Find the right agent evaluation tool
Choose by eval job, lifecycle stage, hosting model, and required evidence.
Arize Phoenix
Adoption note+
Phoenix is strongest when instrumentation and traces already exist or can be added cleanly.
Adoption note+
Choose Langfuse when trace storage and evaluation operations are part of the workflow, not only one-off benchmarks.
Adoption note+
Use LangSmith-specific docs for current plan, retention, and self-hosting details before production adoption.
| Tool | Category | Traces | Datasets | Human review | Sandbox | Hosting |
|---|---|---|---|---|---|---|
| Arize Phoenix | observability | Open source, Self-hosted, Local | ||||
| Langfuse | observability | Cloud, Self-hosted, Open source | ||||
| LangSmith Evaluation | platform | Cloud | ||||
| OpenAI agent evals | platform | Cloud | ||||
| Inspect AI | benchmark | Open source, Local | ||||
| Ragas | framework | Open source, Local |
Trace first when failure shape is unknown
Use traces to see model calls, tool calls, handoffs, retrieved context, and failure points.
Use datasets, graders, metrics, and repeated runs when behavior is stable enough to compare versions.
Use human review, production scoring, and sandboxing when agent actions affect trust, safety, cost, or external systems.
Evidence used for this finder
Evaluation and observability platform for LLM and agent workflows.
Open sourceOpen-source observability and evaluation workflow for traces, scores, prompts, and experiments.
Open sourceLLM engineering platform for traces, datasets, experiments, annotation queues, and eval scores.
Open sourceEvaluation library for LLM applications with metrics, experiments, datasets, and agent/tool-use metrics.
Open sourceOpen-source evaluation framework for coding, agentic, tool-use, reasoning, multimodal, and sandboxed evaluations.
Open sourceOpenAI platform workflow for evaluating agent traces with graders, datasets, and eval runs.
Open sourceAgent Evaluation Tool Finder FAQ
Use this route to choose eval infrastructure before building a full agent quality loop.
What should I capture before evaluating an AI agent?+
Capture the user input, model calls, tool calls, retrieved context, intermediate steps, final answer, latency, cost, and failure notes. Without traces or repeatable datasets, most eval results are hard to debug or compare.
Should I start with traces or benchmark datasets?+
Start with traces when you do not yet know where the agent fails. Start with benchmark or regression datasets when the task is stable enough to run the same cases across versions.
Why does the finder warn about OpenAI Evals?+
OpenAI documentation says the general Evals platform is on a deprecation timeline. OpenAI agent-eval and dataset surfaces can still be useful for OpenAI-based workflows, but long-term infrastructure decisions should verify the current supported path first.