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Arize Phoenix: open-source AI observability and evaluation

Phoenix is an open-source AI observability and evaluation platform: tracing, LLM-as-judge evals and experiments, self-hostable and vendor-neutral.

Published on June 11, 20264 min readView on GitHub

Debugging and evaluating AI systems requires visibility into every step. Arize Phoenix is an open-source observability and evaluation platform, with OpenTelemetry-based tracing that works with LangChain, LlamaIndex, OpenAI and more, all self-hostable and vendor-neutral.

What is Phoenix?

Phoenix does OpenInference-standard tracing (over OpenTelemetry), LLM-as-judge evaluations, dataset and experiment tracking and a prompt playground. It runs locally or in a container, which helps in privacy-sensitive workflows.

Key features

  • OpenInference/OpenTelemetry tracing, framework-agnostic
  • LLM-as-judge evaluations and experiments
  • Dataset tracking and prompt playground
  • Self-hostable (notebook, container or app)

How Reche uses it

Observing and evaluating is what turns an AI system from a gamble into engineering. Reche instruments products with tools like Phoenix, so quality and cost are measured and improved, not estimated in the dark.

Want to implement this in your product?

Reche's initial diagnosis defines scope, timeline, and budget. Credited to the project if you move forward.