Phoenix

Phoenix is an open-source platform designed for evaluating, experimenting with, and optimizing AI products, specifically Large Language Models (LLMs). It provides comprehensive LLM observability by allowing users to trace, visualize, and debug LLM applications in real-time. Built on OpenTelemetry, Phoenix offers seamless data collection, transparency, and avoids vendor lock-in. The tool is ideal for AI engineers, developers, and data scientists working with LLMs who need to understand model behavior, identify failures, and improve performance. It helps in debugging complex LLM decision-making, flagging poor responses, and troubleshooting issues related to retrieval and tool execution. Phoenix aims to integrate observability directly into the development process, encouraging continuous improvement of LLM applications before and after deployment. Key functionalities include interactive prompt playgrounds for iteration, streamlined evaluation libraries with pre-built templates and human feedback integration, and dataset clustering/visualization to uncover performance issues using embeddings. Its open-source nature and self-hostable option provide flexibility and control for small teams to large enterprises.