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) What people are using
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
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) 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) sharing results clearly → standalone HTML report is the simplest path. (Promptfoo)
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) LangSmith if you want a full workflow with datasets, experiments, exports, and online monitoring. (LangChain Docs)
**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)
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)
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)
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) Move to LangSmith or Braintrust if you want a more polished team workflow. (LangChain Docs) Use Langfuse or Phoenix when live traces and production monitoring start to matter. (Langfuse)
**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/))