Evaluation & Testing Tools
Evaluation tools help you measure, benchmark, and improve the quality of your LLM applications and agents. They provide automated metrics for relevance, faithfulness, toxicity, and more, along with CI/CD integration to catch regressions before they reach production.
DeepEval
An open-source LLM evaluation framework that provides 14+ research-backed metrics for systematically testing LLM applications. DeepEval integrates with pytest for unit-testing LLM outputs, supports custom metrics, and connects to the Confident AI platform for analytics and collaboration.
Key Features
- 14+ built-in metrics including faithfulness, answer relevancy, hallucination, and bias
- Pytest integration for unit-testing LLM outputs in your existing CI/CD pipeline
- Synthetic test data generation from documents for comprehensive coverage
- Confident AI dashboard for tracking evaluation results over time
Integrations
PromptFoo
An open-source tool for testing, evaluating, and red-teaming LLM applications. PromptFoo enables side-by-side comparison of prompts and models with configurable assertions, making it easy to iterate on prompt quality and catch regressions across model changes.
Key Features
- Side-by-side prompt and model comparison with tabular result views
- Configurable assertions (contains, regex, model-graded, JavaScript) for automated pass/fail
- Red-teaming module for automated adversarial testing and vulnerability detection
- CI-friendly CLI and GitHub Actions integration for automated evaluation
Integrations
Ragas
A framework specifically designed for evaluating RAG (Retrieval-Augmented Generation) pipelines with automated, reference-free metrics. Ragas measures both the retrieval quality and the generation quality of your RAG system, helping you identify whether problems originate in retrieval or generation.
Key Features
- RAG-specific metrics: context precision, context recall, faithfulness, answer relevancy
- Reference-free evaluation — no ground truth labels needed for most metrics
- Test set generation from documents for building evaluation datasets automatically
- Component-level analysis to isolate retrieval vs. generation quality issues
Integrations
Comparison
Choosing the right evaluation tool depends on what you are evaluating and how you want to integrate it.
| Feature | DeepEval | PromptFoo | Ragas |
|---|---|---|---|
| Primary Focus | General LLM testing | Prompt comparison & red-teaming | RAG pipeline evaluation |
| Metrics Count | 14+ built-in | Custom assertions | 8+ RAG-specific |
| CI/CD Integration | pytest native | CLI + GitHub Actions | Python scripts |
| Red-teaming | Basic | Extensive (built-in module) | No |
| Test Data Generation | Yes (synthetic) | No | Yes (from documents) |
| Best For | Teams wanting pytest-based LLM unit tests | Prompt engineering and adversarial testing | Dedicated RAG pipeline quality measurement |