{"slug": "looking-for-simple-ways-to-evaluate-an-ai-agent", "title": "Looking for simple ways to evaluate an AI agent", "summary": "Promptfoo is recommended as the primary evaluation tool for AI agents focused on documentation and RAG tasks, with Ragas and LangSmith suggested for deeper analysis. The guidance from Promptfoo, Hugging Face, LangChain, Braintrust, Ragas, Langfuse, Phoenix, and DeepEval highlights measuring retrieval quality, answer accuracy, and relevance. A phased approach starts with Promptfoo for output comparison and regression spotting, then adds advanced tools for observability and agentic metrics as needed.", "body_md": "Seems some options:\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](https://huggingface.co/learn/cookbook/rag_evaluation))\n\nWhat people are using\n\n1. 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](https://www.promptfoo.dev/docs/getting-started/))\n\n2. 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](https://docs.langchain.com/langsmith/evaluation))\n\n3. 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](https://www.braintrust.dev/docs/evaluate))\n\n4. 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](https://docs.ragas.io/en/stable/getstarted/rag_testset_generation/))\n\n5. 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](https://langfuse.com/docs/evaluation/overview))\n\n6. 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](https://github.com/confident-ai/deepeval))\n\nMy recommendation for your case\n\nI would split your situation into two phases.\n\nPhase 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](https://www.promptfoo.dev/docs/getting-started/))\n**seeing weak points or regressions** → pass/fail stats plus repeatable runs against the same cases. ([Promptfoo](https://www.promptfoo.dev/docs/configuration/outputs/))\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](https://huggingface.co/learn/cookbook/rag_evaluation))\n**sharing results clearly** → standalone HTML report is the simplest path. ([Promptfoo](https://www.promptfoo.dev/docs/configuration/outputs/))\n\nPhase 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](https://docs.ragas.io/en/stable/getstarted/rag_testset_generation/))\n**LangSmith** if you want a full workflow with datasets, experiments, exports, and online monitoring. ([LangChain Docs](https://docs.langchain.com/langsmith/evaluation))\n**Braintrust** if collaboration and URL-sharing matter most. ([Braintrust](https://www.braintrust.dev/docs/evaluate))\n**Langfuse** if you want an open-source platform with tracing plus evals. ([Langfuse](https://langfuse.com/docs))\n**DeepEval** only if tool use and multi-step traces become important. ([GitHub](https://github.com/confident-ai/deepeval))\n\nWhat to evaluate first\n\nFor a docs assistant, the best first eval loop is not “did the agent seem smart.” It is:\n\n**Did it retrieve the right context?**\n**Was the answer correct?**\n**Was the answer grounded in the retrieved docs?**\n**Was the answer complete enough?**\n**Did it abstain when the docs did not support an answer?**\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](https://docs.langchain.com/langsmith/evaluate-rag-tutorial))\n\nWhy 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](https://docs.langchain.com/langsmith/evaluation-approaches))\n\nOne 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](https://huggingface.co/learn/cookbook/llm_judge))\n\nBottom line\n\nIf you want the cleanest beginner path:\n\n**Start with Promptfoo** for comparisons and easy sharing. ([Promptfoo](https://www.promptfoo.dev/docs/configuration/outputs/))\n**Add Ragas** if you need help generating better eval cases from your docs. ([Ragas](https://docs.ragas.io/en/stable/getstarted/rag_testset_generation/))\n**Move to LangSmith or Braintrust** if you want a more polished team workflow. ([LangChain Docs](https://docs.langchain.com/langsmith/evaluation))\n**Use Langfuse or Phoenix** when live traces and production monitoring start to matter. ([Langfuse](https://langfuse.com/docs/evaluation/overview))\n**Use DeepEval only when agent behavior is truly more than simple docs QA.** ([GitHub](https://github.com/confident-ai/deepeval))\n\nThe shortest strong recommendation is: **Promptfoo first, LangSmith second, Ragas as the RAG-specific add-on.** ([Promptfoo](https://www.promptfoo.dev/docs/configuration/outputs/))", "url": "https://wpnews.pro/news/looking-for-simple-ways-to-evaluate-an-ai-agent", "canonical_source": "https://discuss.huggingface.co/t/looking-for-simple-ways-to-evaluate-an-ai-agent/175062#post_3", "published_at": "2026-08-20 21:11:34+00:00", "updated_at": "2026-08-20 21:14:54.295843+00:00", "lang": "en", "topics": ["ai-tools", "ai-agents", "machine-learning"], "entities": ["Promptfoo", "Ragas", "LangSmith", "Braintrust", "Langfuse", "Phoenix", "DeepEval", "Hugging Face"], "alternates": {"html": "https://wpnews.pro/news/looking-for-simple-ways-to-evaluate-an-ai-agent", "markdown": "https://wpnews.pro/news/looking-for-simple-ways-to-evaluate-an-ai-agent.md", "text": "https://wpnews.pro/news/looking-for-simple-ways-to-evaluate-an-ai-agent.txt", "jsonld": "https://wpnews.pro/news/looking-for-simple-ways-to-evaluate-an-ai-agent.jsonld"}}