izam-mohammed/ragrank
🎯 Your free LLM evaluation toolkit helps you assess the accuracy of facts, how well it understands context, its tone, and more. This helps you see how good your LLM applications are.
Specialized for RAG pipeline evaluation with metrics like response relevancy, context understanding, and factual accuracy. Built as a Python toolkit that integrates with OpenAI's API by default but supports custom LLM models, enabling flexible assessment workflows through a dataset-to-metrics evaluation pattern. Provides structured evaluation results exportable to dataframes for analysis and integration with downstream data processing pipelines.
Stars
45
Forks
14
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 14, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/izam-mohammed/ragrank"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Related tools
modelscope/evalscope
A streamlined and customizable framework for efficient large model (LLM, VLM, AIGC) evaluation...
Kareem-Rashed/rubric-eval
Independent framework to test, benchmark, and evaluate LLMs & AI agents locally.
justplus/llm-eval
大语言模型评估平台,支持多种评估基准、自定义数据集和性能测试。支持基于自定义数据集的RAG评估。
relari-ai/continuous-eval
Data-Driven Evaluation for LLM-Powered Applications
Addepto/contextcheck
MIT-licensed Framework for LLMs, RAGs, Chatbots testing. Configurable via YAML and integrable...