Safety ResearchAutomated release watch

@arizeai/phoenix-evals@2.7.0

Arize Phoenix releases published @arizeai/phoenix-evals@2.7.0. Minor Changes - b00c105: Classification evaluators, including every built-in, can now run on AI SDK decision models such as openai.decisionModel("gpt-6-luna") . The label comes from experimental decide , with label probabilities (and the provider's confidence, when reported) in metadata . The…

Human read

Why this signal matters

Arize Phoenix releases published @arizeai/phoenix-evals@2.7.0. This automated release-watch entry was generated from the project's official GitHub release feed and is kept separate from human-reviewed editorial analysis. Release notes: Minor Changes - b00c105: Classification evaluators, including every built-in, can now run on AI SDK decision models such as openai.decisionModel("gpt-6-luna") . The label comes from experimental decide , with label probabilities (and the provider's confidence, when reported) in metadata . The rendered prompt is sent as plain text, and refusals throw the AI SDK's Experimental DecisionRefusalError . Decide spans now record the decision state, question and answer. New exports: isDecisionModel and the DecisionModel type. This replaces the evaluation model support from the previous release, and changes what evaluation models such as TypeSafe's Jev are sent. They now go through experimental decide too. Message prompts are joined into plain text instead of being serialized as JSON state. ai is pinned to 7.0.137 and @ai-sdk/otel to 1.0.137 , because the decision API is experimental and has…

Agent parse

Actionable summary

Treat @arizeai/phoenix-evals@2.7.0 as an official release signal. Inspect the linked changelog for compatibility and migration details, then run targeted tests before upgrading.

Agent usefulness
77/100
Confidence
96%
Canonical data
JSON + Markdown
Next actions

What builders should check

  • Review the official Arize Phoenix releases release notes before upgrading.
  • Check compatibility, migration, and security notes against your current agent stack.
  • Run focused regression tests before production rollout.
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