{
  "$type": "site.standard.document",
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  "path": "/t/looking-for-simple-ways-to-evaluate-an-ai-agent/175062#post_2",
  "publishedAt": "2026-04-08T01:00:55.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "Hugging Face",
    "Promptfoo",
    "LangChain Docs",
    "Braintrust",
    "Ragas",
    "Langfuse",
    "GitHub"
  ],
  "textContent": "Seems some options:\n\n* * *\n\nFor 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)\n\n## What people are using\n\n### 1. Promptfoo\n\nThis 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)\n\n### 2. LangSmith\n\nThis 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)\n\n### 3. Braintrust\n\nThis 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)\n\n### 4. Ragas\n\nThis 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)\n\n### 5. Langfuse or Phoenix\n\nThese 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)\n\n### 6. DeepEval\n\nThis 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)\n\n## My recommendation for your case\n\nI would split your situation into two phases.\n\n### Phase 1: keep it simple\n\nUse **Promptfoo** as the main eval runner. It is the best fit for your stated needs:\n\n  * **comparing outputs** → built-in side-by-side web view and HTML reports. (Promptfoo)\n  * **seeing weak points or regressions** → pass/fail stats plus repeatable runs against the same cases. (Promptfoo)\n  * **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)\n  * **sharing results clearly** → standalone HTML report is the simplest path. (Promptfoo)\n\n\n\n### Phase 2: add depth only when you need it\n\nIf your system grows beyond simple QA over docs, add one of these:\n\n  * **Ragas** if the bottleneck is building a useful evaluation set from your docs. (Ragas)\n  * **LangSmith** if you want a full workflow with datasets, experiments, exports, and online monitoring. (LangChain Docs)\n  * **Braintrust** if collaboration and URL-sharing matter most. (Braintrust)\n  * **Langfuse** if you want an open-source platform with tracing plus evals. (Langfuse)\n  * **DeepEval** only if tool use and multi-step traces become important. (GitHub)\n\n\n\n## What to evaluate first\n\nFor a docs assistant, the best first eval loop is not “did the agent seem smart.” It is:\n\n  1. **Did it retrieve the right context?**\n  2. **Was the answer correct?**\n  3. **Was the answer grounded in the retrieved docs?**\n  4. **Was the answer complete enough?**\n  5. **Did it abstain when the docs did not support an answer?**\n\n\n\nThat 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)\n\n## Why this matters\n\nA 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)\n\n## One caveat\n\nLLM-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)\n\n## Bottom line\n\nIf you want the cleanest beginner path:\n\n  * **Start with Promptfoo** for comparisons and easy sharing. (Promptfoo)\n  * **Add Ragas** if you need help generating better eval cases from your docs. (Ragas)\n  * **Move to LangSmith or Braintrust** if you want a more polished team workflow. (LangChain Docs)\n  * **Use Langfuse or Phoenix** when live traces and production monitoring start to matter. (Langfuse)\n  * **Use DeepEval only when agent behavior is truly more than simple docs QA.** (GitHub)\n\n\n\nThe shortest strong recommendation is: **Promptfoo first, LangSmith second, Ragas as the RAG-specific add-on.** (Promptfoo)",
  "title": "Looking for simple ways to evaluate an AI agent"
}