Back to all tools
Open Source
Tool Comparison
LM Evaluation Harness
The industry-standard LLM benchmark suite — evaluate any model on 60+ tasks from one CLI.
VS
At a Glance
| Attribute | LM Evaluation Harness | Ragas |
|---|---|---|
| License / Pricing | Open Source | Open Source |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.5/5 | 4.3/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 4 listed | 5 listed |
| Categories | LLM Evaluation | RAG FrameworksAI Observability |
Key Features
LM Evaluation Harness
- 60+ built-in benchmarks: MMLU, GSM8K, HumanEval, TruthfulQA, HellaSwag
- Evaluate any HuggingFace model, OpenAI API, or local model
- Few-shot prompting with configurable shot count
- Parallelized evaluation across GPUs
- Used by Hugging Face Open LLM Leaderboard
- Custom task support via YAML config
Ragas
- Faithfulness — measures if the answer is grounded in retrieved context
- Answer Relevance — checks if the answer addresses the question
- Context Precision and Recall — evaluates retriever quality
- LLM-as-judge evaluation — no labeled ground truth needed
- Integrates with LangChain, LlamaIndex, and any RAG pipeline
- Testset generation — automatically create evaluation datasets
Real-World Use Cases
LM Evaluation Harness
Benchmark a fine-tuned model before deployment
Install the harness: pip install lm-eval
Add LLM capability regression tests to CI
Select a fast subset of tasks (e.g. hellaswag with 100 samples)
Ragas
Benchmark RAG pipeline before going to production
Generate a test set from your documents with Ragas TestsetGenerator
CI quality gate for RAG changes
Add a Ragas evaluation step to your GitHub Actions pipeline
Integrations
LM Evaluation Harness
huggingfaceopenaiwandbmlflow
Ragas
langchainllamaindexopenailangsmithwandb