LLM eval tools compared, which one for what (7 picks)

Reddit r/AI_Agents News

Summary

A comparison of seven popular LLM evaluation tools, split between in-code libraries (promptfoo, DeepEval, Ragas) and tracing/experiment platforms (Langfuse, Arize Phoenix, LangSmith, Braintrust), with guidance on when to use each.

Split into libraries you run in code and platforms that store traces and experiments. Plenty of teams use one of each. * **promptfoo** for prompt regression tests and red-teaming from a YAML file and a CLI. Results are stored locally by default. * **DeepEval** if you like pytest. You write evals as tests and run them in CI. * **Ragas** when the thing you're scoring is retrieval quality and grounded answers. * **Langfuse** for open-source tracing plus LLM-as-judge scoring, self-hostable if data can't leave your servers. * **Arize Phoenix** for OpenTelemetry-based traces and evals you can run locally. * **LangSmith** if you're on LangChain or LangGraph, though it works without them too. * **Braintrust** for a managed platform built around datasets and experiment comparisons. Commercial, with a free tier. Whichever you pick, I'd start with a few dozen hand-labelled examples from real traffic before trusting any automated metric. What are you running in CI?
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