# Looking for simple ways to evaluate an AI agent
**URL:**
**Author:**  [@Kirderp](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FKirderp)%5C
**Post date:** [April 7, 2026, 7:22pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Flooking-for-simple-ways-to-evaluate-an-ai-agent%2F175062%2F1 "2026-04-07T19:22:10Z")
I’m building an AI agent that answers questions based on documentation/small knowledge base and I’m trying to figure out a simple way to evaluate if it is working well.
I have used [test.qlankr.com](http://test.qlankr.com), which looks interesting, but I’m wondering if there are any other eval tools people here use that are beginner-friendly and make it easy to share results clearly.
What I’m mainly looking for is something that helps with:
- comparing outputs
- seeing weak points or regressions
- seeing where the agent gives incomplete orbad
- sharing result with other people without making it look too complicated
Curious to find out what people here are using for this.
Cheers
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**Author:**  [@John6666](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FJohn6666)%5C
**Post date:** [April 8, 2026, 1:00am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Flooking-for-simple-ways-to-evaluate-an-ai-agent%2F175062%2F2 "2026-04-08T01:00:55Z")
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 "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/ "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 "LangSmith Evaluation - Docs by LangChain"))
### 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 "Evaluate systematically - Braintrust"))
### 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/ "Testset Generation for RAG"))
### 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 "Evaluation of LLM Applications"))
### 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 "confident-ai/deepeval: The LLM Evaluation Framework"))
## 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/ "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/ "Output Formats - Results Export and Analysis"))
- **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 "RAG Evaluation"))
- **sharing results clearly** → standalone HTML report is the simplest path. ([Promptfoo](https://www.promptfoo.dev/docs/configuration/outputs/ "Output Formats - Results Export and Analysis"))
### 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/ "Testset Generation for RAG"))
- **LangSmith** if you want a full workflow with datasets, experiments, exports, and online monitoring. ([LangChain Docs](https://docs.langchain.com/langsmith/evaluation "LangSmith Evaluation - Docs by LangChain"))
- **Braintrust** if collaboration and URL-sharing matter most. ([Braintrust](https://www.braintrust.dev/docs/evaluate "Evaluate systematically - Braintrust"))
- **Langfuse** if you want an open-source platform with tracing plus evals. ([Langfuse](https://langfuse.com/docs "Langfuse Overview"))
- **DeepEval** only if tool use and multi-step traces become important. ([GitHub](https://github.com/confident-ai/deepeval "confident-ai/deepeval: The LLM Evaluation Framework"))
## What to evaluate first
For a docs assistant, the best first eval loop is not “did the agent seem smart.” It is:
1. **Did it retrieve the right context?**
2. **Was the answer correct?**
3. **Was the answer grounded in the retrieved docs?**
4. **Was the answer complete enough?**
5. **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 "Evaluate a RAG application - Docs by LangChain"))
## 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 "Application-specific 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 "Using LLM-as-a-judge 🧑⚖️ for an automated and versatile ..."))
## 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/ "Output Formats - Results Export and Analysis"))
- **Add Ragas** if you need help generating better eval cases from your docs. ([Ragas](https://docs.ragas.io/en/stable/getstarted/rag_testset_generation/ "Testset Generation for RAG"))
- **Move to LangSmith or Braintrust** if you want a more polished team workflow. ([LangChain Docs](https://docs.langchain.com/langsmith/evaluation "LangSmith Evaluation - Docs by LangChain"))
- **Use Langfuse or Phoenix** when live traces and production monitoring start to matter. ([Langfuse](https://langfuse.com/docs/evaluation/overview "Evaluation of LLM Applications"))
- **Use DeepEval only when agent behavior is truly more than simple docs QA.** ([GitHub](https://github.com/confident-ai/deepeval "confident-ai/deepeval: The LLM Evaluation Framework"))
The shortest strong recommendation is: **Promptfoo first, LangSmith second, Ragas as the RAG-specific add-on.** ([Promptfoo](https://www.promptfoo.dev/docs/configuration/outputs/ "Output Formats - Results Export and Analysis"))
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**Author:**  [@alikhaters](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Falikhaters)%5C
**Post date:** [August 20, 2026, 9:11pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Flooking-for-simple-ways-to-evaluate-an-ai-agent%2F175062%2F3 "2026-08-20T21:11:34Z")
For simple evals, I’d split output quality from trajectory quality. First: did the final answer match ground truth? Later: did the agent choose sensible steps, avoid loops, know when to stop, and preserve uncertainty?
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**Author:**  [@m-sanchez](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fm-sanchez)%5C
**Post date:** [October 6, 2026, 3:30am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Flooking-for-simple-ways-to-evaluate-an-ai-agent%2F175062%2F4 "2026-10-06T03:30:59Z")
late to this but one thing the answers above dont cover. once you have outputs from two versions of the agent on the same questions, check the difference is real before calling it a regression. with 50-100 questions a few flips either way is just noise
what works for me is freezing the questions and expected answers before changing anything, and not editing them after seeing results. then run old and new on exactly the same questions and compare them paired, ie only look at the questions where one got it right and the other didnt (mcnemar), not two accuracy numbers side by side
promptfoo gives you the side by side view, the paired test is a few lines on top. i wrote two small typescript libs for those parts if useful, frozen-eval and ab-significance (i built them) [GitHub - m-sanchez/ab-significance: Did model B really beat model A, or is it sampling noise? Paired McNemar and bootstrap CI on the common-valid subset. Zero dependencies. · GitHub](https://github.com/m-sanchez/ab-significance)