🐢 Open-Source Evaluation & Testing library for LLM Agents

AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation

The open-source LLMOps platform: prompt playground, prompt management, LLM evaluation, and LLM observability all in one place.

RAG evaluation without the need for "golden answers"

Dataset and benchmark for RAG on company internal documents.

Red Teaming python-framework for testing chatbots and GenAI systems.

Convert and validate your Markdown, then choose the best chunking strategy for your RAG pipeline.

RAG boilerplate with semantic/propositional chunking, hybrid search (BM25 + dense), LLM reranking, query enhancement agents, CrewAI orchestration, Qdrant vector search, Redis/Mongo sessioning, Celery ingestion pipeline, Gradio UI, and an evaluation suite (Hit-Rate, MRR, hybrid configs).

Lightweight RAG provenance middleware. Verifies every claim in an LLM response is grounded in a retrieved source - without an LLM call.

⚡️ The "1-Minute RAG Audit" — Generate QA datasets & evaluate RAG systems in Colab, Jupyter, or CLI. Privacy-first, async, visual reports.

Open source framework for evaluating AI Agents

Evaluation Framework for LLM applications in Java and Kotlin

smallevals — CPU-fast, GPU-blazing fast offline retrieval evaluation for RAG systems with tiny QA models.

The E2E AI testing tool | No ML Overhead

This project aims to compare different Retrieval-Augmented Generation (RAG) frameworks in terms of speed and performance.

A framework for systematic evaluation of retrieval strategies and prompt engineering in RAG systems, featuring an interactive chat interface for document analysis.

Learn Retrieval-Augmented Generation (RAG) from Scratch using LLMs from Hugging Face and Langchain or Python

RAG Chatbot for Financial Analysis