Compare Promptfoo, DeepEval, and LangSmith as LLM eval frameworks: when to use each?

An **LLM eval framework** is the tooling you use to score model and pipeline outputs systematically instead of eyeballing them. The three named here solve overlapping but different problems, so the interview wants you to match each to a job rather than crown a single winner. Promptfoo is config-first. You write test cases in a YAML file and run them, much like unit tests for prompts. It works with any LLM API and slots cleanly into a CI pipeline, so it shines when you want prompt changes gated before merge. DeepEval is Python-first. It plugs into pytest, so eval feels like normal software testing, and it ships RAG metrics like faithfulness and context recall out of the box. That makes it a strong default for a retrieval system. LangSmith is tracing-first. It started as a debugging and observability tool inside the LangChain ecosystem, and its eval features are a secondary surface. Its biggest payoff comes from automatic tracing, which is weak when your app was not built with LangChain. For a non-LangChain RAG system, the clean answer is DeepEval for metrics, Promptfoo for CI regression, and skip LangSmith.

These three tools target different jobs, so you compose rather than pick one. Promptfoo is a config-driven, framework-agnostic test runner. You declare cases in YAML and run them in CI. It is the best fit for prompt regression gating before merge. Think of it as unit tests for prompts. DeepEval is a Python-native framework with pytest integration. It ships first-class RAG metrics: faithfulness, context precision, context recall, and answer relevance. Eval feels like normal software testing. It is also framework-agnostic, so it works with any retrieval stack. LangSmith is tracing-first and rooted in the LangChain ecosystem. Its core value is automatic, deep tracing of LangChain and LangGraph runs. Eval is a secondary surface. Outside LangChain, the tracing hooks are shallow and the eval offering is less specialized. For a non-LangChain RAG system the recommendation is DeepEval as the metrics engine. It gives the retrieval-specific metrics you need without custom code. Add Promptfoo as a complementary CI runner for prompt-level regression. Skip LangSmith here. Its main advantage is tracing tied to an ecosystem you are not using, so you pay integration cost for little gain.