Every Evals & Observability comparison and buyer's guide for building AI agents — 7 pieces and counting. Each is a head-to-head or a “best X for Y” roundup with a sources-backed verdict.
Almost every hallucination detector measures one thing — whether the answer is grounded in the context it was given. That is not the same as whether the answer is true.
Three open-source tools dominate LLM red teaming — but they aren't rivals. One scans a model, one is a framework for building attacks, one is a CI gate. Pick by layer.
A prompt registry lets you change prompts without a deploy. On its own, that just lets you change them faster — not better. The tools that compound tie every version to an eval.
They look like a difficulty ladder. They're three orthogonal axes — and only one of them measures the thing that decides whether your agent survives contact with real users.
Both libraries emit OpenTelemetry spans for your agent. They disagree on what to name the attributes — and that disagreement, not the instrumentation, is your real lock-in.
Three popular eval frameworks that look interchangeable answer three different questions — pick the one that matches the question you actually have.
The real choice isn't which dashboard looks nicer — it's what unit of work you trace and who owns the trace data after the agent finishes.