts and model parameters. The platform is vendor-agnostic and integrates with popular frameworks like LangGraph, LlamaIndex, OpenAI Agents, and Claude Agent SDK, as well as major LLM providers.\n\nYou run Phoenix as a server (locally via `phoenix serve`, in Docker, or in Kubernetes) and instrument your application to send traces to it. The platform then lets you replay traces, benchmark performance, version and organize test datasets, and iterate on prompts and retrieval strategies. It also includes an MCP server endpoint for integration with coding agents like Claude Code and Cursor.","worth_installing":"Yes, if you are building or maintaining LLM applications and need observability. The platform is actively maintained, has strong community adoption (11052 stars), and offers a comprehensive feature set for tracing, evaluation, and experimentation. The 51 dependencies and unclear license (Elastic-2.0) warrant review before production use; verify license terms and whether your deployment model (local, cloud, or self-hosted) aligns with your requirements."},"id":"arize-phoenix","links":{"html":"https://skillfed.io/packages/arize-phoenix","md":"https://skillfed.io/packages/arize-phoenix.md","pypi":"https://pypi.org/project/arize-phoenix/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-13","license_spdx":null,"license_treatment":"unclear","name":"arize-phoenix","python_support":"supports_current","summary":"AI Observability and Evaluation"},"popularity":{"monthly_downloads":2331841,"position":3132,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"20.2.0"}