agentevals vs phoenix
Open source AI agent evaluation and observability compared
agentevals and phoenix both help teams evaluate AI behavior using traces and support agent or LLM development. agentevals focuses on scoring existing OpenTelemetry traces, while phoenix combines tracing with datasets, experiments, and application-level evaluation.
agentevals: Evaluate AI Agents from OpenTelemetry Traces
agentevals scores AI agent behavior from existing OpenTelemetry traces, without rerunning agents or making extra model calls. It suits teams building instrumented agents that need local evaluation, golden-set checks, or CI quality gates.
phoenix: Observe and Evaluate AI Applications
Arize Phoenix is a self-hosted platform for tracing, evaluating, and troubleshooting LLM applications. It helps AI engineers inspect runtime behavior and test changes to prompts, models, and retrieval.
| agentevals | phoenix | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | NOASSERTION |
| Stars | 162 | 11.7k |
| Forks | 28 | 1.2k |
| Last analyzed | Oct 4, 2026 | Oct 3, 2026 |
Key differences
- agentevals scores recorded Jaeger JSON or OTLP traces against golden sets and custom rules; phoenix captures application traces and supports evaluations of responses and retrieval.
- agentevals is designed to evaluate existing runs without rerunning agents; phoenix also supports experiments, prompt comparisons, and replaying traced calls.
- agentevals offers an offline CLI, a trace-inspection UI, and an OTLP receiver; phoenix provides a self-hosted platform that can run locally or in containers or the cloud.
- agentevals is licensed under Apache-2.0; phoenix's repository metadata says NOASSERTION, while its README describes Elastic License 2.0, so review its license terms.
- agentevals is under active development and warns of possible breaking changes; phoenix's supplied project information does not give a comparable maturity note.
Choose agentevals if you…
- need to score existing traces without rerunning agents or making extra model calls.
- want golden-set checks or trace-based CI quality gates for instrumented agents.
- need an offline CLI or local tools for inspecting and evaluating traces.
Choose phoenix if you…
- want tracing alongside datasets, evaluations, and experiments for LLM applications.
- need to compare prompt, model, or retrieval changes and evaluate retrieval performance.
- prefer a self-hosted platform with integrations across frameworks and providers.
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This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.