There isn’t a single “best” framework for every custom test set, but if your main goal is evaluating LLMs on your own datasets/tasks, the strongest general-purpose choices are usually:

Best overall for custom test sets

lm-eval-harness

Why it’s popular:

• Widely used and trusted
• Supports lots of model backends
• Easy to define custom tasks/test sets
• Good for benchmarking and reproducible comparisons
• Open-source and extensible

Best when:

• You want a standard evaluation pipeline
• You care about repeatability and comparisons across models
• Your custom test set is mostly prompt → response / classification / multiple-choice style

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Best for product-style / app-level evals

promptfoo

Why it’s strong:

• Very easy to set up custom test cases
• Great for regression testing prompts and model outputs
• Supports assertions, grading, and comparisons across models
• Nice CI/CD integration

Best when:

• You’re evaluating real application behavior
• You want to test prompts, tools, RAG, or agents
• You need fast iteration with custom examples

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Best for qualitative + LLM-as-judge workflows

LangSmith or DeepEval

Why they’re useful:

• Designed for LLM app evaluation
• Support custom datasets and judge-based scoring
• Good for RAG, summarization, extraction, and agent workflows
• Useful tracing/debugging if you’re using LangChain

Best when:

• Your eval requires human-like judgments
• You need to evaluate chains, retrievers, or agents
• You want observability plus evals

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Best for research-style experimentation

OpenAI Evals

Why it’s useful:

• Flexible for custom evals
• Good if you want to build your own benchmarks
• Useful for structured test generation and scoring

Best when:

• You’re doing model research
• You want to define bespoke eval logic
• You’re okay with more setup effort

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Quick recommendation

If you want a simple answer:

• For custom benchmark-style datasets: lm-eval-harness
• For app/prompt regression testing: promptfoo
• For LLM app workflows with judges/traces: LangSmith or DeepEval

My practical pick

If you’re starting from scratch and want the most flexible option for custom test sets, I’d usually recommend:

promptfoo for application evaluation
lm-eval-harness for benchmark evaluation

If you want, I can also recommend the best framework based on your exact use case:

• classification
• RAG
• summarization
• agents/tools
• code generation
• safety/toxicity
• human preference evals