AI Observability and Evaluation

Phoenix is an open-source AI observability platform designed for experimentation, evaluation, and troubleshooting. It provides: Tracing - Trace your LLM application's runtime using OpenTelemetry-based instrumentation. Evaluation - Leverage LLMs to benchmark your application's performance using response and retrieval evals. Datasets - Create versioned datasets of examples for experimentation, evaluation, and fine-tuning. Experiments - Track and evaluate changes to prompts, LLMs, and retrieval. Playground- Optimize prompts, compare models, adjust parameters, and replay traced LLM calls. Prompt Management- Manage and test prompt changes systematically using version control, tagging, and experimentation. Phoenix is vendor and language agnostic with out-of-the-box support for popular frameworks (OpenAI Agents SDK, Claude Agent SDK, LangGraph, Vercel AI SDK, Mastra, CrewAI, LlamaIndex, DSPy) and LLM providers (OpenAI, Anthropic, Google GenAI, Google ADK, AWS Bedrock, OpenRouter, LiteLLM, and more). Phoenix runs practically anywhere, including your local machine, a Jupyter notebook, a containerized deployment, or in the cloud.