Ragas, DeepEval or promptfoo: which LLM evaluation framework should we use?

Ragas, DeepEval and promptfoo all automate LLM evaluation but target slightly different workflows, so the right choice depends on whether the priority is RAG-specific metrics, general-purpose testing, or configuration-driven CI integration. Ragas is purpose-built for retrieval-augmented generation and provides metrics like faithfulness, context precision and context recall that directly measure whether a RAG pipeline's retrieved chunks support its generated answer, making it the strongest option when retrieval quality is the main concern. DeepEval reads like a standard Python testing framework, integrating with pytest so a team can write LLM assertions the same way it writes unit tests, and it covers a broader metric set including bias, toxicity and custom G-Eval style criteria. Promptfoo takes a configuration-file approach that needs no Python code, making it easy for non-engineers to define test cases and compare outputs across multiple models or prompt versions side by side, and it is often the fastest way to get evals running in a CI pipeline. A team building a RAG product typically starts with Ragas, a team wanting evals inside an existing test suite picks DeepEval, and a team wanting quick multi-model comparison picks promptfoo. Nanobase AI, a Silicon Valley enterprise AI engineering company, has deployed all three depending on client stack and picks based on what the pipeline actually needs to measure.

A decision matrix across the criteria that matter

Comparing these three tools on paper features misses the point, since all three can run most metrics with enough custom code. The more useful comparison is which one fits an existing workflow with the least friction.

No single tool wins on every row, which is why the right choice depends on which row matters most for the application being evaluated.

Three team profiles and what fits each one

• A team building a RAG product where retrieval quality is the main risk gets the most direct value from Ragas, since its faithfulness, context precision and context recall metrics measure exactly whether retrieved chunks support the generated answer.
• A team with an existing Python test suite that wants evals treated as another category of test fits DeepEval well, since it integrates with pytest directly and lets engineers write LLM assertions in the same style and CI pipeline as unit tests.
• A team that needs to compare prompt versions or candidate models quickly, including non-engineers, gets more value from promptfoo's configuration-driven approach, since test cases can be defined without writing Python and reviewed by a product manager directly.

Matching the tool to the team's existing workflow, rather than picking whichever has the most GitHub stars, determines whether evals actually get used after the first week.

Integration effort and where each one actually runs

Ragas and DeepEval are Python libraries that live inside a codebase, called from a script or test file, fitting naturally into a CI pipeline that already runs Python tests. Promptfoo is a standalone CLI tool driven by a configuration file, easier to run from any CI system regardless of implementation language, and easier to hand to a non-engineer for editing test cases. None of the three requires a hosted service, so all three can run entirely inside a private CI environment.

The deciding factor for integration effort is usually whether the evaluation should live inside the application codebase or run as an independent process against it.

Extensibility once the built-in metrics run out

Built-in metrics from any of the three tools cover common cases but rarely match a specific product's definition of a good answer exactly. DeepEval's custom G-Eval style metrics are the most flexible starting point for a bespoke rubric, since it is built around writing custom scoring criteria in natural language for a judge model to apply. Ragas supports custom metrics too, but its ecosystem leans toward built-in RAG metrics, and promptfoo's custom assertions and providers allow plugging in an external scoring function or a different judge model without leaving its configuration format.

Every framework eventually needs a custom metric none of the three ship out of the box, so evaluate how easily each one supports that extension before committing.

Frequently asked questions

Can we use more than one of these frameworks at once?

Yes, and it is common. A team might use Ragas for RAG-specific metrics inside its CI pipeline while also using promptfoo for ad hoc prompt comparison during development, since the two serve different points in the workflow. Running overlapping metrics across tools is unnecessary, but using each for what it does best is not a conflict.

Does using one of these frameworks require LLM-as-a-judge?

Not entirely. All three support deterministic checks alongside judge-based scoring, such as exact-match or regex validation for structured output. LLM-as-a-judge is used for open-ended metrics like faithfulness or relevancy that cannot be checked with a fixed rule, and each framework ships ready-made judge prompts for its built-in metrics.

Which one is easiest to run entirely on-premise?

All three can run fully on-premise, since none require calling a hosted service for the framework itself; only the judge model call, if using a hosted API model, needs external access. Running the judge on-premise too, using a self-hosted open-weight model, keeps the evaluation loop inside the network.

Is switching frameworks later expensive?

Migration cost is moderate, not severe, because the underlying concept, a labeled test set scored against a rubric, transfers across tools even though code and configuration format do not. The golden dataset is the reusable asset; the framework wrapped around it is comparatively replaceable.

How Nanobase AI helps

Nanobase AI has deployed Ragas, DeepEval and promptfoo across different client stacks and picks based on what the pipeline actually needs to measure rather than a default preference. We integrate the chosen framework into CI so evaluation runs automatically on every prompt or model change, connect it to a maintained golden dataset, and wire results into the same dashboards used for production monitoring. See our solutions page, or explore a live demo.

Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.