Evaluation & observability
Arize-ai/phoenix
Phoenix is an open-source observability and evaluation platform for AI applications. It supports traces, datasets, experiments and evaluation across retrieval and agent workflows. It helps teams replace anecdotal prompt testing with inspectable evidence about system behaviour.
THE PRACTICAL EXPLANATION
Phoenix is an open-source observability and evaluation platform for AI applications. It supports traces, datasets, experiments and evaluation across retrieval and agent workflows. It helps teams replace anecdotal prompt testing with inspectable evidence about system behaviour.
WHERE TO USE IT
Use it during development and production diagnosis of RAG systems, agents and model-powered applications, especially where failures need to be traced across several steps.
WHO MAY USE IT
AI engineers, quality teams and product owners who need to understand why a system produced an outcome and whether changes improved it.
HOW TO USE IT
Instrument one end-to-end workflow, create a dataset of representative cases and define evaluations tied to user risk. Compare versions through experiments, then inspect failed traces rather than relying only on an average score.
GETTING THE BEST RESULTS
WHY IT MAY BE USEFUL
Open-source observability and evaluation for AI applications.
Best considered for: Tracing, datasets, experiments and evaluation of agents and RAG.
READ BEFORE YOU ADOPT IT
Metrics need to reflect the actual business risk, not just model convenience.
Confirm the current licence, maintenance status, dependency risk, data path, model access, tool permissions and human approval points at the source. A public repository is inspectable raw material—not proof that a system is secure, supported or suitable for your production environment.