evalbench 0.7.0

Lightweight, modular library for evaluating LLM outputs with built-in metrics, custom extensions, and agentic workflows.

EvalBench

EvalBench is a plug-and-play Python package for evaluating outputs of large language models (LLMs) across a variety of metrics - from response quality and retrieval accuracy to hallucination and prompt alignment.

It now includes agentic workflows: just describe what you want to understand or improve about your LLM outputs, and EvalBench will plan and execute a tailored sequence of evaluation, interpretation, and recommendation steps — automatically!

🚀 Key Features:

• 18+ built-in metrics covering coherence, relevance, hallucination, BLEU, ROUGE, MRR, and more
• User-defined custom metrics with a simple decorator-based API
• Modular architecture to group related metrics and share inputs
• Agentic execution: EvalBench can reason about your goal and execute the necessary steps (evaluate → interpret → recommend)
• Batch support, configurable output (print/save), and JSON-compatible results

📊 Modules and Metric Categories:

Module	Metrics
response_quality	conciseness_score, coherence_score, factuality_score
reference_based	bleu_score, rouge_score, meteor_score, semantic_similarity_score, bert_score
contextual_generation	faithfulness_score, hallucination_score, groundedness_score
retrieval	recall_at_k_score, precision_at_k_score, ndcg_at_k_score, mrr_score
query_alignment	context_relevance_score
response_alignment	response_relevance_score, response_helpfulness_score
user defined module	User-registered custom metrics

🧠 Agentic Workflow:

EvalBench follows a three-step agentic pipeline, automatically triggered based on user instructions:

1. Evaluation – Runs relevant metrics to score model outputs. EvalBench intelligently selects which metrics to use if not explicitly specified.
2. Interpretation – Analyzes the evaluation results and highlights potential issues with model behavior.
3. Recommendation – Suggests improvements to prompts, model setup, data inputs, or evaluation strategy.

Just write your request in plain language — EvalBench will take care of the rest.

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🚀 Usage

pip install evalbench

All usage examples, including how to write your own custom metrics and how to use the agentic pipeline in practice, are available in this Jupyter notebook:

👉 View the Notebook

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💡 Use Cases

EvalBench is ideal for:

• Evaluating LLM apps like summarizers, chatbots, and search agents using built-in metrics
• Integrating custom, domain-specific metrics into the EvalBench's ecosystem
• Getting automatic eval → interpret → recommend pipelines from natural language instructions
• Rapidly iterating on model outputs, prompts, and evaluation strategies

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🚧 Coming Soon

• Dataset evaluation integration
• Ecosystem integration - langchain/llama_index hooks
• CLI support