You are selecting an LLM eval framework for a production RAG system built without LangChain. Compare Promptfoo, DeepEval, and LangSmith on capabilities and ecosystem fit, and recommend which to use and why.
Free · 2 AI evals / day
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.
Quick reference
TL;DR
For a non-LangChain RAG system, lead with DeepEval (pytest-native, built-in RAG metrics), add Promptfoo for CI prompt regression, and skip LangSmith because its value is tracing tied to the LangChain ecosystem.
Imagine you are buying tools to test a kitchen. Promptfoo is like a checklist machine: you write the dishes and rules in a simple form, and it runs the same check every time the menu changes, perfect for catching surprises on an automated assembly line. DeepEval is like a food inspector who already knows the exact tests for a buffet, freshness, correct ingredients, nothing missing, and it plugs straight into the testing habits your cooks already have. LangSmith is more like a security-camera system that watches every step in one specific kitchen brand. If your kitchen was not built by that brand, the cameras only catch part of the room. So you pick the inspector who knows your kind of food, add the checklist machine for the assembly line, and skip the cameras built for someone else's kitchen.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.
Your stack is deeply built on LangChain and LangGraph end to end Then LangSmith's automatic tracing becomes its strongest asset and the ecosystem fit flips in its favor.
You only need prompt regression gating, with no semantic RAG metrics Promptfoo alone in CI is enough, and adding DeepEval would be unused machinery for that narrow goal.
Your team works entirely outside Python, in TypeScript or Go DeepEval's pytest-native design loses its main ergonomic win, so a language-neutral runner fits better.
The task has cheap exact-truth checks like extraction or classification Exact match against gold labels is faster and unbiased, so heavy eval frameworks add little value.
Quick scan · 2–4 min
Everything important, quickly.
5 min: characterize each tool on its lead axis (Promptfoo CI, DeepEval RAG metrics, LangSmith tracing), justify DeepEval plus Promptfoo for a non-LangChain RAG system, and separate offline eval from online observability.
Real products, models, and research that use this idea.
1DeepEval ships faithfulness, context precision, and context recall as built-in metrics, judged by a configurable model like GPT-5.5 or Claude Opus 4.7.
2Promptfoo runs YAML-defined prompt sweeps in GitHub Actions, gating prompt diffs the way Jest gates code in a CI pipeline.
3LangSmith auto-traces LangChain and LangGraph runs, which is its core draw and exactly what a non-LangChain app cannot use.
4Langfuse and Arize Phoenix provide framework-neutral OpenTelemetry tracing, the role teams reach for when they skip LangSmith.
5RAGAS pioneered the claim-level faithfulness metric that DeepEval and others now expose as a built-in RAG evaluator.
Interviewer follow-ups
What an interviewer would ask next. Try answering before peeking at the approach.
QHow would you run DeepEval and Promptfoo together in one CI pipeline without duplication?
Sign in or go Pro to see the approach for this follow-up.
QIf you still want tracing without LangChain, what replaces LangSmith?
Sign in or go Pro to see the approach for this follow-up.
QWhat changes in your recommendation if the system is high volume and judge cost dominates?
Sign in or go Pro to see the approach for this follow-up.
Don't say this
Red flags and common mistakes that signal junior thinking. Click to expand.
Most common mistake
Treating framework choice as one winner take all decision. The right answer usually composes a metrics library with a CI runner, and ignores tools whose value is locked to an ecosystem you do not use.