# Looking for simple ways to evaluate an AI agent

> Source: <https://discuss.huggingface.co/t/looking-for-simple-ways-to-evaluate-an-ai-agent/175062#post_3>
> Published: 2026-08-20 21:11:34+00:00

Seems some options:

For your kind of agent, the simplest practical answer is: **start with Promptfoo**, then add **Ragas** or **LangSmith** only when you need more depth. Your system sounds closer to a **docs/RAG assistant** than a broad autonomous agent, so the first things to measure are usually **retrieval quality**, **answer accuracy**, and **answer relevance/completeness**, not elaborate multi-step planning. That framing matches Hugging Face’s RAG evaluation cookbook and LangSmith’s RAG tutorial. ([Hugging Face](https://huggingface.co/learn/cookbook/rag_evaluation))

What people are using

1. Promptfoo

This is the easiest beginner-friendly choice when you mainly want to **compare outputs**, **spot regressions**, and **share results without a heavy platform**. Its getting-started guide says it opens a web view for comparing outputs, and its output docs explicitly support a **shareable standalone HTML report** with **sorting, filtering, side-by-side comparisons, and pass/fail statistics**. ([Promptfoo](https://www.promptfoo.dev/docs/getting-started/))

2. LangSmith

This is a strong next step when you want a more complete workflow: **datasets**, **offline evals**, **experiment comparison**, **filters/exports**, and **online evaluations** for production traces. LangSmith also separates RAG evaluation from agent evaluation and has tutorials for both. ([LangChain Docs](https://docs.langchain.com/langsmith/evaluation))

3. Braintrust

This is a good option when the main priority is **team review** and **clear sharing in a browser UI**. Braintrust’s docs say playgrounds let you **compare configurations side by side** and **share results via URL**, while experiments are **immutable snapshots** that remain comparable over time. It also supports more complex agent code through remote evals. ([Braintrust](https://www.braintrust.dev/docs/evaluate))

4. Ragas

This is especially useful for a documentation or knowledge-base assistant because it focuses on **RAG evaluation** and can **generate a test set from your own documents**. Its docs also expose metrics for both **RAG** and **agentic workflows**. ([Ragas](https://docs.ragas.io/en/stable/getstarted/rag_testset_generation/))

5. Langfuse or Phoenix

These become useful once you care about **observability**, **debugging traces**, and **live quality monitoring**. Langfuse’s docs center on **datasets**, **experiments**, and **evaluation** as repeatable checks that catch regressions before shipping. Phoenix provides pre-built evaluators for **document relevance**, **correctness**, **tool selection**, and **tool invocation**, and has RAG evaluation tutorials. ([Langfuse](https://langfuse.com/docs/evaluation/overview))

6. DeepEval

This is more useful when your system is truly agentic, not just retrieval-plus-answering. Its docs and repo highlight agent metrics such as **task completion** and **tool correctness**, including checks on whether the right tools were called with the right arguments. ([GitHub](https://github.com/confident-ai/deepeval))

My recommendation for your case

I would split your situation into two phases.

Phase 1: keep it simple

Use **Promptfoo** as the main eval runner. It is the best fit for your stated needs:

**comparing outputs** → built-in side-by-side web view and HTML reports. ([Promptfoo](https://www.promptfoo.dev/docs/getting-started/))
**seeing weak points or regressions** → pass/fail stats plus repeatable runs against the same cases. ([Promptfoo](https://www.promptfoo.dev/docs/configuration/outputs/))
**finding incomplete or bad answers** → pair Promptfoo with a small rubric or LLM-as-judge checks. Hugging Face’s cookbook explicitly uses **LLM-as-a-judge** for RAG evaluation. ([Hugging Face](https://huggingface.co/learn/cookbook/rag_evaluation))
**sharing results clearly** → standalone HTML report is the simplest path. ([Promptfoo](https://www.promptfoo.dev/docs/configuration/outputs/))

Phase 2: add depth only when you need it

If your system grows beyond simple QA over docs, add one of these:

**Ragas** if the bottleneck is building a useful evaluation set from your docs. ([Ragas](https://docs.ragas.io/en/stable/getstarted/rag_testset_generation/))
**LangSmith** if you want a full workflow with datasets, experiments, exports, and online monitoring. ([LangChain Docs](https://docs.langchain.com/langsmith/evaluation))
**Braintrust** if collaboration and URL-sharing matter most. ([Braintrust](https://www.braintrust.dev/docs/evaluate))
**Langfuse** if you want an open-source platform with tracing plus evals. ([Langfuse](https://langfuse.com/docs))
**DeepEval** only if tool use and multi-step traces become important. ([GitHub](https://github.com/confident-ai/deepeval))

What to evaluate first

For a docs assistant, the best first eval loop is not “did the agent seem smart.” It is:

**Did it retrieve the right context?**
**Was the answer correct?**
**Was the answer grounded in the retrieved docs?**
**Was the answer complete enough?**
**Did it abstain when the docs did not support an answer?**

That structure lines up with LangSmith’s RAG tutorial, which centers **answer relevance**, **answer accuracy**, and **retrieval quality**, and with Phoenix’s retrieval/document relevance evaluators. ([LangChain Docs](https://docs.langchain.com/langsmith/evaluate-rag-tutorial))

Why this matters

A lot of teams jump straight to “agent evaluation,” but your current problem is more specific. LangSmith’s docs distinguish **RAG evaluation** from **agent trajectory evaluation**, and the DeepLearning.AI agent-eval course makes the same point by separating **output quality** from **path/trajectory quality**. For a small documentation assistant, the output and retrieval layer usually matters first; trajectory evaluation matters later, once the system starts choosing tools, routing, or looping through steps. ([LangChain Docs](https://docs.langchain.com/langsmith/evaluation-approaches))

One caveat

LLM-as-a-judge is useful, but it needs care. Hugging Face’s guide says it is powerful and versatile, but also says it does **not work well out of the box** unless you set it up carefully. So it is best used as a repeatable grader on a fixed test set, not as a magical final truth source. ([Hugging Face](https://huggingface.co/learn/cookbook/llm_judge))

Bottom line

If you want the cleanest beginner path:

**Start with Promptfoo** for comparisons and easy sharing. ([Promptfoo](https://www.promptfoo.dev/docs/configuration/outputs/))
**Add Ragas** if you need help generating better eval cases from your docs. ([Ragas](https://docs.ragas.io/en/stable/getstarted/rag_testset_generation/))
**Move to LangSmith or Braintrust** if you want a more polished team workflow. ([LangChain Docs](https://docs.langchain.com/langsmith/evaluation))
**Use Langfuse or Phoenix** when live traces and production monitoring start to matter. ([Langfuse](https://langfuse.com/docs/evaluation/overview))
**Use DeepEval only when agent behavior is truly more than simple docs QA.** ([GitHub](https://github.com/confident-ai/deepeval))

The shortest strong recommendation is: **Promptfoo first, LangSmith second, Ragas as the RAG-specific add-on.** ([Promptfoo](https://www.promptfoo.dev/docs/configuration/outputs/))