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Arize Phoenix

Open-source AI observability and evaluation platform for traces, experiments, prompts, datasets, and debugging.

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Resource overview Use when a product needs visibility into model calls, retrieval, tool calls, latency, failures, prompts, eval scores, and production regressions.
Why use this

What it helps you do

  • Use this to move AI work from manual judgment to repeatable testing, tracing, metrics, prompt experiments, and quality improvement loops.
Who can use this

Best fit users

  • AI engineers, QA teams, platform teams, and product teams responsible for reliability, regression testing, and measurable AI quality.
Prerequisites

What to know first

  • Representative test cases, logs or traces, target quality metrics, and enough production context to know what good and bad outputs look like.
System requirements

Environment needed

  • Python environment for most workflows, access to model calls or traces, datasets for evaluation, and storage for experiment results when running repeatedly.
Community

Signals and discussion

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